Prognostics of transformer paper insulation using statistical particle filtering of on-line data

Catterson, Victoria M.; Melone, J.; Segovia-García, María del Carmen

doi:10.1109/mei.2016.7361101

Cited by 14 publications

(23 citation statements)

References 15 publications

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“…estimated with autoregressive-moving-average models [12]), or PDF of the RUL (e.g. derived using particle filters [16]). …”

Section: A Prognostics Techniques and Adaptation Of Prognostics Resultsmentioning

confidence: 99%

“…• C. The probabilistic degradation of the system was analysed based on the Bayesian particle filtering approach [16]. To this end, it is necessary to rewrite the physics-of-failure degradation equation (7) as a recurrence relation [16],…”

Section: Asset-level Prognostics Models and Parametrizationmentioning

confidence: 99%

“…To this end, it is necessary to rewrite the physics-of-failure degradation equation (7) as a recurrence relation [16],…”

Section: Asset-level Prognostics Models and Parametrizationmentioning

confidence: 99%

“…This is caused by the small differences in the activation energy required to break cellulose chains. Therefore the process noise must account for the uncertainties surrounding the activation energy process [16]. In this case a normal distribution with N (0, 20) was used.…”

Section: Asset-level Prognostics Models and Parametrizationmentioning

confidence: 99%

“…There are several asset-specific prognostics applications reported in the literature. For instance, transformer prognostics has the potential to improve maintenance planning and potentially extend the useful life of power transformers [16]. However, the effects of prognostics at the system-level cannot be easily or automatically established and quantified.…”

mentioning

confidence: 99%

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Improved Dynamic Dependability Assessment Through Integration With Prognostics

et al. 2017

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This version is available at https://strathprints.strath.ac.uk/60422/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Abstract-The use of average data for dependability assessments results in a outdated system-level dependability estimation which can lead to incorrect design decisions. With increasing availability of online data, there is room to improve traditional dependability assessment techniques. Namely, prognostics is an emerging field which provides asset-specific failure information which can be reused to improve the system level failure estimation. This paper presents a framework for prognosticsupdated dynamic dependability assessment. The dynamic behaviour comes from runtime updated information, asset interdependencies, and time-dependent system behaviour. A case study from the power generation industry is analysed and results confirm the validity of the approach for improved near real-time unavailability estimations.

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“…estimated with autoregressive-moving-average models [12]), or PDF of the RUL (e.g. derived using particle filters [16]). …”

Section: A Prognostics Techniques and Adaptation Of Prognostics Resultsmentioning

confidence: 99%

Section: Asset-level Prognostics Models and Parametrizationmentioning

confidence: 99%

“…To this end, it is necessary to rewrite the physics-of-failure degradation equation (7) as a recurrence relation [16],…”

Section: Asset-level Prognostics Models and Parametrizationmentioning

confidence: 99%

Section: Asset-level Prognostics Models and Parametrizationmentioning

confidence: 99%

mentioning

confidence: 99%

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Improved Dynamic Dependability Assessment Through Integration With Prognostics

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On the use of dynamic reliability for an accurate modelling of renewable power plants

Chiacchio

D’Urso

Famoso

et al. 2018

Energy

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Renewable energies are a key element of the modern sustainable development. They play a key role in contributing to the reduction of the impact of fossil sources and to the energy supply in remote areas where the electrical grid cannot be reached. Due to the intermittent nature of the primary renewable resource, the feasibility assessment, the performance evaluation and the lifecycle management of a renewable power plant are very complex activities. In order to achieve a more accurate system modelling, improve the productivity prediction and better plan the lifecycle management activities, the modelling of a renewable plant may consider not only the physical process of energy transformation, but also the stochastic variability of the primary resource and the degradation mechanisms that affect the aging of the plant components resulting, eventually, in the failure of the system. This paper presents a modelling approach which integrates both the deterministic and the stochastic nature of renewable power plants using a novel methodology inspired from reliability engineering: the Stochastic Hybrid Fault Tree Automaton. The main steps for the design of a renewable power plant are discussed and implemented to estimate the energy production of a real photovoltaic power plant by means of a Monte Carlo simulation process. The proposed approach, modelling the failure behavior of the system, helps also with the evaluation of other key performance indicators like the power plant and the service availability.

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Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

Aizpurua

Catterson

Papadopoulos

et al. 2017

Reliability Engineering & System Safety

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Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry

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Prognostics of transformer paper insulation using statistical particle filtering of on-line data

Abstract: Prognostics of transformer remaining life can be achieved through a statistical technique called particle filtering, which gives a more accurate prediction than standard methods by quantifying sources of uncertainty

Cited by 14 publications

References 15 publications

Improved Dynamic Dependability Assessment Through Integration With Prognostics

Improved Dynamic Dependability Assessment Through Integration With Prognostics

On the use of dynamic reliability for an accurate modelling of renewable power plants

Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

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