A smart fault-detection approach with feature production and extraction processes

In this paper, a smart fault-detection approach with feature production and extraction procedures is developed for industrial ball bearing systems. By developing a set of chaotic-mapping systems composed of a main system and data-feeding system with appropriate parameters, the vibration signals of different fault states captured from ball bearings in the time domain can be mathematically mapped to the chaotic domain for feature production. Furthermore, through the designed feature extraction process, the relevant Euclidean feature values (EFVs) can be obtained for the classification of four different fault states. Three fault conditions at diameters of 7 mil, 14 mil, and 21 mil and a depth of 0.011 inches are illustrated for performance investigations. The experimental results show that the proposed smart detection approach is effective and feasible for identifying different fault states in real time. (C) 2019 Published by Elsevier Inc.