Conditional health monitoring using vibration signatures

Condition health monitoring of dynamic systems based on vibration signatures has generally relied upon Fourier based analysis as a means of translating vibration signals in time domain into the frequency domain. The wavelet packet transform is introduced as an alternative means of extracting time-frequency information from vibration signatures. Moreover, with the aid of statistical based feature selection criteria, many feature components containing little discriminant information could be discarded resulting in a feature subset with reduced number of parameters. This significantly reduces the long training time that is often associated with neural network classifier and increases the generalization ability of the neural network classifier. To validate the feature extraction algorithm proposed, the simulations have been performed with the benchmark data known as Westland vibration data set. The results show significant improvement when the data is subjected to various white, colored and pink noise.