Title: Research on adaptive diagnosis algorithm for fuel injection failure system of construction machinery diesel engine
Authors: Yanfei Chen; Chengping Liu
Addresses: Information Construction and Management Office, Nanjing Vocational Institute of Transport Technology, Nanjing, Jiangsu, China ' School of Rail Transportation, Nanjing Vocational Institute of Transport Technology, Nanjing, Jiangsu, China
Abstract: Shipping is the main transportation mode of international bulk trade at present. Diesel engine is the main power source of shipping ships. The stable and reliable operation of diesel engine injection system affects the safe navigation of ships. In order to find the fault of diesel fuel injection system in time, an adaptive diagnosis algorithm of fault system based on adaptive genetic algorithm and Elman neural network is constructed. The simulation results show that the output membership of the improved GA Elman neural network adaptive fault detection model is maintained within the range of [0.81, 0.95], the membership accuracy is high and the absolute error is small. The fault diagnosis accuracy of the improved GA Elman neural network adaptive fault detection model is 95.67%, which can effectively carry out adaptive diagnosis of fault problems and predict the occurrence of fault problems in advance. It provides judgment basis for maintenance personnel, improves maintenance efficiency, ensures the normal operation of diesel engine, and provides a new research idea for fault adaptive diagnosis of construction machinery.
Keywords: fuel injection system; fault diagnosis; Elman neural network; genetic algorithm.
International Journal of Wireless and Mobile Computing, 2022 Vol.22 No.1, pp.23 - 28
Received: 03 Sep 2021
Accepted: 22 Jan 2022
Published online: 27 Apr 2022 *