Abstract
The present study applies the one-dimensional convolutional neural network (1D-CNN) to propose an intelligent approach of the feature extraction for the analog circuit diagnosis. The raw signals based on various soft faults from the output terminal of the circuit under test (CUT) are collected with appropriate data acquisition system to implement a data-driven fault diagnosis. The data-driven diagnosis process is typically encapsulated in two distinct blocks, including the feature extraction and the classification. In this study, the designed 1D-CNN model efficiently combines the aforementioned two phases into a single diagnosis body with fast learning rate and accurate classification. The main advantages of the 1D-CNN are: 1) it can be directly established to the raw signal with proper training so that it is more applicable in real applications; 2) its compact architecture and configuration has reasonable applicability in complex analog circuits; 3) convolutional kernels guarantee that the hierarchical features can be extracted from raw data with better anti-interference performance. Moreover, since the method can extract high-level features of raw signals, it resolves the necessity to employ other per-processing methods for the hand-crafted feature transformation. The performance of the proposed 1D-CNN model is evaluated through three benchmark circuits on the SIMULINK platform. Obtained results are compared with other intelligent fault diagnosis methods. The experimental results show that the 1D-CNN can be utilized effectively as the feature exactor and faults classifier for analog circuits.
Highlights
With high integration and wide application of analog circuits in modern electronic systems, the fault diagnosis of analog circuits has become a fundamental issue to perform reasonable maintenance and maintain the reliable operation of the system
The training dataset is applied for constructing the 1D convolutional neural network (1D-CNN) modeling with feature extraction and fault classification, and the testing data is used for validation study
Feature extraction results of circuit-1 indicate that the deep and intrinsic feature of each incipient faults can be separated from raw signals by the proposed 1D-CNN model
Summary
With high integration and wide application of analog circuits in modern electronic systems, the fault diagnosis of analog circuits has become a fundamental issue to perform reasonable maintenance and maintain the reliable operation of the system. INDEX TERMS Analog circuit fault diagnosis, convolutional neural network, data-driven method, faults classification, feature extraction. H. Yang et al.: Data-Driven Feature Extraction for Analog Circuit Fault Diagnosis Using 1-D CNN In order to resolve these shortcomings, a compact 1D convolutional neural network (1D-CNN) is considered for analog circuit fault diagnosis with an automatic feature extraction process.
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