A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

Ge, Jike; Niu, Tianyu; Xu, Di; Yin, Guibin; Wang, Yaping

doi:10.3390/e22030290

Cited by 30 publications

(13 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The EMD is used to decompose the vibration signal, isolating and denoising high frequency IMFs using Pearson correlation coefficients and the wavelet semi-soft threshold, respectively. The Eigen vector of the signal is used as a feature vector for the SVM to classify faults into inner race, outer race and rolling elements, with an accuracy of 100% [42]; however, the 100% accuracy seems to be on the higher end. Delprete et al [43] used orthogonal empirical mode decomposition analysis (a time-frequency) to detect faults in the inner and outer raceway of the bearings using vibration signals.…”

Section: Introductionmentioning

confidence: 99%

A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals

Altaf

Akram

Khan

et al. 2022

Sensors

View full text Add to dashboard Cite

In condition based maintenance, different signal processing techniques are used to sense the faults through the vibration and acoustic emission signals, received from the machinery. These signal processing approaches mostly utilise time, frequency, and time-frequency domain analysis. The features obtained are later integrated with the different machine learning techniques to classify the faults into different categories. In this work, different statistical features of vibration signals in time and frequency domains are studied for the detection and localisation of faults in the roller bearings. These are later classified into healthy, outer race fault, inner race fault, and ball fault classes. The statistical features including skewness, kurtosis, average and root mean square values of time domain vibration signals are considered. These features are extracted from the second derivative of the time domain vibration signals and power spectral density of vibration signals. The vibration signal is also converted to the frequency domain and the same features are extracted. All three feature sets are concatenated, creating the time, frequency and spectral power domain feature vectors. These feature vectors are finally fed into the K- nearest neighbour, support vector machine and kernel linear discriminant analysis for the detection and classification of bearing faults. With the proposed method, the reduction percentage of more than 95% percent is achieved, which not only reduces the computational burden but also the classification time. Simulation results show that the signals are classified to achieve an average accuracy of 99.13% using KLDA and 96.64% using KNN classifiers. The results are also compared with the empirical mode decomposition (EMD) features and Fourier transform features without extracting any statistical information, which are two of the most widely used approaches in the literature. To gain a certain level of confidence in the classification results, a detailed statistical analysis is also provided.

show abstract

Section: Introductionmentioning

confidence: 99%

A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals

Altaf

Akram

Khan

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

Section: Simulation Experiments Validationmentioning

confidence: 99%

“…The simulation movement was composed of the superimposed shock movement and the modulated movement. The sub-constructions of the shock movement and the modulated movement are shown in Equations ( 21) [33] and (22). To simulate the failure of the bearing under operating conditions in order to find the best method for extracting the bearing vibration signal features, the sampling frequency was set to 8192 Hz, and the number of sampling points was set to 4096. y = y 0 e −2πgf n t 0 sin πf n (1 − g 2 (t 0 − KT) (21) x =(1 + cos(2πf r t)) cos(2πf z t) (22) where y 0 is the displacement constant, set to 5; g is the damping coefficient, set to 0.5; f n is the intrinsic frequency, set to 1000 Hz; t 0 is the single-cycle sampling interval; K is the number of repetitions of the shock movement; T is the repetition period, set to 0.025 s; f r is the amplitude modulation frequency, set to 70 Hz; f z is the carrier frequency, set to 560 Hz.…”

Section: Simulation Experiments Validationmentioning

confidence: 99%

Remaining Useful Life Prediction Model for Rolling Bearings Based on MFPE–MACNN

Wang

Zhang

et al. 2022

Entropy

Self Cite

View full text Add to dashboard Cite

Aiming to resolve the problem of redundant information concerning rolling bearing degradation characteristics and to tackle the difficulty faced by convolutional deep learning models in learning feature information in complex time series, a prediction model for remaining useful life based on multiscale fusion permutation entropy (MFPE) and a multiscale convolutional attention neural network (MACNN) is proposed. The original signal of the rolling bearing was extracted and decomposed by resonance sparse decomposition to obtain the high-resonance and low-resonance components. The multiscale permutation entropy of the low-resonance component was calculated. Moreover, the locally linear-embedding algorithm was used for dimensionality reduction to remove redundant information. The multiscale convolution module was constructed to learn the feature information at different time scales. The attention module was used to fuse the feature information and input it into the remaining useful life prediction module for evaluation. The appropriate network structure and parameter configuration were determined, and a multiscale convolutional attention neural network was designed to determine the remaining useful life prediction model. The results show that the method demonstrates effectiveness and superiority in degrading the feature information representation and improving the remaining useful life prediction accuracy compared with other models.

show abstract

“…In addition, the application of increasingly mature intelligent algorithms and the combination of different classical classification models make further progress in the study of fault diagnosis technology [35][36][37]. Bazan, Scalassara et al [38], a combination model of artificial neural network, SVM and K-nearest neighbor intelligent system was proposed for fault diagnosis of induction motors.…”

Section: Literature Reviewmentioning

confidence: 99%

Fault Diagnosis of High-Speed Brushless Permanent-Magnet DC Motor Based on Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

Liu

Zhao³

et al. 2021

Symmetry

View full text Add to dashboard Cite

With the development of reliability theory, people realized that “absolutely reliable” machines could not be made. With its incomparable advantages, the high-speed permanent-magnet brushless DC motor is usually used in the symmetrical structure of high-speed operation working systems, which at present are widely used in aerospace and other fields. The structure of the manufacturing process involves a strict processing, but in the process of work failure could still occur. No matter what field the high-speed permanent magnet brushless DC motor is applied to, it is very important to identify states and run fault diagnosis, which is of great significance to maintain the reliability of the motor and its working system. In this study, the fault diagnosis method of a high-speed permanent-magnet brushless DC motor is studied, and a combination model of modified gray wolf optimization algorithm (MGWO) and support vector machine (SVM) have been proposed for the motor fault diagnosis research. Based on the traditional gray wolf optimization (GWO) algorithm, the optimization performance of the algorithm is improved by initializing the population through a tent map and introducing a sine wave dynamic adaptive factor. Then the modified algorithm is used to optimize the internal parameters of SVM to improve the diagnostic accuracy of the model. Through the signal acquisition test, the current signals under different fault states and faultless states were collected, and the current signal data set required for the experiment is obtained. The experimental result showed that, compared with GWO or sailfish optimization (SFO) optimized SVM models, Extreme learning machine and Back Propagation neural network classical classification models, the fault diagnosis accuracy of the proposed model is the highest, proving the excellent classification performance and good robustness of the MGWO-SVM model.

show abstract

A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

Cited by 30 publications

References 31 publications

A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals

A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals

Remaining Useful Life Prediction Model for Rolling Bearings Based on MFPE–MACNN

Fault Diagnosis of High-Speed Brushless Permanent-Magnet DC Motor Based on Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

Contact Info

Product

Resources

About