Abstract
A diagnostic prototype expert system was built using characteristics of measured sound and vibration for the automobile. For the utilities of this system, a 1/3 octave filter (bandpass filter) and an A/D converter were used for data acquisition and then the data were analyzed using signal processing technique, statistical analysis, and pattern recognition using a hamming network algorithm. In order to raise the reliability of the diagnostic results by considering many operating variables and the condition of the automobile to be diagnosed, a fuzzy inference technique was applied combining several kinds of information. The results were displayed as a graphical method to help the novice in the diagnostic field. The validation of this diagnostic system was checked through a simple rotating simulator and automobile and it showed acceptable performance for a diagnostic process. Also, for the case of wrong decision making, this system included a learning algorithm and its knowledge base can be extended through that procedure. Therefore, as a result, it is expected that this system can be developed by implementing more simplicity and flexibility to the external environment.
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