Background: Correct classifying of analog circuit faults is helpful in the health management of the circuit. It is difficult to be implemented because of the lack of proper feature extraction methods and accurate fault diagnosis models.
Objective: T-SNE based core components extraction method and PSO-ELM-based fault diagnosis model are presented to improve the diagnostic accuracy of analog circuit fault diagnosis.
Methods: Firstly, circuit output signals are collected, and they are transformed to wavelet coefficients. Then, the high-dimensional wavelet coefficients are processed by t-SNE to generate lowdimensional core components as features. The Extreme Learning Machine (ELM) based diagnosing model is constructed by using the features, and the key parameters of ELM are optimized by using Particle Swarm Optimization (PSO) algorithm. Finally, the constructed PSO-ELM diagnosis model is employed to identify different analog circuit faults.
Results: Leapfrog filter circuit and three-phase bridge circuit fault diagnosis experiments are implemented to demonstrate the proposed t-SNE based features extraction method and PSO-ELM based fault diagnosis model. Also, comparisons are performed to verify the high performance of proposed fault diagnosis methods.
Conclusion: The proposed t-SNE based core components extraction method and PSO-ELM diagnosis model are effective to improve the fault diagnosis accuracy of the analog circuit.