Abstract
The fault diagnosis of analog circuits faces problems, such as inefficient feature extraction and fault identification. To solve the problems, this paper combines the circle model and the extreme learning machine (ELM) into a fault diagnosis method for the linear analog circuit. Firstly, a circle model for the voltage features of fault elements was established in the complex domain, according to the relationship between the circuit response, element position and circuit topology. To eliminate the impacts of tolerances and signal aliasing, the 3D feature was introduced to make the indistinguishable features in fuzzy groups distinguishable. Fault feature separability is very important to improve the fault diagnosis accuracy. In addition, an effective classier can improve the precision and the time taken. With less computational complexity and a simpler process, the ELM algorithm has a fast speed and a good classification performance. The effectiveness of the proposed method is verified by simulation. The simulation results show the ELM-based algorithm classifier with the circle model can enhance precision and reduce time taken by about 80% in comparison with other methods for analog circuit fault diagnosis. To sum up, this proposed method offers a fault diagnosis method that reduces the complexity in generating fault features, improves the isolation probability of faults, speeds up fault classification, and simplifies fault testing.
Highlights
Since the 1970s, analog circuit fault diagnosis has become a research hotspot in the field of electronic testing, and gradually formed a relatively complete theoretical system [1]
For modern intelligent diagnosis of analog circuit faults, the key lies in the extraction of fault features and the design of classification algorithms
The proposed method of circle model-based feature extraction and the extreme learning machine (ELM) classification algorithm combination will fully inherit the completeness of the circle model and the fast classification of the ELM, and achieve higher accurate and efficient fault diagnosis of analog circuit
Summary
Since the 1970s, analog circuit fault diagnosis has become a research hotspot in the field of electronic testing, and gradually formed a relatively complete theoretical system [1]. The SVM or LS-SVM algorithm is used to map the lower-dimensional nonlinear response space into the higher-dimensional feature space for effective classification This algorithm has a much higher time cost because of complex computation of SVM, processes of test signal generator and test structure, and has unstable testing accuracy because of reduced precision in case of compressing the sampled space. For modern intelligent diagnosis of analog circuit faults, the key lies in the extraction of fault features and the design of classification algorithms. The proposed method of circle model-based feature extraction and the ELM classification algorithm combination will fully inherit the completeness of the circle model and the fast classification of the ELM, and achieve higher accurate and efficient fault diagnosis of analog circuit
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