A novel binary bat algorithm with chaos and Doppler effect in echoes for analog fault diagnosis

A novel modified binary bat algorithm (MBBA) with chaos and Doppler effect in echoes is presented in this paper to deal with the fault diagnosis problem of analog circuits with tolerance. The proposed method focuses on further mimicking the bats' behaviors and improving the binary bat algorithm (BBA) in view of biology. The proposed algorithm utilizes the bats' self-adaptive compensation for Doppler effect in echoes and the chaotic optimization method to improve the BBA. The elitist strategy is also used to store all the possible global best solutions, which will provide us with more possible choices in practice. Since single feature extraction method has limitations during fault diagnosis of analog circuits with tolerance, several kinds of feature extraction methods are used together to build a big feature set firstly in our method, and then, the new proposed MBBA is used to select the optimal feature subset simultaneously optimize the classifier parameters for the analog fault diagnosis. Three analog circuits' examples are given to demonstrate the effectiveness and generalization ability of our method, and other methods are also used to do comparisons. The results indicate that combining different kinds of feature extraction method together to optimally select the feature subset simultaneously optimize the classifier parameters by MBBA can obtain better performance in dealing with analog fault diagnosis problems, and our proposed method provides a better tradeoff between fault diagnosis accuracy and total cost, which is superior to other methods. Therefore, it is an effective method for analog fault diagnosis and has broad application prospects in practice.