A Real-Time Data-Driven Algorithm for Health Diagnosis and Prognosis of a Circuit Breaker Trip Assembly

Biswas, Saugata S.; Srivastava, Anurag K.; Whitehead, Dave

doi:10.1109/tie.2014.2362498

Cited by 56 publications

(20 citation statements)

References 13 publications

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“…The selected papers [16][17][18] contribute to data-driven fault diagnosis. In [16] by Zhu et al, a novel fault classification mechanism is presented by developing probabilistic principal component analyser under hidden Markov model framework.…”

Section: Table 1 Selected Fault Diagnosis Papers In the Ssmentioning

confidence: 99%

“…The proposed fault classification method is tested on the Tennessee Eastman benchmark process. The paper [17], contributed by Biswas et al, addresses a real-time data-driven algorithm for health diagnosis and prognosis for a circuit breaker trip assembly by using a programmable intelligent electronic device stationed at the remote substation. The comprehensive health detection algorithm has a real time module as well as a predictive module, both of which can provide a clear indication about the present and future health of the circuit breaker trip coil arrangement.…”

Section: Table 1 Selected Fault Diagnosis Papers In the Ssmentioning

confidence: 99%

“…Twelve selected papers pay attention on fault diagnosis methods and applications [10][11][12][13][14][15][16][17][18][19][20][21], and the other eleven papers are concentrated on real-time fault tolerant control and applications [22][23][24][25][26][27][28][29][30][31][32]. We are going to overview the selected papers following fault diagnosis techniques and fault-tolerant control techniques sequentially.…”

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confidence: 99%

“…Therefore, knowledge-based diagnosis method is also called data-driven fault diagnosis methods (e.g., see the survey paper [10,11]). The selected research papers [12][13][14][15][16][17][18][19][20][21] fall into the respective categories readily (e.g., see Table 1). …”

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confidence: 99%

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Real-time fault diagnosis and fault-tolerant control

Gao

Ding

Cecati³

2015

IEEE Trans. Ind. Electron.

275

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he has been a Professor of control engineering and the head of the Institute for Automatic Control and Complex Systems (AKS) at the University of Duisburg-Essen, Germany. His research interests are model-based and data-driven fault diagnosis, fault-tolerant systems and their application in industry with a focus on automotive systems, mechatronic and chemical processes.Carlo Cecati (M'90-SM'03-F'06) is a Professor of industrial electronics and drives and the coordinator of the Ph.D. course on renewable energy and systainable building at DISIM -University of L'Aquila, L'Aquila, Italy. He is also Chief International Academic Adviser at Harbin Institute of Technology, Harbin, China. His research and technical interests cover several aspects of power electronics, distributed generation, and smart grids. In these areas he has published more than 130 journal and conference papers. Since nineties, he has been an active member of IEEE-IES; currently he is a Senior AdCom Member

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Section: Table 1 Selected Fault Diagnosis Papers In the Ssmentioning

confidence: 99%

Section: Table 1 Selected Fault Diagnosis Papers In the Ssmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Real-time fault diagnosis and fault-tolerant control

Gao

Ding

Cecati³

2015

IEEE Trans. Ind. Electron.

275

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“…This can be thought of as predicting the time remaining before a likely system failure, which is referred to as the remaining useful life (RUL). In the literature [1][2][3][4][5], existing prognostics approaches can generally be divided into three categories: physics-based, data-driven, and hybrid-based approaches. Physics-based approaches incorporate prior knowledge of physical and/or analytical models with measured data to predict the future degradation behavior of a system and its RUL.…”

Section: Introductionmentioning

confidence: 99%

An Empirical Investigation on a Multiple Filters-Based Approach for Remaining Useful Life Prediction

Trinh

Kwon

2018

Machines

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Feature construction is critical in data-driven remaining useful life (RUL) prediction of machinery systems, and most previous studies have attempted to find a best single-filter method. However, there is no best single filter that is appropriate for all machinery systems. In this work, we devise a straightforward but efficient approach for RUL prediction by combining multiple filters and then reducing the dimension through principal component analysis. We apply multilayer perceptron and random forest methods to learn the underlying model. We compare our approach with traditional single-filtering approaches using two benchmark datasets. The former approach is significantly better than the latter in terms of a scoring function with a penalty for late prediction. In particular, we note that selecting a best single filter over the training set is not efficient because of overfitting. Taken together, we validate that our multiple filters-based approach can be a robust solution for RUL prediction of various machinery systems.

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Condition Monitoring and Asset Management in the Smart Grid

Bangalore

Tjernberg

2016

Smart Grid Handbook

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One of the main characteristics of a smart grid is the availability of large volumes of data, for example, gained from sensors. This data can be utilized as a tool to estimate the state of the system as a whole or any component within the system. In order to achieve actionable information from the variety of data that is available from smart grids, it is important to use the correct mathematical and signal processing tools. Furthermore, the future smart grid is expected to have high levels of reliability. This can be achieved by integrating the condition monitoring systems with maintenance management, wherein the focus is shifted from corrective maintenance to predictive condition‐based maintenance.This chapter introduces the concept of reliability‐centered asset management (RCAM). The RCAM approach provides the possibility of both qualitative and quantitative analysis toward optimal maintenance strategy. Furthermore, various issues with condition monitoring in smart grids have been discussed along with some literature that suggest possible solutions for these issues. Finally, a detailed case study of a data‐based condition monitoring method based on artificial neural network is presented to demonstrate one of the many possibilities to use data from various measurement systems to reach actionable decisions.

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A Real-Time Data-Driven Algorithm for Health Diagnosis and Prognosis of a Circuit Breaker Trip Assembly

Cited by 56 publications

References 13 publications

Real-time fault diagnosis and fault-tolerant control

Real-time fault diagnosis and fault-tolerant control

An Empirical Investigation on a Multiple Filters-Based Approach for Remaining Useful Life Prediction

Condition Monitoring and Asset Management in the Smart Grid

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