Bearing fault detection based on time-frequency representations of vibration signals

Rolling element bearings are widely used in modern machinery and play an important role in industrial applications. Tough environments under which they work make them subject to failure. The classical strategy is to collect bearing vibration signals and denoise the signals to detect fault features by using signal processing techniques. Although the noise is reduced with this strategy, the fault features may be weakened or even destroyed as well. Different from the classical denoising techniques, stochastic resonance is able to extract weak features embedded in heavy noise by utilizing noise instead of eliminating noise. The single stochastic resonance, however, fails to extract the fault features when the signal-to-noise ratio of the bearing vibration signals is very low. To address this problem, this paper investigates the enhancement methods of stochastic resonance and develops a cascaded stochastic resonance-based weak feature extraction method for bearing fault detection. Two sets of vibration signals collected respectively from an experimental bearing and a bearing inside a train wheel pair are utilized to demonstrate the proposed method. The results show that the method is superior to the other enhancement methods in extracting weak features of bearing faults.

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Deep Transfer Learning for Machine Diagnosis: From Sound and Music Recognition to Bearing Fault Detection

Applied Sciences ◽

10.3390/app112411663 ◽

2021 ◽

Vol 11 (24) ◽

pp. 11663

Author(s):

Eugenio Brusa ◽

Cristiana Delprete ◽

Luigi Gianpio Di Maggio

Keyword(s):

Fault Detection ◽

Learning Strategies ◽

Transfer Learning ◽

Fine Tuning ◽

Sound Recognition ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Detection ◽

Sound Detection ◽

Music Recognition

Today’s deep learning strategies require ever-increasing computational efforts and demand for very large amounts of labelled data. Providing such expensive resources for machine diagnosis is highly challenging. Transfer learning recently emerged as a valuable approach to address these issues. Thus, the knowledge learned by deep architectures in different scenarios can be reused for the purpose of machine diagnosis, minimizing data collecting efforts. Existing research provides evidence that networks pre-trained for image recognition can classify machine vibrations in the time-frequency domain by means of transfer learning. So far, however, there has been little discussion about the potentials included in networks pre-trained for sound recognition, which are inherently suited for time-frequency tasks. This work argues that deep architectures trained for music recognition and sound detection can perform machine diagnosis. The YAMNet convolutional network was designed to serve extremely efficient mobile applications for sound detection, and it was originally trained on millions of data extracted from YouTube clips. That framework is employed to detect bearing faults for the CWRU dataset. It is shown that transferring knowledge from sound and music recognition to bearing fault detection is successful. The maximum accuracy is achieved using a few hundred data for fine-tuning the fault diagnosis model.

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Detrended fluctuation analysis of vibration signals for bearing fault detection

2011 IEEE Conference on Prognostics and Health Management ◽

10.1109/icphm.2011.6024364 ◽

2011 ◽

Cited By ~ 9

Author(s):

Jie Liu

Keyword(s):

Fault Detection ◽

Detrended Fluctuation Analysis ◽

Fluctuation Analysis ◽

Vibration Signals ◽

Bearing Fault ◽

Bearing Fault Detection ◽

Detrended Fluctuation

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A Hybrid SVD-Based Denoising and Self-Adaptive TMSST for High-Speed Train Axle Bearing Fault Detection

Sensors ◽

10.3390/s21186025 ◽

2021 ◽

Vol 21 (18) ◽

pp. 6025

Author(s):

Feiyue Deng ◽

Chao Liu ◽

Yongqiang Liu ◽

Rujiang Hao

Keyword(s):

Fault Detection ◽

High Speed ◽

Working Environment ◽

Permutation Entropy ◽

Mechanical Equipment ◽

Noise Interference ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Detection ◽

Self Adaptive

Fault detection of axle bearings is crucial to promote the safe, efficient, and reliable running of high-speed trains. In recent decades, time−frequency analysis (TFA) techniques have been widely used in mechanical equipment fault diagnoses. Time-reassigned multisynchrosqueezing transform (TMSST), as a novel time−frequency representation (TFR) algorithm, is more suitable for dealing with strong frequency-varying signals. However, TMSST TFR results are subject to noise interference. It is difficult to extract the accurate time−frequency (TF) fault feature of the axle bearing under a complex working environment. In addition, determination of the TMSST algorithm parameters depends on the personnel’s subjective experience. Therefore, the TMSST result has a great randomicity and has the disadvantage of having a poor reliability. To address the above issues, a hybrid SVD-based denoising and self-adaptive TMSST is proposed for axle bearing fault detection in this paper. The main improvements of the proposed algorithm include the following two aspects: (1) An SVD-based denoising method using the maximum SV mean to determine the reasonable SV order is adopted to eliminate noise interference and to reserve useful fault impulse information. (2) A new evaluation metric, named time−frequency spectrum permutation entropy (TFS-PEn), is put forward for the quantitative evaluation of the performance of TFR for the TMSST, and then a water cycle algorithm (WCA)-based optimized TMSST can adaptively determine the optimal algorithm parameters. In both the simulation and experimental tests, the superiority and effectiveness of the proposed method is compared with the TMSST, short-time Fourier transform (STFT), MSST, wavelet transform (WT), and Hilbert-Huang transform (HHT) methods. The results show that the proposed algorithm has a better performance for extracting the weak fault features of axle bearing under a strong background noise environment.

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Vibration-Based Bearing Fault Detection and Diagnosis via Image Recognition Technique Under Constant and Variable Speed Conditions

Applied Sciences ◽

10.3390/app8081392 ◽

2018 ◽

Vol 8 (8) ◽

pp. 1392 ◽

Cited By ~ 7

Author(s):

Moussa Hamadache ◽

Dongik Lee ◽

Emiliano Mucchi ◽

Giorgio Dalpiaz

Keyword(s):

Fault Detection ◽

Image Recognition ◽

Fault Detection And Diagnosis ◽

Variable Speed ◽

Vibration Signals ◽

Bearing Faults ◽

Bearing Fault ◽

Bearing Fault Detection ◽

Detection And Diagnosis ◽

Recognition Technique

This paper addresses the application of an image recognition technique for the detection and diagnosis of ball bearing faults in rotating electrical machines (REMs). The conventional bearing fault detection and diagnosis (BFDD) methods rely on extracting different features from either waveforms or spectra of vibration signals to detect and diagnose bearing faults. In this paper, a novel vibration-based BFDD via a probability plot (ProbPlot) image recognition technique under constant and variable speed conditions is proposed. The proposed technique is based on the absolute value principal component analysis (AVPCA), namely, ProbPlot via image recognition using the AVPCA (ProbPlot via IR-AVPCA) technique. A comparison of the features (images) obtained: (1) directly in the time domain from the original raw data of the vibration signals; (2) by capturing the Fast Fourier Transformation (FFT) of the vibration signals; or (3) by generating the probability plot (ProbPlot) of the vibration signals as proposed in this paper, is considered. A set of realistic bearing faults (i.e., outer-race fault, inner-race fault, and balls fault) are experimentally considered to evaluate the performance and effectiveness of the proposed ProbPlot via the IR-AVPCA method.

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