Identification of Multiple Faults in Gearbox Based on Multipoint Optional Minimum Entropy Deconvolution Adjusted and Permutation Entropy.

Huer Sun,Qunfeng Zeng,Xiaohua Liang,Chao Wu

doi:10.3390/e20110850

Huer Sun, Qunfeng Zeng + Show 2 more

Open Access

https://doi.org/10.3390/e20110850

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Journal: Entropy	Publication Date: Nov 6, 2018
Citations: 11	License type: CC BY 4.0

Affiliation: North University of China, Xi'an Jiaotong University

Abstract

The weak compound fault feature is difficult to extract from a gearbox because the signal components are complex and inter-modulated. An approach (that is abbreviated as MRPE-MOMEDA) for extracting the weak fault features of a transmission based on a multipoint optimal minimum entropy deconvolution adjustment (MOMEDA) and the permutation entropy was proposed to solve this problem in the present paper. The complexity of the periodic impact signal was low and the permutation entropy was relatively small. Moreover, the amplitude of the impact was relatively large. Based on these advantages, the multipoint reciprocal permutation entropy (MRPE) was proposed to track the impact fault source of the weak fault feature in gearbox compound faults. The impact fault period was indicated through MRPE. MOMEDA achieved signal denoising. The optimal filter coefficients were solved using MOMEDA. It exhibits an outstanding performance for noise suppression of gearbox signals with a periodic impact. The results from the transmission show that the proposed method can identify multiple faults simultaneously on a driving gear in the 4th gear of the transmission.

Highlights

A gearbox plays an important role in the transmission system, such as in the automobile industry.the manufacturing cost of gearboxes is relatively high, so early fault detection is important.The structure of a gearbox is complex and high assembly standards are required
The compound fault diagnoses of a gearbox were investigated during normal operation
The weak fault feature extraction method based on multipoint optimal minimum entropy deconvolution adjustment (MOMEDA) and permutation entropy was studied in the present paper

Summary

Introduction

A gearbox plays an important role in the transmission system, such as in the automobile industry. The minimum entropy deconvolution (MED) method has been shown to be an effective deconvolution method and has been employed in rotating machinery fault diagnosis [8] It still has some limitations [9,10]. A method called the multipoint optional minimum entropy deconvolution adjustment (MOMEDA) was further developed from the minimum entropy deconvolution (MED) method It has been proven as an efficient tool to extract the periodic impulses in the diagnosis of gearbox faults [13]. MED was proposed using kurtosis; MCKD was proposed using correlated kurtosis (CK); MOMEDA was proposed using MKurt as the fault indicator These methods are based on kurtosis, ignoring the intrinsic characteristics of the signals.

The Multipoint Optimal Minimum Entropy Deconvolution Adjustment

Basic Principle of Permutation Entropy

Multipoint Reciprocal Permutation Entropy

Experimental

Conclusions