Study On State Detection Based Fault Diagnosis Method Of Complicated Electronic System

Fault diagnosis of complicated electronic system is an intricate and difficult work. Different from usual fault diagnosis, the faults of complicated electronic systems are hierarchical, propagable, correlative and uncertain. Because of those particular characteristics, any single accustomed diagnosis method such as signal processing based method, analytic model based method or knowledge based method is not good enough to diagnose effectively and get comprehensive and logical fault conclusions. According to the characteristics of complicated electronic systems, this paper presents a state detecting based fault diagnosis method, which disassembles the whole fault diagnosis process into three phases: fault detecting, fault locating and fault recognizing. The main works of this paper include:1. Study on fault detection technology based on wavelet transform singularities analysis. Fault detection is the base of fault diagnosis. Faults of systems can be detected by observing different signals such as voltage, current, temperature, image, et al.. If fault occurs, it will make singularities in those signals. The locations and Lipschitz exponents of singularities contain abundant information about faults, and are important for faults diagnosis. In this paper, the method of measuring singularities based on wavelet transform modulus maxima is studied. Then, we improve the classical method of estimating Lipschitz exponents invented by Mallat, and present a novel algorithm. The result of experiment demonstrates that the method of this paper is more precise and robust than that of classical methods.2. Study on fault locating algorithm based on Bayes maximal posteriori probability principle. In this paper, a fault location algorithm based on Bayes maximal posteriori probability principle is studied. First, the multi-signal model of complicated electronic system is built. Based on the model, the algorithm utilizes the information of apriori probabilitys of faults to compute the maximal posteriori probabilitys according to Bayes theory. This is induced as set cover problem (SCP), and solved by Lagrange relax algorithm. The component that has the maximal posteriori probability is diagnosed as the fault component. This fault location algorithm has ratiocinative ability and can avoid combination blast.3. The apriori fault probabilities of Components are important for the fault locating algorithm based on Bayes maximal posteriori probability principle. In this paper, an idea that uses faults apriori probabilities distributing functions of components to replace that of system is presented to improve the original fault locating algorithm. Because the probabilities distributing functions of components include time information, the results of fault location are more suitable to actual running state of systems.4. For the case that can not get faults distributing functions of components, a fuzzy fault locating algorithm based on false alarm probabilities and detection probabilities of sensors is presented. First, N sensors can create N dimensions fault observation space, in which a fuzzy function is designed to describe the comparability between actual observation vectors and fault character vectors. Finally, a fuzzy multi-fault diagnosis algorithm is presented which induces the problem of fault diagnosis as classification problem of fault observation vectors in fault observation space.5. SVM based fault recognition. The essence of fault recognition is pattern recognition. It is difficult for complicated electronic systems to get sufficient fault samples. In this paper, SVM is used to do fault recognition because of its excellent little samples study ability. First, the standard SVM is studied, and the disadvantages of usual SVM multi-classification methods are analysed. Then, a genetic algorithm (GA) based SVM multi-classification decision-tree optimization algorithm is presented. This algorithm can create optimal or near-optimal decision-tree self-adaptively according to actual instances. Experiment results show that the proposed algorithm can improve recognition efficiency, control error accumulation and at the same time ensure recognition precision.6. Application of the state detecting based fault diagnosis method. The methods proposed in this paper are applied in a fault diagnosis instance of radar receiver. A radar receiver fault diagnosis experiment system is designed, which demonstrates how the theories and methods studied in this paper work in actual fault diagnosis of complicated electronic systems.