Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines

Yan, Ruqiang; Liu, Yongbin; Gao, Robert X.

doi:10.1016/j.ymssp.2011.11.022

Cited by 337 publications

(234 citation statements)

References 29 publications

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“…If the value of embedding dimension is too large, embedded dimension value will not reflect the subtle change accurately, which can affect computing efficiency of permutation entropy. Yan [21] found that when 6 m  and 3   , permutation entropy can better reflect the subtle changes of mechanical system. Therefore, this paper permutation entropy embedding dimension 6 m  and time delay 3   .…”

Section: Permutation Entropymentioning

confidence: 99%

Research of Planetary Gear Fault Diagnosis Based on Permutation Entropy of CEEMDAN and ANFIS

Kuai¹,

Pang²,

Li³

2018

Preprint

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functions are adaptively adjusted according to training samples. Finally, the fuzzy inference rules are determined, and the optimal ANFIS is obtained. The overall recognition rate of the test sample used for ANFIS is 90.75%, and the recognition rate of gear with one missing tooth is relatively high. The recognition rates of different fault gears based on the method can also achieve better results. Therefore, the proposed method can be applied to planetary gear fault diagnosis effectively.

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Section: Permutation Entropymentioning

confidence: 99%

Research of Planetary Gear Fault Diagnosis Based on Permutation Entropy of CEEMDAN and ANFIS

Kuai¹,

Pang²,

Li³

2018

Preprint

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“…The concept of permutation entropy was also employed to analyze electroencephalographic signals [41,42] as well as the working status characterization of rotary machines [43]. Based on the Shannon entropy, the permutation entropy is defined by calculating the probability density function of a time series with permutation of specific order.…”

Section: Permutation Entropymentioning

confidence: 99%

Multi-Scale Analysis Based Ball Bearing Defect Diagnostics Using Mahalanobis Distance and Support Vector Machine

et al. 2013

Entropy

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Abstract:The objective of this research is to investigate the feasibility of utilizing the multi-scale analysis and support vector machine (SVM) classification scheme to diagnose the bearing faults in rotating machinery. For complicated signals, the characteristics of dynamic systems may not be apparently observed in a scale, particularly for the fault-related features of rotating machinery. In this research, the multi-scale analysis is employed to extract the possible fault-related features in different scales, such as the multi-scale entropy (MSE), multi-scale permutation entropy (MPE), multi-scale root-mean-square (MSRMS) and multi-band spectrum entropy (MBSE). Some of the features are then selected as the inputs of the support vector machine (SVM) classifier through the Fisher score (FS) as well as the Mahalanobis distance (MD) evaluations. The vibration signals of bearing test data at Case Western Reserve University (CWRU) are utilized as the illustrated examples. The analysis results demonstrate that an accurate bearing defect diagnosis can be achieved by using the extracted machine features in different scales. It can be also noted that the diagnostic results of bearing faults can be further enhanced through the feature selection procedures of FS and MD evaluations.

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“…The types of features are usually classified into three categories, time domain features, frequency domain features, and time-frequency domain features. The time-frequency domain features are always based on timefrequency analysis, combined with the concept of spectrum, entropy, and complexity, for example, the Rényi entropy [5], the permutation entropy [6], and the general mathematical morphology particle [7]. In general, mechanical equipment undergoes a complete degradation from normal stage to failure.…”

Section: Introductionmentioning

confidence: 99%

Rolling Element Bearing Performance Degradation Assessment Using Variational Mode Decomposition and Gath-Geva Clustering Time Series Segmentation

Wang

et al. 2017

International Journal of Rotating Machinery

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By focusing on the issue of rolling element bearing (REB) performance degradation assessment (PDA), a solution based on variational mode decomposition (VMD) and Gath-Geva clustering time series segmentation (GGCTSS) has been proposed. VMD is a new decomposition method. Since it is different from the recursive decomposition method, for example, empirical mode decomposition (EMD), local mean decomposition (LMD), and local characteristic-scale decomposition (LCD), VMD needs a priori parameters. In this paper, we will propose a method to optimize the parameters in VMD, namely, the number of decomposition modes and moderate bandwidth constraint, based on genetic algorithm. Executing VMD with the acquired parameters, the BLIMFs are obtained. By taking the envelope of the BLIMFs, the sensitive BLIMFs are selected. And then we take the amplitude of the defect frequency (ADF) as a degradative feature. To get the performance degradation assessment, we are going to use the method called Gath-Geva clustering time series segmentation. Afterwards, the method is carried out by two pieces of run-to-failure data. The results indicate that the extracted feature could depict the process of degradation precisely.

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Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines

Cited by 337 publications

References 29 publications

Research of Planetary Gear Fault Diagnosis Based on Permutation Entropy of CEEMDAN and ANFIS

Research of Planetary Gear Fault Diagnosis Based on Permutation Entropy of CEEMDAN and ANFIS

Multi-Scale Analysis Based Ball Bearing Defect Diagnostics Using Mahalanobis Distance and Support Vector Machine

Rolling Element Bearing Performance Degradation Assessment Using Variational Mode Decomposition and Gath-Geva Clustering Time Series Segmentation

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