Abstract: Engine is the core component of the armored equipment's power system. Aiming at the problems that the traditional detection technology is difficult to accurately and quickly diagnose the engine fault in a large number of faulty equipment, the diagnosis workload is large, and the efficiency is low, an engine fault diagnosis technology based on the BA-RVM algorithm is proposed. By merging the various parameter index data of typical armored equipment engines, the model is trained by using the data of the acquisition parameter index and the engine fault, so that the model can predict the fault type based on the parameters of the engine. In the model training, the bat algorithm (BA) is proposed to adjust the kernel parameter width of the correlation vector machine algorithm (RVM), so as to obtain the prediction model of the RVM optimal parameters. Finally, the experimental verification of 12/200ZL water-cooled exhaust turbocharged diesel engine is carried out. The experimental results show that, compared with BP algorithm and SVM algorithm, BA-RVM algorithm reduces the error rate of fault diagnosis by 66.67% and 62.5%, respectively.

Key words: engine fault, bat algorithm, correlation vector machine algorithm, fault diagnosis