Copula-Based Statistical Health Grade System Against Mechanical Faults of Power Transformers

Hu, Chao; Wang, Pingfeng; Youn, Byeng D.; Lee, Wook-Ryun; Yoon, Joung Taek

doi:10.1109/tpwrd.2012.2202406

Cited by 41 publications

(21 citation statements)

References 25 publications

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“…A separability measure for multi-class cases was obtained from an average of separability measures for all possible combinations of two-class problems, as shown in Eq. (8). The separability results of time-and frequency-domain features using the three evaluation metrics are shown in the following sections.…”

Section: Quantitative Study Of Anomaly Diagnosismentioning

confidence: 99%

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Datum unit optimization for robustness of a journal bearing diagnosis system

Jeon

Jung

Youn

et al. 2015

Int. J. Precis. Eng. Manuf.

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This paper describes a robust diagnosis method for a rotor system with a journal bearing. To enhance the robustness of a journal bearing diagnosis system, it is of great importance to define an optimum datum unit for featuring anomaly states of the rotor system. To support the research goal, this study makes use of three measures for class separation, including Kullback-Leibler divergence (KLD), Fisher discriminant ratio (FDR), and a newly proposed measure: probability of separation (PoS). From the viewpoint of class separability, this work found that PoS is more attractive than other methods for quantification of class separation. PoS offers favorable properties like normalization, boundedness, and high sensitivity. A generic algorithm integrated with one of three measures consistently suggested the optimum datum units among the feasible datum units. Optimum datum units were found to be one-cycle for time-domain features and sixty-cycles for frequency-domain features. The support vector machine (SVM) classifier with the optimum datum units was used for diagnosing a normal and three anomaly states. The health classification results showed that the proposed optimum datum units can outperform other datum units.

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Section: Quantitative Study Of Anomaly Diagnosismentioning

confidence: 99%

“…S i,j can be obtained by using one of the three measures-KLD, FDR, or PoS. Four health classes entail six (= 4 C 2 ) possible combinations (8). …”

mentioning

confidence: 99%

Datum unit optimization for robustness of a journal bearing diagnosis system

Jeon

Jung

Youn

et al. 2015

Int. J. Precis. Eng. Manuf.

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show abstract

“…Recent works [17][18][19][20][21][22][23][24][25] exploit vibration measurements for mechanical fault diagnosis of power transformers, but they use a large set of sensors, e.g., at least 10 in [17][18][19], 15 in [22], or 40 in [21] for each transformer. Recent works on power transformer analysis [17][18][19][20][21][22][23][24][25], and battery analysis [45][46][47], adopt computation intensive probabilistic algorithms, not implementable in low-cost embedded computing systems.…”

Section: Critical Analysis Of the State-of-the-art For Ups Predictivementioning

confidence: 99%

“…Recent works on power transformer analysis [17][18][19][20][21][22][23][24][25], and battery analysis [45][46][47], adopt computation intensive probabilistic algorithms, not implementable in low-cost embedded computing systems. Moreover, the above works usually follow a centralised approach instead of a distributed networking approach as in this paper.…”

Section: Critical Analysis Of the State-of-the-art For Ups Predictivementioning

confidence: 99%

“…The weight of these power transformers can be up to a thousand of Kg, whereas their voltage and current levels are up to several hundreds of kVA [8,[17][18][19][20][21][22][23][24][25][26][27][28][29][30]. The causes of degradation for high power transformers can be of an electrical nature, such as over-current and over-voltage conditions, or of a thermal nature, over-temperature condition, and of a mechanical nature.…”

Section: Degradation In Power Transformers and Specifications For Thementioning

confidence: 99%

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Distributed Measuring System for Predictive Diagnosis of Uninterruptible Power Supplies in Safety-Critical Applications

Saponara

2016

Energies

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This work proposes a scalable architecture of an Uninterruptible Power Supply (UPS) system, with predictive diagnosis capabilities, for safety critical applications. A Failure Mode and Effect Analysis (FMEA) has identified the faults occurring in the energy storage unit, based on Valve Regulated Lead-Acid batteries, and in the 3-phase high power transformers, used in switching converters and for power isolation, as the main bottlenecks for power system reliability. To address these issues, a distributed network of measuring nodes is proposed, where vibration-based mechanical stress diagnosis is implemented together with electrical (voltage, current, impedance) and thermal degradation analysis. Power system degradation is tracked through multi-channel measuring nodes with integrated digital signal processing in the transformed frequency domain, from 0.1 Hz to 1 kHz. Experimental measurements on real power systems for safety-critical applications validate the diagnostic unit.

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Case Studies: Prognostics and Health Management (PHM)

Youn

Wang

2018

Springer Series in Reliability Engineering

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Copula-Based Statistical Health Grade System Against Mechanical Faults of Power Transformers

Cited by 41 publications

References 25 publications

Datum unit optimization for robustness of a journal bearing diagnosis system

Datum unit optimization for robustness of a journal bearing diagnosis system

Distributed Measuring System for Predictive Diagnosis of Uninterruptible Power Supplies in Safety-Critical Applications

Case Studies: Prognostics and Health Management (PHM)

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