Incipient machine degradation assessment by MF-MTS system

This paper presents an incipient machine degradation assessment method based on multifractal theory and Mahalanobis-Taguchi system (MTS), which help to differentiate the incipient fault stage and assess the degradation degree as well. According to different machine degradation states, the feature parameters from multifractal aspects are first calculated and further optimized by a MTS statistical method, based on which incipient faults can be subsequently identified and diagnosed accurately. A comparative application study of the effectiveness of the proposed method is carried out through case study of bearings. It is also proved to be a powerful and comprehensive tool for machine's elaborate condition monitoring management which combines a unified representation in both current and predictive perspectives of the fault degradation behavior.