Fault diagnosis of planetary gear based on entropy feature fusion of DTCWT and OKFDA

Chen, Xihui; Li, Yong; Peng, Liping

doi:10.1177/1077546317742506

Cited by 13 publications

(15 citation statements)

References 24 publications

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“…The authors also provide the conclusion that fault detection is possible without information about the operating conditions. Another effective method for planetary gear consists of two steps, including entropy feature fusion of dual-tree complex wavelet transform and optimized kernel Fisher discriminant analysis [133]. Zhang et al [134] improve dual-tree complex wavelet transform and combine it with minimum entropy deconvolution to diagnose the composite fault of a gearbox.…”

Section: Other Typical Entropy Theories Application On Gearmentioning

confidence: 99%

“…Authors Methodologies 1 Bokoski and Jurii [132] wavelet packet transform + Rényi entropy 2 Chen et al [133] entropy feature fusion of dual-tree complex wavelet transform + optimized kernel Fisher discriminant analysis 3 Zhang et al [134] minimum entropy deconvolution + improved dual-tree complex wavelet transform 4 Cheng et al [135] ensemble empirical mode decomposition + sample entropy 5 Chen et al [136] fuzzy entropy 6 Zhang et al [137] continuous vibration separation + minimum entropy deconvolution 7 Tang et al [138] hierarchical Instantaneous energy density dispersion entropy + dynamic time warping 8 Cai et al [139] combining product function + multipoint optimal minimum entropy deconvolution adjusted…”

Section: Indexmentioning

confidence: 99%

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Related Entropy Theories Application in Condition Monitoring of Rotating Machineries

Liu

Zhi

Zhang

et al. 2019

Entropy

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Rotating machinery plays an important role in various kinds of industrial engineering. How to assess their conditions is a key problem for operating safety and condition-based maintenance. The potential anomaly, fault and failure information can be obtained by analyzing the collected condition monitoring data of the previously deployed sensors in rotating machinery. Among the available methods of analyzing sensors data, entropy and its variants can provide quantitative information contained in these sensing data. For implementing fault detection, diagnosis, and prognostics, this information can be utilized for feature extraction and selecting appropriate training data for machine learning methods. This article aims to review the related entropy theories which have been applied for condition monitoring of rotating machinery. This review consists of typical entropy theories presentation, application, summary, and discussion.

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Section: Other Typical Entropy Theories Application On Gearmentioning

confidence: 99%

Section: Indexmentioning

confidence: 99%

Related Entropy Theories Application in Condition Monitoring of Rotating Machineries

Liu

Zhi

Zhang

et al. 2019

Entropy

View full text Add to dashboard Cite

show abstract

“…In the aspect of pattern recognition, the fault recognition technology based on mathematical model is the core of fault diagnosis and usually more precise [12][13][14]. Due to the powerful learning and recognition ability, neural networks are excellent pattern recognition model and have been widely used in the field of fault diagnosis of rolling bearing [15].…”

Section: Introductionmentioning

confidence: 99%

A fault pattern recognition method for rolling bearing based on CELMDAN and fuzzy entropy

Liu¹,

Liu²,

Jiaxin³

et al. 2020

J. vibroeng.

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The vibration signal of rolling bearing often has the characteristics of strong noise, nonlinearity and non-stationary, so the accurate fault feature information cannot be obtained directly from the measured vibration signal. Therefore, a fault pattern recognition method for rolling bearing based on complete ensemble local mean decomposition with adaptive noise (CELMDAN) and fuzzy entropy is deeply studied. Firstly, the reason of modal aliasing existing in local mean decomposition (LMD) method is explained. According to the previous methods for modal aliasing processed in other methods, CELMDAN method is proposed. The experiment proves that the proposed CELMDAN method can better handle the vibration signals with nonlinear and non-stationary. Then, the principle and properties of the fuzzy entropy are introduced in detail, and the fault feature of rolling bearing can be reflected. Finally, extreme learning machine (ELM) is introduced as the pattern recognition method based on the effective fault feature of rolling bearing. Combined with the verification of experimental signal, it is proved that the proposed method can extract the fault features of rolling bearing accurately and effectively, and the fault pattern recognition of rolling bearing can be realized.

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“…A good fault feature can effectively represent different fault signals [ 14 ]. It not only needs to reflect the difference between the different kinds of signals, but also needs to ensure the similarity between the same kind of signals [ 15 ]. The filter vector based on the optimal kurtosis position can effectively reflect the periodicity and amplitude characteristics of the original fault impulse signals [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Research on Bearing Fault Diagnosis Method Based on Filter Features of MOMLMEDA and LSTM

Chen

Pang

2019

Entropy

Self Cite

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As the supporting unit of rotating machinery, bearing can ensure efficient operation of the equipment. Therefore, it is very important to monitor the status of bearings accurately. A bearing fault diagnosis mothed based on Multipoint Optimal Minimum Local Mean Entropy Deconvolution Adjusted (MOMLMEDA) and Long Short-Term Memory (LSTM) is proposed. MOMLMEDA is an improved algorithm based on Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA). By setting the local kurtosis mean as a new selection criterion, it can effectively avoid the interference of false kurtosis caused by noise and improve the accuracy of optimal kurtosis position. The optimal filter designed by optimal kurtosis position has periodic and amplitude characteristics, which are used as the fault feature in this paper. However, this feature has temporal characteristics and cannot be used as input of general neural network directly. LSTM is selected as the classification network in this paper. It can effectively avoid the influence of the temporal problem existing in feature vectors. Accurate diagnosis of bearing faults is realized by training classification neural network with samples. The overall recognition rate is up to 93.50%.

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Fault diagnosis of planetary gear based on entropy feature fusion of DTCWT and OKFDA

Cited by 13 publications

References 24 publications

Related Entropy Theories Application in Condition Monitoring of Rotating Machineries

Related Entropy Theories Application in Condition Monitoring of Rotating Machineries

A fault pattern recognition method for rolling bearing based on CELMDAN and fuzzy entropy

Research on Bearing Fault Diagnosis Method Based on Filter Features of MOMLMEDA and LSTM

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