Fault diagnosis for analog circuit has become a prominent factor in improving the reliability of integrated circuit due to its irreplaceability in modern integrated circuits. In fact fault diagnosis based on intelligent algorithms has become a popular research topic as efficient feature extraction and selection are a critical and intricate task in analog fault diagnosis. Further, it is extremely important to propose some general guidelines for the optimal feature extraction and selection. In this paper, based on wavelet analysis, we will study the problems of mother wavelets selection, number of decomposition levels, and candidate coefficients selection by using a four-op-amp biquad filter circuit. After conducting several comparative experiments, some general guidelines for feature extraction for this type of analog circuits fault diagnosis are derived.

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Abstract. Aiming at the difference of the different optimization target weights in receiving-departure line, the comprehensive optimization model of the optimization target weight was proposed. Firstly, the exponential multiplication of each optimized target value was used as the comprehensive optimization target, by adjusting the target exponent weight to affect the scheduling results. Then, the adaptive pheromone updating method and the adaptive pheromone total method were used to improve the performance of the algorithm based on the hybrid ant colony algorithm. It was verified by the Lanzhou station that the Model and Algorithm can adjust the multi-objective optimization weights in the scheduling results by adjusting the exponential parameters of each optimization target.