DEVELOPMENT OF THE RISK-BASED MAINTENANCE OPTIMIZATION SYSTEM FOR FOSSIL POWER PLANTS

Cost reduction in electric power generation is a major management concern, and it is therefore necessary to reduce maintenance expenses while upholding plant reliability. A maintenance optimization system 'FREEDOM', which uses RBM technique, DCF (discounted cash flow) and NPV (net present value) calculation functions, has been newly developed. This system probabilistically evaluates the lifetime of boiler and turbine and quantitatively calculates the risk defined as the cumulative probability of failure multiplied by the consequence of failure. Economically optimized timing of inspection and alternative countermeasure such as repair and replacement are then recommended. This system has already been applied to seven plants in Japan, and its effectiveness has been confirmed.

Assessment of Performance Based Road Maintenance Practices in Nepal

Roads are the major asset of any country and to conserve them maintenance is essential. The delay of maintenance works can lead the road in the stage of expensive rehabilitation and reconstruction. In Nepal, Department of Roads (DoR) is responsible for maintenance of strategic roads of country. The maintenance practice of the road under Department of Road is predominantly based on Strengthen Maintenance Division Process where several maintenance activities like Routine maintenance, Recurrent Maintenance, Periodic Maintenance and Emergency Maintenance are planned and carried out. The maintenance work is based on traditional quantity and unit price based short term maintenance contracts, where maintenance of physical works is outsourced. Client demands the contractor what work is to be done, when to be do it and the extent of the work. The contractor is paid on the basis of a defined output or quantity accomplished, and the schedule of the rates. DoR engineers required full time supervision of work. Pilot based performance based maintenance practice of road started in Nepal in 2003. The study assesses performance road maintenance practices of road maintenance in Nepal on the basis of the framework of cost efficiency, level of service effectiveness and quality of service. The study concluded that implementation of Performance Based Maintenance Contract (PBMC) was not successful in Nepal as Road Asset condition was not maintained to performance standard. One of the reasons is the weak service delivery of contractor alongside weak contract enforcement from the road agency.

Study on the Methods of Equipment Maintenance Management

This thesis discusses the importance of equipment maintenance management, then introduces the development tendency of equipment maintenance management. In the end it analyses and studies the design of a computerized maintenance management system, which offers an integration technique.

Prediction Approach to Life on Wing for Civil Aeroengine

To reduce engine maintenance cost and support safe operation, a prediction method of engine life on wing was proposed. This method is a kind of regression model which is a function of the condition monitoring and failure data. Key causes of engine removals were analyzed, and the life limit due to performance deterioration was predicted by proportional hazards model. Then the scheduled removal causes were considered as constraints of engine life to predicte the finai life on wing. Application of the proposed prediction method to the case of CF6-80C2A5 engine fleet in an airline proved its effectiveness.

A Logical Framework for Knowledge Base Maintenance

The maintenance sequences of a knowledge base and their limits are intro-duced. Some concepts used in knowledge base maintenance, such as new laws,user's rejections, and reconstructions of a knowledge base are defined; the re-lated theorems are proved. A procedure is defined using transition systems; itgenerates maintenance sequences for a given user's model and a knowledge base.It is proved that all sequences produced by the procedure are convergent, andtheir limit is the set of true sentences of the model. Some computational aspectsof reconstructions are studied. An Rcalculus is given to deduce a reconstruc-tion when a knowledge base meets a user's rejection. The work is comparedwith AGM's theory of belief revision.

Modeling on Spare Parts Inventory Control Under Condition Based Maintenance Strategy

In order to optimize the spare parts inventory, we present a decision-making model under condition based maintenance policy for a single equipment system subjected to continuous and random deterioration. Firstly,a probability model of the spare parts support is established, according to the requirement of a predetermined probability of stockout. It can determine the optimal spare parts stock level. Secondly, the spare parts ordering decision is made according to the equipment deterioration level, and it can optimize the spare parts ordering. The objectives of this model are to minimize the spare parts inventory, and the expected total operating cost. Thirdly,a numerical example is given to illustrate this model. The results prove that the optimal preventive maintenance threshold obtained from the proposed model can satisfy the spare parts support requirements.

Developing a predictive maintenance model for vessel machinery

The aim of maintenance is to reduce the number of failures in equipment and to avoid breakdowns that may lead to disruptions during operations.The objective of this study is to initiate the development of a predictive maintenance solution in the shipping industry based on a computational artificial intelligence model using real-time monitoring data.The data analysed originates from the historical values from sensors measuring the vessel´s engines and compressors health and the software used to analyse these data was R.The results demonstrated key parameters held a stronger influence in the overall state of the components and proved in most cases strong correlations amongst sensor data from the same equipment.The results also showed a great potential to serve as inputs for developing a predictive model,yet further elements including failure modes identification,detection of potential failures and asset criticality are some of the issues required to define prior designing the algorithms and a solution based on artificial intelligence.A systematic approach using big data and machine learning as techniques to create predictive maintenance strategies is already creating disruption within the shipping industry,and maritime organizations need to consider how to implement these new technologies into their business operations and to improve the speed and accuracy in their maintenance decision making.

An adaptive-order particle filter for remaining useful life prediction of aviation piston pumps

An accurate estimation of the remaining useful life（RUL） not only contributes to an effective application of an aviation piston pump, but also meets the necessity of condition based maintenance（CBM）. For the current RUL evaluation methods, a model-based method is inappropriate for the degradation process of an aviation piston pump due to difficulties of modeling, while a data-based method rarely presents high-accuracy prediction in a long period of time. In this work,an adaptive-order particle filter（AOPF） prognostic process is proposed aiming at improving long-term prediction accuracy of RUL by combining both kinds of methods. A dynamic model is initialized by a data-driven or empirical method. When a new observation comes, the prior state distribution is approximated by a current model. The order of the current model is updated adaptively by fusing the information of the observation. Monte Carlo simulation is employed for estimating the posterior probability density function of future states of the pump＇s degradation.With updating the order number adaptively, the method presents a higher precision in contrast with those of traditional methods. In a case study, the proposed AOPF method is adopted to forecast the degradation status of an aviation piston pump with experimental return oil flow data, and the analytical results show the effectiveness of the proposed AOPF method.

DATA DRIVEN MODELING FOR POWER TRANSFORMER LIFESPAN EVALUATION

Large sized power transformers are important parts of the power supply chain. These very critical networks of engineering assets are an essential base of a nation＇s energy resource infrastructure. This research identifies the key factors influencing transformer normal operating conditions and predicts the asset management lifespan. Engineering asset research has developed few lifespan forecasting methods combining real-time monitoring solutions for transformer maintenance and replacement. Utilizing the rich data source from a remote terminal unit （RTU） system for sensor-data driven analysis, this research develops an innovative real-time lifespan forecasting approach applying logistic regression based on the Weibull distribution. The methodology and the implementation prototype are verified using a data series from 161 kV transformers to evaluate the efficiency and accuracy for energy sector applications. The asset stakeholders and suppliers significantly benefit from the real-time power transformer lifespan evaluation for maintenance and replacement decision support.

Security Analysis Model, System Architecture and Relational Model of Enterprise Cloud Services

Cloud computing is one of the fastest growing and popular computer technologies, and there are more and more enterprise services based on the cloud computing. In order to save costs, more and more enterprises and their employees have hired the enterprise cloud services, and put much important information in the cloud gradually. Cloud service systems have become the main targets of malicious attacks. However, the cloud computing technologies are still not perfect, and the management and maintenance of enterprise cloud services are more complex compared to traditional network services of cloud computing. So, enterprise cloud services are more likely to encounter some security problems, and the influenced scale of these security problems is broad. But there are few researches on the security of enterprise cloud services. In this paper, we analyze the software as a service（Saa S） enterprise cloud services and introduce the research status of security problems in cloud computing environment. Combining with the analysis of the characteristics and application architecture of Saa S enterprise cloud services, we propose the security problems analysis model, the analysis system architecture and the relational model. Our researches can support further research of the automatic generation of solutions and guide the deployment of security policies of Saa S enterprise cloud services.