| Summary: | 碩士 === 中原大學 === 機械工程研究所 === 92 === The machine runs with vibration. If the machine vibrates oversized, it maybe has the fault. Regarding the motor fault diagnosis, we can use many kinds of vibrations analysis method to diagnose. Each vibrations analysis method has different characteristics. Sometimes using the sole analysis method, we can't correctly diagnose the fault. In order to raise the rate of diagnosis ability, this article proposes the pro and con inference and the mix inference method which include frequency spectrum analysis, waterfall analysis, and orbital analysis.
In positive reasoning, the frequency and waterfall analysis apply the Back-Propagation Neural Network (BPNN) and subnet theory. We establish separately the rotor, the bearing, and the electrical machinery neural network. We use the neural network to inference the possible fault from the reason. Orbital analysis means to use artificial recognition orbit by graph to distinct the possible fault type.
The negative inference and the mix inference ways use the approximate reasoning and positive reasoning result with weighting computation to the inference result. The approximate reasoning method uses Hamming distance theory. We calculate relevance between experiment signal data and define fault signal data by the approximate reasoning method.
Based on the pro and con inference and the mix inference method, we develop the professional system of motor fault diagnosis. We also test for three practical examples to do positive reasoning, negative reasoning and mix reasoning. From each inference way, output data proves that mix inference diagnosis method is reasonable, reliable and accurate.
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