Filters Bank Derived from the Wavelet Transform for Real Time Change Detection in Signal

The aim of this paper is to detect the faults in industrial systems, through on-line monitoring. The faults that are concerned correspond to changes in frequency components of the signal. Thus, early fault detection, which reduces the possibility of catastrophic damage, is possible by detecting the changes of characteristic features of the signal. This approach combines the filters bank technique, for extracting frequency and energy characteristic features, and the dynamic cumulative sum method (DCS), which is a recursive calculation of the logarithm of the likelihood ratio between two local hypotheses. The main contribution is to derive the filters coefficients from the wavelet in order to use the filters bank as a wavelet transform. The advantage of our approach is that the filters bank can be hardware implemented and can be used for online detection.