An Analog Circuit Fault Diagnosis Approach Based on Improved Wavelet Transform and MKELM

Correct diagnosing analog circuit fault is beneficial to the circuit's health management, and its core challenge is extracting essential features from the circuit's output signals. Wavelet transform is a classical features extraction method whose performance relies on its wavelet basis function deeply. However, there are no satisfying rules to discover an optimal wavelet basis function for wavelet transform. In this paper, an improved wavelet transform with optimal wavelet basis function selection strategy is proposed. In the strategy, the optimal wavelet basis function is selected based on calculating the distance score and mean score of its features, and the features extracted by the optimal wavelet basis function are considered as the best features of signals. Subsequently, the features are split into training data and testing data randomly and evenly. By using the training data, a multiple kernel extreme learning machine (MKELM) based diagnosing model is initialized, and the parameters of MKELM are yielded by using particle swarm optimization algorithm. Finally, the MKELM is used to identify the faults of testing data for the purpose of verifying its performance. Fault diagnosis experiments of three circuits are performed to show the proposed optimal wavelet basis function selection strategy and MKELM's establishing process. Comparison experiments are performed to verify that the optimal wavelet basis function selection strategy is effective and MKELM is better than other classifiers in analog circuit fault diagnosis.