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$S$ -parameter data of analog high-speed channels or interconnects are based on Laplace transform techniques, i.e., in the continuous frequency domain. However, $S$ -parameter data are typically presented in tabular form. This fact does not mean that the data are coming from digital systems. The channels or interconnects are analog. Yet there is a proliferation of methods using the fast Fourier transform (FFT), which is inherently used for digital systems, applied to sampled analog $S$ -parameter data. In a previous article, we used vector fitting (VF) and bilinear transformation (BT) to place the analog $S$ -parameter data into the z-domain while developing a sampling theorem in the frequency domain (the dual of sampling in the time domain). In this article, we analyze whether using the BT, with its known frequency warping effect, has any impact on the channel impulse response when taking the inverse FFT (IFFT). In addition, the discrete Hilbert transform (DHT) is used to check for causality on the z-domain. It will be shown that by using the DHT, the $S$ -parameters can be classified as causal or not, without needing $S$ -parameter data at infinite, or data extrapolation providing that the $S$ -parameters complies with the sampling criterion. Examples to validate the discussed methods will be shown. [ABSTRACT FROM AUTHOR]

The application occasion of vortex flowmeter is complex and changeable, and it is easily interfered by various transient impacts, which may cause the flowmeter to fail to measure correctly. In order to find a reliable solution, it is the key to find a method to identify the parameters of transient impact interference output by vortex sensor effectively. Based on the analysis of the vortex flowmeter piping system, a real number form of the composite Laplace wavelet with unilateral attenuation is used to describe each main mode of transient impact interference output by the vortex sensor. Considering that the transient impact interference has multi-modal characteristics, including dense modes, the objective function is constructed, a genetic algorithm combined with BFGS quasi-Newton method is used to search for the optimal estimation of the objective function, and a parameter identification method of the transient impact interference based on the optimal estimation of the objective function is proposed. The effectiveness and reliability of the parameter identification method for the transient impact interference output by vortex sensor are verified by simulation experiments and transient impact interference platform experiments. [ABSTRACT FROM AUTHOR]

Induction motors are important equipment in modern industry. However, the occurrence of fatigue failure following an extended period of operation invariably results in a catastrophic failure. As a result, monitoring and diagnosing induction motors is critical to avoiding unplanned shutdowns caused by premature failures. This article aims to develop an effective method for motor fault detection using time–frequency contents of vibration signals and an attention-based convolutional neural network model. First, the vibration signals are collected and labeled into five different categories: normal condition, outer ring fault, inner ring fault, misalignment condition, and broken rotor bar. Then, using the Morlet function, continuous wavelet transform (CWT) converts the vibratory time-series signals to the scalogram feature images. The time–frequency feature images are created after downsampling and converting the measured vibration signals to the frequency domain. These images are then resized and fed into the proposed convolutional attention neural network (CANN) to identify various induction motor failures. The experimental results demonstrate that the suggested model can provide an excellent diagnosis accuracy of 99.43%, significantly better than the state-of-the-art deep learning approaches for fault diagnosis. Moreover, the developed model’s robustness is validated against adversarial attacks based on the fast gradient sign method (FGSM) by including white Gaussian noise. [ABSTRACT FROM AUTHOR]

For rotary machines, the measured vibration signals contain multiple frequency modulation components with much machine health information. Decomposing vibration signals of rotary machines into a series of sub-signal components with physical meaning is of great significance for a deep understanding of the machine properties in solving fault diagnosis problems. However, accurately detecting and decomposing frequency modulation components is a challenging problem. This article proposed a multiple frequency modulation components detection and decomposition approach for rotary machine fault diagnosis. Specifically, the amplitude and concentration at the orders (defined as multiple of the rotating frequency) of each signal to be analyzed are calculated, and thresholds are set to distinguish noise and non-noise signal components. Then the cross correlation between the non-noise signal components and an order ruler (constructed with normalized amplitude only on the orders of characteristic components of the faulty signal) is calculated to obtain the instantaneous frequencies (IFs) of these signal components. Finally, a variational algorithm framework defined only by the characteristic components of the faulty signal is used to achieve signal decomposition. The proposed method fully uses the inherent geometric relationship of the frequency modulation signal in the angular domain and is robust to strong environmental noise. Both the simulation example and the actual application cases demonstrated the effectiveness of the proposed method, which shows the potential in solving real industrial problems. [ABSTRACT FROM AUTHOR]

Noncontact vital sign measurements based on millimeter-wave radar can realize long-range detection of respiratory and heartbeat signals, therefore it is gradually applied in more and more scenes. With an increase in the number of millimeter-wave radar devices, the mutual interference between radar signals will occur, which will increase the noise floor. Moreover, respiratory and heartbeat signals are relatively weak and easy to be submerged by the high noise floor. To solve this problem, we present a novel method for suppressing mutual interference and extracting vital signs using improved morphological component analysis (IMCA) and an adaptive parameter optimization variational mode decomposition (APVMD) algorithm. The IMCA algorithm is used to suppress the mutual interference based on the different sparsities of components in different sparse domains, and this improvement solves the inability of the MCA algorithm to detect the interfered signal. Then, the APVMD algorithm is used to extract respiratory and heartbeat signals from the signal containing residual noise. The rate of energy loss is used as the index to optimize the parameters, and the step size of the penalty value is adaptively adjusted according to the center frequency of the mode. The simulation and field experiment results show that the proposed method can significantly improve the signal-to-noise ratios (SNRs) of respiratory and heartbeat signals in the case of mutual interference between radar signals. Moreover, a comparison with other interference suppression and extraction methods shows that the proposed method better improves the SNR and accuracy. [ABSTRACT FROM AUTHOR]

This article presents a strategy for the description of new test waveforms for static electricity meters to be included in international standards. The need of extending the existing standardization frame arises from several recent studies that have reported conducted electromagnetic interference problems of type-approved static electricity meters, resulting in significant errors in the measured electricity consumption. The proposed method is based on discrete wavelet transform and allows for a compact and parsimonious representation of test waveforms, suitable for inclusion in standards. Very few wavelet parameters are concentrating the relevant information to accurately reproduce all the characteristics that the meters need to be tested against. The same parsimonious description cannot be performed with the current practices based on Fourier transform methods since the new test signals need to be highly non-sinusoidal. The discrete wavelet transform is proposed as a more effective tool to sparsely describe the most relevant waveform features. The effect of different discrete wavelet transform decomposition settings on compactness and reconstruction accuracy is studied using suitable metrics. Finally, results from experimental validation with several different waveforms are presented to demonstrate that the error-inducing features can be preserved using only 0.1% of the original signal information. [ABSTRACT FROM AUTHOR]

The vibration signals produced by rotating machinery are mostly non-stationary, and there are numerous methods for dealing with them. People’s expectations for time–frequency analysis (TFA) results are increasing all the time. The emergence of post-processing algorithms based on the short-time Fourier transform (STFT) provides scholars with new ideas, but such algorithms heavily rely on the window length selected by STFT and have significant uncertainty. To address this issue, we propose the generalized S-synchroextracting transform, a new time–frequency post-processing algorithm (GS-SET). The algorithm extracts the coefficients on the TF ridge of the generalized S-transform (GST) to remove the majority of the dispersed TF energy, allowing the time–frequency representation (TFR) to achieve optimal TF resolution. The results of the analog signal processing show that the method can characterize the signal clearly and accurately, and it has good noise robustness. To process the fault signals of the three groups of rolling bearings, we use different TFA methods. The results show that the method can more precisely determine the characteristic frequency of the faulty bearing. Finally, the superiority of this method is demonstrated further by processing data from Case Western Reserve University’s faulty bearing database. [ABSTRACT FROM AUTHOR]

Dynamic response of the seismic sensor is crucial, particularly for ultra-weak vibration detection. The related researches almost focus on traditional electrical ones. However, for fiber-optic seismic sensor (FOSS), the dynamic response, as well as the on-site results, is rarely reported. This study thoroughly evaluates the theoretical mechanism for improving the dynamic response of FOSS and its applications in the oilfield. The Ricker wavelet is utilized to simulate the applied seismic signal, and its dynamic response output is investigated. The influences of peak frequency and damping ratio on resonance wave suppression and correlation coefficient improvement are discussed. Besides, the perforation shot detection based on ten-level 3-component FOSS arrays is demonstrated in the oilfield. The basic FOSSs and damping-optimized FOSSs are both manufactured for dynamic response comparison. The signal features, including attenuation time, correlation coefficient, and principal frequency component distribution, all confirm the dynamic response improvement of the proposed damping-optimized FOSS array. [ABSTRACT FROM AUTHOR]

With the increasing train speed, catenary fluctuation and pantograph vibration are intensified, the pantograph–catenary contact force (PCCF) at the dropper point increases, and the current collection quality of pantograph–catenary contact deteriorates. This study performs the location analysis of the PCCF characteristics based on the simulation method to reduce the PCCF value at the dropper point. First, the key wavelength component and the corresponding amplitude component (ac) of the PCCF at the dropper point were located using the time-frequency representation, which is the continuous wavelet transform (CWT). It was found that the PCCF signal at each dropper point considered the dropper spacing wavelength (DW) and span wavelength (SW) as the time-frequency characteristics (TFC) components; however, its contribution to the current collection quality for each dropper point was different. Second, the ac change rule of the DW and SW was analyzed by changing the tension and linear density of the catenary. Third, the sensitivity analysis method was used to quantitatively analyze the relationship between the catenary design parameters and the ac of the PCCF at the dropper point. Moreover, the quantitative analysis of PCCF was conducted based on the positioning analysis. It is concluded that the influence of catenary tension and linear density of messenger wire on the key ac is significant. Finally, the catenary structure was optimized via the parameter sensitivity coefficient when the train was running at 450 km/h speed. The results show that the current collecting quality of pantograph–catenary is improved effectively. [ABSTRACT FROM AUTHOR]

In electroencephalography (EEG)-based affective brain–computer interfaces (aBCIs), there is a consensus that EEG features extracted from different frequency bands and channels have different abilities in emotion expression. Besides, EEG is so weak and non-stationary that easily causes distribution discrepancies for EEG data collected at different times; therefore, it is necessary to explore the affective activation patterns in cross-session emotion recognition. To address these two problems, we propose a self-weighted semi-supervised classification (SWSC) model in this article for joint EEG-based cross-session emotion recognition and affective activation patterns mining, whose merits include: 1) using both the labeled and unlabeled samples from different sessions for better capturing data characteristics; 2) introducing a self-weighted variable to learn the importance of EEG features adaptively and quantitatively; and 3) mining the activation patterns including the critical EEG frequency bands and channels automatically based on the learned self-weighted variable. Extensive experiments are conducted on the benchmark SEED_IV emotional dataset and SWSC obtained excellent average accuracies of 77.40%, 79.55%, and 81.52% in three cross-session emotion recognition tasks. Moreover, SWSC identifies that the Gamma frequency band contributes the most and the EEG channels in prefrontal, left/right temporal, and (central) parietal lobes are more important for cross-session emotion recognition. [ABSTRACT FROM AUTHOR]

Specific to the full-range detection demand for the magnetic anomaly targets such as metal in shallow strata, transportation pipelines, and mined-out regions during the geophysical survey, based on the transient electromagnetic method, using the Gauss inverse Laplace transform, a magnetic excitation model for metal targets in medium-shallow strata is proposed. By using the adjustment of the electrical parameters such as frequency, amplitude, and phase of the excitation current, a spatial multimode magnetic excitation field is formed, an array magnetic excitation detection device is developed, and a distribution model for the array magnetic excitation field is established. The hardware-in-the-loop simulation results show that the frequency and amplitude of the excitation signal are directly proportional to the spectral amplitude and the induced amplitude of the secondary field of the signal, where the signal has the most spectral components when selecting a frequency of 25 Hz. The duty cycle of the excitation signal is of nonlinear correlation to the magnitude of the signal frequency spectrum. When the duty cycle is 50%, the signal spectral components are the least and concentrated in the part with lower frequency, and when the duty cycle is lower or higher than 50%, the spectral components increase and spread to the middle and high frequencies. The indoor test results indicate that changing the excitation signal parameters can also increase the detection range and depth to a certain extent without changing the sensor parameters. It can also reduce the detection blind area to a certain extent. Therefore, the conclusion of this article can solve the problem of large detection blind areas in transient electromagnetic detection, and the application field can also be extended from deep mineral detection to new areas, such as medium and shallow unexploded bombs, metal pipelines, and goafs. [ABSTRACT FROM AUTHOR]

This article proposes a novel approach for the detection of interharmonics using sparse signal decomposition (SSD) based on matching pursuit (MP) on the overcomplete dictionary. First, an overcomplete dictionary is designed according to the characteristics of harmonics and interharmonics. Then, use the MP algorithm to obtain the sparse decomposition of the signal. Thus, the interharmonic components are extracted from the signal, and the estimation of frequency, amplitude, and start and end time can be computed from the parameter set of the atoms that best match signal residues. An imperialist competitive algorithm (ICA) is employed to search for the best matching atoms in the continuous parameter space, which can avoid the limitation of the step sizes on the detection accuracy and greatly reduce the computational complexity. Search function and differential evolution operator are introduced in the assimilation stage to solve the issue that ICA is likely to get stuck at a local optimum. The proposed method is tested using simulated and realistic signals. The experimental results demonstrate the effectiveness and robustness of the proposed method using the SSD technique in detection and location of interharmonics, even under noisy conditions. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) biometric recognition as a personal identification method is receiving more and more attention because it can support live verification results. Compared with other biometric-based methods, it can provide higher security performance. The difficulty of the problem lies in how to stably extract ECG signal features and achieve real-time verification. In this study, a new type of sparse representation learning framework called statistical $n$ -best adaptive Fourier decomposition (SAFD) originated by Qian is adopted in ECG biometric identification. Adaptive Fourier decomposition (AFD) is a recently developed combination of transform-based signal decomposition and sparse representation method, which can adaptively select the atoms from a redundant dictionary through orthogonal processing. The advantage of the AFD-type methods is that each atom in the dictionary has a precise mathematical formula with good analytic properties. This characteristic is significantly distinguished it from other existing sparse representations, where the atoms learned are usually matrix data and cannot be described mathematically. The proposed SAFD extends the existing $n$ -best AFD from processing single signal to multi-signals and implements the $n$ -best AFD in the stochastic Hardy space. Therefore, the small number of learned atoms by SAFD is sufficient to capture internal structure and robustness of the signal and generate a discriminative representation that reflects the time–frequency characteristics of signals. It is very suitable for non-stationary signals like ECG. The proof of convergence of the algorithm is presented. Extensive experiments are conducted on five public databases collected in different realistic conditions, and an average identification accuracy of 98.0% is achieved. In addition, less than 1 ms for one matching process makes it possible to be implemented in real time. Experimental results demonstrate that the proposed method can achieve superior performance compared to other state-of-the-art ECG biometric identification methods. [ABSTRACT FROM AUTHOR]

In the status quo, dementia (DEM) is yet to be cured. Precise diagnosis prior to the onset of the symptoms can prevent the rapid progression of the emerging cognitive impairment. Recent progress has shown that electroencephalography (EEG) is the promising and cost-effective test to facilitate the detection of neurocognitive disorders. However, most of the existing works have been using only resting-state EEG. The efficiencies of EEG signals from various cognitive tasks, for DEM classification, have yet to be thoroughly investigated. In this study, we designed four cognitive tasks that engage different cognitive performances: attention, working memory, and executive function. We investigated these tasks by using statistical analysis on both time and frequency domains of EEG signals from three classes of human subjects: DEM, mild cognitive impairment (MCI), and normal control (NC). We also further evaluated the classification performances of two features extraction methods: principal component analysis (PCA) and filter bank common spatial pattern (FBCSP). We found that the working memory-related tasks yielded good performances for DEM recognition in both cases using PCA and FBCSP. Moreover, FBCSP with features combination from four tasks revealed the best sensitivity of 0.87 and the specificity of 0.80. To our best knowledge, this is the first work that concurrently investigated several cognitive tasks for DEM recognition using both statistical analysis and classification scores. Our results yielded essential information to design and aid in conducting further experimental tasks to early diagnose DEM patients. [ABSTRACT FROM AUTHOR]

Moisture damp has been identified as one of the most harmful agents of multilayer composited insulation, which can seriously accelerate its degradation, cause unexpected failure, and even lead to catastrophic accidents. This article proposes a nonintrusive method for moisture inspection of multilayer insulation structure based on terahertz (THz) technology with a focus on the cable terminal of the electric multiple units (EMUs). First, a three-layer scattering model is established based on the actual structure of the composite cable terminal, and theoretical analysis is performed to reveal the potential application mechanism. Based on that, an artificial model of a three-layer structure is then built on which the insulation damp test is carried out. Finally, the THz spectrum and THz imaging tests are performed. Results show that the frequency-domain signal can effectively identify the moisture inside the multilayer composited insulation. The imaging results in the frequency domain of the sample can accurately determine the geometric parameters of the damp area. By further analyzing the time-domain spectroscopy, the thickness of insulation affected by moisture can be identified quantitatively. This article verifies the feasibility of THz technology in inspecting the moisture dampness of multilayer composite insulation and its potential application in the damp condition assessment of EMU cable terminals. [ABSTRACT FROM AUTHOR]

Indoor wideband electromagnetic radiation (EMR) over seven days and potential radiation sources were measured and analyzed in this article. The research results show that Wi-Fi and base station equipment are the main factors causing indoor electromagnetic exposure, and the radiation intensity of the former is much higher than the latter when the equipment is working. Also, the change of EMR over time conforms to the users’ daily work and rest cycle and contains a significant low-frequency component with a 24-h cycle and complex high-frequency components. Besides, the intensive use of electronic products by users has been proven to have a great impact on the frequency characteristics of the electric field, and the complexity of the time–frequency characteristics of EMR time series has increased, resulting in poor prediction results of the models such as the seasonal autoregressive moving average (SARIMA) model and the long short-term memory (LSTM) neural network. To this end, a prediction scheme combining wavelet transform and time-series analysis methods is proposed in this article. To verify the effectiveness of the proposed hybrid method, the samples of three days are exploited to perform a loop test of the prediction accuracy under different prediction steps. Besides, the results are compared with those obtained by single SARIMA and LSTM. The experimental results show that the proposed hybrid prediction method effectively predicts electric field radiation with complex frequency characteristics and significantly outperforms other methods in terms of prediction accuracy and prediction length. [ABSTRACT FROM AUTHOR]

As a typical nonstationary signal, rolling bearing fault signals exhibit dynamic characteristics and low signal-to-noise ratio, which lead to fuzzy spectrograms from traditional time–frequency analysis (TFA) methods. It is a challenging task to accurately represent its instantaneous frequency (IF) trajectory. To solve this problem, this article proposes a TFA method which is called match-extracting chirplet transform for analyzing multicomponent dynamic signals. It generates a time–frequency (TF) representation of high energy concentration by constructing a basis function that matches the chirp rate with the original signal at any TF point. A matching extraction operator is built to extract the best matching ridges on the IF trajectory to further improve the quality of the spectrum. The proposed method can effectively characterize the dynamic characteristics of multicomponent signals and reduce the energy divergence. Meanwhile the algorithm retains signal reconstruction and can be used to recover significant components by adaptively searching modal ridges. Numerical verification shows that this method has a better performance in TF location and noise robustness. Finally, the validity of this method in mechanical fault diagnosis is verified by applying it to actual bearing signals. [ABSTRACT FROM AUTHOR]

The article presents and summarizes the research results of the UTC-UTC(PL) differences predicting procedure for the Polish Time Scale UTC(PL) developed at the Institute of Metrology, Electronics and Computer Science of the University of Zielona Góra. The research has been conducted in close cooperation with the Time and Frequency Laboratory of Central Office of Measures, responsible for the realization of the UTC(PL). The planned experiment covered a 50-month period, in which the UTC(PL) timescale has been carried out using, as a master clock, atomic clocks with different properties: commercial cesium atomic clock 5071A Opt. 001 (first case) and VCH-1003M hydrogen maser (second and third cases). In case of the developed predicting procedure by means of group method of data handling (GMDH)-type neural network (NN), the transitional period (second case) in which the master clock had been replaced by the new clock, is also very important. For GMDH-type NN, input data as a TS1 and TS2 time series have been prepared, based on the already-known differences of UTC(PL) from UTC and UTC rapid timescales and on determined differences between UTC(PL) and the master clock steering the UTC(PL). The method of preparing TS1 and TS2 time series is an important part of the developed procedure for predicting UTC(PL) differences from UTC, before they will be calculated and published by fr. Bureau International des Poids et Mesures (BIPM). The obtained research results confirmed that the developed predicting procedure by means of GMDH-type NN ensures a proper quality of maintaining the national timescale. The best quality of UTC(PL) predicting has been achieved for the third case, both for data prepared according to the TS1 and TS2 time series. This is confirmed by the calculated values of the prediction quality measures and the Modified Allan deviation. On the other hand, the second case confirmed the possibility of using the proprietary development of the TS2 time series to predict UTC-UTC(k) differences. This time series is a very good alternative for the TS1 time series, when the amount of data in this series is insufficient due to replacement of the clock or its failure. [ABSTRACT FROM AUTHOR]

Induction machines (IMs) are essential components of many industrial installations and, therefore, their faults must be detected early. Fault detection using current spectrum analysis is attracting an increasing interest as a condition-based monitoring (CBM) technique. However, its use to detect rotor asymmetries in high-power IMs, which operate at very low slip, is particularly challenging, due to the closeness of the characteristic fault harmonics to the fundamental component, separated only a few MHz. Their reliable detection in harsh industrial environments requires a very high spectral resolution, that is, long acquisition times and a huge number of current samples, which hinders its implementation on embedded, online devices with limited computing resources. To address this problem, this article presents a novel combination of diagnostic techniques, the use of the rectified current as diagnostic signal, and the Goertzel algorithm as signal processing tool. This unique combination allows for an optimized implementation of the Goertzel algorithm, which provides a high spectral resolution in the full load range of the machine, with a low computational cost and a negligible memory footprint. This proposal is validated experimentally with the fault diagnosis of a high-power medium-voltage industrial motor. [ABSTRACT FROM AUTHOR]

Strong frequency-varying signal always undergoes a rapid frequency change within a short time duration. Converting its 1-D waveform into the 2-D time-frequency (TF) plane is convenient for us to understand the structure of the strong frequency-varying signal. Time-reassigned multisynchrosqueezing transform (TMSST) is a recently proposed time-frequency analysis (TFA) method, which owns the capability to provide an energy concentrated time-frequency representation (TFR) result for the strong frequency-varying signal. However, the fixed-point iterative estimation manner of the group delay in the TMSST method would lead to some non-reassigned TF points and further affect the readability of the TFR result. To address this issue, a post-processing method for the TMSST is proposed in this study. In the proposed method, the group delay of the strong frequency-varying signal is estimated by local maximizing the TFR result provided by TMSST in the time direction, and then the TF coefficients from the TMSST are rearranged among the time direction on the basis of the new estimated group delay. Results from simulation and experimental case studies verify that the proposed method can provide a more energy-concentrated TFR result in comparison with the other existing TFA methods. [ABSTRACT FROM AUTHOR]

Windowing digital signals is essential to digital signal processing. The matter is a complicated one, and choosing a window function is always a tradeoff between certain undesired effects on one hand and important benefits on the other. In this article, a new window class of windows called sine ramp windows is presented, which is very fast decaying with regard to the frequency resolution. These new windows improve the attenuation of far-distant spectral leakage while maintaining an acceptable minimal signal duration, which is beneficial, e.g., in the harmonic analysis of power systems in transient conditions. This window class is then extensively benchmarked against the state of science using common figures of merit proving its advantages, which are then shown in two application examples. [ABSTRACT FROM AUTHOR]

Heart sounds are an important basis for evaluating heart disease. In recent years, auxiliary diagnosis technology of cardiovascular disease based on the detection of heart sound signals has become a research hotspot. A lightweight automatic heart sound classification method is presented in this study. It only needs to perform simple preprocessing on the heart sound data, and a neural network model is constructed to automatically extract the time–frequency features of heart sound data: where the convolution module extracts the frequency-domain characteristics, the loop module extracts the time-domain characteristics, and the features are then fused in series and parallel. Finally, the classification and recognition of heart sounds are realized based on the fusion features. At the same time, methods, such as group convolution, global average pooling, and gated loop mechanisms, are used to reduce the parameters and training time of the neural network. The experimental data from the PhysioNet database are used for testing. The recognition accuracy of the series feature fusion heart sound classification model is 95.00%, and the model size is 0.6 MB. In addition, the parallel feature fusion heart sound classification model is 95.50%, and the model size is 1.36 MB. Therefore, the lightweight heart sound classification model designed in this study contains fewer parameters and higher recognition accuracy. This makes it suitable for deployment in embedded devices. It also has important research significance for the development of portable heart sound detection equipment. [ABSTRACT FROM AUTHOR]

Pulsed eddy current testing has attracted increasing attention on ferromagnetic materials evaluation as it has abundant time–frequency domain features that can be utilized to characterize the thickness of the materials. Few studies have given a theoretical analysis to explain the relationship between the features and the thickness. In this article, the decay process of eddy current response to a ferromagnetic casing is analyzed qualitatively, and a concise expression of the decay rate is derived. The expression reveals the relations between the decay rate and the casings parameters. A method based on the decay rate, thus, is given for the wall thinning evaluation. Both numerical and practical experiments are carried out for verifying the method, and the experiment results demonstrate that the method is barely influenced by testing coils and the inner radius of a ferromagnetic casing. The wall thinning of a ferromagnetic casing can be evaluated appropriately. [ABSTRACT FROM AUTHOR]

Detection of wideband passive radar signals, accomplished by instantaneous frequency measurement (IFM) systems, is crucial for gaining a profound knowledge of environmental circumstances. The need for fast pulse detection has hindered the investigation of frequency extraction with higher accuracy in IFM receivers. A noteworthy research in recent years shows that fast and reliable frequency extraction in high bit-count (HBC) IFMs while attaining a better frequency accuracy [called intelligent, reliable, and design-independent (InReDI)] is feasible. In addition, some investigated process-intensive algorithms like the minimum mean squared error (MMSE) estimator promise excellent frequency accuracy. In this work, we present a new method of MMSE algorithm modified by a piecewise linear approximation [named linear approximation of MMSE (LAMMSE)]. By utilizing the InReDI methodology and the novel LAMMSE algorithm, we propose a new architecture for a fast, accurate, and reliable frequency extraction (FAREx) in HBC digital IFMs (DIFM) receivers. The proposed FAREx architecture resolves the compromise challenge between frequency accuracy and extraction speed that has been long lingered in DIFM receivers. We have implemented the new architecture using Xilinx field-programmable gate array (FPGA) on a fabricated 15 bit, 2–4 GHz, and 70 dB dynamic range IFM receiver. The measurement results show an average frequency rms error of 1.43 MHz for a CW signal, 2.49 MHz average rms error for a 50 ns pulsed input signal, and 2.7 MP/s detection speed, representing a state-of-the-art receiver and a breakthrough compared to the latest works. [ABSTRACT FROM AUTHOR]

Rotating machines often operate under varying-speed working conditions due to the demands of diverse applications and complicated functionalities. Vibration signals of their key components, such as bearings, are analyzed to detect an early fault in case of unexpected failures. Order tracking is a widely used method of handling the nonstationarity of varying-speed vibration signals. However, signals after order tracking are not strictly cyclostationary, i.e., they exhibit accumulated cyclic disturbances easily masked by strong background noises when the bearing fault is at an early stage. Detecting the bearing fault from order-tracked signals with both accumulated cyclic disturbances and strong background noises is very challenging because cyclic disturbances and background noises are two mutually constraining characteristics. Herein, a generalized autocorrelation function (GeACF) is proposed to address this challenge. The autocorrelation function (ACF) is proven to be a special case of the proposed GeACF, which provides more options than ACF for order-tracked signals contaminated with strong background noises. By tuning a free parameter, GeACF can achieve a balanced tradeoff between these two mutually constraining characteristics. The superior performance of the GeACF method over its conventional counterparts is demonstrated through simulation and case studies. [ABSTRACT FROM AUTHOR]

Magnetic-resonance sounding (MRS) is a direct, quantitative, accurate, and efficient technology that has been applied in the fields of groundwater detection, regional water-resource investigation, and geological-disaster-risk early warning. However, owing to strong environmental noise, the signal-to-noise ratio (SNR) of data collected by MRS instruments is low; thus, providing reliable results for subsequent hydrological interpretation is difficult. To solve this issue, we propose a method based on the synchroextracting transform (SET) to eliminate harmonic noise from MRS data in an environment with strong noise. Compared with the harmonic modeling cancellation (HMC) and three existing data processing algorithms, the SET can effectively eliminate the harmonic noise in a single record and process multiple types of harmonic noise in stacked data at a time. The calculation amount and time are much lower than those of the HMC algorithm, and the calculation accuracy is similar to that of the HMC algorithm. More importantly, after the harmonics are eliminated by the SET method, the impact on the free induced decay (FID) signal is small, whereas the other three methods severely distort the FID signal. Considering that FID signals may exhibit the characteristics of multiexponential decay and that its Larmor frequency is close to a certain harmonic frequency, we study the applicable scope of the SET. Finally, by processing the results of field-measured data, the validity and practicability of SET are further verified. We also discuss the advantages of the SET method and existing problems to provide future research directions. [ABSTRACT FROM AUTHOR]

In recent years, as reliance on factory automation increases, real-time surveillance techniques for electrical systems have received substantial attention. In particular, the fault diagnosis of shielded cables has become crucial in the industrial sector due to their roles in interconnecting each electrical element. The time–frequency-domain reflectometry (TFDR), which is an advanced cable diagnostic technique, has been used to diagnose various types of shielded cable with high accuracy in fault location. However, in the case of reflected signals with a low signal-to-noise ratio (SNR) caused by any soft faults, the method faces ambiguities in interpreting the presence of failures and locating the faults. Thus, this article proposes an algorithm that simultaneously enhances the fault detection and localization performance of TFDR. In addition, the proposed method provides a statistical model-based threshold for fault detection. The performance of the proposed algorithm is tested via three experiments on actual shielded cables, and the efficacy of the proposed method is verified based on statistical analyses with theoretical discussion. [ABSTRACT FROM AUTHOR]

One of the key characteristics of modern devices is time-awareness, which demands frequency source of good accuracy, precision, and stability. Unfortunately, all high-quality sources are expensive and bulky. An alternative is to add a companion clocking circuit to the primary source for performing frequency compensation in real time, under the guidance of a good time or frequency reference. The companion circuit aims at counteracting the primary source’s systematic error and environment-induced variations. In this work, a frequency synthesizer having the features of small frequency granularity and fast frequency switching is adopted as such companion circuit. It functions as a tuning tool for periodically adjusting device’s output frequency under the supervision of 1 PPS signal locked to global positioning system (GPS). This clock disciplining method can also be used for steering output frequency away from the specified value and toward a user-defined target. The main focus of this method is on improving frequency accuracy. The key advantage of the proposed method is low-cost and high-accessibility. It provides a new perspective, or devises a new instrument, for dealing with the sophisticated task of real-time frequency compensation. Our primary goal is to significantly simplify the engineering effort of this task without sacrificing the quality of clock signal noticeably. An field-programmable gate array (FPGA)-based system is created to demonstrate its capability of frequency compensation and steering. Further, 20 such systems are used to study the precision of a multi-node organization, as an example of using this instrument as a syntonization tool. The handy implementation on FPGA illustrates the advantage of DIY (do it yourself), making this method conveniently applicable for a variety of user scenarios. [ABSTRACT FROM AUTHOR]

Time–frequency (TF) analysis (TFA) provides an effective tool to characterize nonstationary signals with time-varying features. However, the TFA of gas turbine’s vibration signals is a challenging topic due to high complexity and strong nonstationarity. There is an obstacle to generate more accurate and sharper TF results for such multicomponent signals. This article proposes a novel TFA technique, named local maximum synchrosqueezing chirplet transform (LMSSCT), to deal with this problem. This method can not only well match window function and modulated frequency but produce an unbiased instantaneous frequency (IF) estimator to correct the deviation caused by strong frequency modulation (FM) in TF results. We give the theoretical analysis that this method is an improvement of classical local maximum synchrosqueezing transform (LMSST), and we also prove that it allows for perfect signal reconstruction. The numerical validation shows that the proposed method can be employed to effectively address the multicomponent signals with complex FM laws, even those with heavy noise. The experimental analysis on the test-bench signal and the vibration signal of a dual-rotor gas turbine validates that this method can capture more detailed features that are helpful to identify the origins of abnormal vibration of gas turbine. [ABSTRACT FROM AUTHOR]

This article proposes an integrator-less method for phase angle estimation of unbalanced three-phase voltage systems under adverse conditions. It mainly unites abc-to-dq transform, delayed signal cancellation scheme, and a phase error compensation algorithm. It does not demand online operation of integrators, and thus, associated discretization error and instability issue are not present in the proposed method. It is also an unconditionally stable technique as it uses a real open-loop structure. Moreover, the method is relatively simple and computationally efficient due to circumventing online operations of the complex functions, such as square root, trigonometric, and inverse trigonometric. Under alike transient time conditions, the proposed method is more resistant to harmonic disturbances when compared to integrator-based similar competing techniques, such as an enhanced frequency adaptive demodulation approach and a phase error compensation-based open-loop method. The usefulness of the offered method is proved using simulated and experimental results. [ABSTRACT FROM AUTHOR]

The oscillator fluctuations are described as the phase or frequency-noise spectrum or in terms of a wavelet variance as a function of the measurement time. The spectrum is generally approximated with the “power law,” i.e., a Laurent polynomial with integer exponents of the frequency. This article provides: 1) the analytical expression of the response of the wavelet variance parabolic variance (PVAR) to the frequency-noise spectrum in the general case of noninteger exponents of the frequency and 2) a useful approximate expression of the statistical uncertainty. In turn, PVAR is relevant in that it replaces the widely used modified Allan variance (MVAR), featuring the identification of noise processes with fewer data. [ABSTRACT FROM AUTHOR]

The study of dispersive signals has received widespread attention in numerous fields, such as structural health monitoring, nondestructive testing, and geological exploration. The decomposition of multicomponent dispersive signals generated by real systems is challenging since the group delay (GD) of each component varies nonlinearly with frequency and may overlap with each other. Meanwhile, the amplitude mutation components also bring difficulties to the decomposition. Aiming at these problems and inspired by the dispersion compensation method (DCM) and generalized dispersive mode decomposition (GDMD), we propose the nonlinear dispersive component decomposition method (NDCD) in this article. We establish an optimization model with a hybrid regularization term for frequency domain signal decomposition. By solving the optimization problem using composite split denoising (CSD) and the split augmented Lagrange shrinkage algorithm (SALSA), we can get the signal components along with high-resolution time-frequency representation. Simulation signals, bearing fault diagnosis, and Lamb wave experiments show that NDCD has better decomposition ability for signals containing mutation amplitude and overlapped GD. Moreover, it also has better noise robustness and lowers computational complexity, better than methods such as GDMD, DCM, synchrosqueezing transform (SST), time-reassigned SST (TSST), and TSET. [ABSTRACT FROM AUTHOR]

Schizophrenia (SZ) is a psychiatric disorder characterized by cognitive dysfunctions, hallucinations, and delusions, which may lead to lifetime disability. Detection and diagnosis of SZ by visual inspection is subjective, difficult, and time-consuming. Electroencephalogram (EEG) signals are widely used to detect the SZ as they reflect the conditions of the brain. Conventional machine learning methods involve many lengthy manual steps, such as decomposition, feature extraction, feature selection, and classification. In this article, automated identification of SZ is proposed using a combination of time–frequency analysis and convolutional neural network (CNN) to overcome the limitations of feature extraction-based methods. Three press button tasks are analyzed to segregate normal subjects and SZ patients. The EEG signals are subjected to continuous wavelet transform, short-time Fourier transform, and smoothed pseudo-Wigner–Ville distribution (SPWVD) techniques to obtain scalogram, spectrogram, and SPWVD-based time–frequency representation (TFR) plots, respectively. These 2-D plots are fed to pretrained AlexNet, VGG16, ResNet50, and CNN. We have obtained an accuracy of 93.36% using the SPWVD-based TFR and CNN model. In comparison to the benchmark AlexNet, ResNet50, and VGG16 networks, the developed CNN model with four convolutional layers not only requires fewer learnable parameters but also is computationally efficient and fast. This clearly indicates that our proposed method combining the SPWVD-CNN model has performed better than the state-of-the-art transfer learning techniques. Our developed model is ready to be tested with more EEG data and can aid psychiatrists in their diagnosis. [ABSTRACT FROM AUTHOR]

Nonstationary signal decomposition (SD) is a primary procedure to extract monotonic components or modes from electroencephalogram (EEG) signals for the development of robust brain–computer interface (BCI) systems. This study proposes a novel automated computerized framework for proficient identification of motor and mental imagery (MeI) EEG tasks by employing empirical Fourier decomposition (EFD) and improved EFD (IEFD) methods. Specifically, the multiscale principal component analysis (MSPCA) is rendered to denoise EEG data first, and then, EFD is utilized to decompose nonstationary EEG into subsequent modes, while the IEFD criterion is proposed for a single conspicuous mode selection. Finally, the time- and frequency-domain features are extracted and classified with a feedforward neural network (FFNN) classifier. Extensive experiments are conducted on four multichannel motor and MeI data sets from BCI competitions II and III using a tenfold cross-validation strategy. Results compared with the other existing methods demonstrated that the highest classification accuracies of 99.82% (data set IV-a), 93.33% (data set IV-b), 91.96% (data set III), and 88.08% (data set V) in subject-specific scenarios, while 82.70% (data set IV-a) in the subject-independent framework are achieved for IEFD with FFNN classifiers collectively. The overall exploratory results authenticate that the proposed IEFD-based automated computerized framework not only outperforms the conventional SD methods but is also robust and computationally efficient for the development of subject-dependent and subject-independent BCI systems. [ABSTRACT FROM AUTHOR]

One of the most popular technologies adopted for indoor localization is ultrawideband impulse radio (IR-UWB). Due to its peculiar characteristics, it is able to overcome the multipath effect that severely reduces the capability of receivers (sensors) to estimate the position of transmitters (tags) in complex environments. In this article, we introduce a new low-cost real-time locating system (RTLS) that does not require time synchronization among sensors and uses a one-way communication scheme to reduce the cost and complexity of tags. The system is able to evaluate the position of a large number of tags by computing the time difference of arrival (TDOA) of UWB pulse sequences received by at least three sensors. In the presented system, the tags transmit sequences of 2-ns UWB pulses with a carrier frequency of 7.25 GHz. Each sensor processes the received sequences with a two-step correlation analysis performed first on a field-programmable gate array (FPGA) chip and successively on an on-board processor. The result of the analysis is the time of arrival (TOA) of the tag sequence at each sensor and the ID of the associated tag. The results are sent to a host PC implementing trilateration algorithm based on the TDOA computed among sensors. We will describe the characteristics of the custom hardware that has been designed for this project (tag and sensor) as well as the processing steps implemented that allowed us to achieve an optimum localization accuracy of 10 cm. [ABSTRACT FROM AUTHOR]

Compound faults diagnosis of bearings and gears in gearboxes is a full-challenging task. The rational usage of meshing resonance is probable to be one of the breakthroughs to solve this problem. However, the existing detection method of meshing resonance is sensitive to noise and has risks of false demodulation. Meanwhile, it is difficult to guarantee that isolated components only contain one fault characteristic. Based on the mentioned problems, a spectrum of full-band preprocessing and 2-D separation method is raised. First, the inverted editing method of the original signal is proposed to reduce noise in the full frequency band. Second, the established resonance detection diagram and the introduced amplitude-level decomposition technique separate the processing results in the frequency and amplitude dimensions. Finally, the separate components are detected, respectively, by envelope analysis. Both the simulation analysis and experimental verification do support the effectiveness of this method. In addition, this article compares the existing method and solves the essential problems as well. [ABSTRACT FROM AUTHOR]

The synchrosqueezing transform (SST) and its ameliorated methods can enhance the energy concentration of the vibration signals in time-frequency domain, but it does not show enough effect for the weak feature extraction. Besides, many large-scale rotating machines such as aerospace engines and gas engines often work under complex vibrations, and this increases the difficulty for health monitoring based on vibration signals. This article presents a time-frequency analysis (TFA) named bilateral second-order SST (BFSST2). It can sharpen the instantaneous frequencies (IFs) like second-order SST (FSST2), and more importantly, it can extract the weak features such as amplitude-modulation frequency-modulation IFs and small-amplitude time-varying IFs which are easily ignored on time-frequency plane. To achieve the aforementioned target, the IFs are first obtained by employing FSST2, and then a bilateral function representing a novel negative-positive TFA is structured by combining the hyperbolic tangent function, exponential function, and sign function together. After that the large-amplitude IFs are transformed into small-negative amplitude ones by the product with the bilateral function, and this can not only enhance the original weak feature but also avoid its confusion with the transformed small-negative amplitude ones. The proposed BFSST2 is validated by a numerical simulation. At last, two case studies are given to illustrate its effectiveness in aerospace engine vibration monitoring. [ABSTRACT FROM AUTHOR]

This article presents a method to obtain a enhanced frequency resolution two-phase lock-in amplifier (LIA) with two channels in a microcontroller using minimum external circuitry. The timer peripheral of the microcontroller was used to generate the square wave signals for both the excitation and the quadrant demodulation, simplifying the waveform synthesis when compared to the use of digital-to-analog converters (DACs). To improve the frequency resolution of the generated signals, the timer signal was dithered to achieve fractional frequency values, while demodulation was executed in real time with signal acquisition. The real and imaginary parts of the signal are then transmitted to the computer by the universal serial bus (USB) interface while the computer sends commands to change the frequency, phase, or control other features. As proof of work, quartz tuning fork (QTF) sensors were measured due to its high $Q$ -factor. The bandwidth of the lock-in was limited by the acquisition and demodulation sample rate of 300 Ks/s, while we showed 1000 times frequency resolution enhancement when compared to the not dithered signal generation. This approach can be applied in embedded design to measure frequency-dependent sensors with high $Q$ , in scientific instrumentation such as scanning probe microscopes (SPMs), or even for educational purposes to teach the working principle of the LIAs. [ABSTRACT FROM AUTHOR]

Variational nonlinear chirp mode decomposition (VNCMD) is a recently proposed time-frequency analysis method that combines variational mode decomposition with the sparse representation framework. It can effectively decompose crossed and adjacent nonlinear chirp components. However, due to its optimization target’s ridge regression characteristic, VNCMD cannot accurately reconstruct amplitude mutation components in fast-time-varying signals. Based on the framework of VNCMD and the sparsity and smoothness of Elastic Network Regression, we propose the nonlinear chirp component decomposition (NCCD) algorithm to decompose nonlinear chirps with strong piecewise linear characteristics accurately. We establish our new joint optimization model by adding weighted $l_{1}$ norm regulation term to the optimized objective function of VNCMD and solves the model by alternating direction method of multipliers. Meanwhile, we use the dynamic path optimization (DPO) algorithm to extract the time-frequency distribution ridge and initialize the instantaneous frequency. Simulation signals suggest that our method has higher decomposition accuracy for fast-time-varying nonstationary signals, smaller end effect, better convergence and noise robustness, and lower computational complexity. Furthermore, experimental signals of rotor rubbing suggest that our method can be more effectively applied to process strong modulation vibration signals than other methods such as synchrosqueezing transform (SST), synchro-extracting transform (SET), time reassigned-SST (TSST), and VNCMD. [ABSTRACT FROM AUTHOR]

Low probability of intercept (LPI) radars using linear frequency-modulated continuous waveforms (LFMCWs) is rapidly proliferating in the military domain and posing a serious challenge to conventional electronic support receivers (ESRs). In this article, we provide a practical approach that can be used in digital ESR systems for real-time LFMCW radar signal detection and parameter extraction under challenging SNR levels. We proposed a modified version of the Wigner–Ville distribution (WVD), namely, short-time Wigner distribution (STWD), postprocessing steps, e.g., column-based thresholding of STWD combined with the Hough transform (HT), and a field-programmable gate array (FPGA) design as an embedded solution with low computational complexity. The performance of the proposed system is tested with MATLAB and hardware simulations using a signal database generated with different signal-to-noise ratio (SNR) levels. The system is then embedded into a Virtex-7 FPGA where the performance evaluations showed satisfactory detection results down to −6-dB SNR level, and parameter extraction results down to −8-dB SNR level. [ABSTRACT FROM AUTHOR]

The frequency response function (FRF) is a fundamental property of a linear time-invariant system that describes the system’s frequency-dependent behavior. Accurately measuring the FRF of multivariable lightly damped systems in a time-efficient manner is difficult due to spectral leakage and long-lasting transient phenomena. In the past, local rational modeling techniques have been introduced that mitigate these difficulties. This article presents an overview of the existing techniques for multivariable systems and proposes a new local rational modeling technique that is both consistent and stochastically efficient in measuring the nonparametric FRF. The proposed technique is successfully validated on simulation examples of a generic multivariable lightly damped system. Its practical applicability is illustrated by characterizing the FRF of an aluminum plate. [ABSTRACT FROM AUTHOR]

Fault diagnosis of rolling bearing has always been the focus of research in the engineering field. Due to the nonstationary conditions, this work has suffered huge challenges. The adaptive chirp mode decomposition (ACMD) is a signal decomposition method developed recently. By using a recursive framework embedded with an iterative algorithm, it can decompose signals one by one. Although this scheme has strong adaptability, the accuracy of reconstructed signals is very rough. Moreover, the estimated components will even deviate from the true results as the iteration progresses when two components are very close. In order to solve the above problems, a novel two-step ACMD is proposed in this article. Specifically, the recursive scheme without an iterative algorithm is only used to decompose signals first, which can determine the number of components. Then, the joint-estimation framework using an iterative algorithm is used to achieve high-resolution component reconstruction. The main idea of this two-step method is to let different schemes do what they do best. Subsequently, we proposed a new fault diagnosis method for rolling bearing under variable speeds based on the proposed two-step ACMD. Finally, both simulation and actual bearing diagnosis cases verify the potential of the proposed method and successfully diagnosed different kinds of rolling bearing faults. [ABSTRACT FROM AUTHOR]

Motor current signature analysis has become a widespread fault diagnosis technique for induction machines (IMs), because it is noninvasive and requires low resources of hardware (a current sensor) and software (a fast Fourier transform). Nevertheless, its industrial application faces practical problems. One of its most challenging scenarios is the detection of broken bars in IMs working at very low slip, like large machines with a very small rated slip, or unloaded induction motors in off-line tests. In these cases, the leakage of the main supply component can hide the fault harmonics, even with a severe fault. Diverse solutions to this problem have been proposed, such as the use of smoothing windows, advanced spectral estimators, or the removal of the supply component. Nevertheless, these methods modify the spectral content of the current signal or add a high computational burden. In this work, a new approach is proposed, based on the analysis of the current with a very fine spectrum, obtained via simple zero padding, followed by the extraction of a practically leakage-free conventional, coarse spectrum. The method is experimentally validated by the diagnosis of a broken bar fault in a 3.15-MW induction motor. [ABSTRACT FROM AUTHOR]

Playing a vital role in the safe operation of equipment, intelligent fault detection has been extensively applied in mechanical systems. However, the complicated mapping of signal symptoms and multicomponent failure modes under sharp speed variation makes it hard. The classification error is large due to insufficient samples in the fault condition. Aiming at these problems, a novel multitask redundant lifting adversarial network (MRLAN) is proposed. First, inspired by redundant second-generation wavelet transform, a reconstruction network is established to generate high-quality time–frequency representation of vibration signal, thus eliminating speed fluctuations and expanding training samples. The reconstruction network consists of transposed convolution layer, resume update layer, resume predict layer, and merge layer. Then, multitask category discriminator, which forms dual discriminator together with distance discriminator, is constructed to separate the features of multiple components, thereby realizing multiple tasks (multicomponent fault location and single-component health assessment). Finally, the model is alternately optimized through adversarial process between the generation network and the double discriminators. The proposed method is validated by two cases and other methods are also established to show its superiority. The results show satisfactory performance of MRLAN in multicomponent fault diagnosis under sharp speed fluctuation and little training data. [ABSTRACT FROM AUTHOR]

As the complexity increases in modern power systems, power quality (PQ) analysis considering interharmonics has become a challenging and important task. This article proposes a novel decomposition and estimation method for instantaneous PQ indices (PQIs) monitoring in single- and three-phase systems with interharmonics and transient disturbances. To separate the interharmonic components, a set of new scaling filter and wavelet filter with narrow transition bands is designed for the undecimated wavelet packet transform (UWPT). Furthermore, a two-stage decomposition method for multitone voltage and current signals is proposed. The Hilbert transform (HT) is applied to calculate the instantaneous amplitude and phase of each frequency component, which accordingly allows the monitoring of different PQI parameters. Numerical tests are conducted to check the performance of the proposed method. The test results show that, compared to other conventional approaches, instantaneous PQIs estimated by the proposed method present significant advances for tracking transitory changes in power systems and could be considered as a helpful tool for high-accuracy PQ detections. [ABSTRACT FROM AUTHOR]

Blade tip timing (BTT) is a noncontacting vibration measurement method for rotating blade condition monitoring. The BTT sampling frequency is proportional to the number of probes under uniform probe layout and constant rotating speed. However, the limitation of the probe installation space in aeroengines causes the lack of probes, which leads to the inherent undersampled feature of the BTT signal. Thus, frequency identification of nonuniform undersampled signal has become a popular field in BTT signal processing. Multiple signal classification (MUSIC) is an array signal processing method that has been applied to this field. Nevertheless, the frequency traversal in MUSIC is a time-consuming process that will affect the online monitoring performance. Its running time is highly related to the dimension of the noise subspace. The subspace dimension reduced MUSIC (SDR-MUSIC) is proposed in this article to reduce the running time of MUSIC by reducing the dimension of noise subspace. Simulations and experiments are conducted to testify that SDR-MUSIC can significantly reduce the running time of MUSIC while ensuring accuracy. [ABSTRACT FROM AUTHOR]

In this article, the amplitude measurement of multiple and simultaneously received linear chirp signals is investigated. The achievable scalability, that is, the maximum number of simultaneously transmitted superimposed signals that a receiver can characterize with a given target accuracy, is explored using simulations. The effect of chirp duration and bandwidth on the estimation accuracy and on the scalability is investigated. The effect of inaccuracies affecting the realized chirp duration and bandwidth is also discussed. It is shown that the scalability of the proposed approach is competitive with respect to multiple access methods, such as frequency-division multiple access or code-division multiple access. It is also shown that accurate amplitude measurements are achievable even when the collected signal is the superimposition of up to 20 chirp signals with amplitudes differing by three orders of magnitude. [ABSTRACT FROM AUTHOR]

The harsh working environment of compressor threats the integrality of bladed disk. Crack is one of the most fatal faults of rotating blades. Detecting the crack of blades at early stage could ensure the safe operation of gas turbine and save economic costs. Blade tip timing (BTT) is a noncontact blade vibration measurement technique, which is widely used in recent years. In this article, a procedure of crack detection using BTT data is proposed. First, some typical parameters are taken into consideration, including resonance frequency, amplitude change of natural frequency, amplitude of blade tip, phase, and interblade spacing. It has been found that the frequency of the blade will decrease with the increment of crack length and reduction of crack height. In addition, the resonance frequency of the bladed disk will split when the blade cracks. The amplitude of different orders of resonance frequency varies with cracks. The amplitude of the first order changes very slightly, whereas the second and third orders are relatively obvious. The blade tip vibration amplitude and phase change with cracks without obvious regularity. The interblade distance will change due to the appearance of cracks. According to the above rules, the frequency and the interblade distance are adopted as crack diagnosis indicators. First, we use the Lomb–Scargle periodogram to quickly determine whether the crack appears and then use the blade frequency and interblade distance to locate the number of the blade having crack. In the experimental test, BTT data measured on a titanium bladed disk is used to verify the effectiveness of our method. [ABSTRACT FROM AUTHOR]

In this article, we propose a single-vibration sensor-based method for detecting incipient faults in squirrel cage induction motors (SCIMs). We consider defects in different parts of the bearing (inner raceway, outer raceway, cage train, and rolling element) and in a single bar of the rotor. The vibration signal is dominated by the fundamental rotational frequency and its harmonics. The dominant components result in numerical errors while estimating the relatively indistinct fault-specific spectral components. In this article, we precondition the vibration signal by suppressing multiple dominant components using an extended Kalman filter-based method. The suppression of the dominant components reduces the spectral leakage, exposes minute fault components, and improves the overall amplitude estimation. Subsequently, we estimate the fault frequency and amplitude using an accurate and low-complexity Rayleigh-quotient-based spectral estimator. The thresholds for fault detection are determined from a small number of healthy data, and an adaptive minimum distance-based detector is used for hypothesis testing. The proposed test improves detection and reduces false alarms under noisy conditions. We test the complete algorithm using data from a 22-kW SCIM laboratory setup. The proposed method has achieved 100% accuracy with the publicly available 12-kHz drive-end bearing data from Case Western Reserve University, Cleveland, OH, USA. [ABSTRACT FROM AUTHOR]

A distributed phase measurement method of frequency-domain electromagnetic detection is proposed. The theoretical analysis of the previous literature shows that in electromagnetic detection applications, phase as an independent measurement parameter can reduce the diversity of solution, and it also has the advantage of greater steepness than the amplitude. Currently, there are few literature dealing with the phase in the practical application of audio electromagnetic detection, because it is difficult to obtain the phase from the separated devices. A synchronization recording system is designed based on the most widely used GPS and fiducial timestamp (FIDTS) technology. The phase errors caused by FIDTS, hardware circuit noise, and FFT spectrum leakage are analyzed. In addition, the synchronization data lost by GPS can be supplemented by interpolation. To prove the effectiveness of the method, a test platform was built in the laboratory. System test results show that the synchronization error between any two systems is less than 48 ns, and the phase error is less than 0.173° at 10 kHz. The experimental result of abnormal loop shows that the phase is more accurate than the amplitude. [ABSTRACT FROM AUTHOR]