Fault Diagnosis of Planetary Gear Based on Fuzzy Entropy of CEEMDAN and MLP Neural Network by Using Vibration Signal

Chen, Xihui; Liu, Chang; Li, Yong

doi:10.1051/itmconf/20171108002

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…The process of fuzzy entropy is used to measure the irregularity, complexity, and stability for each IMF component. The steps for the process of fuzzy entropy are as follows 21 : Step 1: Assume that a time series is denoted as IMF(i)=(φ(1),φ(2),…,φ(N)), where N is the length of times series. Then, the mean u0(t) of m consecutive IMF(i) values can be calculated as follows where parameter m is called the embedding dimension and is a positive integer.…”

Section: Fault Feature Extraction Based On Fuzzy Entropy Of Emdmentioning

confidence: 99%

Multi-fault diagnosis of rolling bearing using fuzzy entropy of empirical mode decomposition, principal component analysis, and SOM neural network

Zair

Rahmoune

Benazzouz

2018

Proceedings of the Institution of Mechanical Engineers, Part C:

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The condition monitoring and multi-fault diagnosis of rolling bearing is a very important research content in the field of the rotating machinery health management. Most researches widely used empirical mode decomposition in tandem with principal component analysis which is applied for feature extraction. But this method may lead to imprecise classification. In this paper, we propose a new method of rolling bearing multi-fault diagnosis, by combining the fuzzy entropy of empirical mode decomposition, principal component analysis, and self-organizing map neural network. The empirical mode decomposition process allows the vibration signal to be decomposed into a series of intrinsic mode functions. For each intrinsic mode function, we obtained the fault feature information. The proposed approach combines the fuzzy function and sample entropy to obtain fuzzy entropy. By this combination, we can reflect the complexity and the irregularity in each intrinsic mode function component. The fuzzy entropy of empirical mode decomposition used to construct the vectors is defined as the input of the principal component analysis. This principal component analysis is used to reduce the dimension of the feature vectors. Finally, the reduced feature vectors are chosen as input of self-organizing map network for automatic fault diagnosis and fault classification. The obtained results show that the proposed approach makes it possible to correctly assess the degradation of rolling bearing and to obtain recognition of high-sensitivity defects for different types of bearing faults.

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