The Fault Diagnosis of Diesel Fuel Supply System Based on BP Neural Network Optimized by Genetic Algorithm

Abstract

As an important power source of diesel engine, the fuel supply system has the characteristics of high failure rate and being difficult to be detected, which is critical to detect and diagnose the fault for the reliable and stable operation of diesel engine. In the paper, principal component analysis (PCA) was used to process the initial data, back-propagation (BP) neural network was used to diagnose fault of diesel engine, BP diagnosis efficacy was analyzed, then the BP neural network fault diagnosis system was optimized by being introduced in the genetic algorithm , and the comparison results of training error and diagnostic analysis showed that: the fault diagnosis of BP neural network optimized by genetic algorithm is more accurate than that of traditional BP, which can effectively improve the fault diagnosis accuracy and reduce the training time.