A Variety of Engine Faults Detection Based on Optimized Variational Mode Decomposition-Robust Independent Component Analysis and Fuzzy C-Mean Clustering

Abstract

As one kind of current main power sources, internal combustion engines require high-reliability to ensure mechanical systems working well in normal operation. This paper studies vibration signals in order to detect multiple types of faults by a single channel signal. First, for the decomposition level of variational mode decomposition (VMD) which needs to be chosen non-automatically, this paper analyzes the features of various faults and optimizes the iteration initial values of center frequency so as to reduce the adverse effect of inappropriate decomposition level. Moreover, considering that VMD cannot decompose different signal sources in the same frequency, robust independent component analysis (ICA) is an excellent method to overcome this challenge. Then, the fourth-order cumulant of restructured signals from VMD and robust ICA is taken as fault indexes. Finally, because of the high error rate of original fuzzy C-Mean clustering, Euclidean distance between test points and cluster center of fuel injection failure is taken as a fault detection threshold in order to achieve high-recognition rate. In conclusion, through optimizing several algorithms, the purpose of detecting multiple types of faults by single channel signal is achieved in the current research.