Sparse Representation of Transients Based on Wavelet Basis and Majorization-Minimization Algorithm for Machinery Fault Diagnosis

Mathematical Problems in Engineering ◽

10.1155/2014/696051 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 8

Author(s):

Wei Fan ◽

Gaigai Cai ◽

Weiguo Huang ◽

Li Shang ◽

Zhongkui Zhu

Keyword(s):

Fault Diagnosis ◽

Sparse Representation ◽

Background Noise ◽

Augmented Lagrangian ◽

Signal To Noise Ratio ◽

Wavelet Basis ◽

Mm Algorithm ◽

Novel Method ◽

Machinery Fault Diagnosis ◽

Detection Effect

Vibration signals captured from faulty mechanical components are often associated with transients which are significant for machinery fault diagnosis. However, the existence of strong background noise makes the detection of transients a basis pursuit denoising (BPD) problem, which is hard to be solved in explicit form. With sparse representation theory, this paper proposes a novel method for machinery fault diagnosis by combining the wavelet basis and majorization-minimization (MM) algorithm. This method converts transients hidden in the noisy signal into sparse coefficients; thus the transients can be detected sparsely. Simulated study concerning cyclic transient signals with different signal-to-noise ratio (SNR) shows that the effectiveness of this method. The comparison in the simulated study shows that the proposed method outperforms the method based on split augmented Lagrangian shrinkage algorithm (SALSA) in convergence and detection effect. Application in defective gearbox fault diagnosis shows the fault feature of gearbox can be sparsely and effectively detected. A further comparison between this method and the method based on SALSA shows the superiority of the proposed method in machinery fault diagnosis.

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Sparse Representation Classification with Structured Dictionary Design Strategy for Rotating Machinery Fault Diagnosis

IEEE Access ◽

10.1109/access.2020.3036250 ◽

2020 ◽

pp. 1-1 ◽

Cited By ~ 1

Author(s):

Yun Kong ◽

Tianyang Wang ◽

Zhaoye Qin ◽

Fulei Chu

Keyword(s):

Fault Diagnosis ◽

Sparse Representation ◽

Design Strategy ◽

Rotating Machinery ◽

Sparse Representation Classification ◽

Machinery Fault Diagnosis ◽

Structured Dictionary

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Sparse representation based on adaptive multiscale features for robust machinery fault diagnosis

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406214557343 ◽

2014 ◽

Vol 229 (12) ◽

pp. 2303-2313 ◽

Cited By ~ 14

Author(s):

Huijie Zhu ◽

Xinqing Wang ◽

Yang Zhao ◽

Yanfeng Li ◽

Wenfu Wang ◽

...

Keyword(s):

Fault Diagnosis ◽

Sparse Representation ◽

Evidence Theory ◽

Engineering Application ◽

Final Decision ◽

Extraction Scheme ◽

Adaptive Feature Extraction ◽

Global And Local ◽

Improved Accuracy ◽

Machinery Fault Diagnosis

In machinery fault diagnosis, it is fairly time consuming and expertise-demanded for manually selecting features, so it is profitable to automate this process for rapid and robust fault diagnosis. An automatic and adaptive feature extraction scheme via K-SVD algorithm was proposed in this paper, and without additional classifier, the fault detection was directly implemented by sparse representation. Higher animals apply the integration of global and local information to identify unknown objects for better recognition. Enlightened by this mechanism, the judgments by global and local frequency features were fused for better diagnosis by evidence theory. This fusion not only improved the successful rate, but also presented the reliability of diagnosis, which provided worthwhile recommendations and was essential to final decision. Verified in bearing fault diagnosis, the results demonstrated that the proposed scheme improved accuracy, robustness and efficiency, and this scheme had potential value for engineering application.

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Research on the Sparse Representation for Gearbox Compound Fault Features Using Wavelet Bases

Shock and Vibration ◽

10.1155/2015/560171 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Chunyan Luo ◽

Changqing Shen ◽

Wei Fan ◽

Gaigai Cai ◽

Weiguo Huang ◽

...

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Sparse Representation ◽

Background Noise ◽

Practical Application ◽

Impulse Responses ◽

Wavelet Bases ◽

Potential Approach ◽

Fault Features ◽

Engineering Practices

The research on gearbox fault diagnosis has been gaining increasing attention in recent years, especially on single fault diagnosis. In engineering practices, there is always more than one fault in the gearbox, which is demonstrated as compound fault. Hence, it is equally important for gearbox compound fault diagnosis. Both bearing and gear faults in the gearbox tend to result in different kinds of transient impulse responses in the captured signal and thus it is necessary to propose a potential approach for compound fault diagnosis. Sparse representation is one of the effective methods for feature extraction from strong background noise. Therefore, sparse representation under wavelet bases for compound fault features extraction is developed in this paper. With the proposed method, the different transient features of both bearing and gear can be separated and extracted. Both the simulated study and the practical application in the gearbox with compound fault verify the effectiveness of the proposed method.

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Atomic decomposition and sparse representation for complex signal analysis in machinery fault diagnosis: A review with examples

Measurement ◽

10.1016/j.measurement.2017.02.031 ◽

2017 ◽

Vol 103 ◽

pp. 106-132 ◽

Cited By ~ 44

Author(s):

Zhipeng Feng ◽

Yakai Zhou ◽

Ming J. Zuo ◽

Fulei Chu ◽

Xiaowang Chen

Keyword(s):

Fault Diagnosis ◽

Sparse Representation ◽

Signal Analysis ◽

Atomic Decomposition ◽

Complex Signal ◽

Machinery Fault Diagnosis

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Sparse Representation Convolutional Autoencoder for Feature Learning of Vibration Signals and Its Applications in Machinery Fault Diagnosis

IEEE Transactions on Industrial Electronics ◽

10.1109/tie.2021.3128895 ◽

2021 ◽

pp. 1-1

Author(s):

Mengqi Miao ◽

Yuanhang Sun ◽

Jianbo Yu

Keyword(s):

Fault Diagnosis ◽

Sparse Representation ◽

Feature Learning ◽

Vibration Signals ◽

Convolutional Autoencoder ◽

Machinery Fault Diagnosis

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Selecting the Optimal FM System for Children With Cochlear Implants

Perspectives on Hearing and Hearing Disorders in Childhood ◽

10.1044/hhdc18.1.19 ◽

2008 ◽

Vol 18 (1) ◽

pp. 19-24

Author(s):

Erin C. Schafer

Keyword(s):

Speech Recognition ◽

Cochlear Implants ◽

Empirical Research ◽

Background Noise ◽

Signal To Noise Ratio ◽

Evidence Based ◽

Signal To Noise ◽

Speech Processor ◽

System Input ◽

Optimal Type

Children who use cochlear implants experience significant difficulty hearing speech in the presence of background noise, such as in the classroom. To address these difficulties, audiologists often recommend frequency-modulated (FM) systems for children with cochlear implants. The purpose of this article is to examine current empirical research in the area of FM systems and cochlear implants. Discussion topics will include selecting the optimal type of FM receiver, benefits of binaural FM-system input, importance of DAI receiver-gain settings, and effects of speech-processor programming on speech recognition. FM systems significantly improve the signal-to-noise ratio at the child's ear through the use of three types of FM receivers: mounted speakers, desktop speakers, or direct-audio input (DAI). This discussion will aid audiologists in making evidence-based recommendations for children using cochlear implants and FM systems.

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COMPOUND FAULT DIAGNOSIS OF BEARINGS USING AN IMPROVED SPECTRAL KURTOSIS BY MAXIMUM CORRELATION KURTOSIS DECONVOLUTION (MCKD).

June-2020 - International Journal of Engineering Sciences & Research Technology ◽

10.29121/ijesrt.v9.i8.2020.8 ◽

2020 ◽

Vol 9 (8) ◽

pp. 66-72

Keyword(s):

Fault Diagnosis ◽

Resonance Frequency ◽

Background Noise ◽

Maximum Correlation ◽

Vibration Signals ◽

Filter Length ◽

Frequency Bands ◽

Noise Interference ◽

Spectral Kurtosis

This paper discusses the use of Maximum Correlation kurtosis deconvolution (MCKD) method as a pre-processor in fast spectral kurtosis (FSK) method in order to find the compound fault characteristics of the bearing, by enhancing the vibration signals. FSK only extracts the resonance bands which have maximum kurtosis value, but sometimes it might possible that faults occur in the resonance bands which has low kurtosis value, also the faulty signals missed due to noise interference. In order to overcome these limitations FSK used with MCKD, MCKD extracts various faults present in different resonance frequency bands; also detect the weak impact component, as MCKD also dealt with strong background noise. By obtaining the MCKD parameters like, filter length & deconvolution period, we can extract the compound fault feature characteristics.

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