ON THE SELECTION OF INFORMATIVE WAVELETS FOR MACHINERY DIAGNOSIS

A new method of machinery fault diagnosis based on wavelet analysis and using an extension to Mallat and Zhangʹs matching pursuit for machinery diagnosis is presented. Instead of the `best matchingʹ criterion, a mutual information measure is used to search a redundant wavelet dictionary for a small set of wavelets that carry meaningful information about machinery faults. With these informative wavelets treated as feature extractors, this approach effectively facilitates the diagnosis of machinery faults of a non-stationary nature. This method has been applied to the detection of diesel engine malfunctions. The results show that both the sensitivity and the reliability of this approach are good.