Abstract
Rolling element bearing is one of the most commonly used supporting parts in rotating machinery, and it is also one of the most easily failing rotating parts. It is of great safety and economic significance to study the effective fault diagnosis method of rolling element bearing. The fault characteristic signal of rolling bearing is often affected by other interference signals in practical engineering, and the situation is much more serious when the rolling bearing fault occurs in gearbox. Besides, only a limited number of measuring points are used in the process of rolling bearing fault signal acquisition due to the limitation of sensors installation condition. In some sense, the above two factors often cause the result that the fault diagnosis of rolling bearing is the problem of underdetermined blind source separation. The independence and non-Gaussian characteristic of the observed signals are the prerequisite of most of existent blind source separation methods. Unlike traditional blind source separation methods, SCA originating from sparse representation is an effective method to solve the problem of underdetermined blind source separation, because it does not require the independence or non-Gaussian characteristics of the observed signals, and it only makes full use of the sparse characteristics of the observed signals to extract the source signal from the observed signals. Based on these, a sparse component analysis (SCA) method based on linear clustering (LC) named LC-SCA is proposed for the purpose of underdetermined blind source separation of vibration signals of rolling element bearing, and the LC is introduced into SCA to improve the computation efficiency of SCA. The effectiveness of the proposed method is verified by simulation and experiment. In addition, the superiority of the method is verified by comparison with the other related methods such as constrained independent component analysis (cICA) and SCA.
Highlights
As the key and most commonly used supporting part in modern high-speed and large-scale rotating machinery, effective fault diagnosis of rolling element bearing provides important safety and economic significance for health monitoring of rotating machinery. e collected vibration signals of rolling bearing are usually from multiple sources on practical engineering occasions, and the fault diagnosis of rolling bearing is a process of signal blind source separation to some extent
A sparse component analysis method based on linear clustering named LC-SCA is proposed for underdetermined source separation of rolling bearing vibration signals. e paper is organized as follows: Section 1 is dedicated to introduction, and Section 2 discusses the theory of the proposed method
An SCA method based on linear clustering named LC-SCA is proposed for underdetermined blind source separation, and it has the advantages of simple calculation theory and more efficient separation result compared with the other blind source separation methods such as SCA and constrained independent component analysis (cICA)
Summary
As the key and most commonly used supporting part in modern high-speed and large-scale rotating machinery, effective fault diagnosis of rolling element bearing provides important safety and economic significance for health monitoring of rotating machinery. e collected vibration signals of rolling bearing are usually from multiple sources on practical engineering occasions, and the fault diagnosis of rolling bearing is a process of signal blind source separation to some extent. Kinds of blind source separation methods [1,2,3,4,5] and other advanced methods [6, 7] for analyzing vibration signals of rolling element bearing have been arising. In [15], a new algorithm for approximately estimating matrix A was proposed, which solved the major problems in underdetermined sparse component analysis in the field of (semi)blind source separation. Several kinds of underdetermined blind source separation methods have been arising as stated above, most of them focus on the study of other areas of signal processing such as audio signal and image signal, and very limited numbers of them are focusing on fault diagnosis of rotating machinery. A sparse component analysis method based on linear clustering named LC-SCA is proposed for underdetermined source separation of rolling bearing vibration signals.
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