A Joint Fault Diagnosis Scheme Based on Tensor Nuclear Norm Canonical Polyadic Decomposition and Multi-Scale Permutation Entropy for Gears

Ge, Mao; Lv, Yong; Yi, Cancan; Zhang, Yi; Chen, Xiangjun

doi:10.3390/e20030161

Cited by 11 publications

(17 citation statements)

References 31 publications

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“…Ge et al [75] present a joint fault diagnosis scheme via tensor nuclear norm canonical polyadic decomposition (TNNCPD) and multiscale permutation entropy (MSPE) to classify four gear states. BPNN isused to perform fault classi cation.…”

Section: Results Discussionmentioning

confidence: 99%

“…e results illustrate that their proposed scheme can accurately recognize di erent gear working conditions, while the performance of WT is worse. It is worth noting that Ge et al [75] adopt the same set of signals in the experiment as we used in this paper. e comparisons between the performance in [75] and our proposed method are shown in Table 11.…”

Section: Results Discussionmentioning

confidence: 99%

“…It is worth noting that Ge et al [75] adopt the same set of signals in the experiment as we used in this paper. e comparisons between the performance in [75] and our proposed method are shown in Table 11. e test accuracy of our result is not as perfect as that in [75], nevertheless it is signi cantly better than WT.…”

Section: Results Discussionmentioning

confidence: 99%

“…e comparisons between the performance in [75] and our proposed method are shown in Table 11. e test accuracy of our result is not as perfect as that in [75], nevertheless it is signi cantly better than WT.…”

Section: Results Discussionmentioning

confidence: 99%

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A Fault Diagnosis Method for One-Dimensional Vibration Signal Based on Multiresolution tlsDMD and Approximate Entropy

Dang

et al. 2019

Shock and Vibration

Self Cite

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Dynamic mode decomposition (DMD) has certain advantages compared with the traditional fault signal diagnosis method. By exploiting the strength of DMD algorithm in signal processing, this paper proposes a joint fault diagnosis scheme to extract the spatial and temporal patterns and evaluate them for the complexity to diagnose the fault for one-dimensional mechanical signal. The multiscale method is adopted to decompose the reconstructed matrix of standard DMD modes into multiple scales with a given level parameter. Total least squares DMD algorithm is performed on each level to solve the noise sensitivity problem. Approximate entropy (ApEn) is performed on the grouped multiscale spatiotemporal modes that represent the dynamic characteristic information of the original signal. ApEn values are used as a fault recognizer to identify fault types. By applying the algorithm on three experimental mechanical vibration data, we verify the effectiveness of the proposed method. The result demonstrates that the proposed scheme can effectively recognize different fault forms as a fault diagnosis method.

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Section: Results Discussionmentioning

confidence: 99%

Section: Results Discussionmentioning

confidence: 99%

Section: Results Discussionmentioning

confidence: 99%

Section: Results Discussionmentioning

confidence: 99%

See 2 more Smart Citations

A Fault Diagnosis Method for One-Dimensional Vibration Signal Based on Multiresolution tlsDMD and Approximate Entropy

Dang

et al. 2019

Shock and Vibration

Self Cite

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“…Additionally, this method can not only effectively solve the disturbance of noise to fault characteristics, but also competently detect weak fault signals. Simultaneously, the MPE method can solve the multi-scale coupling problem between multiple faults and accurately reflect the dynamic mutation capability of the fault system [9].…”

Section: Introductionmentioning

confidence: 99%

Fault identification technology for gear tooth surface wear based on MPE method by MI and improved FNN algorithm

Zhou¹,

Cui²,

Liu³

et al. 2019

Vib. proced.

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Multiscale Permutation Entropy (MPE) is a presented nonlinear dynamic technology for measuring the randomness and detecting the nonlinear dynamic change of time sequences and can be used effectively to extract the nonlinear dynamic wear fault feature of gear tooth surface from vibration signals of gear set. To solve the subjectivity drawback of threshold parameter selection process in MPE method, a joint calculation method based on the Mutual Information (MI) and improved False Nearest Neighbor (FNN) principle for calculating threshold parameters for MPE method was presented in this article. Then, the influence of threshold parameters on the identification accuracy of fault features with the MPE was studied by analyzing simulation data. Through the simulation analysis, the effectiveness of the proposed MPE method is validated. Finally, the wear failure test of spur gear was carried out, and the proposed method was applied to analyze the experimental data of fault signal. Meanwhile, the vibration characteristics of the fault signal are acquired. The analysis results show that the proposed method can effectively realize the fault diagnosis of gear box and has higher fault identification accuracy than the existing methods.Keywords: multiscale permutation entropy, mutual information, improved false nearest neighbor, delay time, embedded dimension, scale factor and fault feature.

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Gear fault detection based on instantaneous frequency estimation using variational mode decomposition and permutation entropy under real speed scenarios

Sharma

2020

Wind Energy

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Structural compactness with multiple meshing points excites multiple vibrations in a planetary gearbox of a wind turbine operating at varying speeds. The nonstationary, multicomponent vibration signals result in complex modulations challenging the effectiveness of a signal processing technique‐based fault diagnosis method. This paper aims to detect gear tooth faults under varying speed conditions based on the instantaneous frequency (IF) estimate of the decomposed component. Vibration signals were decomposed using variational mode decomposition (VMD), which is capable of frequency demodulation by exhibiting mono‐oscillatory components, followed by the IF estimation to select the most sensitive decomposed mode. The selected sensitive mode was further analyzed to deduce envelope spectrums for the extraction of frequency components highlighting fault symptoms. Furthermore, permutation entropy (PE) was evaluated to characterize the presence of randomness due to fault severity and to validate the proposed signal processing approach. In this paper, the vibration signals simulating planet gear faults were analyzed using the proposed signal processing approach. Symptoms of planet tooth faults were analyzed using the experimental investigation under real‐time varying speed to validate and exhibit the effectiveness of the proposed fault diagnosis approach for a planetary gearbox.

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A Joint Fault Diagnosis Scheme Based on Tensor Nuclear Norm Canonical Polyadic Decomposition and Multi-Scale Permutation Entropy for Gears

Cited by 11 publications

References 31 publications

A Fault Diagnosis Method for One-Dimensional Vibration Signal Based on Multiresolution tlsDMD and Approximate Entropy

A Fault Diagnosis Method for One-Dimensional Vibration Signal Based on Multiresolution tlsDMD and Approximate Entropy

Fault identification technology for gear tooth surface wear based on MPE method by MI and improved FNN algorithm

Gear fault detection based on instantaneous frequency estimation using variational mode decomposition and permutation entropy under real speed scenarios

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