Research on ELM Soft Fault Diagnosis of Analog Circuit Based on KSLPP Feature Extraction

Gan, Xusheng; Qu, Hong; Meng, Xiangfu; Wang, Chunlan; Zhu, Jie

doi:10.1109/access.2019.2923242

Cited by 30 publications

(14 citation statements)

References 20 publications

(19 reference statements)

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“…The penalty parameter C and kernel parameter γ of SVM are tuned according to IH-PSO. According to formulas (7) to (9), the position and velocity of the particles are updated to generate a new particle group.…”

Section: B Fault Diagnosis Process Based On Ih-pso Optimized Svmmentioning

confidence: 99%

“…Soft fault, also known as parametric fault [5], [6] is caused by parametric drift, which will not cause damage to the circuit hardware. Due to the continuity of components parameters, the insufficiency of diagnostic information in actual test circuits, as well as the tolerance impact of analog components [7]- [9], parameter fault diagnosis is rather complicated. It is rather troublesome for traditional methods like signal processing or analytical models to meet the needs.…”

Section: Introductionmentioning

confidence: 99%

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Fault Diagnosis of Analog Circuits Based on IH-PSO Optimized Support Vector Machine

et al. 2019

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Because of its excellent small sample learning abilities and simple network structure, support vector machine (SVM) is widely applied in various pattern recognition fields, e.g., face recognition, scene classification, fault diagnosis, etc. Due to the complexity and diversity of analog circuit faults, the diagnosis accuracy and stability of SVM classifier optimized by traditional particle swarm optimization (PSO) are unsatisfactory. Therefore, this paper proposes an improved hybrid particle swarm optimization (IH-PSO) algorithm to optimize SVM, which is applied in the fault diagnosis of analog circuits. Compared with the traditional PSO algorithm, the proposed IH-PSO mainly has three improvements, namely, the oppositionbased learning population initialization, the nonlinear time-varying inertia weight, and the new position updating strategy with a spiral convergence mechanism. The performance of the proposed IH-PSO algorithm is verified by 12 commonly used benchmark functions and experimental results show that the proposed IH-PSO algorithm overcomes the deficiencies of the traditional PSO algorithm, such as slow convergence speed and trapping into local optimums. In addition, to further verify the performance of IH-PSO algorithm, the IH-PSO optimized SVM is applied to solve analog circuits fault diagnosis problems. Extensive experiments are carried out and results indicate that the proposed method has better performances both in diagnosis accuracy and stability compared with that of the traditional method.

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Section: B Fault Diagnosis Process Based On Ih-pso Optimized Svmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Analog Circuits Based on IH-PSO Optimized Support Vector Machine

et al. 2019

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“…The extreme learning machine (ELM) proposed by Guang-Bin Huang et al in 2004 provides a unified learning platform with a wide range of feature mapping types, and thus can approximate any continuous objective function, making classification of disjoint areas possible. Since they have better scalability, stronger generalization and faster learning speeds than SVMs [16], ELMs have been widely used in the fields of fault diagnosis, such as transmission line fault diagnosis [17], rolling bearing fault diagnosis [18][19][20], and analog circuit fault diagnosis [21,22]. In the field of transformer fault diagnosis, Hasmat Malik and Sukumar Mishra [23] applied a principal component analysis on International Electrotechnical Commission Technical Committee (IEC TC) 10 DGA data to find the most relevant variables first and then used an ELM to classify early transformer faults.…”

Section: Introductionmentioning

confidence: 99%

A Transformer Fault Diagnosis Method Based on Parameters Optimization of Hybrid Kernel Extreme Learning Machine

Hai²,

Feng

et al. 2021

IEEE Access

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Dissolved gas analysis (DGA) is a widely used method for diagnosing internal transformer defects. The traditional single intelligent diagnostic method cannot efficiently process large amounts of incomplete defect information with DGA, which affects the accuracy of fault diagnosis. To this end, this paper proposes a transformer fault diagnosis method based on the optimization of kernel parameters and weight parameters of a kernel extreme learning machine (KELM). First, based on Mercer's theorem, we combine the radial basis kernel function with the polynomial kernel function to construct a new hybrid kernel function. Then, the gray wolf optimization (GWO) algorithm and the differential evolution (DE) algorithm are combined to improve the diversity of the gray wolf population, enhance the searchability of GWO, and prevent GWO from falling into a local optimum during the iterative process. Finally, the kernel parameters and weight parameters of the hybrid kernel function are optimized by using the modified grey wolf optimization (MGWO) algorithm. The International Electrotechnical Commission Technical Committee (IEC TC) 10 transformer fault data and constructed hybrid feature set is used as the input set of the model, the model is simulated and analyzed, and the transformer fault data collected at a site are used for training and verification. The simulation results on the two sets of data show that the method can accurately and effectively diagnose transformer faults, and has a higher fault diagnosis accuracy rate than traditional methods.

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“…Since establishment of modeling approaches is a great challenge, data-driven approaches have become more popular as they combine feature extraction and classification techniques into a single learning body. Reviewing the literature indicates that commonly used data-driven approaches are the artificial neural network (ANN) [17]- [22], support vector machine (SVM) [23], [24], and other machine learning methods [27], [28]. Zhao et al [17] proposed an intelligent solution based on the deep belief network (DBN), which has higher classification accuracy and lower requirements on data.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Feature Extraction for Analog Circuit Fault Diagnosis Using 1-D Convolutional Neural Network

Yang¹,

Meng²,

Wang³

2020

IEEE Access

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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. INDEX TERMS Analog circuit fault diagnosis, convolutional neural network, data-driven method, faults classification, feature extraction.

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Research on ELM Soft Fault Diagnosis of Analog Circuit Based on KSLPP Feature Extraction

Cited by 30 publications

References 20 publications

Fault Diagnosis of Analog Circuits Based on IH-PSO Optimized Support Vector Machine

Fault Diagnosis of Analog Circuits Based on IH-PSO Optimized Support Vector Machine

A Transformer Fault Diagnosis Method Based on Parameters Optimization of Hybrid Kernel Extreme Learning Machine

Data-Driven Feature Extraction for Analog Circuit Fault Diagnosis Using 1-D Convolutional Neural Network

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