Open AccessAutomatic Transmission Fault Symptom Identification by Apply of Neural Network and D-S Evidence Theory
Author(s) -
O Ryongsik,
Jiangwei Chu,
Zhigang Sun,
Myongchol Ri,
MyongSu Sim,
Yongchol Kim,
SunGol Ryu,
Chunlei Li,
Cholsong Hwang,
KwangBok Kim
Publication year - 2021
Publication title -
international journal of scientific research in science and technology
Language(s) - English
Resource type - Journals
eISSN - 2395-602X
pISSN - 2395-6011
DOI - 10.32628/ijsrst2183163
Subject(s) - artificial neural network , fault (geology) , computer science , identification (biology) , artificial intelligence , fuzzy logic , transmission (telecommunications) , automatic transmission , time delay neural network , pattern recognition (psychology) , machine learning , data mining , engineering , telecommunications , mechanical engineering , clutch , botany , seismology , biology , geology
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.