Analog circuit fault diagnosis based on Quantum Clustering based Multi-valued Quantum Fuzzification Decision Tree (QC-MQFDT)

Cui, Yiqian; Shi, Junyou; Wang, Zili

doi:10.1016/j.measurement.2016.07.018

Cited by 30 publications

(7 citation statements)

References 37 publications

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“…The fault diagnosis problem of analog circuits can be considered to be far from a definitive solution, even if in the last years a large amount of research has been dedicated to it (e.g., [1][2][3][4][5][6][7][8][9][10][11][12]). This is because the growing complexity of electronic circuits and the increasing number of applications characterized by the coexistence of digital and analog systems.…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Classifier with Multi-Valued Neurons for Analog Circuit Fault Diagnosis

et al. 2021

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In this paper, we present a new method designed to recognize single parametric faults in analog circuits. The technique follows a rigorous approach constituted by three sequential steps: calculating the testability and extracting the ambiguity groups of the circuit under test (CUT); localizing the failure and putting it in the correct fault class (FC) via multi-frequency measurements or simulations; and (optional) estimating the value of the faulty component. The fabrication tolerances of the healthy components are taken into account in every step of the procedure. The work combines machine learning techniques, used for classification and approximation, with testability analysis procedures for analog circuits.

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Section: Introductionmentioning

confidence: 99%

A Neural Network Classifier with Multi-Valued Neurons for Analog Circuit Fault Diagnosis

et al. 2021

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“…Segatori et al [26] proposed a distributed fuzzy decision tree learning scheme shaped according to the MapReduce programming model, which relies on a novel distributed fuzzy discretizer based on fuzzy information entropy. In our previous work, we proposed a quantum clustering based multivalued quantum fuzzification decision tree (QC-MQFDT) [27]. This decision tree uses adaptive fuzzification method to discretize continuous-valued data and constructs a quantum fuzzy entropy to evaluate the information of each attribute, which makes the decision tree more stable and robust.…”

Section: Introductionmentioning

confidence: 99%

GMM Clustering-Based Decision Trees Considering Fault Rate and Cluster Validity for Analog Circuit Fault Diagnosis

Shi

Wang

2019

IEEE Access

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Traditional decision trees for fault diagnosis often use an ID3 construction algorithm. For promoting the accuracy and efficiency of decision trees, considering the cluster validity and fault rates, this paper proposes two improved trees, CV-DTs and FR-DTs. This paper mainly has two highlights. The first highlight is to propose a CV-DT which is constructed by an improved ID3 algorithm considering the cluster validity index. A new cluster validity index which can compare the cluster validities of different attributes is proposed to modify the information gain. This method selects the splitting attributes with higher classification credibility and increases the diagnostic accuracy. The second highlight is to propose an FR-DT which is constructed by an improved ID3 algorithm considering the fault rates. This algorithm not only considers the partitioning ability of each attribute, but also considers the isolation priority of faults with higher fault rates. This method decreases the average diagnostic steps and promotes the diagnostic efficiency. Through a simulation case and a real board case, these decision trees are proved to be effective diagnostic tools which have higher accuracies or efficiencies in analog circuit. INDEX TERMSCluster validity, decision tree, fault rate, GMM clustering, ID3 algorithm.

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“…The key to parametric fault diagnosis is the selection and optimization of the classifier. The classification algorithm, back propagation (BP) neural network (NN) [13], neuromorphic analyzers [14], extreme learning machine (ELM) [15][16][17], decision tree support vector machine (DTSVM) [18], quantum clustering-based multi-valued quantum fuzzification decision tree (QC-MQFDT) [19], and Gaussian Bernoulli deep belief network (GB-DBN) [20] were used in fault diagnosis of the analog circuits. The average fault diagnosis rate is more than 90% in the fixed training samples and test samples.…”

Section: Introductionmentioning

confidence: 99%

Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised Algorithm

et al. 2019

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The parametric fault diagnosis of analog circuits is very crucial for condition-based maintenance (CBM) in prognosis and health management. In order to improve the diagnostic rate of parametric faults in engineering applications, a semi-supervised machine learning algorithm was used to classify the parametric fault. A lifting wavelet transform was used to extract fault features, a local preserving mapping algorithm was adopted to optimize the Fisher linear discriminant analysis, and a semi-supervised cooperative training algorithm was utilized for fault classification. In the proposed method, the fault values were randomly selected as training samples in a range of parametric fault intervals, for both optimizing the generalization of the model and improving the fault diagnosis rate. Furthermore, after semi-supervised dimensionality reduction and semi-supervised classification were applied, the diagnosis rate was slightly higher than the existing training model by fixing the value of the analyzed component.

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Analog circuit fault diagnosis based on Quantum Clustering based Multi-valued Quantum Fuzzification Decision Tree (QC-MQFDT)

Cited by 30 publications

References 37 publications

A Neural Network Classifier with Multi-Valued Neurons for Analog Circuit Fault Diagnosis

A Neural Network Classifier with Multi-Valued Neurons for Analog Circuit Fault Diagnosis

GMM Clustering-Based Decision Trees Considering Fault Rate and Cluster Validity for Analog Circuit Fault Diagnosis

Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised Algorithm

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