Soft fault diagnosis for analog circuits based on slope fault feature and BP neural networks

Hu, Mei; Wang, Hong; Hu, Geng; Yang, Shiyuan

doi:10.1016/s1007-0214(07)70079-2

Cited by 38 publications

(21 citation statements)

References 8 publications

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“…Pouts proposed a fault dictionary information as the membership function of input variables, and the shape of membership function is determined by the element parameter values in fuzzy diagnosis system, the method is compared with the fault dictionary have greatly improve performance [12].…”

Section: Modern Methods Of Analog Circuit Fault Diagnosismentioning

confidence: 99%

The Analysis of Analog Circuits Fault Diagnosis Methods

Zhang¹,

Zhang²

2016

Proceedings of the 6th International Conference on Electronic, Mechanical, Information and Management Society

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Abstract. Focusing on the requirement of analog circuit fault and diagnosis, authors summarize the analog circuit fault classifier and analysis the corresponding analog circuit fault diagnosis methods based reviewing a great number of literatures. The fault diagnosis strategies are discussed from the two fields, one is the traditional fault diagnosis methods, and the other one is modern intelligent fault diagnosis method.

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Section: Modern Methods Of Analog Circuit Fault Diagnosismentioning

confidence: 99%

The Analysis of Analog Circuits Fault Diagnosis Methods

Zhang¹,

Zhang²

2016

Proceedings of the 6th International Conference on Electronic, Mechanical, Information and Management Society

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“…For analog circuits and systems, soft fault diagnosis is difficult and complex on account of the tolerance effect and circuit nonlinearity. To overcome these issues, many emerging diagnosis methods have been developed, for instance the diagnosis method integrated with neural network [16], [17], the slope-based modeling method [18], [19], and fuzzy analysis method [20], [21]. Notably, there has not yet been any open published literature which is available for fault diagnosis of multiple memristor circuits, although the effectiveness of the nano-scale combination circuits have been proved in numerous fields, especially suitable for implementing artificial synapses [22]- [24].…”

Section: Introductionmentioning

confidence: 99%

Multiple Memristor Circuit Parametric Fault Diagnosis Using Feedback-Control Doublet Generator

Dong

et al. 2016

IEEE Access

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The memristor was first theorized as an electrical element, which provided the missing link between the charge and the flux. Due to the advantages of nano-scale size, multiple interconnected memristors have demonstrated unique overall characteristics, which are ideal for the utilization in neuromorphic systems. However, compared with the individual memristor circuit, a little work is explored about the overall behavior of the multiple memristive systems. In particular, the lack of a fault diagnosis approach for composite memristive network structures makes all the corresponding applications unstable and shaky. In this paper, the extraordinary properties of multiple memristor circuits are further investigated with comprehensive formula derivation and scientific computer simulations. Furthermore, a special feedback-control doublet generator is designed for implementing the fuzzy-based parametric fault diagnosis of multiple memristor circuits, which offers huge benefits in terms of accuracy and time consumption. Finally, the entire scheme is validated by an illustrative example. INDEX TERMSMultiple memristor circuits, parametric fault diagnosis, feedback-control, doublet generator, fuzzy-based.

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“…It is not applicable to the continuous parameter shifting (CPS) fault. To diagnose the single CPS fault, invariance of node-voltage sensitivity method and slope modeling method are proposed in [46], [48], and [49], respectively. The basic idea is that in linear circuit, no matter how much the parameter shifting is, the ratio of voltages on two test nodes is invariable.…”

Section: Introductionmentioning

confidence: 99%

“…However, they cannot diagnose the CPS fault; 2) the CPS fault is solved in [46]- [62]. However, most of the proposed methods [50], [52]- [62] rely on the transfer function; and 3) the techniques proposed in [46] and [48] are the most simple and effective methods which can model all possible CPS fault occurred in linear circuit with only two simulations. However, at least two test nodes are needed to diagnose single fault.…”

Section: Introductionmentioning

confidence: 99%

Complex Field Fault Modeling-Based Optimal Frequency Selection in Linear Analog Circuit Fault Diagnosis

Yang

Liu

et al. 2014

IEEE Trans. Instrum. Meas.

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Owing to the lack of feasible fault modeling method, hard (open and short) faults, and discretized parametric faults are still the mostly used fault models. These models cannot characterize all soft (parameter shifting) faults because that the parameter of analog element be of continuity character. To address this concern, a complex field fault modeling method is presented first. If fault happens to passive element x i in linear analog circuit, the real (U r ) and imaginary (U j ) parts of faulty voltage phasor U°(U°= U r + j U j ) must satisfy binary quadratic function F i (U r , U j ) = 0, which is independent from the value of element x i and uniquely determined by its location and the nominal circuit under test. Hence, the binary quadratic function can model any continuous parameter shifting (soft) or hard fault. Second, to avoid calculating the explicit expression of binary quadratic function, a simulation-based method is given to obtain the locus of the change in the function F z in the complex plane. Besides, the nominal (fault-free) point, the loci determined by the binary quadratic functions might intersect with each other. It is referred to as aliasing problem in this paper. This problem can be solved by adjusting the frequency of the input signal. Therefore, a constraint optimization method is proposed to select optimal test frequencies. The proposed test generation and compression methods, when combined with the complex field modeling method, can pinpoint much more fault components with less test points and frequencies when compared with the other methods.

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Soft fault diagnosis for analog circuits based on slope fault feature and BP neural networks

Cited by 38 publications

References 8 publications

The Analysis of Analog Circuits Fault Diagnosis Methods

The Analysis of Analog Circuits Fault Diagnosis Methods

Multiple Memristor Circuit Parametric Fault Diagnosis Using Feedback-Control Doublet Generator

Complex Field Fault Modeling-Based Optimal Frequency Selection in Linear Analog Circuit Fault Diagnosis

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