A Fault Diagnosis Method of Modular Analog Circuit Based on SVDD and D–S Evidence Theory

Abstract

In the actual fault diagnosis process of an analog circuit, there is often a problem due to the lack of fault samples, leading to the low-accuracy of diagnostic models. Therefore, using positive samples that are easy to obtain to establish diagnostic models became a research hotspot in the field of analog circuit fault diagnosis. This paper proposes a method based on Support Vector Data Description (SVDD) and Dempster–Shafer evidence theory (D–S evidence theory) for fault diagnosis of modular analog circuit. Firstly, the principle of circuit module partition is proposed to divide the analog circuit under test, and the output port of each module is selected as test point. Secondly, the paper extracts the feature of the time-domain and frequency-domain output signals of the circuit module through Principal Component Analysis (PCA). Thirdly, four state detection models based on SVDD are established to judge the working state of each circuit module, including TSG, TSP, FSG, and FSP state detection model. Finally, the D–S theory is introduced to integrate the test results of each model for locating fault circuit module. To verify the effectiveness of the proposed method, the dual bandpass filter circuit is selected for simulation and hardware experiment. The results show that the proposed method can locate the analog fault effectively and has a higher diagnosis accuracy.

An Exploration into the Fault Diagnosis of Analog Circuits Using Enhanced Golden Eagle Optimized 1D-Convolutional Neural Network (CNN) with a Time-Frequency Domain Input and Attention Mechanism

“…The position update of the golden eagle is determined based on the attacking behavior and the cruising behavior. The step size of the two behaviors of the ith golden eagle in iteration t is denoted as Equation (8), where r 1 and r 2 are hyperparameters in the range of 0 to 1, and the p t a and p t c are calculated using Equations ( 9) and (10), which are the coefficients of the attack vector and the coefficients of the cruise vector in iteration t. T is the maximum number of iterations. ∥ A i ∥ and ∥ C i ∥ are the Euclidean norms of the attack vector and the cruise vector, computed based on Equations (11) and (12).…”

“…Hard faults can be analyzed based on the basic mechanism of the circuit to locate the faulty component and determine the type of fault, and research in this area has been intensive and has achieved a relatively mature stage [ 7 ]. In contrast, soft faults have infinite possibilities of fault values due to the continuity of component parameters and the diversity of output signals in the normal state due to the influence of component tolerances, making fault diagnosis difficult to realize, which is becoming a hot issue in current research [ 8 , 9 , 10 ]. C. Alippi et al [ 11 ] proposed a harmonic analysis-based method for fault diagnosis in analog electronic circuits, using a “simulation before test” approach.…”

A Method of Diagnosing Analog Circuit Soft Faults Using Boruta Features and LightGBM

“…Ping Song et al [11] introduced a novel approach for fault feature extraction utilizing fractional Fourier transform (FRFT), and SVM was employed to train the extracted features to achieve the effect of diagnosing and categorizing the faults. Peng Sun et al [12] introduced a fault diagnosis method for modular analog circuits, utilizing support vector data description (SVDD) and integrating Dempster-Shafer (abbreviated as DS) evidence theory. They performed simulation and hardware experiments on a double-bandpass filter circuit, achieving favorable results.…”

Mechanical Equipment Performance Degradation Assessment Method Based on Improved Degradation Feature Extraction and Sample Completeness

“…18,19 In the fault detection of building chiller with insufficient fault data, Zhao et al 20 adopted the pattern recognition method based on the SVDD model to obtain higher fault detection accuracy and better fault detection performance compared with the PCA method. Sun et al 21 proposed a modular analog circuit fault diagnosis method based on SVDD and D-S evidence theory, which achieved higher diagnostic accuracy in the actual fault diagnosis process of analog circuits lacking fault samples and could effectively locate simulated faults. The SVDD model, which has strong robustness and good classification effect under the condition of small samples, can be modeled only by single-class state samples.…”