A novel classification method combining adaptive local iterative filtering with singular value decomposition for fault diagnosis

Yong Lv,Han Xiao,Yi Zhang,Cancan Yi,Zhang Dang

doi:10.21595/jve.2017.18512

Abstract

As a novel time-frequency analysis method, adaptive local iterative filtering (ALIF) can decompose the time series into several stable components which contain the main fault information. In addition, the amplitude of singular value obtained by singular value decomposition (SVD) can reflect the energy distribution. Naturally, there are certain differences in the energy produced by different faults such as the broken tooth, wearing and normal. Thus, a novel method of mechanical fault classification method based on adaptive local iterative filtering and singular value decomposition is proposed in this paper. Firstly, ALIF method decomposed the original vibration signal into a number of stable components to establish an initial feature vector matrix. Then, the singular values energy corresponding to the feature matrix is employed as a criterion to identify various faults. Compared with the conventional EMD method by simulation experiments, ALIF method has obvious superiority in solving modal aliasing, which is more conducive to the advanced analysis. In this paper, the proposed method is employed to extract the fault information of rolling bearing fault signals from Case Western Reserve University Bearing Data Center. To further verify the effectiveness of the method, the case study is conducted at Drivetrain Diagnostics Simulator. To further illustrate the effectiveness of the method, the results obtained by this method are compared with EMD and EEMD. The results indicated the proposed method performs better in the classification of different mechanical faulty modes.

Highlights

Gears and rolling bearings are the most damaged parts in the mechanical equipment [1,2,3,4,5,6]
This paper was organized as follows: the basic principle and characteristics of the proposed fault classification method based on the singular value energy spectrum and adaptive local iterative filtering (ALIF) were introduced in the second chapter
The research work in this paper elaborates on the theoretical effectiveness of the proposed method based on the adaptive local iterative filtering (ALIF) and singular value decomposition (SVD)

Summary

Introduction

Gears and rolling bearings are the most damaged parts in the mechanical equipment [1,2,3,4,5,6]. The energy which is extracted from each component can better reveal the inherent characteristics of fault information This method can produce the problems of mode aliasing and uncertain order of the decomposition, which is harmful for feature extraction and different fault classification. Since the influence of above non-linear factors and the complexity of the signal components, the faults of gear and rolling bearing parts are abnormal or different in the running process. A novel fault classification method jointed adaptive local iterative filtering and singular value decomposition is proposed in this paper. This paper was organized as follows: the basic principle and characteristics of the proposed fault classification method based on the singular value energy spectrum and adaptive local. The conclusions of the study and the necessary discussions were given in the fifth section

The theory of adaptive local iterative filtering

Numerical simulation analysis

Application to processing of case western reserve university bearing data

Application to drivetrain diagnostics simulator

Findings

Conclusions