Nonlinear Soft Fault Diagnosis of Analog Circuits Based on RCCA-SVM

Li, Yang; Zhang, Rui; Guo, Yinjing; Huan, Pengfei; Zhang, Manlin

doi:10.1109/access.2020.2982246

Cited by 23 publications

(13 citation statements)

References 41 publications

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“…The SVM's is robust for fault diagnostics problems [48]- [50] and with its kernel-compatible architecture, has dependable classification capabilities [16], [20], [25], [51]; hence, the motivation for employing the SVM* for fault classification of our proposed solenoid pumps for achieving accurate FD&I.…”

Section: B Meta-heuristic Methods For Feature Selectionmentioning

confidence: 99%

A Multi-Domain Diagnostics Approach for Solenoid Pumps Based on Discriminative Features

Akpudo

Hur

2020

IEEE Access

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Accurate condition monitoring of industrial cyber-physical systems/components demands the use of reliable fault detection and isolation (FD&I) methodologies. Meta-heuristic algorithms for feature selection have good exploration capability for optimal discriminative feature selection for fault isolation/classification of which the Binary Particle swarm optimization (BPSO) is superior to its counterparts. This study presents a robust approach for vibration-based failure diagnostics of electromagnetic/solenoid pumps which employ a multi-domain feature extraction procedure (statistical time-domain and frequencydomain features, Mel frequency cepstral coefficients, and continuous wavelet coefficients) for capturing linear and nonlinear properties from the signals. Compared with other filter and wrapper methods for supervised feature selection, a hybrid filter-wrapper (Pearson's correlation-BPSO (ρ-BPSO)) feature selection procedure is proposed for global search of optimal discriminative (uncorrelated) features for fault diagnosis with an RBF-kernel support vector machine (SVM*). Subsequently, a practical case study involving five VSC63A5 solenoid pumps at various operating/fault conditions is presented for validating the performance of the proposed approach. Results show the superior performance of the proposed hybrid filterwrapper approach against filter-based and wrapper-based techniques for discriminative feature selection. Also, the proposed ρ-BPSO-SVM* diagnostics model performance was compared with other standard fault isolation/classification methods.

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Section: B Meta-heuristic Methods For Feature Selectionmentioning

confidence: 99%

A Multi-Domain Diagnostics Approach for Solenoid Pumps Based on Discriminative Features

Akpudo

Hur

2020

IEEE Access

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“…Numerous methods devoted to soft fault diagnosis have been developed in the last decades, e.g. [4, 5, 9, 10, 13, 16, 17, 21, 22, 25, 28]. Most works in this area concentrate on single fault diagnosis, e.g.…”

Section: Preliminariesmentioning

confidence: 99%

Soft fault diagnosis of non‐linear circuits having multiple DC solutions

Tadeusiewicz

Hałgas

2020

IET Circuits, Devices & Systems

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This study deals with a single soft fault diagnosis of non-linear circuits having multiple DC solutions. The key problem of the diagnosis is arranging a measurement test. Creation of the test for the above-defined class of circuits is discussed in detail. It is assumed that only input and output terminals are accessible in the circuit. The test is created based on some singlevalued input or transfer characteristics traced in a modified circuit. The modification does not disturb the integrity of the circuit and is realised by connecting some components to the accessible terminals. A very fast method for tracing the characteristics is developed. Having appropriate characteristics several values of the exciting voltage are chosen and the corresponding values of voltage or current defined by the characteristic are measured in the course of testing. The method for arranging the measurement test is illustrated using four non-linear circuits having multiple DC solutions and three of them were built in bipolar and complementary metal-oxide-semiconductor (CMOS) technology. One of the circuits is comprehensibly diagnosed using a method for detecting, locating, and estimating values of different faulty parameters exploiting the proposed test.

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“…One influential model-based approach is the geometric programming algorithm [2]- [4], which models the circuit performance with posynomial approximation. Other modeling strategies also exist, including artificial neural network (ANN) [5]- [7], support vector machine (SVM) [8], and Gaussian process regression (GPR) [9]- [14]. The disadvantage that prevents model-based methods from being widely used is that an accurate performance model is always hard to derive manually or requires a large set of simulation data to approximate.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Batch-Constrained Bayesian Optimization Approach for Analog Circuit Synthesis via Multiobjective Acquisition Ensemble

Zhang

Yang

Yan

et al. 2022

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Bayesian optimization is a promising methodology for analog circuit synthesis. However, the sequential nature of the Bayesian optimization framework significantly limits its ability to fully utilize real-world computational resources. In this paper, we propose an efficient parallelizable Bayesian optimization algorithm via Multi-objective ACquisition function Ensemble (MACE) to further accelerate the optimization procedure. By sampling query points from the Pareto front of the probability of improvement (PI), expected improvement (EI) and lower confidence bound (LCB), we combine the benefits of state-of-theart acquisition functions to achieve a delicate tradeoff between exploration and exploitation for the unconstrained optimization problem. Based on this batch design, we further adjust the algorithm for the constrained optimization problem. By dividing the optimization procedure into two stages and first focusing on finding an initial feasible point, we manage to gain more information about the valid region and can better avoid sampling around the infeasible area. After achieving the first feasible point, we favor the feasible region by adopting a specially designed penalization term to the acquisition function ensemble. The experimental results quantitatively demonstrate that our proposed algorithm can reduce the overall simulation time by up to 74× compared to differential evolution (DE) for the unconstrained optimization problem when the batch size is 15. For the constrained optimization problem, our proposed algorithm can speed up the optimization process by up to 15× compared to the weighted expected improvement based Bayesian optimization (WEIBO) approach, when the batch size is 15.

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Nonlinear Soft Fault Diagnosis of Analog Circuits Based on RCCA-SVM

Cited by 23 publications

References 41 publications

A Multi-Domain Diagnostics Approach for Solenoid Pumps Based on Discriminative Features

A Multi-Domain Diagnostics Approach for Solenoid Pumps Based on Discriminative Features

Soft fault diagnosis of non‐linear circuits having multiple DC solutions

An Efficient Batch-Constrained Bayesian Optimization Approach for Analog Circuit Synthesis via Multiobjective Acquisition Ensemble

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