An Improved Sparse Representation Based on Local Orthogonal Matching Pursuit for Bearing Compound Fault Diagnosis

Cai, Yi; Ran, Le; Tang, Jiayin; Zhuang, Zhe; Zhou, Qiuyang; Lin, Jianhui

doi:10.1109/jsen.2022.3210450

Cited by 13 publications

(3 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The sparse representation-based method has been proven to be a prevalent tool in compound fault diagnosis due to its several advantages including different component matching, signal denoising, and signal separation without mode mixing. Generally, sparse representation theory mainly contains two aspects: overcomplete dictionary construction [74][75][76][77][78][79][80] and sparse coefficient solution [82][83][84][85][86].…”

Section: ) Sparse Representation-based Methodsmentioning

confidence: 99%

“…For sparse representation-based methods, solving the sparse coefficients is pivotal and has a significant influence on the performance of compound fault signal decomposition. Many approximate methods have been proposed for dealing with the sparse coefficient solution, among which Convex Relaxation Optimization (CRO) [82][83][84] and Orthogonal Matching Pursuit (OMP) [85,86] have attracted the most attention in compound fault diagnosis. For instance, Huang et al proposed a compound fault diagnosis method for gearboxes, in which a multi-source fidelity sparse representation algorithm was developed to convert the signal reconstruction problem into a multivariate sparse convex optimization problem [83].…”

Section: ) Sparse Representation-based Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

Huang

Xia

Zhang

et al. 2023

JDMD

View full text Add to dashboard Cite

Compound fault, as a primary failure leading to unexpected downtime of rotating machinery, dramatically increases the difficulty in fault diagnosis. To deal with the difficulty encountered in implementing compound fault diagnosis (CFD), researchers and engineers from industry and academia have made numerous significant breakthroughs in recent years. Admittedly, many systematic surveys focused on fault diagnosis have been conducted by reputable researchers. Nevertheless, previous review articles paid more attention to fault diagnosis with several single or independent faults, resulting in that there is still lacking a comprehensive survey on CFD. Therefore, to fulfill the above requirements, it is necessary to provide an in-depth overview of fault diagnosis methods or algorithms for compound faults of rotating machinery and uncover potential challenges or opportunities that would guide and inspire readers to devote their efforts to promoting fault diagnosis technology more effective and practical. Specifically, the backgrounds including the related definitions and a new taxonomy of CFD methods are detailed according to the way of implementing compound fault recognition. Then, the state-of-the-art applications of CFD are overviewed based on relevant publications in the past decades. Finally, the challenges and opportunities associated with implementing CFD are concluded and followed by a conclusion for ending this survey. We believe that this review article can provide a systematic guideline of CFD from different aspects for potential readers and seasoned researchers.

show abstract

Section: ) Sparse Representation-based Methodsmentioning

confidence: 99%

Section: ) Sparse Representation-based Methodsmentioning

confidence: 99%

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

Huang

Xia

Zhang

et al. 2023

JDMD

View full text Add to dashboard Cite

show abstract

“…A noise-aware statistical threshold is designed to automatically remove noise and achieve superior performances in IBD tasks [ 7 ]. Some other CSL-based IBD methods adopt the shift-invariant dictionary [ 36 , 37 ], local matching pursuit [ 38 , 39 ], and sparse filters [ 40 ] to detect impulsive sources. The nonlocal similarity of feature waveforms is also exploited to further enhance the deconvolution performance of the CSL model [ 41 ].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Multi-D-Norm-Driven Sparse Unfolding Deconvolutional Network for Bearing Fault Diagnosis

Lin,

Zhang,

et al. 2024

Sensors

View full text Add to dashboard Cite

Impulsive blind deconvolution (IBD) is a popular method to recover impulsive sources for bearing fault diagnosis. Its underpinnings are in the design of objective functions based on prior knowledge of impulsive sources and a transfer function to describe transmission path influences. However, popular objective functions cannot retain waveform impulsiveness and periodicity cyclostationarity simultaneously, and the single convolution operation of IBD methods is insufficient to describe transmission paths composed of multiple linear and nonlinear units. Inspired by the MaxPooling period modulation intensity (MPMI) and convolutional sparse learning (CSL), an adaptive multi-D-norm-driven sparse unfolding deconvolution network (AMD-SUDN) is proposed in this paper. The core strategy is that one target vector with simultaneous impulsiveness and cyclostationarity is constructed automatically through the MPMI; then, this vector is substituted into the multi D-norm to design objective functions. Moreover, an iterative soft threshold algorithm (ISTA) for the CSL model is derived, and its iterative steps are unfolded into one deconvolution network. The algorithm’s performance and the hyperparameter configuration are investigated by a set of numerical simulations. Finally, the proposed AMD-SUDN is applied to detect the impulsive features of bearing faults. All comparative results verify that the proposed AMD-SUDN achieves a better deconvolution accuracy than state-of-the-art IBD methods.

show abstract

Adaptive multiscale wavelet-guided periodic sparse representation for bearing incipient fault feature extraction

Niu,

Jiang,

Yao

2024

Sci. China Technol. Sci.

View full text Add to dashboard Cite

An Improved Sparse Representation Based on Local Orthogonal Matching Pursuit for Bearing Compound Fault Diagnosis

Cited by 13 publications

References 40 publications

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

An Adaptive Multi-D-Norm-Driven Sparse Unfolding Deconvolutional Network for Bearing Fault Diagnosis

Adaptive multiscale wavelet-guided periodic sparse representation for bearing incipient fault feature extraction

Contact Info

Product

Resources

About