Fault Diagnosis Cases Research Articles

Fault Classification Based on Improved Evidence Theory and Multiple Neural Network Fusion

Combination results of the evidence theory will be out of accord when the evidences highly conflict with the real condition, some improved methods can solve this problem, but the convergence speed is rather slow. Furthermore, the combination of these improved methods will diverge when the evidences are consistent, thus limiting the application of evidence theory in condition monitoring system. In view of this, a novel evidence combination approach based on evidence confidence is proposed, and a multiple neural network fusion model is constructed for fault classification on the basis of this improved evidence combination method. A case of gear fault diagnosis using the proposed model is studied. The fault feature space is divided into several subspaces, and the corresponding sub-neural network classifiers are established. The output of these sub-neural network classifiers are used as the combination evidences. Finally, different faults are classified through combining the obtained evidences by the novel combination method. Comparing with the classification results of traditional evidence theory method, other representative improved methods and traditional neural network method, experiment results indicate the effectiveness of this improved evidence fusion method.

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In order to increase the diagnostic accuracy of ground-source heat pump (GSHP) system, especially for multiple-simultaneous faults, the paper proposes a multi-source information fusion based fault diagnosis methodology by using Bayesian network, due to the fact that it is considered to be one of the most useful models in the filed of probabilistic knowledge representation and reasoning, and can deal with the uncertainty problem of fault diagnosis well. The Bayesian networks based on sensor data and observed information of human being are established, respectively. Each Bayesian network consists of two layers: fault layer and fault symptom layer. The Bayesian network structure is established according to the cause and effect sequence of faults and symptoms, and the parameters are studied by using Noisy-OR and Noisy-MAX model. The entire fault diagnosis model is established by combining the two proposed Bayesian networks. Six fault diagnosis cases of GSHP system are studied, and the results show that the fault diagnosis model using evidences from only sensor data is accurate for single fault, while it is not accurate enough for multiple-simultaneous faults. By adding the observed information as evidences, the probability of fault present for single fault of “Refrigerant overcharge” increases to 100% from 99.69%, and the probabilities of fault present for multiple-simultaneous faults of “Non-condensable gas” and “Expansion valve port largen” increases to almost 100% from 61.1% and 52.3%, respectively. In addition, the observed information can correct the wrong fault diagnostic results, such as “Evaporator fouling”. Therefore, the multi-source information fusion based fault diagnosis model using Bayesian network can increase the fault diagnostic accuracy greatly.