A New Method for Weak Fault Feature Extraction Based on Improved MED

Junlin Li,Jingsheng Jiang,Xiaohong Fan,Liangchao Chen,Huaqing Wang,Jianfeng Yang,Wenbin Liu,Liuyang Song

doi:10.1155/2018/9432394

Abstract

Because of the characteristics of weak signal and strong noise, the low-speed vibration signal fault feature extraction has been a hot spot and difficult problem in the field of equipment fault diagnosis. Moreover, the traditional minimum entropy deconvolution (MED) method has been proved to be used to detect such fault signals. The MED uses objective function method to design the filter coefficient, and the appropriate threshold value should be set in the calculation process to achieve the optimal iteration effect. It should be pointed out that the improper setting of the threshold will cause the target function to be recalculated, and the resulting error will eventually affect the distortion of the target function in the background of strong noise. This paper presents an improved MED based method of fault feature extraction from rolling bearing vibration signals that originate in high noise environments. The method uses the shuffled frog leaping algorithm (SFLA), finds the set of optimal filter coefficients, and eventually avoids the artificial error influence of selecting threshold parameter. Therefore, the fault bearing under the two rotating speeds of 60 rpm and 70 rpm is selected for verification with typical low-speed fault bearing as the research object; the results show that SFLA-MED extracts more obvious bearings and has a higher signal-to-noise ratio than the prior MED method.

Highlights

Rolling bearings are one of the most widely used elements in rotary machines
When a rolling bearing fault is weak at an early stage or at a low shaft speed, weak fault features are often embedded in background noise
In [4], wavelet transformation is applied to the problem of weak, abnormal vibration signal extraction, but reliance upon a single wavelet transform makes the extraction of complex vibration signals difficult [5, 6]

Summary

Introduction

Rolling bearings are one of the most widely used elements in rotary machines. The mechanical failure of bearings may result in financial loss, and even death. When a rolling bearing fault is weak at an early stage or at a low shaft speed, weak fault features are often embedded in background noise. The fast Fourier transform cannot obtain ideal transient extraction, even as an increased signal-to-noise ratio assists weak fault feature extraction. Many scholars have reviewed the problem of fault diagnosis taken from weak signals and have made progress. Huang et al in 1998 proposed empirical mode decomposition (EMD), an adaptive signal processing method that yields intrinsic mode functions (IMFs) [11]. EMD algorithms are widely used for rolling bearing fault type identification. Wu and Huang proposed ensemble empirical mode decomposition (EEMD) to overcome the shortcoming of mode mixing, but end effects remain a problem.

Methods

Results

Conclusion