Adaptive multiscale and dual subnet convolutional auto-encoder for intermittent fault detection of analog circuits in noise environment

In avionics , industrial electronic systems, analog circuits are one of the most commonly used components. Intermittent faults (IFs) are a no fault found (NFF) state in analog circuits that are difficult to detect. In addition, the presence of noise may obscure critical information about the state of the circuit. Considering these challenges, this paper proposes an adaptive multiscale and dual subnet convolutional auto-encoder (AMDSCAE) to detect IFs. The proposed method can adaptively assign different attention to each scale and then fuse the multiscale information, which has better noise robustness. Then, the fault reconstruction error is amplified by the dual subnet structure to enhance the IF detection ability and find weaker faults. Considering the difficulty of obtaining fault sample labels, the proposed model requires only fault-free samples in the training process. In three typical analog filter circuit experiments, AMDSCAE has better noise immunity and can detect weaker IFs.(c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes an adaptive multiscale and dual subnet convolutional auto-encoder (AMDSCAE) to detect intermittent faults (IFs) in analog circuits. The AMDSCAE method can assign different attention and fuse multiscale information adaptively, resulting in better noise robustness. Additionally, the dual subnet structure enhances the IF detection ability and can detect weaker faults. Experimental results on three typical analog filter circuits demonstrate that AMDSCAE has better noise immunity and can detect weaker IFs.