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Rolling bearings are vital components in rotating machinery, and their reliable operation is crucial for maintaining the stability and efficiency of mechanical systems. However, fault detection in rolling bearings is often hindered by noise interference in complex industrial environments. To overcome this challenge, this paper presents a novel fault diagnosis method for rolling bearings, combining Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs), integrated with the envelope analysis and adaptive mean filtering techniques. Initially, envelope analysis and adaptive mean filtering are applied to suppress random noise in the bearing signals, thereby enhancing the visibility of fault features. Subsequently, a deep learning model that combines a CNN and a GRU is developed: the CNN extracts spatial features, while the GRU captures the temporal dependencies between these features. The integration of the CNN and GRU significantly improves the accuracy and robustness of fault diagnosis. The proposed method is validated using the CWRU dataset, with the experimental results achieving an average accuracy of 99.25%. Additionally, the method is compared to four classical fault diagnosis models, demonstrating superior performance in terms of both diagnostic accuracy and generalization ability. The results, supported by various visualization techniques, show that the proposed approach effectively addresses the challenges of fault detection in rolling bearings under complex industrial conditions. [ABSTRACT FROM AUTHOR]

Traditional machine learning techniques depend on both the training data and the target data having the same data distribution and feature space. The performance may be unsatisfactory if there is a discrepancy in data distribution between the training data and the target data, which is known as a cross-domain learning issue. In this study, a technique for addressing this issue based on using the envelope analysis features coded as inputs to the Artificial Neural Networks (ANN) is presented. Specifically, three major cross-domain difficulties were resolved, including defect identification of different roller bearing types, rotating speeds, and loading conditions. The envelope approach is used to extract more distinguishing features at fundamental fault frequencies from the original signal, with the benefit of derived features being independent of both roller bearing type and operating conditions of rotating machines. The diagnosis outcomes were achieved experimentally through three different types of rollers bearing. Meanwhile, for each bearing type, three data sets were obtained at three different rotational speeds with different levels of fault categories to simulate all the cross-domain tasks. Moreover, the ANN model was trained using data of a particular bearing type and a certain operating state, whereas data of other bearing types were utilized to test the cross-domain performance of the proposed technique. At the same loading condition, the results indicate a 99.5% average success rate for bearing Koyo 1205, a 98.33% average accuracy for bearing NU 205, and a 97.3% average accuracy for the defective bearing kit. Furthermore, the results indicate that the suggested technique can deliver accurate cross-domain detection. Also, the experimental results proved that the suggested technology is a potential strategy for industrial applications. [ABSTRACT FROM AUTHOR]

Purpose: In rotating machines, bearings are essential components, but these have a limited life and are subject to degradation. This paper discusses detecting anomalies in the bearing's various components, such as the inner ring, outer ring, and ball, by processing the bearing's vibration signal. Methodology: Self-Organizing Maps (SOMs) and Variational Mode Decomposition (VMD) are used to automate diagnosing bearing defects. The approach's performance was evaluated using the Bearing Data Center (BDC) database, which includes data collection, preprocessing, and SOM algorithm implementation. Results and Conclusions: The proposed method accurately classifies bearing fault types and can aid in early detection and proactive maintenance to prevent catastrophic equipment failure. The findings also show that VMD decomposition and envelope demodulation effectively detect various bearing faults. Therefore, using advanced machine learning techniques to accurately diagnose rotating machine failures is necessary. [ABSTRACT FROM AUTHOR]

This paper proposes a highly reliable multi-fault diagnosis scheme for low-speed rolling element bearings using an effective time–frequency envelope analysis and a Bayesian inference based one-against-all support vector machines (probabilistic-OAASVM) classifier. The proposed method first performs a wavelet packet transform based envelope analysis on an acoustic emission signal to select sub-bands of the signal that contain the most intrinsic and pertinent information about the defects. Frequency- and time-domain fault features are extracted only from selected sub-bands for fault classification. Traditional one-against-all SVMs (OAASVM), a widely used multi-class pattern recognition technique, employ an arbitrary combination of a series of binary classifiers yielding overlapped feature spaces, where a data sample can be unclassifiable. To address this limitation, we formulate the feature space of OAASVM as an appropriate Gaussian process prior (GPP) and interpret OAASVM results as a posterior probability estimation procedure using Bayesian inference under this GPP. The efficacy of the proposed probabilistic-OAASVM classifier is verified for low-speed rolling element bearings under various conditions. Experimental results indicate that the proposed method outperforms the state-of-the-art algorithms for multi-fault classification of low-speed bearings, yielding a 4.95–20.67% improvement in the average classification accuracy. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the vibration signal of wind turbine bearings affected by strong background noise and other equipment excitation sources leaded to the difficulty of feature extraction for early bearing weak fault features, a wind turbine bearing fault diagnosis method based on maximum correlation kurtosis deconvolution (MCKD), optimized by multi-objective beluga whale optimization (MOBWO) algorithm was proposed. Firstly, based on the powerful global and local search capabilities of MOBWO, the key parameters of MCKD were optimized, and the optimal parameter combination of MOBWO was obtained. Secondly, the optimized MCKD was employed to process the original signal by deconvolution operation for eliminating the influence of background noise and other equipment excitation sources and highlighting the bearing periodic pulse signal. Then, the envelope spectrum method was used to process the deconvolution signal to perform the extraction of bearing fault characteristic frequency, and the obtained fault characteristic frequency values were compared with the theoretical calculation values for diagnosis. Finally, in order to validate the effectiveness of MOBWO-MCKD, the experiments were conducted on the actual collected inner and outer ring fault data of wind turbine bearings. The results show that the fault feature extraction method based on MOBWO-MCKD can effectively eliminate the background noise and other excitation source interference of the early bearing weak fault features. The inner ring signal envelope spectrum shows the inner ring failure frequency fIR=125.87Hz and 2fIR=251.74Hz. The envelope spectrum of the outer ring signal can be seen as the outer ring failure frequency that fOR=84.47Hz, 2fOR=168.94Hz, 3fOR=253.41Hz, which has a certain application value for the extraction of early weak fault characteristics of wind turbine bearings in practical engineering. [ABSTRACT FROM AUTHOR]

Existing train bearing trackside acoustic diagnosis methods mostly focus on doppler effect removal and spatial filter optimization, while ignoring the impact noise and cyclostationary noise in the trackside environment. To address this problem, a trackside acoustic diagnosis method combining beamforming and target band selection for train axlebox is proposed in this study. The proposed method acquires train bearing array acoustic signals by microphone array, corrects the signal distortion by time domain interpolation resampling method, extracts the target bearing direction signal using beamforming spatial domain filter, selects the optimal demodulation band and extracts the band-pass signal using ICS2gram, and the envelope analysis of the band-pass signal is carried out to realize bearing diagnosis. The experimental results show that the proposed method can effectively avoid the influence of impact noise and cyclostationary noise in the trackside sound field environment, accurately extract the target bearing signals and diagnose the bearing faults, showing better effect when compared with the existing methods. [ABSTRACT FROM AUTHOR]

The hole edge crack monitoring of metal structures with holes is of great significance for ensuring flight safety and enhancing the reliability of aircraft structures. In order to monitor the crack growth at the hole edge, the fatigue loading test of porous aluminum alloy plate containing the corner crack at the hole edge is carried out, and the a-N curve of the test piece of porous aluminum alloy plate and the center wavelength offset of the optical fiber grating strain sensor during the crack growth at the hole edge are obtained. The damage identification algorithms such as envelope analysis method and BP neural network are used to process and analyze the test data. The monitoring model that can identify the crack growth at the hole edge with the center wavelength offset of the optical fiber grating strain sensor is established, and verified with test parts. The results show that the established monitoring model can effectively identify the propagation and penetration of the corner crack at the hole edge, and the accuracy of monitoring the propagation length of the corner crack at the hole edge has reached 97.2%, which can be applied to the ground fatigue test of the whole aircraft, aircraft structure health monitoring and other scenarios in the future.

Rolling element bearings (REBs) are an essential part of rotating machinery. A localised defect in a REB typically results in periodic impulses in vibration signals at bearing characteristic frequencies (BCFs), and these are widely used for bearing fault detection and diagnosis. One of the most powerful methods for BCF detection in noisy signals is envelope analysis. However, the selection of an effective band-pass filtering region presents significant challenges in moving towards automated bearing fault diagnosis due to the variable nature of the resonant frequencies present in bearing systems and rotating machinery. Cepstrum Pre-Whitening (CPW) is a technique that can effectively eliminate discrete frequency components in the signal whilst detecting the impulsive features related to the bearing defect(s). Nevertheless, CPW is ineffective for detecting incipient bearing defects with weak signatures. In this study, a novel hybrid method based on an improved CPW (ICPW) and high-pass filtering (ICPW-HPF) is developed that shows improved detection of BCFs under a wide range of conditions when compared with existing BCF detection methods, such as Fast Kurtogram (FK). Combined with machine learning techniques, this novel hybrid method provides the capability for automated bearing defect detection and diagnosis without the need for manual selection of the resonant frequencies. The results from this novel hybrid method are compared with a number of established BCF detection methods, including Fast Kurtogram (FK), on vibration signals collected from the project I2BS (An EU Clean Sky 2 project 'Integrated Intelligent Bearing Systems' collaboration between Schaeffler Technologies and the University of Southampton. Safran Aero Engines was the topic manager for this project) and those from three databases available in the public domain—Case Western Reserve University (CWRU), Intelligent Maintenance Systems (IMS) datasets, and Safran jet engine data—all of which have been widely used in studies of this kind. By calculating the Signal-to-Noise Ratio (SNR) of each case, the new method is shown to be effective for a much lower SNR (with an average of 30.21) compared with that achieved using the FK method (average of 14.4) and thus is much more effective in detecting incipient bearing faults. The results also show that it is effective in detecting a combination of several bearing faults that occur simultaneously under a wide range of bearing configurations and test conditions and without the requirement of further human intervention such as extra screening or manual selection of filters. [ABSTRACT FROM AUTHOR]

In modern power systems or new energy power stations, the medium voltage circuit breakers (MVCBs) are becoming more crucial and the operation reliability of the MVCBs could be greatly improved by online monitoring technology. The purpose of this research is to put forward a fault diagnosis approach based on vibration signal envelope analysis, including offline fault feature training and online fault diagnosis. During offline fault feature training, the envelope of the vibration signal is extracted from the historic operation data of the MVCB, and then the typical fault feature vector M is built by using the wavelet packet-energy spectrum. In the online fault diagnosis process, the fault feature vector T is built based on the extracted envelope of the real-time vibration signal, and the MVCB states are assessed by using the distance between the feature vectors T and M. The proposed method only needs to handle the envelope of the vibration signal, which dramatically reduces the signal bandwidth, and then the cost of the processing hardware and software could be cut down. [ABSTRACT FROM AUTHOR]

In the presence of the pandemic caused by covid-19, digital platforms were implemented to continue with the education processes from isolation. In the case of higher education that imparts various areas of knowledge, different results were identified in the use of the same digital platforms. In response to this, it was necessary to analyze how the technological characteristics implemented at a higher level in Mexico have influenced the participation, attendance and evaluation of students by area of knowledge. Therefore, a data envelopment analysis was performed to measure the efficiency of the inputs and outputs described by the literature; the limitations of the research was circumscribed by the amount of data to be analyzed. As a result, it was identified that the greater the number of features added to the digital platforms, the better levels of participation, attendance and evaluation of students by academic area. Likewise, when considering future lines of research, it is considered ideal to elaborate on the various methods of digital evaluation, such as the digital portfolio and feedback.

Obstructive sleep apnea (OSA) severity assessment is based on manually scored respiratory events and their arbitrary definitions. Thus, we present an alternative method to objectively evaluate OSA severity independently of the manual scorings and scoring rules. A retrospective envelope analysis was conducted on 847 suspected OSA patients. Four parameters were calculated from the difference between the nasal pressure signal's upper and lower envelopes: average (AV), median (MD), standard deviation (SD), and coefficient of variation (CoV). We computed the parameters from the entirety of the recorded signals to perform binary classifications of patients using three different apnea–hypopnea index (AHI) thresholds (5-15-30). Additionally, the calculations were undertaken in 30-second epochs to estimate the ability of the parameters to detect manually scored respiratory events. Classification performances were assessed with areas under the curves (AUCs). As a result, the SD (AUCs ≥ 0.86) and CoV (AUCs ≥ 0.82) were the best classifiers for all AHI thresholds. Furthermore, non-OSA and severe OSA patients were separated well with SD (AUC = 0.97) and CoV (AUC = 0.95). Respiratory events within the epochs were identified moderately with MD (AUC = 0.76) and CoV (AUC = 0.82). In conclusion, envelope analysis is a promising alternative method by which to assess OSA severity without relying on manual scoring or the scoring rules of respiratory events. [ABSTRACT FROM AUTHOR]

Study objective: Traditionally, age-related deterioration of sleep architecture in older individuals has been evaluated by visual scoring of polysomnographic (PSG) recordings with regard to total sleep time and latencies. In the present study, we additionally compared the non-REM sleep (NREM) stage and delta, theta, alpha, and sigma wave stability between young and older subjects to extract features that may explain age-related changes in sleep. Methods: Polysomnographic recordings were performed in 11 healthy older (72.6 ± 2.4 years) and 9 healthy young (23.3 ± 1.1 years) females. In addition to total sleep time, the sleep stage, delta power amplitude, and delta, theta, alpha, and sigma wave stability were evaluated by sleep stage transition analysis and a novel computational method based on a coefficient of variation of the envelope (CVE) analysis, respectively. Results: In older subjects, total sleep time and slow-wave sleep (SWS) time were shorter whereas wake after sleep onset was longer. The number of SWS episodes was similar between age groups, however, sleep stage transition analysis revealed that SWS was less stable in older individuals. NREM sleep stages in descending order of delta power were: SWS, N2, and N1, and delta power during NREM sleep in older subjects was lower than in young subjects. The CVE of the delta-band is an index of delta wave stability and showed significant differences between age groups. When separately analyzed for each NREM stage, different CVE clusters in NREM were clearly observed between young and older subjects. A lower delta CVE and amplitude were also observed in older subjects compared with young subjects in N2 and SWS. Additionally, lower CVE values in the theta, alpha and sigma bands were also characteristic of older participants. Conclusion: The present study shows a decrease of SWS stability in older subjects together with a decrease in delta wave amplitude. Interestingly, the decrease in SWS stability coincided with an increase in short-term delta, theta, sigma, and alpha power stability revealed by lower CVE. Loss of electroencephalograms (EEG) variability might be a useful marker of brain age. [ABSTRACT FROM AUTHOR]

Bearings are one of the most widely used components of rotary machines. To keep these bearings running in the best condition, several techniques for the early diagnosis of faults are applied to enable continuous monitoring of their condition and avoid unexpected faults that may cause damage to humans and materials. Several works have focused on the development of such technologies, including those that apply artificial intelligence, in the classification and diagnosis of faults. This work reports on a multi-layer perceptron (MLP) to classify the conditions of faulty bearings, using the envelope analysis method to extract the faulty features of the bearings. The proposed architecture is implemented on a field programmable gate array (FPGA) board, where the Digilent Zybo Z7-20 platform with a Zynq-7000 FPGA circuit from Xilinx was selected as the target. The Case Western Reserve University (CWRU) dataset, which is considered the standard reference for testing bearing fault classifications, is used to evaluate the performances. The results of the implemented embedded system are first compared to those obtained through MATLAB simulations and then to those obtained from the literature. These practical results provide an average accuracy of 95 and 89% for the fault-type identification and fault-severity identification, respectively. [ABSTRACT FROM AUTHOR]

Rolling element bearing is a core component in the rotating machine. The performance of the whole machine is mainly dominated by the performance condition of the rolling element bearing. The Initial Fault Time (IFT) is a beginning landmark of the unhealthy condition of bearings. In order to identify accurately and rapidly the IFT under the weak fault signatures and heavy background noise, an identification method of the IFT is proposed by the monitoring indicator and envelope analysis with Weighted Empirical Mode Decomposition (WEMD) and Infogram. The monitoring indicator is constructed by the variation coefficient of the summation of the multiple standardized statistical features of the vibration signal. The approximate IFT can be obtained by the minimum before the early stage of the continuous increase in the monitoring indicator. Whereafter, a more accurate IFT can be detected by envelope analysis with WEMD and Infogram based on interval-halving backtracking strategy. The proposed method is verified by the tested dataset provided by Intelligent Maintenance System (IMS). The results show that the proposed method is efficient, rapid and simple for identifying the IFT. [ABSTRACT FROM AUTHOR]

• A novel low-speed bearing damage detection method based on synchronous averaging. • An innovative vibration signal processing model considering pulse width. • A new envelope extraction algorithm with sound mathematical background. • Demonstrated advantages by numerical simulations as well as field examples. The health condition of low-speed rolling bearing, such as the main bearing in wind turbines which bears the heavy dead weight and operates under variable speeds, has a big impact on the safe operation of the machinery. Therefore, damage detection of low-speed bearings plays a key role in the health management of large-scale rotating machinery. However, in popular vibration based bearing damage detection algorithms, due to the fact that the additional vibration features incurred by the low-speed operated bearing damage are typically weak in amplitude and low in frequency contents, the additional responses caused by damage are difficult to be isolated by conventional algorithms. Especially, in the cases of variable speed operations, the smearing effect caused by Fourier transform makes it more difficult to extract the damage features by spectrum analysis based methods. To deal with these issues, we developed a damage detection procedure specially designed for bearings operated at low and variable speeds. According to the dynamic properties of the vibration signals incurred by a low-speed bearing with damage, an envelope analysis method based on synchronous averaging with sliding narrow band-pass filters is designed and developed for extracting damage features in the low frequency range. The fundamental theory used in the method is derived first. Then, a damage detection signal processing procedure is constructed based on the elaborated theory. The feasibility and advantages of the proposed methodology are validated by numerical simulations as well as the measured data from a wind turbine field example. [ABSTRACT FROM AUTHOR]

In this paper, the dynamic behavior of rolling element bearings with localized faults on the inner and outer rings is investigated. A nonlinear mathematical model is developed with five degrees of freedom considering rotor unbalance. In this bearing model, the nonlinearity is caused by the Hertzian contact forces and the radial internal clearance. The fourth-order Runge–Kutta technique is used to solve the coupled nonlinear equations of motion numerically. Nonlinear vibration response of the rotor and bearing housing can be obtained in both time and frequency domains. An experimental verification of the numerical model is presented where experimental measurements for defective ball bearings are compared with the numerical results. Envelope spectra of the numerical results show similar behavior to that of the measured experimental signals. A parametric analysis is conducted to investigate the effect of system parameters on the nonlinear dynamic response using time waveforms, orbit plots, frequency spectra and bifurcation diagrams. The presented results demonstrate that the dynamic response shows periodic, quasi-periodic and chaotic motions because of varying rotational speeds and defect width. The proposed model contributes toward improved design and better health monitoring of bearings in practice. [ABSTRACT FROM AUTHOR]

The generation of synthetic vibration signals enables the testing of novel machine diagnostic methods without the costly introduction of real failures. One of major goals of vibration-based condition monitoring is the early detection of bearing faults. This paper presents a novel modeling technique based on the combination of the known mechanical properties of a modeled object (phenomenological part) and observation of a real object (behavioral part). The model uses the real pulse response of bearing housing, along with the external instantaneous machine speed profile. The presented method is object-oriented, so it is applicable to a large group of machinery. [ABSTRACT FROM AUTHOR]

This paper presents an approach to studying rolling element bearing damage under the interference of impact loading. In the experimental part, a series of bearing tests was performed by using the twin-disc test device with artificially damaged bearings. This was followed by analysis of the measured acceleration response data in impact-free condition as well as under the influence of the impact loading. The results showed successful detection of the bearing outer race damage by using typical bearing damage detection approaches regardless whether the impact loading was applied to the system or not. In turn, recognition of the bearing rolling element damage required specific signal processing. [ABSTRACT FROM AUTHOR]

For the problem of inconsistent quantitative standards for running status analysis of rolling bearings, this paper uses principal component analysis (PCA) to extract a new index F, which is the joint parameters of time domain and frequency domain, and by establishing the value of F to analyze the running states of the rolling bearings. Firstly, the acceleration sensors are used to collect the vibration signal of the whole life cycle of the rolling bearings. Secondly, empirical mode decomposition (EMD) method is used to denoise the acquired vibration signal. Then, the main components of the denoised vibration signal are used to propose the characteristic parameters and synthesized into new parameter indicators. Finally, envelope analysis spectrum is used to analyze the fault classification under the new parameter index. The exepriment results show that the whole life cycle of the rolling bearings can be classified into five different operating periods by using the new parameter index, and each period represents a different bearing operating state.

The induction motors (IMs) are undoubtedly the most used machines in industries because of the advantages they offer such as simplicity, service continuity and low cost. Due to wear and tear, the motor suffers different types of mechanical and electrical failures. Depending on the criticality of the plant motors, it could be necessary to implement predictive techniques in order to detect the faults before they can cause unnecessary downtime. Therefore, in this paper, the research approach was to develop a low cost measurement system based on a micro controller platform for machine diagnosis. The FRDM K64F developing board was selected as the most suitable for satisfying the system conditions, and it was used to collect induction motor`s current data. In order to validate the accuracy of the developed system, the Frequency Transfer Functions (FRF) of the developed measurement system and the standard system (NI USB-6009) were compare. It showed a flat frequency spectrum from 0 to 1 KHz, with small fluctuations of about 0.25 dB standard deviation. A fully automated test bench was implemented, which allows to perform all the measurement tests with the IMs, and in this case, the detection and diagnosis of broken bars. Around 240 tests were performed with varying loads, different rotation speeds, and with different severity damage levels in the rotor. The data analysis procedure for broken rotor bar detection and motor diagnosis was performed by the Motor Current Signature Analysis (MCSA), FFT and Enveloped Analysis (EA). Finally, the research approach was successfully accomplished, by the team by developing a software firmware measurement ultra-low cost development platform for machine diagnosis. It was also developed a proper antialiasing filter to reduce industrial noise. The effectiveness of the proposed system is detecting a weak fault in a noise signal. It was found out a new consistent and robust parameter called the pole pass frequency (fpsf), which could be used as a diagnosis parameter for detection of broken rotor bars faults, with their damage severity degree. The detected parameter can be found around 2.6 Hz, and it increases in amplitude with increasing damage severity.

Background: The acoustic emission (AE) analysis has been used increasingly for gearbox diagnostics. Since AE signals are of non-linear, non-stationary and broadband nature, traditional signal processing techniques such as envelope spectrum must be carefully applied to avoid a wrong fault diagnosis. One signal processing technique that has been used to enhance the demodulation process for vibration signals is the empirical mode decomposition (EMD). Until now, the combination of both techniques has not yet been used to improve the fault diagnostics in gearboxes using AE signals. Purpose: In this research we explore the use of the EMD to improve the demodulation process of AE signals using the Hilbert transform and enhance the representation of a gear fault in the envelope spectrum. Methods: AE signals were measured on a planetary gearbox (PG) with a ring gear fault. A comparative signal analysis was conducted for the envelope spectra of the original AE signals and the obtained intrinsic mode functions (IMFs) considering three types of filters: highpass filter in the whole AE range, bandpass filter based on IMF spectra analysis and bandpass filter based on the fast kurtogram. Results: It is demonstrated how the results of the envelope spectrum analysis can be improved by the selection of the relevant frequency band of the IMF most affected by the fault. Moreover, not considering a complementary signal processing technique such as the EMD prior the calculation of the envelope of AE signals can lead to a wrong fault diagnosis in gearboxes. Conclusion: The EMD has the potential to reveal frequency bands in AE signals that are most affected by a fault and improve the demodulation process of these signals. Further research shall focus on overcome issues of the EMD technique to enhance its application to AE signals. [ABSTRACT FROM AUTHOR]

Increasing industrial competitiveness has led to an increased global interest in condition monitoring. In this sector, rotating machinery plays an important role, where the bearing is one of the most critical components. Many vibration-based signal treatments are already being used to identify features associated with bearing faults. The information embedded in such features are employed in the construction of health indicators, which allow for evaluation of the current operating status of the machine. In this work, the use of contour maps to represent the diagnosis map of a bearing, used as a health map, is presented for the first time. The results show that the proposed method is promising, allowing for the satisfactory detection and evaluation of the severity of bearing damage. In this initial stage of the research, our results suggest that this method can improve the classification of bearing faults and, therefore, optimise maintenance processes. [ABSTRACT FROM AUTHOR]

Envelope analysis is a widely used tool for fault detection in rotating machines. In envelope analysis, impulsive noise contaminates the measured signal, making it difficult to extract the features of defects. This paper proposes a time–frequency envelope analysis that overcomes the effects of impulsive noises. Envelope analysis is performed by dividing the signal into several sections through a time window. The effect of impulsive noises is eliminated by using the frequency characteristics of the short time rectangular wave. The proposed method was verified through simulation and experimental data. The simulation was conducted by mathematically modeling a cyclo-stationary process that characterizes rotating machinery signals. In addition, the effectiveness of the method was verified by the measured data of normal and defective air-conditioners produced on the actual assembly line. This simple proposed method is effective enough to detect the faults. In the future, the approaches of big data and deep learning will be required for the development of the prognostic health-management framework. [ABSTRACT FROM AUTHOR]

To multi-faults of a gearbox, the strong fault feature vibration of gearing always interfered with rolling element bearing weak fault vibration. The traditional methods are difficult to extract the weak bearing fault feature. In order to solve the problem, a vibration analysis scheme for multi-faults vibration of gearbox based on discrete random separation（DRS） is proposed. In the proposed method, the resonance parameters of the gearbox vibration is obtained based on the fast kurtogram algorithm at first, built a bandpass filter that included resonance parameters, and the envelope is extracted by Hilbert transform. Then, converted envelope to the angle domain based on the equi-angle increment resampling to remove speed fluctuation. Next, the SANC is performed to separate the envelope in the angle domain. As a result, the gear fault corresponding envelope component and the bearing fault related envelope component are obtained; Finally, the spectrum analysis is carry out on the two envelope components respectively for the fault diagnosis information. Test results show that this method can be used to extract the gear and bearing fault features effectively.

Featured Application: The proposed method of this paper is for the fault diagnosis of railway wheelset bearings. A wheelset bearing is one of the main components of the train bogie frame. The early fault detection of the wheelset bearing is quite important to ensure the safety of the train. Among numerous diagnostic methods, envelope analysis is one of the most effective approaches in the detection of bearing faults which has been amply applied, but its validity greatly depends on the informative frequency band (IFB) determined. For the wheelset bearing faulty signal, it is often difficult to identify the IFB and extract fault characteristics due to the influence of complex operating conditions. To address this problem, a novel method to select optimal IFB, called the Mkurtogram, is proposed for railway wheelset bearings fault diagnosis. It takes the multipoint kurtosis (Mkurt) of unbiased autocorrelation (AC) of the squared envelope signal generated from sub-bands as assessment indicator for the first time. The fundamental concept which inspires this proposed method is to make full use of regular periodicity of AC of squared envelope signal. In the AC domain, the impulsiveness and periodicity, two distinctive signatures of the repetitive transients, have achieved a united representation by Mkurt. A simulated signal with multiple interferences and two experimental signals collected from wheelset bearings are applied to verify its performances and advantages. The results indicate that the proposed method is more effective to extract the wheelset bearings fault feature under complex interferences. It can not only decrease the influence of large impulse interference and the discrete harmonics interference, but also effectively overcome the influence of amplitude fluctuation caused by variable working conditions. Moreover, based on the periodic directivity of Mkurt, the proposed method also can be applied to the compound faults diagnosis of the wheelset bearing. [ABSTRACT FROM AUTHOR]

This study represents an effective approach for detection and classification of bearing faults in brushless DC (BLDC) motors based on hall‐sensor signal analysis. The envelope analysis and Hilbert–Huang transform are used to extract features from the time and frequency domains of each signal. A new feature selection technique is proposed based on the combination of the genetic algorithm strength and the advantage of the binary state transition algorithm. The genetic algorithm explores search space through cross‐over operator while the binary state transition algorithm is based on four special transformation operators in the local exploitation capabilities. The artificial neural network and support vector machine are used as the classifier. Each model is separately analysed and compared, leading to a high possibility to distinguish the bearing faults. [ABSTRACT FROM AUTHOR]

Envelope analysis is a dominant approach in detecting modulated fault signals in rotating machinery. The Hilbert transform is a popular method used to obtain envelopes. However, envelopes based on the Hilbert transform are only suitable for narrowband signals. Signals are usually filtered before envelope demodulation, which leads to the challenging issue of filter selection. A modulated fault signal detection method using the upper and lower envelopes of the signals is proposed. This method can demodulate the modulated fault signals directly from the raw data, which can include wideband and narrowband signals. The proposed method uses a simple algebraic operation instead of a transform function, so it is convenient in practical applications. It is tested with some simulated signals and some real‐measured data. The test results show that the method can effectively demodulate a modulated fault signal directly from the raw data. [ABSTRACT FROM AUTHOR]

Predictive maintenance strategies are established in the industrial context on account of their benefits in terms of costs abatement and machine failures reduction. Among the available techniques, vibration-based condition monitoring (VBCM) has notably been applied in many bearing fault detection problems. The health indicators construction is a central issue for VBCM, since these features provide the necessary information to assess the current machine condition. However, the relation between vibration data and its sources intimately related to bearing damage is not effortlessly definable from a diagnostic perspective. This study discloses a diagnostic investigation performed both on the vibration signal and on the contact pressure signal that is supposed to be one of main forcing terms in the dynamic equilibrium of the damaged bearing. Envelope analysis and spectral kurtosis (SK) are applied to extract and compare diagnostic features from both signals, referring to the Case Western Reserve University (CWRU) case-study. Namely, health indicators are constructed by means of physical considerations based on the effect of faults on the signal power contents. These indicators show to be promising not only for damage detection but, also, for damage severity assessment. Moreover, they provide an invaluable reading key of the link occurring between the contact pressure path and the vibration response. [ABSTRACT FROM AUTHOR]

Demodulation is one of the most useful techniques for the fault diagnosis of rotating machinery. The commonly used demodulation methods try to select one sensitive sub-band signal that contains the most fault-related components for further analysis. However, a large number of the fault-related components that exist in other sub-bands are ignored in the commonly used envelope demodulation methods. Based on a weighted-empirical mode decomposition (EMD) de-noising technique and time–frequency (TF) impulse envelope analysis, a multi-scale demodulation method is proposed for fault diagnosis. In the proposed method, EMD is first employed to divide the signal into some IMFs (intrinsic mode functions). Then, a new weighted-EMD de-noising technique is presented, and different weights are assigned to IMFs for construction according to their fault-related degrees; thus, the fault-unrelated components are suppressed to improve the signal-to-noise ratio (SNR). After that, continuous wavelet transformation (CWT) is adopted to obtain the time–frequency representation (TFR) of the de-noised signal. Subsequently, the fault-related components in the entire frequency range scale are calculated together, referring to the TF impulse envelope signal. Finally, a fault diagnosis result can be obtained after the fast Fourier transformation of the TF impulse envelope signal. The proposed method and three commonly used methods are applied to the fault diagnosis of a planetary gearbox with a sun gear spalling fault and a fixed shaft gearbox with a crack fault. The results show that the proposed method can effectively detect gear faults and yields better performance than other methods. [ABSTRACT FROM AUTHOR]

This paper presents a MATLAB-based tutorial to conduct fault diagnosis of a rolling element bearing. While there have been so many new developments in this field, no studies have addressed the tutorial aspects in this field to help the engineers learn the concept and implement by their own effort. The three most common techniques—the autoregressive model, spectral kurtosis, and envelope analysis—are selected to demonstrate the bearing diagnosis process. Simulation signal is introduced to help understand the characteristics of fault signal and carry out the process toward the fault identification. The techniques are then applied to the two real datasets to demonstrate the practical applications, one made by the authors and the other by the Case Western Reserve University, which is known as a standard reference in testing the diagnostic algorithms. [ABSTRACT FROM AUTHOR]

Rotating machinery are widely used in industry, and vibration analysis is one of the most common methods to monitor health condition of rotating machinery. However, due to the presence of outliers and interference, vibration signal becomes very complicated in reality, and it is important to reduce the influence of outliers and interference. Since a bandpass filter can eliminate a lot of above influence, it is usually selected to process vibration signal in classic fault diagnosis. The selection of the lower and upper cutoff frequencies of the bandpass filter is very critical. In order to extract fault characteristics from vibration signal, this paper proposes a new method which uses deep reinforcement learning algorithm and the reciprocal of smoothness index to control the bandpass filter to select a frequency band with the highest signal-to-noise ratio. Then, envelope demodulation is performed on the filtered signal so as to diagnose the faults of rotating machinery. Two sets of data collected from the test rig are used to validate the effectiveness of the proposed method. The comparisons with fast kurtogram and GiniIndexgram show the superiority of the proposed method. It also suggests that reinforcement learning has a great potential in the field of mechanical fault diagnosis. [ABSTRACT FROM AUTHOR]

Rotary machines are key equipment in many industrial sectors, from mining operations to advanced manufacturing. Among the critical components of these machines are bearings, gearboxes, rotors, among others. These components tend to present failures, which can be catastrophic, with economic, safety and/or environmental consequences. Among the most established methods for classifying bearing faults, the envelope method has been widely used, with relative success, for several years. However, this method and its variations are difficult to automate and require extensive experience on the part of the analyst. We report that, while traditional methods (e.g., envelope) successfully classified bearing failures less than 45% of the time, machine learning methods were successful more than 62% of the time, and in some cases reaching 67%. This work differs from others in the sense that it uses all the available measurements from a well-known database, not just a subset. In addition, the measurements are taken at the motor base, which are more difficult to classify, and avoid using different segments of the same signal in both training and validation, thus reducing the possibility of overfitting. As a consequence, the results obtained are apparently inferior to those reported elsewhere, but probably closer to what one might expect in practical applications. [ABSTRACT FROM AUTHOR]

This paper presents a simple and reliable improved method based on the three-phase current intersection signal (TPCIS), for induction motor rotor fault diagnosis. This method consists of finding time and amplitude of intersection points instead of using the traditional zero-crossing-time technique. TPCIS is constructed by searching and interpolating the positive and negative crossing points of the three-phase currents. After that, a spectral analysis of this signal is performed to extract the rotor fault signature. The proposed method is theoretically introduced and experimentally validated by testing three induction machines under different load conditions. The experimental results show the effectiveness of the proposed method in identifying the examined defect even under low-load conditions. [ABSTRACT FROM AUTHOR]

Spatiotemporal structures of fluctuations with frequencies lower than the ion cyclotron frequency in a cylindrical magnetized plasma are investigated. Drift-wave and low-frequency zonal flow coexist. Electrostatic potentials of the zonal flow and the drift-wave are distributed widely in radius. The radial wave number profile of the zonal flow has a shear structure at the radial location where the drift-wave has a maximal normalized fluctuation amplitude. On the other hand, the radial wave number profile of the drift-wave shows vortex tilting, resulting in the generation of stationary turbulence Reynolds stress gradient per mass density. The envelope and bispectral analyses indicate significant nonlinear interactions between the zonal flow and the drift-wave. ©2008 The Physical Society of Japan.