Engine misfire fault diagnosis based on SC–ANFIS

Abstract

Misfire fault is a common engine failure which is caused by incomplete combustion in the engine cylinders. Conventionally, the misfire fault is diagnosed manually by mechanics, but the diagnosis process is time-consuming. Therefore, this study aims to explore the feasibility of using Subtractive Clustering based Adaptive Neuro-Fuzzy Inference System (SC-ANFIS) algorithm to assist in diagnosing misfire faults. The Subtractive Clustering (SC) approach initializes the parameters of Adaptive Neuro-Fuzzy Inference System (ANFIS), whereas Back Propagation (BP) and Least Square Estimation (LSE) approaches are implemented to optimize the ANFIS parameters. The proposed algorithm will pre-diagnose the cause of misfire faults based on the engine exhaust gas. In this work, exhaust gases for different causes of misfire faults are collected from Volkswagen 1.8TSI 4-cylinder petrol engine. These collected data are used to train the proposed algorithm. The performances of the proposed algorithm are compared to two commonly used algorithms, namely Fuzzy C-Mean Clustering based ANFIS (FCM-ANFIS) and BP algorithms. The simulation results show the proposed algorithm has improved 2.4% to 5.5% averagely in terms of accuracy, efficiency and stability.