Sparse representation based on local time–frequency template matching for bearing transient fault feature extraction

He, Qingbo; Ding, Xiaoxi

doi:10.1016/j.jsv.2016.01.054

Cited by 73 publications

(23 citation statements)

References 29 publications

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“…Zhang [25] proposed a novel method called kurtosis-based weighted sparse model based on a convex optimization technique; this technique formulated the prior information into a sparse regularization problem and achieved good effect in bearing fault diagnosis. He [26] employed a local time-frequency (TF) domain sparse representation to reconstruct the native pulse waveform structure of fault transients, and proved that the proposed method was superior to traditional the MP and K-singular value decomposition (K-SVD) methods.…”

Section: Introductionmentioning

confidence: 99%

Incipient Fault Diagnosis of Rolling Bearings Based on Impulse-Step Impact Dictionary and Re-Weighted Minimizing Nonconvex Penalty Lq Regular Technique

Liang

2017

Entropy

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Abstract:The periodical transient impulses caused by localized faults are sensitive and important characteristic information for rotating machinery fault diagnosis. However, it is very difficult to accurately extract transient impulses at the incipient fault stage because the fault impulse features are rather weak and always corrupted by heavy background noise. In this paper, a new transient impulse extraction methodology is proposed based on impulse-step dictionary and re-weighted minimizing nonconvex penalty Lq regular (R-WMNPLq, q = 0.5) for the incipient fault diagnosis of rolling bearings. Prior to the sparse representation, the original vibration signal is preprocessed by the variational mode decomposition (VMD) technique. Due to the physical mechanism of periodic double impacts, including step-like and impulse-like impacts, an impulse-step impact dictionary atom could be designed to match the natural waveform structure of vibration signals. On the other hand, the traditional sparse reconstruction approaches such as orthogonal matching pursuit (OMP), L1-norm regularization treat all vibration signal values equally and thus ignore the fact that the vibration peak value may have more useful information about periodical transient impulses and should be preserved at a larger weight value. Therefore, penalty and smoothing parameters are introduced on the reconstructed model to guarantee the reasonable distribution consistence of peak vibration values. Lastly, the proposed technique is applied to accelerated lifetime testing of rolling bearings, where it achieves a more noticeable and higher diagnostic accuracy compared with OMP, L1-norm regularization and traditional spectral Kurtogram (SK) method.

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Section: Introductionmentioning

confidence: 99%

Incipient Fault Diagnosis of Rolling Bearings Based on Impulse-Step Impact Dictionary and Re-Weighted Minimizing Nonconvex Penalty Lq Regular Technique

Liang

2017

Entropy

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“…Subsequently, Zhang et al [133] proposed a weighted sparse model with convex optimization framework for bearing fault diagnosis. He and Ding [134] proposed a local time-frequency template matching method for bearing transient feature extraction. Wang et al [135,136] used the sparse representation method with wavelet dictionary for extracting the transient feature in a faulty gearbox, in which wavelet was selected by correlation filtering.…”

Section: Sparse Decomposition Analysismentioning

confidence: 99%

Basic research on machinery fault diagnostics: Past, present, and future trends

et al. 2017

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Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery fault diagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.

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“…A number of techniques for the detection of abnormal conditions of rolling element bearings are currently available, which include vibration and acoustics [5,6], acoustic emission [7], lubricating oil monitoring [8], and temperature [9]. As a sensitive and effective method, vibration and acoustic measurements are widely used to detect the defects in bearings [10].…”

Section: Introductionmentioning

confidence: 99%

Health Monitoring on the Spacecraft Bearings in High-Speed Rotating Systems by Using the Clustering Fusion of Normal Acoustic Parameters

Wang²,

Liu³

et al. 2019

Applied Sciences

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The rolling bearings in moment wheel assemblies (MWAs) or control moment gyros (CMGs) are not only the core components in spacecrafts but also prone to failure. Therefore, a high reliability is the critical characteristic for spacecraft bearings, and long-life testing on the ground is one of the main means for bearing reliability assessment. In practical applications, a convenient and reliable method is required for monitoring the health status of abnormal bearings in MWAs during the long-life test. In this paper, a monitoring method based on the clustering fusion of normal operation acoustic parameters is proposed for the identification of abnormal bearings. Firstly, the characteristics of MWA's acoustic signal and its feasibility as a monitoring medium are clarified based on tests and modal analysis. Then statistical parameters and sound quality parameters are introduced to characterize the changes caused by bearing faults, and the root mean square (RMS), kurtosis, and sharpness parameters are selected to construct the feature vectors. The K-medoids clustering technology is used to fuse the characteristic parameters, and the safety distance for normal bearing operation can be obtained by a suitable method. Finally, the abnormal index is presented based on the excess rate and excess distance to judge the abnormal states of several types of bearings through tests. The research results indicate that the presented monitoring method based on the clustering of the normal operation acoustic parameters can not only identify various faults of the spacecraft bearing (ball defects, outer ring defects, cage instability, etc.) effectively but also give a quantitative evaluation of the severity of the abnormality.

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Sparse representation based on local time–frequency template matching for bearing transient fault feature extraction

Cited by 73 publications

References 29 publications

Incipient Fault Diagnosis of Rolling Bearings Based on Impulse-Step Impact Dictionary and Re-Weighted Minimizing Nonconvex Penalty Lq Regular Technique

Incipient Fault Diagnosis of Rolling Bearings Based on Impulse-Step Impact Dictionary and Re-Weighted Minimizing Nonconvex Penalty Lq Regular Technique

Basic research on machinery fault diagnostics: Past, present, and future trends

Health Monitoring on the Spacecraft Bearings in High-Speed Rotating Systems by Using the Clustering Fusion of Normal Acoustic Parameters

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