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Author: search.filters.author.Trappey, Amy J. C.Subject: search.filters.subject.Engineering asset management

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Now showing 1 - 5 of 5
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    Incorporating Data Warehouse Technology into Asset Information Management Systems for Large Assets
    (2016)
    Trappey, Amy J. C.
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    Trappey, Charles V.
    ;
    Ma, Lin
    ;
    Chang, Acer C. C.
    ;
    管理科學系
    Large sized engineering assets such as power transformers are critical parts of the power supply networks. Therefore, focusing on early fault diagnosis to maintain large transformers in good condition is an important operational task to the power companies. In order to manage and fully utilize the big data generated from the large number of transformers, this paper incorporates data warehouse technology to a fault diagnosis system for the entire transformer fleet. The research includes two major parts. First, a data warehouse (DW) is designed for the large assets information management. Then, the DW-based intelligent fault diagnosis system is developed and implemented. The DW stores the complete transformers\' data and then different data cubes are defined according to various applications. The fault diagnosis system for power transformers consists of the condition monitoring module, failure diagnosis module, and intelligent decision supports module. The research methodology and prototype system are verified with real data from a series of 161 kV transformers in operations.
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    Integrating Real-Time Monitoring and Asset Health Prediction for Power Transformer Intelligent Maintenance and Decision Support
    (2015-01-01)
    Trappey, Amy J. C.
    ;
    Trappey, Charles V.
    ;
    Ma, Lin
    ;
    Chang, Jimmy C. M.
    ;
    管理科學系
    Large sized transformers are an important part of global power systems and industrial infrastructures. An unexpected failure of a power transformer can cause severe production damage and significant loss throughput the power grid. In order to prevent power facilities from malfunctions and breakdowns, the development of real-time monitoring and health prediction tools are of great interests to both researchers and practitioners. An advanced monitoring tool performs real-time monitoring of key parameters to detect signals of potential failure through data mining techniques and prediction models. Asset managers use the result to develop a suitable maintenance and repair strategy for failure prevention. Principal component analysis (PCA) and back-propagation artificial neural network (BP-ANN) are the algorithms adopted in the research. This chapter utilizes industrial power transformers\' historical data from Taiwan and Australia to train and test the failure prediction models and to verify the proposed methodology. First, PCA detects the conditions of transformers by identifying the state of dissolved gasses. Then, the BP-ANN health prediction model is trained using the key factor values. The integrated engineering asset management database includes nine gases in oil as input factors (N-2, O-2, CO2, CO, H-2, CH4, C2H4, C2H6, and C2H2). After applying the principal components algorithm, the research identifies five factors from the Taiwan operational transformer data and six factors from the Australia data. The integrated PCA and BP-ANN fault diagnosis system yields effective and accurate predictions when tested using Taiwan and Australia data. The accuracy rates are much higher (i.e., 92 and 96 % respectively) when compared to previous result of 69 and 75 %. This research is benchmarked against the DGA heuristic approaches including IEEE\'s Doernenburg and Rogers and IEC\'s Duval Triangle for the experimental fault diagnoses.
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    Intelligent engineering asset management system for power transformer maintenance decision supports under various operating conditions
    (2015-06-01)
    Trappey, Amy J. C.
    ;
    Trappey, Charles V.
    ;
    Ma, Lin
    ;
    Chang, Jimmy C. M.
    ;
    管理科學系
    Large sized transformers are an important part of global power systems and industrial infrastructures. An unexpected failure of a power transformer can cause severe production damage and significant loss throughput the power grid. In order to prevent power facilities from malfunctions and breakdowns, the development of real-time monitoring and fault prediction tools are of great interests to both researches and practitioners. This research develops an intelligent engineering asset nianagement system for power transformer maintenance. The system performs real-time monitoring of key parameters and uses data mining and fault prediction models to detect transformers\' potential failure under various operating conditions. Principal component analysis (PCA) and a back-propagation artificial neural network (BP-ANN) are the algorithms adopted for the prediction model. Historical industrial power transformer data from Taiwan and Australia are used to train and test the failure prediction models and to verify the proposed general methodology as comparative case studies. The PCA algorithm reduces the number of the primary dissolved gasses as the key factor values for BP-ANN prediction modeling inputs. The system yields effective predictions when verified using various operating condition data from Australia and Taiwan power companies. The accuracy rates are much higher when compared to the fault prediction results without using PCA. The intelligent system combining PCA and BP-ANN algorithms, developed in this research, can be adopted by asset managers in different regions to develop suitable maintenance and repair strategies for transformer failure preventions., (C) 2015 Published by Elsevier Ltd.
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    An Intelligent Transformer Maintenance System with Real-time Life Assessment Condition Monitoring
    (2013-12-01)
    Trappey, Amy J. C.
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    Trappey, Charles V.
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    Sun, Yong
    ;
    Ma, Lin
    ;
    Chen, Leo Kuan Ju
    Transformers are critical assets that require continuous monitoring in the generation of electrical power. The management of engineering assets requires real-time diagnosis and preventive maintenance in order to avoid unexpected and catastrophic equipment shut-downs. In addition, the health status and remaining life of the transformer are critical knowledge for proper maintenance and management. This research focuses on oil-immersed transformers as a case study to help managers compile real-time monitoring and sampling data for information systems. The deterioration of the transformation's insulating paper was measured to derive the transformer's remaining life and to project the optimal replacement time. The proposed prediction method uses the Group Method of Data Handling (GMDH) to estimate the remaining life of any given transformer in use. This algorithm uses the dissolved gas-in-oil and furfural formation as the input variables to form the transformer life assessment model. Finally, the derived model is compared to models proposed by the International Electrotechnical Commission (IEC). The results show improved performance over the currently used IEC models.
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    A multi-agent collaborative maintenance platform applying game theory negotiation strategies
    (2013-06-01)
    Trappey, Amy J. C.
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    Trappey, Charles V.
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    Ni, Wei-Chun
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    管理科學系
    ;
    Department of Management Science
    Engineering asset management (EAM) is a broad discipline with distributed functions and services. When engineering assets are capital intensive, management requires specialized expertise for diagnosis, prognosis, maintenance and repairs. The current practice of EAM relies on self maintained experiential rules with coordinated collaboration and outsourcing for maintenance and repairs. In order to enhance the life long asset value and efficiency (from the stakeholder's viewpoint) and after sales service quality (from the asset provider's viewpoint), this research proposes a collaborative maintenance platform that integrates real time data collection with diagnostic and prognostic expertise. The collaborative system combines and delivers services among asset operation sites (the maintenance demanders), the service center (the intermediary coordinator), the system providers, the first tier maintenance collaborators, and the second and lower tier parts suppliers. Multi-agent system technology is used to integrate different systems and databases. Agents with autonomy and authority work to assist service providers and coordinate communications, negotiations, and maintenance decision support. Finally, game theory is used to design the decision models for strategic, tactical, and operational decision making during collaborative maintenance practices.