Wavelets and principal component analysis method for vibration monitoring of rotating machinery

Abstract

Fault diagnosis is playing today a crucial role in industrial systems. To improve relia- bility, safety and efficiency advanced monitoring methods have become increasingly im- portant for many systems. The vibration analysis method is essential in improving con- dition monitoring and fault diagnosis of rotating machinery. Effective utilization of vi- bration signals depends upon effectiveness of applied signal processing techniques. In this paper, fault diagnosis is performed using a combination between Wavelet Transform (WT) and Principal Component Analysis (PCA). The WT is employed to decompose the vibration signal of measurements data in different frequency bands. The obtained decomposition levels are used as the input to the PCA method for fault identification using, respectively, the Q-statistic, also called Squared Prediction Error (SPE) and the Q-contribution. Clearly, useful information about the fault can be contained in some levels of wavelet decomposition. For this purpose, the Q-contribution is used as an evaluation criterion to select the optimal level, which contains the maximum information.Associated to spectral analysis and envelope analysis, it allows clear visualization of fault frequencies. The objective of this method is to obtain the information contained in the measured data. The monitoring results using real sensor measurements from a pilot scale are presented and discussed.