基于CAN总线的发动机PSA故障诊断系统

CAN Bus Based PSA Fault Diagnosis System for Engines

针对传统发动机故障诊断方式存在故障事后检修及查询困难等问题,提出一种基于CAN总线的发动机在线故障诊断系统模型.以CAN总线实时采集的发动机控制单元各传感器状态数据为诊断样本,利用主成分分析(PCA)实现输入变量降维和去相关;采用减法(subtractive)聚类算法完成模糊推理过程;应用自适应神经模糊推理系统(ANFIS)建立起PSA(PCA-subtractive-ANFIS)故障诊断模型.研究表明PSA故障诊断模型是有效的.仿真结果表明,其拟合能力、收敛速度及抗噪能力均优于PCA-BP网络模型.

Based on CAN bus, a new online PSA （PCA-subtractive-ANFIS） fault diagnosis model is proposed to solve the problems that the conventional engine fault diagnosis cannot be predicted and are hard to inquire about their happenings. With the state data acquired by ECU （electronic control utilities） sensors and transmitted by CAN bus for all the elements of an engine taken as samples for diagnosis, the diagnosis model is developed the way the PCA （principal component analysis） is used for dimensions＇ reduction and decorrelation and the subtractive clustering algorithm is introduced to complete the fuzzy inference process, then the adaptive-network-based fuzzy inference system （ANFIS） is applied to the new type model. Simulation results showed that the PSA model is superior to PCA-BP network model in fitting, denoising and convergence rate.