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.