Dynamic maintenance strategies for multiple transformers with Markov models

Intelligent substations in smart grids can provide more information about operating states of transformers by advanced sensors and monitoring units. According to information, operators can identify health conditions of transformers more accurately to determine maintenance strategies more reasonably. Maintenance of transformers can enhance the health condition and improve the reliability of a power system. However, maintenance introduces additional costs into total operating costs. A sophisticated maintenance strategy should be a tradeoff between maintenance costs and reliability enhancement. Based on monitoring information, a dynamic coordinated maintenance strategy for multiple transformers is proposed in this paper. First, a Markov model of an individual transformer is built to demonstrate its deterioration processes. Based on deterioration processes of an individual transformer, deterioration processes of a system with multiple transformers are built. Besides internal deterioration processes of components, external conditions, e.g., weather conditions and availability of servicemen and auxiliary equipment, are also considered in the model. Then, an optimization model is built. A series of dynamic coordinated maintenance strategies can be provided by the proposed optimization model, which is solved by a backward induction algorithm. A test system is used to demonstrate efficiency and accuracy of the method proposed in this paper. © 2014 IEEE.published_or_final_versio

Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

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

Dynamic Coordinated Condition-Based Maintenance for Multiple Components With External Conditions

published_or_final_versio

Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

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

A Condition-Based Maintenance Model for Assets with Accelerated Deterioration Due to Fault Propagation

Complex industrial assets such as power transformers are subject to accelerated deterioration when one of its constituent component malfunctions, affecting the condition of other components, which is a phenomenon called fault propagation. In this paper, we present a novel approach for optimizing condition-based maintenance policies for such assets by modelling their deterioration as a multiple dependent deterioration path process. The aim of the policy is to replace the malfunctioned component and mitigate accelerated deterioration at minimal impact to the business. The maintenance model provides guidance on determining inspection and maintenance strategies to optimize asset availability and operational cost.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TR.2015.243913

Improved dynamic dependability assessment through integration with prognostics

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 prognostics-updated dynamic dependability assessment. The dynamic behaviour comes from runtime updated information, asset inter-dependencies, 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