Abstract
Hidden Markov model (HMM) for the diagnosis of incipient faults in analog circuits is presented. Firstly, output voltage signals under faulty conditions are obtained with simulation. Subsequently, output voltages corresponding to the test frequencies are extracted from the response of analog circuits. Finally, the output voltage vectors are used to form the observation sequences, which are sent to training HMM to accomplish the diagnosis of the incipient faults. The performance of the proposed method is tested, and it indicates that the method is effective and has better recognition capability than the popularly used back-propagation (BP) network.
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