Integration of cost-risk assessment of denial of service within an intelligent maintenance system

As organisations become richer in data the function of asset management will have to increasingly use intelligent systems to control condition monitoring systems and organise maintenance. In the future the UK rail industry is anticipating having to optimize capacity by running trains closer to each other. In this situation maintenance becomes extremely problematic as within such a high-performance network a relatively minor fault will impact more trains and passengers; such denial of service causes reputational damage for the industry and causes fines to be levied against the infrastructure owner, Network Rail. Intelligent systems used to control condition monitoring systems will need to optimize for several factors; optimization for minimizing denial of service will be one such factor. With schedules anticipated to be increasingly complicated detailed estimation methods will be extremely difficult to implement. Cost prediction of maintenance activities tend to be expert driven and require extensive details, making automation of such an activity difficult. Therefore a stochastic process will be needed to approach the problem of predicting the denial of service arising from any required maintenance. Good uncertainty modelling will help to increase the confidence of estimates. This paper seeks to detail the challenges that the UK Railway industry face with regards to cost modelling of maintenance activities and outline an example of a suitable cost model for quantifying cost uncertainty. The proposed uncertainty quantification is based on historical cost data and interpretation of its statistical distributions. These estimates are then integrated in a cost model to obtain accurate uncertainty measurements of outputs through Monte-Carlo simulation methods. An additional criteria of the model was that it be suitable for integration into an existing prototype integrated intelligent maintenance system. It is anticipated that applying an integrated maintenance management system will apply significant downward pressure on maintenance budgets and reduce denial of service. Accurate cost estimation is therefore of great importance if anticipated cost efficiencies are to be achieved. While the rail industry has been the focus of this work, other industries have been considered and it is anticipated that the approach will be applicable to many other organisations across several asset management intensive industrie

The safety case and the lessons learned for the reliability and maintainability case

This paper examine the safety case and the lessons learned for the reliability and maintainability case

Preventive maintenance and replacement scheduling : models and algorithms.

Preventive maintenance is a broad term that encompasses a set of activities aimed at improving the overall reliability and availability of a system. Preventive maintenance involves a basic trade-off between the costs of conducting maintenance/replacement activities and the cost savings achieved by reducing the overall rate of occurrence of system failures. Designers of preventive maintenance schedules must weigh these individual costs in an attempt to minimize the overall cost of system operation. They may also be interested in maximizing the system reliability, subject to some sort of budget constraint. In this dissertation, we present a complete discussion about the problem definition and review the literature. We develop new nonlinear mixed-integer optimization models, solve them by standard nonlinear optimization algorithms, and analyze their computational results. In addition, we extend the optimization models by considering engineering economy features and reformulate them as a multi-objective optimization model. We optimize this model by generational and steady state genetic algorithms as well as by a simulated annealing algorithm and demonstrate the computational results obtained by implementation of these algorithms. We perform a sensitivity analysis on the parameters of the optimization models and present a comparison between exact and metaheuristic algorithms in terms of computational efficiency and accuracy. Finally, we present a new mathematical function to model age reduction and improvement factor parameter used in optimization models. In addition, we develop a practical procedure to estimate the effect of maintenance activity on failure rate and effective age of multi component systems

State of the art in simulation-based optimisation for maintenance systems

Recently, more attention has been directed towards improving and optimising maintenance in manufacturing systems using simulation. This paper aims to report the state of the art in simulation-based optimisation of maintenance by systematically classifying the published literature and outlining main trends in modelling and optimising maintenance systems. The authors investigate application areas and published real case studies as well as researched maintenance strategies and policies. Much of the research in this area is focusing on preventive maintenance and optimising preventive maintenance frequency that will lead to the minimum cost. Discrete event simulation was the most reported technique to model maintenance systems whereas modern optimisation methods such as Genetic Algorithms was the most reported optimisation method in the literature. On this basis, the paper identifies the current gaps and discusses future prospects. Further research can be done to develop a framework that guides the experimenting process with different maintenance strategies and policies. More real case studies can be conducted on multi-objective optimisation and condition based maintenance especially in a production context

A Novel Idea for Optimizing Condition-Based Maintenance Using Genetic Algorithms and Continuous Event Simulation Techniques

Effective maintenance strategies are of utmost significance for system engineering due to their direct linkage with financial aspects and safety of the plants’ operation. At a point where the state of a system, for instance, level of its deterioration, can be constantly observed, a strategy based on condition-based maintenance (CBM) may be affected; wherein upkeep of the system is done progressively on the premise of monitored state of the system. In this article, a multicomponent framework is considered that is continuously kept under observation. In order to decide an optimal deterioration stage for the said system, Genetic Algorithm (GA) technique has been utilized that figures out when its preventive maintenance should be carried out. The system is configured into a multiobjective problem that is aimed at optimizing the two desired objectives, namely, profitability and accessibility. For the sake of reality, a prognostic model portraying the advancements of deteriorating system has been employed that will be based on utilization of continuous event simulation techniques. In this regard, Monte Carlo (MC) simulation has been shortlisted as it can take into account a wide range of probable options that can help in reducing uncertainty. The inherent benefits proffered by the said simulation technique are fully utilized to display various elements of a deteriorating system working under stressed environment. The proposed synergic model (GA and MC) is considered to be more effective due to the employment of “drop-by-drop approach” that permits successful drive of the related search process with regard to the best optimal solutions

Optimizing Rehabilitation and Maintenance of Hospitals

Hospitals are one of the core elements of a health care system that provide medical service to the patients. Hospital facility management is a complex issue as it involves the management of several complex systems that have a direct impact on the delivery of health care issues. This research focuses on two vital aspects of hospital facility management, (1) level of service provided by the hospital and (2) technical aspects of mission critical hospital subsystems. This study proposes two models in order to maintain and improve the level of service delivered to the patients. The first model operates at the macro-level and undertakes the Network-level Hospital Rehabilitation Trade off model (NEHIR). The model optimizes the scheduling of rehabilitation works through the use of genetic algorithm optimization engine. The model features through five modules, (1) Database module that stores the hospitals data, (2) Backward Markov chain module that estimates the transition probability matrix, (3) Deterioration prediction module that predict the future condition of the asset, (4) Rehabilitation Cost optimization and (5) Multi-objective rehabilitation schedule optimization that conducts a tradeoff between the modified rehabilitation cost and the number of unserved patients. The second model operates at the micro-level and undertakes the Hospital-level Reliability Centered Maintenance model (HOREM). The model optimizes the maintenance tasks for critical subsystems and optimize the allocation of maintenance budget among the hospital subsystems. HOREM model is consisted of five modules as follows, (1) Reliability Centered Maintenance module that was used to define the components, functions, functional failure, failure modes, failure consequence and maintenance type for subsystems components, (2) fuzzy logic system module for determining the probability of failure of different replacement/restoration intervals, (3) Monte-Carlo simulation module determining the probability of failure of different inspection intervals, (4) Multi-objective maintenance optimization module that tradeoff between the downtime and maintenance costs and (5) Systems Integration optimization module that optimize the top management maintenance budget on hospitals subsystems. Two case studies were considered for verification and validation. The first case study is comprised of four hospitals was used for NEHIR model validation. The results of NEHIR model showed 8% decrease in number of unserved patients and 20% saving in rehabilitation costs. The second case study was one hospital that was used for validating HOREM model. The results of HOREM model showed 17% reduction in maintenance costs compared to traditional methods for the same downtime

Applications of simulation in maintenance research

The area of asset maintenance is becoming increasingly important as greater asset availability is demanded. This is evident in increasingly automated and more tightly integrated production systems as well as in service contracts where the provider is contracted to provide high levels of availability. Simulation techniques are able to model complex systems such as those involving maintenance and can be used to aid performance improvement. This paper examines engineering maintenance simulation research and applications in order to identify apparent research gaps. A systematic literature review was conducted in order to identify the gaps in maintenance systems simulation literature. Simulation has been applied to model different maintenance sub-systems (asset utilisation, asset failure, scheduling, staffing, inventory, etc.) but these are typically addressed in isolation and overall maintenance system behaviour is poorly addressed, especially outside of the manufacturing systems discipline. Assessing the effect of Condition Based Maintenance (CBM) on complex maintenance operations using Discrete Event Simulation (DES) is absent. This paper categorises the application of simulation in maintenance into eight categories

Prognostics-Based Two-Operator Competition for Maintenance and Service Part Logistics

Prognostics and timely maintenance of components are critical to the continuing operation of a system. By implementing prognostics, it is possible for the operator to maintain the system in the right place at the right time. However, the complexity in the real world makes near-zero downtime difficult to achieve partly because of a possible shortage of required service parts. This is realistic and quite important in maintenance practice. To coordinate with a prognostics-based maintenance schedule, the operator must decide when to order service parts and how to compete with other operators who also need the same parts. This research addresses a joint decision-making approach that assists two operators in making proactive maintenance decisions and strategically competing for a service part that both operators rely on for their individual operations. To this end, a maintenance policy involving competition in service part procurement is developed based on the Stackelberg game-theoretic model. Variations of the policy are formulated for three different scenarios and solved via either backward induction or genetic algorithm methods. Unlike the first two scenarios, the possibility for either of the operators being the leader in such competitions is considered in the third scenario. A numerical study on wind turbine operation is provided to demonstrate the use of the joint decision-making approach in maintenance and service part logistics

Maintenance optimization in industry 4.0

This work reviews maintenance optimization from different and complementary points of view. Specifically, we systematically analyze the knowledge, information and data that can be exploited for maintenance optimization within the Industry 4.0 paradigm. Then, the possible objectives of the optimization are critically discussed, together with the maintenance features to be optimized, such as maintenance periods and degradation thresholds. The main challenges and trends of maintenance optimization are, then, highlighted and the need is identified for methods that do not require a-priori selection of a predefined maintenance strategy, are able to deal with large amounts of heterogeneous data collected from different sources, can properly treat all the uncertainties affecting the behavior of the systems and the environment, and can jointly consider multiple optimization objectives, including the emerging ones related to sustainability and resilience