Probabilistic Principal Component Analysis Assisted New Optimal Scale Morphological Top-Hat Filter for the Fault Diagnosis of Rolling Bearing

Siyu Zhao,Changzheng Chen,Yuanqing Luo

doi:10.1109/access.2020.3019638

Abstract

The early fault impulses of rolling bearing are often submerged by harmonic interferences and background noise. In this paper, a fault diagnosis scheme called probabilistic principal component analysis assisted optimal scale average of erosion and dilation hat filter (OSAEDH-PPCA) is presented for the fault detection of rolling bearing. Based on morphological erosion operator and morphological dilation operator, a new morphological top-hat operator, namely average of erosion and dilation hat (AEDH) operator is firstly proposed to extract the fault impulses in the vibration signal. Simulation analysis shows the filter characteristics of proposed AEDH operator. Comparative analyses demonstrate that the feature extraction property of the AEDH operator is superior to existing top-hat operators. Then, the probabilistic principal component analysis is introduced to enhance the filter property of AEDH for highlighting the fault feature information of rolling bearing further. Experimental signals collected from the test rig and the engineering are employed to validate the availability of proposed method. Experimental results show that the OSAEDH-PPCA can effectively extract the early fault impulses from vibration signal of rolling bearing. Comparison results verify that the OSAEDH-PPCA has advantage in early fault detection of rolling bearing than other morphological filters in existence.

Highlights

In the proposed OSAEDH-principal component analysis (PPCA), after employing PPCA to further process the AEDH results under different scales, the scale corresponding to the maximum feature energy factor (FEF) value is selected as the final output
In this paper, a new OSAEDH-PPCA method is presented for detecting the defect of rolling bearings from vibration signals
Simulation analyses demonstrate the filter characteristics of AEDH operator and verify that the fault feature extraction capability of AEDH operator is superior to the existing morphological top-hat operators

Summary

INTRODUCTION

As an important part of rotating machinery, the failure of rolling bearings is one of the common causes of mechanical. Different from traditional fault diagnosis methods, mathematical morphology (MM) is a novel signal processing method for extracting the fault information of rolling bearing [14], [15]. In order to extract more fault feature information, some scholars proposed morphological top-hat operators. Yan et al [28] employed average of opening and closing hat (AVGH) operator to extract fault impulses from the vibration signal of rolling bearing. The selection of reasonable SE scale is still a hot topic in the study of MFs. In view of the fault impulses are often overwhelmed by noise and interferences, the probabilistic principal component analysis assisted optimal scale average of erosion and dilation hat filter (OSAEDH-PPCA) is proposed for the fault detection of rolling bearing.

STRUCTURE ELEMENT

AVERAGE OF EROSION AND DILATION HAT OPERATOR

PROCEDURES OF THE PROPOSED OSAEDH-PPCA METHOD

APPLICATIONS IN THE FAULT DIAGNOSIS OF ROLLING BEARING

CONCLUSION