Automatic Transmission Fault Symptom Identification by Apply of Neural Network and D-S Evidence Theory

Abstract

At present, the method of identifying the fault symptoms of various machines by combining the neural network and the D-S evidence theory is attracting attention from researchers because the identification time is fast and the diagnosis is accurate. In this paper, it was mentioned a method for identifying the fault symptoms of automatic transmission by combining these two theories. First, it was mentioned a method for identifying fault symptoms of the automatic transmission by combining a fuzzy neural network and an RBF neural network. Next, it was newly described a method to improve the accuracy of fault symptom identification by the D-S evidence theory. In addition, the accuracy of this method was verified by an experimental method. In the experiment Firstly, two sub neural networks are established to recognize the initial symptoms. That is, the first sub-neural network E1 be used as the fuzzy neural network, the second sub-neural network E2 be used as RBF neural network, respectively, for preliminary symptom recognition. And then, these outputs of the two sub neural networks are used as the evidence space of D-S evidence theory, so the global diagnosis is carried out. The results show that the test results are consistent with the actual fault symptoms. The success rate of fault diagnosis up to 96.3%, therefore, on the identification of the automatic transmission fault symptom, effectiveness, and feasibility of the D-S evidence theory based on information fusion is verified.

The fault identification method of high energy-consuming transformer interturn short circuit considering feature coupling

“…The Bayesian inference algorithm is applied to D-S (Dempster-Shafter) evidence theory for the assignment of basic trust. D-S evidence theory [9] is one of the most commonly used feature fusion diagnosis algorithms at present. Based on generalized Bayes theory and human reasoning, fault feature fusion diagnosis under multi-sensing signals is realized.…”