APPLICATION OF DISCRETE WAVELET TRANSFORMS (DWT) AND THE SINGULAR VALUE DECOMPOSITION (SVD) FOR ROTATING MACHINERY FAULTS

摘要

Condition monitoring of machines is gaining importance in industry because of the need to increase reliability and decrease possible loss of production due to machine breakdown, gears and Rolling elements bearing is one of the most sensitive components in rotating machinery and are the most common cause of rotating machinery failure, and are among the most important and frequently encountered components in the vast majority of rotating machines, their carrying capacity and reliability being prominent for the overall machine performance. Therefore, quite naturally, the fault identification of rolling element bearings and gears has been the subject of extensive research; these faults have been detected by using discrete wavelet transform (DWT), and the singular value decomposition (SVD). Vibration signals from rotating machinery having single and multiple point defects on rolling elements bearing in( inner race, outer race, balls, cage),and shafts, gears.....etc, and the combination faults have been considered for analysis. The impulses in vibration signals due to bearings and gears faults are prominent in wavelet decompositions. It is found that the impulses appear periodically with a time period corresponding to characteristic defect frequencies. It has been shown that DWT can be used as an effective tool for detecting single and multiple faults in the rotating machinery; The Singular Value Decomposition (SVD) is a widely used technique to decompose a matrix into several component matrices, exposing many of the useful and interesting properties of the original matrix. The decomposition of a matrix is often called a factorization; this work has the aim to determine how these methods can predict the manifestation of the defect preceding the breakdown and to compare these methods between themselves.