A minimum description length principle based method for signal change detection in machine condition monitoring

This paper proposes a minimum description length (MDL) based method for signal change detection in machine condition monitoring. Our method is grounded on a recently proposed MDL-based sequentially normalized maximum likelihood (SNML) approach to time series and especially signals complexity analysis with an autoregressive (AR) model. Experiments on signal change detection are performed using two data sets, one of which is based on measurements on damages of ball bearings. The results proved the success of the method to distinguish different ball bearing failures.