Truck APS Fault Detection Based on Modified Mahalanobis-Taguchi System

Truck air pressure system (APS) fault detection is an important part of truck fault detection. For the high-dimensional imbalanced data obtained from truck fault detection, to improve the detection rate of APS fault, this paper proposes a modified Mahalanobis-Taguchi system (MMTS) algorithm. MMTS utilizes the ReliefF algorithm and signal-to-noise ratio to select features to construct Mahalanobis space, and uses particle swarm optimization algorithm to optimize the threshold determination method. Experiments show that MMTS has higher classification performance and is more suitable for truck APS fault detection than baseline algorithms for high-dimensional imbalanced data.