Research On New Method Of Condition Monitoring And Fault Diagnosis For Vehicle

As a pillar industry of national economy, automotive industry has been paid more and more attention all over the world. Fierce market competition promotes automobile production and scientific research to improve constantly. Increasing complications of vehicle structure and function bring diversification of fault type and then put forward a higher requirement for fault diagnosis and detection technique of vehicles. As the core component of automobile, engine was selected as the study object in this thesis, and the theory and methods of engine condition monitoring and fault diagnosis were mainly concerned. The study includes signal collection, signal processing, neural networks, fuzzy inference system, information fusion theory, network communication technique of vehicle and virtual instrument technology etc. An integrated test platform of engine fault diagnosis was presented and designed based on the study, and some problems of the traditional engine fault diagnosis were solved. The main contents of the study are as follows:(1) As for the method of diagnosis characteristic extraction, aiming at the vibration signals obtained from engine cylinder, three types of characteristic extraction including time domain analysis, frequency domain analysis and wavelet transformation were studied. Considering that the problems existing in fault diagnosis of engine mechanical structure by vibration signals, and the limitation of self-diagnosis system in engine which can only be applied to diagnosis of the electronic control unit (ECU) fault, multiple condition parameters of engine were selected as feature vector of fault diagnosis model. An on-line fault diagnosis system based on CAN bus and the SAE J1939 protocol was proposed, and then design of the system hardware and software was performed to realize extraction and transmission of diagnosis information.(2) As for the method of fault diagnosis, three classical neural networks (BP network, RBF network, PNN network) and adaptive neural-fuzzy inference system(ANFIS) were mainly studied about their basic principle, model structure and algorithm design. BP network was researched with several improved algorithms, then the improved results were comparably analyzed and guiding ideology was given for algorithm selection reasonably. According to different methods of feature extraction (frequency domain analysis, wavelet transformation and engine condition parameters data), fault diagnosis models of engine have been built based on the neural networks and ANFIS respectively. Aiming at the problem that the condition parameters of engine have correlations, principal component analysis was adopted to realize the dimension reduction and decorrelation. The major characteristics condition parameters which can be used to indicate faults were confirmed. Through the comparative analysis of diagnosis results, the most appropriate diagnosis model of each feature extraction method can finally be determined.(3) An information fusion structural model suitable for engine condition monitoring and fault diagnosis was built. For the multi-sources information of engine fault diagnosis, principal component analysis was adopted for feature level fusion, while D-S evidence theory for decision level fusion. Aiming at the evidence conflict phenomenon existing in the information fusion process, an improved information fusion method based on D-S theory was presented; results of engine fault diagnosis which were obtained separately by BP network, RBF network and ANFIS model have been fused by the improved method, this approach increased diagnosis accuracy, certainty degree and real time, and solved the evidence conflict effectively.(4) According to the research results of this thesis, an integrated system of engine condition monitoring and fault diagnosis was developed. a series of experiments including engine unloaded power detection, fault diagnosis based on the vibration signals of engine cylinder, fault diagnosis based on the condition parameters of engine, had been made on engine experimental bench. The experiment results verify the methods of fault feature extraction and fault diagnosis theory which proposed in this thesis.