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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]

The performance of a time-interleaved analog-to-digital converter (TI-ADC) gets compromised due to offset, gain mismatch, timing mismatch, and bandwidth mismatch. This article analyzes a TI-ADC from a generalized sampling perspective and then develops closed-form expression of the signal-to-noise and distortion ratio (SNDR) of an $N$ -channel TI-ADC when all of gain mismatch, timing mismatch, and bandwidth mismatch are present at the same time. The analysis is then extended to incorporate the effect of offset along with the other three mismatches to arrive at closed-form expression of SNDR in the presence of all the mismatches. The closed-form expressions have been validated through numerical simulations as well. [ABSTRACT FROM AUTHOR]

In low-frequency vibration measurement field, optical dynamic 3-D measurement based on camera-projector system is a novel approach. In this system, linear sinusoidal grating is the most common projection mode, which can meet the requirements of some vibration measurement. However, the dynamic measurement range of amplitude using the combination of linear grating and Fourier transform profilometry (FTP) is limited in some vibration measurement environments, because the linear fringe is periodic. To solve the problem, this article proposes a low-frequency vibration measurement method based on the concentric-circle grating camera-projector system and FTP. The center of the circle is traced dynamically and the coordinate system is transformed with the center of the circle as the origin. The phase distribution of concentric-circle grating is obtained by coordinate system transformation and FTP. By exploring the relationship between phase and relative height, the time-varying curve of the object surface height is recovered, which contains the vibration information. The experiments compare the measurement ability of the concentric-circle and linear grating. The results show that the linear grating and concentric-circle grating with the similar amplitude resolution have different amplitude measurement range. In this experiment, the maximum measurement range of linear grating is about 2.4 mm, while that of concentric-circle grating is more than about 3.9 mm. It means that the concentric-circle grating projection method can measure a large dynamic range of amplitude under the condition that the center of the circle can be traced, and solve the problem of amplitude limitation of linear grating in the use of FTP. [ABSTRACT FROM AUTHOR]

This article evaluates time-domain techniques for the applications of ultrawideband (UWB) chipless radio frequency identification (RFID) readers. In UWB time-domain readers, the chipless RFID tag response to a UWB pulse excitation is simultaneously measured in time domain and then the frequency-domain signature of the tag is calculated by a time-to-frequency conversion method such as fast Fourier transform. A 32-bit UWB chipless RFID tag is designed and three time-domain readers based on impulse radio UWB, chirped-pulse Fourier transform microwave (CP-FTMW), and dual-comb techniques are analyzed and implemented by using microwave equipment including arbitrary waveform generator, network analyzer, and high-speed oscilloscope. It is demonstrated that CP-FTMW reader is a fast, accurate with high signal-to-noise ratio, and high-frequency resolution time-domain technique. Dual-comb reader is slower, however, it offers a viable solution for a low cost and potentially miniaturized and portable chipless RFID readers. [ABSTRACT FROM AUTHOR]

Power inverters are essential components in modern electrical distribution systems. Switches are key elements of a power inverter, and they are the more affected components when a fault occurs. Breakdown of power-semiconductor devices represents around 38% of a power inverter fault. An open-circuit fault will keep the inverter working, jeopardizing its components and degrading its performance. The diagnosis and classification of power inverter faults have become important research subjects to avoid malfunctioning or damage of other components in an electric system. The Fourier transform and the wavelet-based multiresolution analysis (MRA) are the most applied signal processing techniques for identifying open-circuit faults in these devices. They are usually combined with other methods for increasing its effectiveness, which demands a considerable use of computational resources. This work introduces a novel low-computational-cost technique that directly analyzes the electric phase currents from a power inverter, in the time domain, utilizing three rotatory reference systems and basic arithmetic operations to identify open-circuit faults. The proposed method allows determining the specific power supply phase and the faulty switch, or switches, with high certainty. The obtained results are validated through exhaustive analyses against the Fourier transform and the wavelet-based MRA, demonstrating the proposed method suitability for online-monitoring applications, ensuring high sensitivity and accuracy on detecting and isolating open-circuit faults with low computation cost. [ABSTRACT FROM AUTHOR]

The 3-D surface measurement plays a significant role in the industrial area. With the advantages of high resolution and acquisition rate, dual-line-scan camera systems have been gradually studied for dynamic 3-D measurement. However, the partial overexposure and the limited measurement depth range of dual-line-scan cameras are the two main elements affecting the quality of the point cloud. Both of these problems are caused by incorrect pixel matching. Therefore, this article presents a matching strategy for dual-line-scan cameras based on the 1-D background-normalized Fourier transform (1DBNFT) to expand the depth range of measurement and deal with the partial overexposure. The wrapped phases extracted by 1DBNFT, the unwrapped phase extracted by projection distance minimization (PDM), and the matching principle are described in detail. We also analyze the possible errors in the system from three aspects: noncoplanar error, installation error, and motion error. Finally, the comparative experiments and accuracy verification experiments demonstrate the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

Synchrosqueezing transform (SST) is a currently proposed novel postprocessing time–frequency (TF) analysis tool. It has been widely shown that SST is able to improve TF representation. However, so far, how to improve the TF resolution while ensuring the accuracy of signal reconstruction is still an open question, particularly for the vibration signal with time-varying instantaneous frequency (IF) characteristics, due to the fact that the vibration signals of mechanical equipment usually contain many types of noise generated by harsh operating conditions, and the SST will mix these noise into the real signal. Our first contribution is using the Gaussian modulated linear chirp (GMLC) signal model to represent the general nonstationary signals. The GMLC signal model can more accurately represent the time-varying nonstationary signal, compared with the SST signal model composed of linear phase function and constant amplitude. Our second contribution in this work is proposing a method to improve the TF resolution and reconstruction accuracy for nonstationary signals with time-varying IF, which we coined the second-order synchroextracting transform (SET2). In SET2, we apply the GMLC to deduce the nonstationary signal model and then only use the energy at the IF to characterize the TF distribution, which improves the TF while reducing the impact of noise on the real signal. [ABSTRACT FROM AUTHOR]

The velocity of a linearly moving cylindrical conductor can be measured using two methods: the laser-Doppler method and the eddy current method. In this article, we focused on an eddy current linear velocity sensor (LVS) with excellent environmental resistance while analyzing its theory to clarify its characteristics. Consequently, we were able to obtain an exact analytical solution by solving Maxwell’s equation using the Fourier transform. In addition, we built an experimental apparatus that enables cylindrical conductors to move linearly and then used it to conduct an inspection experiment. As a result, a good agreement between the theory and the experiment was obtained for the speed characteristics. Therefore, we could confirm the validity of the theoretical analysis, and thus it has become possible to predict various characteristics related to LVSs. The analysis results of the theory are expected to greatly contribute to the optimization of LVSs. [ABSTRACT FROM AUTHOR]

Phaseless measurement is widely used in various fields, and phase retrieval is a key step in signal reconstruction of phaseless measurement. The occurrence of outliers will cause the optimal solution of the traditional phase retrieval objective function to deviate from the original signal, thereby reducing the reconstruction accuracy. This article modifies the objective function by introducing a weight vector so that its optimal solution will still approximate the original signal when outliers appear and proposes a specific implementation strategy for this idea under the background of Fourier phase retrieval. For the weight vector, we design a two-channel measurement structure using a discrete Fourier transform frequency-domain cyclic shift theorem and mask technique to obtain a weight vector. For the improved objective function, we use the majorization–minimization framework to derive an iterative algorithm to get an optimal solution. Extensive simulation and hardware experiments show that the proposed method can effectively suppress the adverse effects of outliers. [ABSTRACT FROM AUTHOR]

Range and Doppler frequency cell migration induced by the movement of the low-observable target would bring about serious integration loss, which may deteriorate the target detection or imaging performance. Hence, this article proposes a migration correction algorithm based on keystone transform and modified generalized radon Fourier transform (KT-MGRFT). More specifically, KT is first utilized to eliminate the linear range cell migration (RCM) induced by the target unambiguous velocity. Then, MGRFT is presented to estimate the fold factor and acceleration. After that, the residual RCM and Doppler frequency cell migration (DFCM) are compensated by multiplying a reference signal constructed by the estimated fold factor and acceleration. Finally, the coherent integration can be achieved via the inverse Fourier transform (IFT) and the Fourier transform (FT) with respect to range frequency and scaled slow time, respectively. Compared with standard GRFT, the proposed method can remove the searching procedure of unambiguous velocity, and thus, it has lower computational complexity. In addition, through taking the maximum value in searching the range-Doppler frequency domain, the blind speed sidelobe (BSSL) effect is suppressed. Both the numerical simulations and the real experiment with the X-band radar are given to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Change in density will cause errors in measurement for indirect-based level sensing approaches, such as capacitive sensors. In addition, there is growing attention in measuring density accurately not only in laboratory conditions but also in a real-time dynamic environment. To address the above-mentioned issues, an independent instrument based on differential pressure sensors is proposed in this article. Synchronous detection is used to detect the desired signal by calculating the coefficients of the digital Fourier transform. The characterization of the sensor is experimentally determined under a steady-state condition, where a linear response is observed. The thermal behavior of the proposed sensor is studied and compensated using the polynomial fitting technique. Evaluating the uncertainty due to reference values, linearity, and repeatability, the obtained results show a combined uncertainty lower than 7.5 (mg/cm3), mainly limited by observed asymmetric hysteresis at higher temperatures. [ABSTRACT FROM AUTHOR]

Machinery condition monitoring methods that can automatically classify the system health condition using machine learning algorithms often require a large amount of labeled data, which are not always available in real industrial processes. This article proposes a different solution to automatic machinery condition monitoring based on the signal similarity measurement in the time–frequency domain. In the first stage, using short-time Fourier Transform (STFT), time–frequency representation (TFRs) of vibration signals issued for different machinery health conditions are used to create a baseline. In the next stage, the similarities in the time–frequency domain between very limited labeled and test signals are measured using a novel technique. In the third stage, the similarities are compared in order to classify the test signals. In the proposed method, the baseline of the labeled samples could be from only one operating condition, while the test samples are from all possible operating conditions. In addition, to improve the robustness against noise and make the proposed method sensitive to local differences in signals, a multiscale analysis (MSA) using the wavelet packet decomposition (WPD) technique is adopted. As a specific example, effectiveness and robustness of the proposed method in a noisy environment is validated through various bearing defective levels and for different speeds and workloads. Experimental results demonstrate that the proposed solution achieves a high classification accuracy even with very limited labeled samples. [ABSTRACT FROM AUTHOR]

As a well-known homologous information fusion technology, the full spectrum is widely used in orbit analysis of rotating machinery. However, due to the average effect of the Fourier transform, the instantaneous features of the axis orbit cannot be obtained in full spectrum. This limits the application of the full spectrum in a nonstationary vibration analysis. The time–frequency representation (TFR) of axis orbit has been proved to be an effective tool for analyzing nonstationary vibration signals. In this article, a new approach of instantaneous features extraction of harmonic axis orbits is proposed. Furthermore, based on these features, an approach to construct the purified orbit TFR (POTFR) is proposed. First, the estimated speed signal is extracted from the TFR of a single-channel signal by data fitting. Guided by the estimated speed signal, the harmonic components of interest are separated by the Vold–Kalman filter. Furthermore, the parameters of each harmonic instantaneous orbit are calculated from the time domain by using the separated harmonic signals. Based on these parameters, high-resolution POTFRs of all the harmonic orbits are constructed. Finally, the POTFR of the original signal is obtained by combining the POTFRs of all the harmonic components. The proposed approach is illustrated by numerical simulation and is further validated using laboratory experimental signals of oil whip and bistable behavior. The start-up signal from a hydroelectric generating set further demonstrates the superiority of the method. [ABSTRACT FROM AUTHOR]

This article introduces the second-order transient-extracting transform (TET2) to extract transient components from a nonstationary signal. Different from the traditional transient-extracting transform (TET1), the proposed method is based on a more general frequency-domain signal model, termed the Gaussian-modulated linear group delay signal model. The first step of the proposed method is the computation of the second-order group delay (GD) estimator that describes the temporal positioning of signal ridges in the time-frequency (TF) plane. Based on this second-order GD estimator, we can then obtain an energy-concentrated TF representation. Finally, the transient component of interest is reconstructed after filtering out the noise in the TF plane. Thus, the TET2 can be considered as an effective TF filtering tool. Numerical experiments on real and synthetic signals show the effectiveness of the proposed method in the TF filtering application. [ABSTRACT FROM AUTHOR]

The influence of liftoff variation on the peak value and time-to-peak feature of pulsed eddy current (PEC) signals is investigated in this article by simulations and experiments. First, PEC response is analytically modeled via Fourier transform for simulations. In an alternative way, PEC response is formulated with Laplace transform. Using an approximation of Laplace transform-based PEC formulation, the physics that liftoff distance only changes the amplitude of PEC responses rather than shifting the position of peak heights is discovered. Second, simulations were implemented, and the results demonstrate that the time-to-peak feature is insensitive to liftoff variation, i.e., around one-fourth of the peak value. Finally, the experimental results agree well with those from the simulations, which validates the physics that the time-to-peak feature is insensitive to the liftoff effect. [ABSTRACT FROM AUTHOR]

Vibration analysis is a powerful tool for condition monitoring of rotating machinery. In the nonstationary case, this analysis often involves denoising and extraction of the time-varying harmonic components buried within the vibration signal. However, the complexity of many contemporary techniques—especially in relation to nonstationary signals—and their dependence on prior knowledge of the system kinematics in order to be effective is an inhibitor to autonomous fault detection and monitoring of nonstationary systems. In this article, a nonparametric, blind spectral preprocessing approach to simultaneously denoise and extract the harmonic content from nonstationary vibration signals is presented. The proposed approach utilizes mean shift clustering in conjunction with the short-time Fourier transform to separate time-varying harmonics from background noise within the frequency spectrum, without the need for a priori knowledge of the system. The technique is fully invertible, allowing the time signals corresponding to the separated time-varying harmonic and residual components to be reconstructed. The performance of the proposed technique is compared against existing preprocessing methods and validated using several industrial data sets: first, using vibration data obtained from a low-speed, nonstationary industrial automated people mover gearbox, next, using vibration data from an aircraft engine containing outer race faults, and finally, using nonstationary vibration data from a wind turbine containing frequent speed fluctuations. [ABSTRACT FROM AUTHOR]

In this paper, the design of frequency-locked loop (FLL) is proposed based on computationally efficient discrete Fourier transform (DFT) structures. In recent years, the DFT structures are evolved as sliding DFT (SDFT), modulated SDFT, hopping DFT (HDFT), modulated HDFT, and sliding-windowed infinite Fourier transform. Considering their tuned filter characteristics, an attempt has been made to obtain a solution for the instantaneous frequency estimation problem of the input signal under varying center frequency condition. In each DFT structure, the $k$ th bin in-phase and quadrature components are separated for instantaneous signal extraction. The feedback loop is designed around these DFT structures, and it was observed that the frequency responses exhibit flat magnitude and phase interestingly when compared with the open-loop structures. Hence, an adaptive sampling frequency adjustment scheme is proposed for these structures as FLL to estimate the instantaneous frequency of the input signal for the wide variation in center frequency. These FLLs with different DFT structures are tested for dynamic performance and wide operating range. The proposed FLLs are implemented in field-programmable gate array (FPGA), and the experimental investigations have been carried out for frequency estimation. Further experimental investigations on these FLLs as a system on chip were carried out with area and power analysis. [ABSTRACT FROM AUTHOR]

Artificial intelligence evolved as a powerful tool in condition monitoring of the induction motor for early diagnosis of bearing faults. This paper attempted to identify the features to improve the accuracy of diagnosis using the benchmark database of the Case Western Reserve University (CWRU) and the Machinery Fault Prevention Technology (MFPT). In this paper, 18 time-domain features are extracted constituting six proposed time-dependent spectral features (TDSFs) and 12 statistical time-domain features. For the first time, a set of six TDSFs are extracted to diagnose the bearing faults. This involves extracting frequency-domain features using the relationship between Parseval’s theorem and the Fourier transform directly from the time domain. Particle swarm optimization (PSO) and wheel-based differential evolution (WBDE) feature selection algorithms are also implemented to identify four prominent features. It is found that three of the four selected features are TDSF features, and results of selected features revealed the attainment of 90.92% for 48 class identifications of CWRU database and 100% for 17 class identifications of the MFPT database, respectively. [ABSTRACT FROM AUTHOR]

In industrial rotating machinery, the transient signal usually corresponds to the failure of a primary element, such as a bearing or gear. However, faced with the complexity and diversity of practical engineering, extracting the transient signal is a highly challenging task. In this paper, we propose a novel time–frequency analysis method termed the transient-extracting transform, which can effectively characterize and extract the transient components in the fault signals. This method is based on the short-time Fourier transform and does not require extended parameters or a priori information. Quantized indicators, such as Rényi entropy and kurtosis, are employed to compare the performance of the proposed method with other classical and advanced methods. The comparisons show that the proposed method can provide a much more energy-concentrated time–frequency representation, and the transient components can be extracted with a significantly larger kurtosis. The numerical and experimental signals are used to show the effectiveness of our method. [ABSTRACT FROM AUTHOR]

An on-load tap changer (OLTC) is one of the most error-prone parts in the transformer. The stability of the entire power system will be greatly threatened if the OLTC fails. It is necessary to diagnose the mechanical fault of OLTCs. At the same time, the mechanical fault signals are not easy to collect during practical application. It is desirable to have indicators that help to access the condition of the OLTC with a small number of fault samples. This paper describes a strategy for distinguishing different mechanical diagnostics of OLTCs based on dynamic time warping. By synchronization, framing, and Fourier transform, the signal can be represented by an energy matrix, which can be used to distinguish different conditions of the OLTCs and shows the severities of the fault. Some simulated experiments are designed and in order to make the simulations close to reality, all experiments are taken in a tank which is filled with transformer oil. These experiments demonstrate that the proposed approach can effectively diagnose different OLTC faults under different fault severities. [ABSTRACT FROM AUTHOR]

High-resolution time-frequency representation (TFR) method is effective for signal analysis and feature detection. However, for variable speed bearing vibration signal, conventional TFR method is prone to blur and affect the accuracy of the instantaneous frequency estimation. Moreover, the traditional order tracking, relying on equi-angular resampling, usually suffers from interpolation error. To solve such problems, we propose a joint time-frequency (TF) squeezing method and generalized demodulation (GD) to realize variable speed bearing fault diagnosis. The method can represent the time-varying fault characteristic frequency precisely and be free from resampling. First, using fast spectral kurtosis to select the optimal-frequency band which is sensitive to rolling bearing fault, and extracting envelope by Hilbert transform within the selected optimal frequency band. Next, a high-quality TF clustering method based on short-time Fourier transform is applied to the TF analysis of the envelope to get a clear TFR, from which the frequency information for GD is obtained. Finally, processing the basic demodulator via the peak search through the TF analysis results in the TFR for GD to gain a resampling-free-order spectrum. Based on the more precise TF information from the clearer TFR, the bearing fault can be diagnosed via GD without tachometer or any resampling involved, avoiding the amplitude error and low computational efficiency of resampling. Simulation study and experimental signal analysis validate that the proposed method has better performance than those methods based on conventional TF analysis and resampling. [ABSTRACT FROM AUTHOR]

There are many cases of electromagnetic interference caused by supraharmonics emitted by power electronic equipment. However, there is currently no effective method for measuring supraharmonics. This paper proposes a new supraharmonics high-resolution measurement algorithm based on a multiple measurement vectors (MMVs) compressive sensing (CS) model and an orthogonal matching pursuit (OMP) recovery algorithm. First, by introducing an interpolation factor, based on a spectrum array of multiple discrete Fourier transform coefficient vectors and a Dirichlet kernel matrix, an MMVs CS model is constructed. Then, by using the jointly sparse property of high-resolution spectrum array, the MMVs CS model is converted into a single-measurement vector CS model. Third, by using an OMP recovery algorithm, the support set of the high-resolution spectrum array is solved. Finally, by using least squares, the high-resolution spectrum array of supraharmonics is obtained. Simulation results and verification of the measured data show that the algorithm proposed in this paper can improve the frequency resolution by an order of magnitude without increasing the observation time, can shorten the calculation time by $100\times $ compared with the single measurement vector compressive sensing-OMP algorithm, and can compute the amplitude and phase of supraharmonics accurately. Meanwhile, the amplitude fluctuation characteristics of supraharmonics can also be analyzed accurately. This algorithm shows a good application prospect in measuring supraharmonics accurately. [ABSTRACT FROM AUTHOR]

This paper presents a new method for automatic monitoring of noisy power quality, which is based on the Hilbert transform (HT) and the proposed slip-singular value decomposition (SVD)-based noise-suppression algorithm. The proposed method first employs the fast Fourier transform (FFT)-based low-pass filter and HT to obtain the instantaneous fundamental amplitude and the FFT sequence of the signal. Second, the slip-SVD-based noise-suppression algorithm and threshold filtering are used to extract cleaned singular value characteristic waveform of the high-frequency signal. Through judging the instantaneous fundamental amplitude, cleaned singular value characteristic waveform, and the FFT sequence, the presence of disturbances including single and combined disturbances can be easily detected by the proposed method. To demonstrate the effectiveness of the proposed method, extensive tests are conducted on the diverse simulation disturbances and the actual data obtained from the practical power systems of China. The test results show that the proposed method has the advantages such as low false detection rate, good noise tolerance capability, short computational time, fewer parameters, practicability, and compatibility in comparison with the traditional disturbance detection methods. Besides, the proposed method can provide some important features such as amplitude, duration, and frequency for classification. Such advantages make the proposed method to be a good choice for real-time applications. [ABSTRACT FROM AUTHOR]

In-circuit impedance extraction of a time-variant electrical system is essential for real-time condition monitoring purpose. This paper looks into the design consideration of real-time in-circuit monitoring for a time-variant system based on inductive coupling approach and discrete Fourier transform in a moving window. By using two clamped-on-type inductive probes, the proposed setup eliminates the need of voltage sensors to measure the terminal voltages of system-under-monitoring (SUM) and, hence, eliminates direct electrical contact with the SUM. Therefore, it facilitates ease of mounting and disassembly. Experimental results show that with careful design consideration, the proposed method has the ability to measure the in-circuit impedance of a time-variant system in real time, which facilitates useful inputs for early detection of abnormality of the monitored electrical system that is time variant in nature. [ABSTRACT FROM AUTHOR]

Gas–liquid slug flow widely exists in many industrial processes. The structural velocities of slug flow are important for fluid mechanics modeling, scientific researches, and process assurance. To measure the structural velocities of slug flow nonintrusively, a continuous wave ultrasonic Doppler sensor and a conductance sensor are combined to obtain the Doppler shift signal and water holdup, respectively. Due to the intense fluctuation of phase fraction and velocity, the scatters in different regions of the slug flow produce different Doppler shifts. Therefore, the Doppler signal is a signal with multiscale fluctuations containing structural information of slug flow. To extract the structural velocities, the Doppler shift signal was decomposed into several intrinsic mode functions (IMFs) by the empirical mode decomposition, and the main IMFs that reflect the essential characteristics of the signal were determined by cross correlation method. By combining water holdup signal, the short-time Fourier transform (STFT) was applied to the main IMFs to determine the correlation between the specific main IMFs and the structure of a slug unit. Finally, the velocities of the liquid slug nose, the liquid slug, and the liquid film were extracted from the STFT results. To validate the proposed model, theoretical structural velocities were calculated based on Dukler’s model, and the results showed good agreement with the extracted velocities with the average relative difference of 3.34%. For further validation, the total volume flow rate was calculated using a quadratic threshold method. Compared with the reference total volume flow rate, the mean absolute percentage error was 3.53%. [ABSTRACT FROM AUTHOR]

This paper describes the way that white noise (including quantised input section sampling) imparts errors onto frequency and rate-of-change-of-frequency (ROCOF) measurements. The main paper focus concerns the use of filtered heterodyned (i.e., Fourier) analyses for single-phase and three-phase systems, and the filtered Clarke transform for three-phase systems. The rules and equations governing the effect of white noise on frequency and ROCOF are formulated for these techniques, explaining the subtle effects of aliasing, splitting signals and noise into their positive and negative frequency components, and the correlation or decorrelation of noise. It is shown that—as expected—for three-phase ac measurements, averaging three single-phase Fourier measurements produces the same performance against noise as using a method based on Clarke’s transform, if identical filtering is used. Furthermore, by understanding the theory behind the frequency and ROCOF measurement processes, it is shown that to achieve the lowest RMS errors, in the presence of front-end white noise (alone, ignoring other dynamic signal and power quality aspects), a filter which provides ~40 dB/decade attenuation (i.e., a two-boxcar cascade) is recommended for a frequency measurement, but a filter which rolls off at ~60 dB/decade (i.e., a three-boxcar cascade) is recommended for a ROCOF measurement. [ABSTRACT FROM AUTHOR]

Incorporation of automated electrocardiogram (ECG) analysis techniques in home monitoring applications can ensure early detection of myocardial infarction (MI), thus reducing the risk of mortality. Most of the published techniques use advanced signal processing tools, a huge number of ECG features, and complex classifiers, which make their hardware implementation difficult. This paper proposes the use of harmonic phase distribution pattern of the ECG data for MI identification. The morphological and temporal changes of the ECG waveform caused by the presence of MI are reflected in the phase distribution pattern of the Fourier harmonics. Two discriminative features, clearly reflecting these variations, are identified for each of the three standard ECG leads (II, III, and V2). Classification of the healthy and MI data is performed using a threshold-based classification rule and logistic regression. The proposed technique has achieved an average detection accuracy of 95.6% with sensitivity and specificity of 96.5% and 92.7%, respectively, for classifying all types of MI data from the Physionet Physikalisch-Technische Bundesanstalt diagnostic ECG database. The robustness of the algorithm is confirmed with real data as well. The algorithm is also implemented and validated on a microcontroller-based Arduino board, which can serve as a prototype ECG analysis device. Apart from providing comparable performance to other reported techniques, the proposed technique provides distinct advantages in terms of computational simplicity of the features, significantly reduced feature dimension, and use of simple linear classifiers which ensure faster and easier MI identification. [ABSTRACT FROM AUTHOR]

Nonsynchronous sampling conditions are the source of leakage errors in measurements based on digital signal processing. The optimal method to avoid such errors is to generate a synchronous sampling clock, whenever the input signal shows enough period stability to allow this. Such method, however, does not yield a straightforward evaluation of the input signal frequency, which is required in several applications. This paper proposes a modified synchronous clock generator that together with a modified frequency interpolation algorithm provides an accurate measurement of the input signal frequency when the only available information about the sampling clock is a given integer multiple of the input signal fundamental frequency. [ABSTRACT FROM AUTHOR]

Charge accumulation on a solid dielectric surface is one of the critical concerns for the design and optimization of the insulation system in a high-voltage power equipment, since it will lead to the overstress of electrical insulation. Therefore, it is important to obtain the charge density distribution on a solid dielectric surface with high accuracy. The acquisition of surface charge for insulators requires multipoint potential measurements to establish the inverse calculation for the determination of an unknown charge distribution. Up to now, extensive studies have been conducted on this subject; nevertheless, the methods are either too complicated and time consuming, or only applicable for specific arrangements, or with poor accuracy. In this paper, the problem is divided into two categories, i.e., shift-variant system and shift-invariant system, and the basic principle of an improved inversion algorithm is interpreted to solve the problem. The 2-D Fourier transform and Wiener filter techniques are employed in the algorithm for shift-invariant system thus the relationship between potential and charge density can be processed in spatial frequency domain, which tremendously simplifies the conventional procedure. The accuracy and resolution of the algorithm are discussed in detail with the aid of numerical examples. In the end, experiments are conducted and the effectiveness of the algorithm is verified. [ABSTRACT FROM AUTHOR]

Motor current signature analysis requires high sampling rate and a large number of data samples to detect faults at light loading conditions. However, it would be interesting to investigate the alternative methods to bring down these requirements using the principle of sparse signal processing. In this paper, a low-complexity fault detection algorithm based on the sub-Nyquist sampling of the analytic current signal has been proposed and compared to other similar algorithms. The acquired current data is first converted into an analytic signal using a low-cost time-shift-based method. The processing of the analytic current signal has been performed at a rate 20 times lower than the Nyquist criterion using multicoset sampling technique. The computational complexity has been reduced further by partially reconstructing the spectrum only for the regions of interest. The regions of interest were found out from the estimated supply frequency and the armature current magnitude. The supply frequency has been estimated from the transformed analytic current signal. The algorithm has been tested for broken rotor bar (BRB), eccentricity, and broken bearing faults, using the recorded data from a 22-kW induction motor drive at various loading conditions in the laboratory. In addition, the BRB fault has also been tested for motors with 1.5- and 3.7-kW ratings. A receiver operating characteristic curve has been generated to test the performance of the algorithm. The proposed algorithm uses only a single current sensor to acquire one of the armature phase currents. [ABSTRACT FROM AUTHOR]

Nonuniform sampling can be utilized to achieve certain desirable results. Periodic nonuniform sampling can decrease the required sampling rate for signals. Random sampling can be used as a digital alias-free signal processing method in analog-to-digital conversion. In this paper, we first present the fractional spectrum estimation of signals that are bandlimited in the fractional Fourier domain based on the general periodic random sampling approach. To show the estimation effect, the unbiasedness, the variance, and the optimal estimation condition are analyzed. The reconstruction of the fractional spectrum from the periodic random samples is also proposed. Second, the effects of sampling jitters and observation errors on the performance of the fractional spectrum estimation are analyzed, where the new defined fractional characteristic function is used to compensate the estimation bias from sampling jitters. Furthermore, we investigate the fractional spectral analysis from two widely used random sampling schemes, i.e., simple random sampling and stratified random sampling. Finally, all of the analysis results are applied and verified using a radar signal processing system. [ABSTRACT FROM PUBLISHER]

The sinusoidal signal outputted by the vortex flow sensor is often superimposed with the white noise, which will affect the accuracy of spectrum correction. When the signal is truncated by rectangular window, a method (hereinafter referred to as RWBWFCR) combining bilateral correction with weighted average on the basis of Fourier coefficients ratio is proposed so as to improve the antinoise performance of the frequency correction. The correction principle of this method is presented. The effects on the frequency correction of changing the initial phase, the frequency deviation, the calculation points of fast Fourier transform, and the signal-to-noise ratio (SNR) are analyzed. The theoretical accuracy of this method when there is stationary Gaussian white noise is deduced and verified. This method has some advantages, such as less computation load, small storage space, high correction precision, strong anti-interference ability, and is easy to be implemented by a microcontroller in real time. RWBWFCR method is applied to the low-power and antivibration type vortex flowmeter with single sensor to improve the reliability of the digital signal processing method based on the frequency correction. [ABSTRACT FROM PUBLISHER]

Real-time image processing algorithms and associated mathematical formulas that render ultraprecise three-axis visual motion tracking are presented. The principle of three-axis motion tracking is based on lateral sampled white light interferometry (LSWLI) and real-time unbiased image registration. Two distinct regions of interest (ROIs), i.e., in-plane ROI and out-of-plane ROI, in a single 2-D image, taken at a single time instant, are processed in real time. Real-time continuous image registration (CIR) is realized and applied to 2-D shadow projection image content of the object to locate the object’s in-plane position, whereas adaptive 1-D CIR (A1-CIR) is proposed and applied to an LSWLI fringe pattern on the object to determine its out-of-plane position. Processing of images is synchronized with real-time image acquisition to realize three-axis motion tracking. Computer simulations are used to demonstrate bias caused by image registration and to examine its effect on measurement precision. The ability of the proposed approach to eliminate measurement bias is validated. Experimental results are presented and used to examine measurement resolution for positioning and to illustrate measurement precision for long-range motion tracking. [ABSTRACT FROM PUBLISHER]

We propose a novel random triggering-based modulated wideband compressive sampling (RT-MWCS) method to facilitate efficient realization of sub-Nyquist rate compressive sampling systems for sparse wideband signals. Under the assumption that the signal is repetitively (not necessarily periodically) triggered, RT-MWCS uses random modulation to obtain measurements of the signal at randomly chosen positions. It uses multiple measurement vector method to estimate the nonzero supports of the signal in the frequency domain. Then, the signal spectrum is solved using least square estimation. The distinct ability of estimating sparse multiband signal is facilitated with the use of level triggering and time-to-digital converter devices previously used in random equivalent sampling scheme. Compared to the existing compressive sampling (CS) techniques, such as modulated wideband converter (MWC), RT-MWCS is with simple system architecture and can be implemented with one channel at the cost of more sampling time. Experimental results indicate that, for sparse multiband signal with unknown spectral support, RT-MWCS requires a sampling rate much lower than Nyquist rate, while giving great quality of signal reconstruction. [ABSTRACT FROM PUBLISHER]

The design and test of a radio detection and ranging (Radar) sensor signal acquisition and processing platform is presented in this paper. The Radar sensor operates in real time and is suited for surveillance applications in transport systems. It includes a front-end with a continuous-wave frequency-modulated transceiver operating in X-band, with a single transmitter and multiple receivers, and a multichannel high-speed A/D converter. Sensor signal processing and data communication tasks with external hosts are managed by a field-programmable gate array. The signal processing chain includes region of interest selection, multidimensional fast Fourier transform, peak detection, alarm decision logic, data calibration, and diagnostic. By configuring the Radar sensing platform in low-power mode (7-dBm transmitted power), it is possible to detect still and moving targets with a covered range up to 300-m and 30-cm resolution. The measuring range can be increased up to 2 km by adding an extra 34.5-dBm power amplifier. The Radar sensing platform can be configured for a maximum detected speed of 200 km/h, with a resolution of 1.56 km/h, or a speed up to 50 km/h with a resolution of 0.4 km/h. The cross-range resolution depends on the number of receiving channels; a tradeoff can be found between cross-range resolution of the Radar sensor and its complexity and power consumption. With respect to the state of the art of surveillance Radar sensors and light detection and ranging, the proposed solution stands for its high configurability and for the better tradeoff that can be found in terms of covered distance and power consumption. [ABSTRACT FROM AUTHOR]

Transient-based methods for fault diagnosis of induction machines (IMs) are attracting a rising interest, due to their reliability and ability to adapt to a wide range of IM’s working conditions. These methods compute the time–frequency (TF) distribution of the stator current, where the patterns of the related fault components can be detected. A significant amount of recent proposals in this field have focused on improving the resolution of the TF distributions, allowing a better discrimination and identification of fault harmonic components. Nevertheless, as the resolution improves, computational requirements (power computing and memory) greatly increase, restricting its implementation in low-cost devices for performing on-line fault diagnosis. To address these drawbacks, in this paper, the use of the short-frequency Fourier transform (SFFT) for fault diagnosis of induction machines working under transient regimes is proposed. The SFFT not only keeps the resolution of traditional techniques, such as the short-time Fourier transform, but also achieves a drastic reduction of computing time and memory resources, making this proposal suitable for on-line fault diagnosis. This method is theoretically introduced and experimentally validated using a laboratory test bench. [ABSTRACT FROM AUTHOR]

A novel digital signal processing-based method for phase noise measurement on sinusoidal signals is proposed. The method obtains the power spectral density of the phase noise by Fourier transforming its autocorrelation function. The autocorrelation function of the phase noise is in turn derived from the cyclic complementary autocorrelation function of the analytical version of the sinusoidal signal under test. The method proves that, taking advantage of the cyclostationarity properties of the signal under test, it is possible to improve the sensitivity in phase noise measurements by diminishing the effects of the instrumentation noise, which mainly limits the performance of the majority of the digital processing-based approaches. [ABSTRACT FROM AUTHOR]

Most rotary machinery imperfections are related to defects in rolling element bearings. Unfortunately, reliable bearing fault detection still remains a challenging task, especially when bearing defect-related features are nonstationary. A new morphological Hilbert-Huang (MH) technique is proposed in this paper for incipient bearing fault detection. In the proposed MH technique, a new linearity measure method is suggested to demodulate characteristic feature functions, and a mathematical morphological filter is proposed to reduce impedance effect of the measured vibration signal to improve fault detection accuracy. The effectiveness of the proposed MH technique is verified by a series of experimental tests corresponding to different bearing conditions. [ABSTRACT FROM AUTHOR]

Analog-to-digital converters (ADCs) are becoming increasingly more common to be involved in most systems with integrated circuits. One of the difficulties being faced is to be able to accurately and cost-effectively test the continually higher performance of ADCs. A part of this test is being able to assess the dynamic linearity of the ADC through dynamic spectral testing. The standard test method for ADCs can be difficult to implement accurately and cost-effectively due to the stringent requirements. This paper develops an algorithm that relaxes the requirements on the linearity of the test signal and of the need to achieve coherent sampling. The standard test requires that the input signal linearity be about 20 dB purer than the ADC under test along with always maintaining coherent sampling. This algorithm will reduce the purity requirement by allowing the test signal to be less pure than the ADC under test while also completely removing the need for coherent sampling. The proposed method will be able to test a 16-b ADC with a target total harmonic distortion (THD) of around −95 dB using a test input signal that has a THD as bad as −50 dB. A test signal with this level of purity would be easy to design as well as be easily accessible in most systems that are being designed. The accuracy and robustness of these methods are demonstrated through simulation and measurement results. [ABSTRACT FROM AUTHOR]

A novel, highly sensitive double-parameter sensor based on the composite interference structure has been designed and implemented in this paper. It can be regarded as a cascade of the traditional single-mode fiber (SMF)–hollow-core fiber (HCF)–photonic crystal fiber (PCF)-SMF structure, which forms a reflected Fabry–Perot interferometer and a transmitted Mach–Zehnder interferometer at the same time. The HCF is filled with ethanol, which is sensitive to temperature. Then the simultaneous measurement of the temperature and the refractive index (RI) can be achieved by monitoring the reflected spectrum and transmitted spectrum. The fact that these two spectrums do not interfere with each other makes it easier to obtain a demodulation method for simultaneous temperature and RI measurement that does not involve complex fast Fourier transform and matrix computation. The sensitivities of the temperature and the RI can reach up to 0.454 nm/°C and 178.7 nm/RIU, respectively. [ABSTRACT FROM AUTHOR]

Nonlinear contact between tip and sample in tapping mode atomic force microscopy (AFM) systems induces higher harmonics, which may be useful for extraction of some characteristics of the sample. In this paper, a new efficient method for implementing multichannel digital lock-in technique is presented, which is able to measure the amplitude and phase of multiple modulated frequency signals. In order to solve the two most important problems in AFM systems, which are resolution and cost, previous multichannel lock-in amplifier methods are investigated and an improved algorithm for amplitude and phase extraction using fast Fourier transform is developed. In addition, we propose a novel multichannel digital lock-in algorithm including an $N$ -slow digital filter and a multichannel direct digital synthesis block for generating reference signals. We achieve optimal noise reduction using the appropriate decimation filters prior to harmonic extraction. This modification results in a significant hardware reduction in implementation compared with classical designs. Since the precision of the design is sensitive to the sampling frequency, we perform the suitable sampling rate conversion using fractional delay and decimation filters, which enhance the accuracy and noise resiliency of the designed architectures. Experimental results prove the efficiency of the proposed method in multiple modulated frequencies extraction. Also, FPGA implementation results show that the proposed architecture is superior to previous structures, especially in the aspect of area. [ABSTRACT FROM PUBLISHER]

Early fault diagnosis is crucial to reduce the machine downtime. This paper presents a novel method based on symbolic dynamic filtering (SDF) for early fault detection and intrinsic characteristic-scale decomposition (ICD) for fault type recognition. SDF is first applied to extract the fault feature for depicting bearing performance degradation. Then, a fault alarm is triggered using cumulative sum. Finally, the extracted abnormal signal is decomposed by the ICD method, and the kurtosis method is used to select a principal product component that contains most fault information for fault detection. The real life experimental results validate the effectiveness of the proposed method in early detection of bearing fault and fault diagnosis in comparison with Fourier transform, Hilbert envelope spectrum, original local mean decomposition and spectral kurtosis. [ABSTRACT FROM AUTHOR]

Steady signals of a power system sampled asynchronously will generate frequency spectrum leakage and fence effect. Meanwhile, in the case of the existence of interharmonics that are close to harmonics as well as other interharmonics, the actual frequency components cannot be accurately identified and measured on account of the limited frequency resolution. In order to solve the immeasurability of multiple adjacent interharmonics under asynchronous sampling condition due to spectrum interference, a multiple interharmonics spectrum separation and parameter measurement method is proposed. First, an interharmonic simplification model is established, and then, each main frequency component can be separated with the known spectral information using the spectrum separation algorithm. The performance of the proposed method is validated on the synthetic signal. The simulation results show that the proposed method can not only accurately identify the frequency components, but also obtain the parameters with high precision. [ABSTRACT FROM AUTHOR]

The fast acquisition of heart rate (HR) is a challenge in noncontact vital sign detection using a Doppler radar system. Most of the previous studies use long-period time windows to guarantee a sufficient frequency spectrum resolution for HR measurement using the peak searching method on the frequency spectrum. For fast acquisition of HR, the length of the time window is less than 5 s and the accuracy is significantly degraded due to insufficient spectrum resolution. For vital sign detection using complex signal demodulation (CSD), measuring HR variation becomes a difficult job due to respiration harmonic interference and insufficient frequency spectrum resolution. In this paper, a time-window-variation technique is developed for fast acquisition of HR from short-period time windows and measuring HR variation using CSD. Experiments are performed on four human subjects under controlled laboratory conditions by a 5.8-GHz continuous-wave Doppler radar vital sign detection system. The HR measurement results within 2–5-s time windows are compared by applying a simple fast Fourier transform (FFT) and peak searching method, a 10-s sliding-time-window FFT method, and the proposed method. The proposed method achieves the smallest average error among the three techniques. The proposed method has also proved to be able to measure HR variation using CSD. [ABSTRACT FROM AUTHOR]

In this paper, the expressions for the phasor parameter estimates returned by the Taylor-based weighted least-squares (TWLS) approach, achieved using either complex-valued or real-valued variables, are derived. In particular, the TWLS phasor estimator and its derivatives are expressed as weighted sums of the discrete-time Fourier transform (DTFT) of the analyzed waveform and its derivatives. The derived expressions show that the TWLS algorithm is sensitive to lower order harmonics and interharmonics located close to the waveform frequency when few waveform cycles are analyzed. Also, the algorithm sensitivity to wideband noise is explained. The relationship between the TWLS phasor estimator and the waveform DTFT is then specifically analyzed when either a static or a second-order dynamic phasor model is assumed. Moreover, a simple and accurate procedure for evaluating the TWLS estimator of the dynamic phasor parameters is proposed. The derived expressions for the real-valued version are then approximated in order to reduce the required computational burden so as to achieve the simplified TWLS (STWLS) procedure. That procedure can be advantageously employed in real-time low-cost applications when the reference frequency used in the TWLS approach is estimated in runtime to improve estimation accuracy. Finally, computer simulations show that the phasor parameter estimates returned by the STWLS procedure when the waveform frequency is estimated by the interpolated discrete Fourier transform method comply with the M-class of performance if an appropriate number of waveform cycles is considered. [ABSTRACT FROM PUBLISHER]

This paper reports a frequency-adaptive robust technique for the accurate estimation of the single-phase grid voltage fundamental and harmonic parameters. The technique is based on the discrete Fourier transform and a cascaded delayed signal cancellation strategy. There is no stability issue in the technique, since it does not contain any type of feedback loop. It can also be flexibly configured to estimate the parameters of the fundamental and/or one/multiple harmonic(s) from the grid voltage waveform distorted by various harmonics. Moreover, it does not require evaluation of trigonometric and inverse trigonometric functions for implementing on real-time digital signal processor. However, it needs computationally demanding high-order finite-impulse-response filters. The simulation and real-time experimental results are provided to verify the performance of the proposed technique. [ABSTRACT FROM PUBLISHER]

The discrete wavelet transform (DWT) has attracted a rising interest in recent years to monitor the condition of rotating electrical machines in transient regime, because it can reveal the time–frequency behavior of the current’s components associated to fault conditions. Nevertheless, the implementation of the wavelet transform (WT), especially on embedded or low-power devices, faces practical problems, such as the election of the mother wavelet, the tuning of its parameters, the coordination between the sampling frequency and the levels of the transform, and the construction of the bank of wavelet filters, with highly different bandwidths that constitute the core of the DWT. In this paper, a diagnostic system using the harmonic WT is proposed, which can alleviate these practical problems because it is built using a single fast Fourier transform of one phase’s current. The harmonic wavelet was conceived to perform musical analysis, hence its name, and it has spread into many fields, but, to the best of the authors’ knowledge, it has not been applied before to perform fault diagnosis of rotating electrical machines in transient regime using the stator current. The simplicity and performance of the proposed approach are assessed by comparison with other types of WTs, and it has been validated with the experimental diagnosis of a 3.15-MW induction motor with broken bars. [ABSTRACT FROM PUBLISHER]

This paper deals with a low-cost diagnostic technique for polymer electrolyte membrane (PEM) fuel cells (FCs), which exploits the ripple produced by power converters to monitor the equivalent ohmic resistance. While the available literature on this topic is focused on constant-frequency control of the power converter (such as pulsewidth modulation), this paper discusses the measurement issues that arise when hysteresis current control is employed for a dc/dc boost converter, which represents the simplest solution from the implementation point of view, and therefore particularly suitable for low-cost applications. The classic frequency-domain analysis for ohmic resistance identification, based on the Fourier transform, is compared with a time-domain analysis based on a simple identification algorithm, and a real-time implementation of the latter is presented. The experimental results are obtained on a single PEM FC, but the extension to FC stacks for commercial applications is also discussed. [ABSTRACT FROM PUBLISHER]

The new amendment to the phasor measurement unit (PMU) standard C37.118.1a makes several significant changes compared with the standard C37.118.1 (2011). This paper highlights some of the most important changes, with a particular emphasis applied to how those changes relate to the way that an M-class PMU filter needs to be designed. In particular, there is a delicate tradeoff between passband flatness (the bandwidth test) and stopband rejection in the out-of-band (OOB) test. For a PMU algorithm using frequency-tracking and adaptive filters, it is shown that passband flatness can be relaxed to about 2.5 dB, but that the stopband needs to begin up to 14.8% closer to 0 Hz than for a fixed-filter PMU. This is partly due to the exact procedures of the C37.118.1a OOB testing and partly due to the adaptive nature of a frequency-tracking PMU filter section. Both the above lead to modified filter masks being required for frequency-tracking devices compared with the mask required for fixed-filter devices. The M-class PMU with a reporting rate of 25 Hz is the most difficult to design, for reasons given in this paper. The validity of the masks is shown using filter Bode plots and simulated C37.118.1a test results of a fixed-filter and frequency-tracking device, which have been designed to meet the masks defined in this paper. [ABSTRACT FROM PUBLISHER]