Diagnostics and Prognostics Method for Analog Electronic Circuits

Vasan, Arvind Sai Sarathi; Li, Bing; Pecht, Michael

doi:10.1109/tie.2012.2224074

Cited by 166 publications

(50 citation statements)

References 68 publications

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“…Meanwhile, the data-driven techniques do not need to construct an explicit model. Hence, the data-driven approaches have been applied to fault diagnosis in many relative works [9,10].…”

Section: Related Workmentioning

confidence: 99%

“…Thus, it is difficult to effectively recognize the defects of analog electronic systems when only considering the features of time-domain or frequency-domain [15]. As a typical time-frequency domain analysis, wavelet transform (WT) can reveal overlaps in time-frequency domains by decomposing the signal into a set of wavelet coefficients that vary continually over time [10]. Nevertheless, in practice, the measured signals of analog circuits commonly contain random noise, which may lead to misclassification.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, it is necessary to take actions to minimize the impact of random noise. Noise removal can be executed by setting a threshold when computing wavelet coefficients [10]. However, there are some limitations: The threshold needs to be set manually, and the calculation process is time-consuming.…”

Section: Related Workmentioning

confidence: 99%

“…Aminian et al [1] WT + PCA + NN 97 95 Xiao et al [4] FrWT + KPCA + Ridgelet−NN 100 98.52 Yuan et al [9] Entropy + Kurtosis + NN 100 99 Vasan et al [10] WT + entropy, Kurtosis + SVM 99.70 95.69 Song et al [46] FrFT statistical feature + SVM 98.41 95.12 Chen et al [47] WPT As shown in Table 4, it can be observed that our proposed method achieves a better result than that of other listed works, with other exceptions [4,9,10]. However, the fault components in our work have smaller parametric deviation.…”

Section: Workmentioning

confidence: 99%

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Analog Circuit Fault Diagnosis via Joint Cross-Wavelet Singular Entropy and Parametric t-SNE

Wei

et al. 2018

Entropy

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Abstract:In this paper, a novel method with cross-wavelet singular entropy (XWSE)-based feature extractor and support vector machine (SVM) is proposed for analog circuit fault diagnosis. Primarily, cross-wavelet transform (XWT), which possesses a good capability to restrain the environment noise, is applied to transform the fault signal into time-frequency spectra (TFS). Then, a simple segmentation method is utilized to decompose the TFS into several blocks. We employ the singular value decomposition (SVD) to analysis the blocks, then Tsallis entropy of each block is obtained to construct the original features. Subsequently, the features are imported into parametric t-distributed stochastic neighbor embedding (t-SNE) for dimension reduction to yield the discriminative and concise fault characteristics. Finally, the fault characteristics are entered into SVM classifier to locate circuits' defects that the free parameters of SVM are determined by quantum-behaved particle swarm optimization (QPSO). Simulation results show the proposed approach is with superior diagnostic performance than other existing methods.

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“…Meanwhile, the data-driven techniques do not need to construct an explicit model. Hence, the data-driven approaches have been applied to fault diagnosis in many relative works [9,10].…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Workmentioning

confidence: 99%

See 2 more Smart Citations

Analog Circuit Fault Diagnosis via Joint Cross-Wavelet Singular Entropy and Parametric t-SNE

Wei

et al. 2018

Entropy

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“…As time goes by, the electrolyte evaporates, and the equivalent series resistance also changes. Reference [14] concluded the trend of the equivalent series resistance's change as time goes by in Eq. (12) (12) In Eq.…”

Section: Figure 3 Equivalent Circuit Of Electrolytic Capacitormentioning

confidence: 99%

Fault prognostic based on AR-LSSVR for electrolytic capacitor

Yin

et al. 2017

Teh. vjesn.

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Original scientific paper This paper puts forward a method of fault prognostic based on Autoregressive -Support Vector Regression Method (AR-LSSVR) for electrolytic capacitor. Because the electrolytic capacitor is low in cost and large in volume, it is widely used in power electronic circuits. Firstly it introduces the basic model and the fault prognostic algorithm of the AR, LSSVM and AR-LSSVR. The AR-LSSVR prediction model combines the prediction algorithm advantage of the LSSVR and the AR model and complements the two to enhance prediction accuracy. It introduces the flow chart of fault trend prediction based on AR-LSSVR. Finally, the AR-LSSVR model is applied to the Buck circuit. The results indicate that the AR-LSSVR model performs better in trend prediction of electrolytic capacitor. Keywords: AR-LSSVR; electrolytic capacitor; fault prognosis Predviđanje greške primjenom AR-LSSVR za elektrolitički kondenzatorIzvorni znanstveni članka U radu se opisuje metoda predviđanja greške na osnovu Autoregressive -Support Vector Regression Metode (AR-LSSVR) za elektrolitički kondenzator. Budući da je elektrolitički kondenzator jeftin, a velik, uveliko se primjenjuje u elektroničkim krugovima. Najprije se daje osnovni model i algoritam za predviđanje greške za AR, LSSVM i AR-LSSVR. Model AR-LSSVR kombinira prednosti algoritma LSSVR-a i modela AR te ih dopunjuje kako bi se povećala točnost predviđanja. Daje se dijagram toka predviđanja pojave greške na temelju AR-LSSVR. Konačno se AR-LSSVR model primjenjuje na Buck strujni krug. Rezultati pokazuju da je predviđanje greške elektrolitičkog kondenzatora bolje primjenom modela AR-LSSVR.

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Health and Remaining Useful Life Estimation of Electronic Circuits

Vasan¹,

Pecht

2018

Prognostics and Health Management of Electronics

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Diagnostics and Prognostics Method for Analog Electronic Circuits

Cited by 166 publications

References 68 publications

Analog Circuit Fault Diagnosis via Joint Cross-Wavelet Singular Entropy and Parametric t-SNE

Analog Circuit Fault Diagnosis via Joint Cross-Wavelet Singular Entropy and Parametric t-SNE

Fault prognostic based on AR-LSSVR for electrolytic capacitor

Health and Remaining Useful Life Estimation of Electronic Circuits

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