Enhanced periodic mode decomposition and its application to composite fault diagnosis of rolling bearings

Cheng, Jian; Yu, Yang; Shao, Haidong; Pan, Haiyang; Zheng, Jianhua; Cheng, Junsheng

doi:10.1016/j.isatra.2021.07.014

Cited by 36 publications

(17 citation statements)

References 22 publications

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“…Signal decomposition-based methods are similar to pattern recognition that relied on feature engineering, in which the different components are expected to be separated. Scholars and researchers have proposed lots of successful methods for compound fault diagnosis, such as Wavelet Transform (WT) [20][21][22][23][24][25][26][27][28], Variational Mode Decomposition (VMD) [29][30][31][32][33][34], Local Mean Decomposition (LMD) [35], Singular Spectrum Decomposition (SSD) [36,37], Symplectic Geometry Mode Decomposition (SGMD) [38,39], and other methods [40][41][42][43][44][45][46][47][48]. first, the compound fault signals are separated into different empirical models by empirical WT; second, a duffing oscillator which incorporates all single fault frequency is used to establish the fault isolator; finally, all the single faults can be recognized one by one by observing the chaotic motion from the Poincar mapping of the fault isolator outputs [20].…”

Section: ) Signal Decomposition-based Methodsmentioning

confidence: 99%

“…For instance, Tang et al proposed a compound fault detection method with virtual multichannel signals in the angel domain and applied it to monitoring the rolling bearings under varying working conditions [43]. More details can be found in [40][41][42][43][44][45][46][47][48], which are not enumerated here. and Cyclostationary Blind Deconvolution (CYCBD) [63], can enhance weak periodic features and suppress signal noise by constructing a comb filter, thus, have been proven to be an effective tool for separating compound fault with weak components.…”

Section: ) Signal Decomposition-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

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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.

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Section: ) Signal Decomposition-based Methodsmentioning

confidence: 99%

Section: ) Signal Decomposition-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

“…For example, the WT can not subdivide well the high-frequency of the signal, inducing information loss and energy leakage (Meng et al 2016), and the analysis window of STFT needs to be further optimized. These shortcomings make these classic methods not be fully self-adaptive in essential (Yao et al 2018;Cheng et al 2021). As a self-adaptive time-frequency analysis method, EMD is able to handle non-stationary and nonlinear signals better, but it has disadvantages such as over-envelope, under-envelope and end-effect, what's more, its modal aliasing property will lead to the distortion of the decomposed IMFs (Yao et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Multi-fault diagnosis of rolling bearing using two-dimensional feature vector of WP-VMD and PSO-KELM algorithm

jiang

Li²,

Shen

2022

Soft Comput

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In order to achieve accurate fault diagnosis of rolling bearing under random noise, a new fault diagnosis method based on wavelet packet-variational mode decomposition (WP-VMD) and kernel extreme learning machine (KELM) optimized by particle swarm optimization (PSO) is proposed in this paper. Firstly, the time-frequency domain feature vectors of the original rolling bearing fault signals are effectively obtained by preprocessing of WMD and decomposition and reconstruction of VMD.Then, the extracted two-dimensional feature vector is input into the KELM neural network for fault identification, and combined with PSO, KELM parameters were optimized. The experimental results show that the proposed method can effectively diagnose the rolling bearing under random noise, with the features of fast speed, stable performance and high accuracy. By comparison, this paper obtains better accuracy and real-time performance with fewer features, which provides a simple and efficient solution for fault diagnosis of rolling bearings.

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“…Recently, there has been a wide interest in nonlinear dynamical systems in connection with various applications, for example, in the theory of vibrations [1,2]. Therefore, within the framework of the disciplines "Nonlinear Dynamics" and "Theory of Oscillatory Systems" for the direction of applied mathematics and computer science, much attention is paid to the qualitative analysis of nonlinear dynamical systems in order to establish their chaotic or periodic modes [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Construction of maps for dynamic modes and bifurcation diagrams in nonlinear dynamics using the Maple computer mathematics software package

Parovik

Yakovleva²

2022

J. Phys.: Conf. Ser.

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Enhanced periodic mode decomposition and its application to composite fault diagnosis of rolling bearings

Cited by 36 publications

References 22 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

Multi-fault diagnosis of rolling bearing using two-dimensional feature vector of WP-VMD and PSO-KELM algorithm

Construction of maps for dynamic modes and bifurcation diagrams in nonlinear dynamics using the Maple computer mathematics software package

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