Abstract
This paper presents an efficient approach to machine condition monitoring and health diagnosis, based on the Discrete Harmonic Wavelet Packet Transform (DHWPT). Specifically, vibration signals measured from a bearing test bed were decomposed into a number of frequency sub-bands, and key features associated with each sub-band were selected, based on the Fisher linear discriminant criterion. The key features were then used as inputs to a neural network classifiers for assessing the system's health status. Comparing to the conventional approach where statistical parameters from raw vibration signals are used, the presented approach enables higher signal-to-noise ratios and consequently, more effective and intelligent use of the available sensor information, leading to more accurate system health evaluation.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have