Expert Weight Allocation for Diesel Engine Condition Identification Based on Entropy Theory and Fuzzy Logic

摘要

Expert weight allocation is important for the diesel engine condition identification design. The weights of the expert information are affected by many levels. A new method based on the entropy theory and fuzzy logic for the expert weight allocation was proposed in this study. Firstly, the entropy theory was used to analyze the difference between the weights of different experts and the optimal weights to determine the expert assessment level. Thus, a comprehensive weight of condition identification was obtained. Then the fuzzy identification theory and the comprehensive weights were combined to identify the diesel engine condition. The experiment tests of six typical operational conditions were carried out using the diesel engine tester. The analysis results indicated that the six states of the diesel engine could be recognized correctly. The proposed method is effective for the diesel engine condition identification and has the importance of application.