Incipient fault diagnosis of analog circuit with ensemble HKELM based on fused multi-channel and multi-scale features

As an essential part in electronics-rich system, the failure of analog circuits will severely affect the system reliability and security. Incipient fault of analog circuit refers to the early stage of degradation fault where the fault characteristics are generally weak and almost indistinguishable. In order to enhance the reliability of electronic systems, it is necessary to diagnose incipient faults of analog circuits promptly and effectively. Existing approaches generally capture fault characteristics only from single signal, ignoring the valuable information inherent in different domains and scales. To address this problem, a novel diagnostic strategy based on multi-scale feature extraction and multi-channel feature fusion is designed to guarantee the completeness and richness of fault information. In this study, a deep extreme learning machine denoising auto-encoder (DELM-DAE) based method is proposed to conduct unsupervised multi-scale and multi-channel feature fusion to extract distinguishable features for incipient faults. The proposed method has higher learning efficiency and overcomes the common problem of low efficiency in deep learning model training. Meanwhile, in order to improve the ability to distinguish high-resolution features, an ensemble hybrid kernel extreme learning machine with novel roulette selection and weighted voting scheme is proposed to enhance the recognition performance and stability. In the verification experiment, the diagnosis accuracy on four typical circuits all reaches above 98%, which demonstrates that the proposed incipient fault diagnosis method for analog circuits has more conspicuous performance than other state-of-the-art methods.