Fault diagnosis method of vehicle engine via HOSVD–HOALS hybrid algorithm-based multi-dimensional feature extraction

Abstract

Engine faults, which are difficult to be accurately diagnosed, seriously affect the normal running of vehicles. To solve this problem, a novel fault diagnosis method via HOSVD–HOALS​ hybrid algorithm-based multi-dimensional feature extraction was proposed for a vehicle engine. First, multiple source signals-based engine status samples with the form of third-order tensors were constructed to retain the correlation among sample data. Then, by analyzing the high-dimensional characteristics of the constructed tensor samples, the Tucker decomposition was employed to realize the dimension reduction of the samples. Simultaneously, combining the high order singular value decomposition (HOSVD) and high order alternation least square (HOALS), a hybrid algorithm was proposed to solve the optimal low-dimensional core tensors and features of the constructed tensor samples. Finally, based on the extracted features, the fault pattern of the vehicle engine was recognized by using the derived tensor-based K-nearest neighbor (K-NN) and fuzzy C-mean (FCM) algorithms, respectively. Research results show that the average accuracy of the fault diagnosis via HOSVD–HOALS hybrid algorithm-based multi-dimensional feature extraction can reach 96.25% which is the highest compared with that via the principal component analysis (PCA), HOSVD, HOALS, and direct tensor sample method, respectively, and the computation time required for the fault diagnosis is greatly shortened, which can provide theoretical and technical support for the fault diagnosis of vehicle engines.