A condition-based maintenance policy for multi-component systems with a high maintenance setup cost

Condition-based maintenance (CBM) is becoming increasingly important due to the development of advanced sensor and ICT technology, so that the condition data can be collected remotely. We propose a new CBM policy for multi-component systems with continuous stochastic deteriorations. To reduce the high setup cost of maintenance, a joint maintenance interval is proposed. With the joint maintenance interval and control limits of components as decision variables, we develop a model for the minimization of the average long-run maintenance cost rate of the systems. Moreover, a numerical study on a case of a wind power farm consisting of a large number of non-identical components is performed, including a sensitivity analysis. At last, our policy is compared to a corrective-maintenance-only policy

Regional Data Archiving and Management for Northeast Illinois

This project studies the feasibility and implementation options for establishing a regional data archiving system to help monitor and manage traffic operations and planning for the northeastern Illinois region. It aims to provide a clear guidance to the regional transportation agencies, from both technical and business perspectives, about building such a comprehensive transportation information system. Several implementation alternatives are identified and analyzed. This research is carried out in three phases. In the first phase, existing documents related to ITS deployments in the broader Chicago area are summarized, and a thorough review is conducted of similar systems across the country. Various stakeholders are interviewed to collect information on all data elements that they store, including the format, system, and granularity. Their perception of a data archive system, such as potential benefits and costs, is also surveyed. In the second phase, a conceptual design of the database is developed. This conceptual design includes system architecture, functional modules, user interfaces, and examples of usage. In the last phase, the possible business models for the archive system to sustain itself are reviewed. We estimate initial capital and recurring operational/maintenance costs for the system based on realistic information on the hardware, software, labor, and resource requirements. We also identify possible revenue opportunities. A few implementation options for the archive system are summarized in this report; namely: 1. System hosted by a partnering agency 2. System contracted to a university 3. System contracted to a national laboratory 4. System outsourced to a service provider The costs, advantages and disadvantages for each of these recommended options are also provided.ICT-R27-22published or submitted for publicationis peer reviewe

Joint condition-based maintenance and load-sharing optimization for two-unit systems with economic dependency

Many production facilities consist of multiple and functionally exchangeable units of equipment, such as pumps or turbines, that are jointly used to satisfy a given production target. Such systems often have to ensure high levels of reliability and availability. The deterioration rates of the units typically depend on their production rates, implying that the operator can control deterioration by dynamically reallocating load among units. In this study, we examine the value of condition-based load-sharing decisions for two-unit systems with economic dependency. We formulate the system as a Markov decision process and provide optimal joint condition-based maintenance and production policies. Our numerical results show that, dependent on the system characteristics, substantial cost savings of up to 40% can be realized compared to the optimal condition-based maintenance policy under equal load-sharing. The structure of the optimal policy particularly depends on the maintenance setup cost and the penalty that is incurred if the production target is not satisfied. For systems with high setup costs, the clustering of maintenance interventions is improved by synchronizing the deterioration of the units. On the contrary, for low setup costs, the deterioration levels are desynchronized and the maintenance interventions are alternated

Integrated optimization of maintenance interventions and spare part selection for a partially observable multi-component system

Advanced technical systems are typically composed of multiple critical components whose failure cause a system failure. Often, it is not technically or economically possible to install sensors dedicated to each component, which means that the exact condition of each component cannot be monitored, but a system level failure or defect can be observed. The service provider then needs to implement a condition based maintenance policy that is based on partial information on the systems condition. Furthermore, when the service provider decides to service the system, (s)he also needs to decide which spare part(s) to bring along in order to avoid emergency shipments and part returns. We model this problem as an infinite horizon partially observable Markov decision process. In a set of numerical experiments, we first compare the optimal policy with preventive and corrective maintenance policies: The optimal policy leads on average to a 28% and 15% cost decrease, respectively. Second, we investigate the value of having full information, i.e., sensors dedicated to each component: This leads on average to a 13% cost decrease compared to the case with partial information. Interestingly, having full information is more valuable for cheaper, less reliable components than for more expensive, more reliable components

Optimal maintenance of multi-component systems: a review

In this article we give an overview of the literature on multi-component maintenance optimization. We focus on work appearing since the 1991 survey "A survey of maintenance models for multi-unit systems" by Cho and Parlar. This paper builds forth on the review article by Dekker et al. (1996), which focusses on economic dependence, and the survey of maintenance policies by Wang (2002), in which some group maintenance and some opportunistic maintenance policies are considered. Our classification scheme is primarily based on the dependence between components (stochastic, structural or economic). Next, we also classify the papers on the basis of the planning aspect (short-term vs long-term), the grouping of maintenance activities (either grouping preventive or corrective maintenance, or opportunistic grouping) and the optimization approach used (heuristic, policy classes or exact algorithms). Finally, we pay attention to the applications of the models.literature review;economic dependence;failure interaction;maintenance policies;grouping maintenance;multi-component systems;opportunistic maintenance;maintencance optimization;structural dependence

Predictive group maintenance for multi-system multi-component networks

Predictive maintenance has become highly popular in recent years due to the emergence of novel condition monitoring and data analysis techniques. However, the application of predictive maintenance at the network-level has not seen much attention in the literature. This paper presents a model for predictive group maintenance for multi-system multi- components networks (MSMCN). These networks are composed of multiple systems that are, in turn, composed of multiple components. In particular, the hierarchical structure of the MSMCN enables different representations of dependences at the network and system levels. The key novelty in the paper is that the designed approach combines analytical and numerical techniques to optimize the predictive group maintenance policy for MSMCNs. Moreover, we introduce a genetic algorithm with agglomerative mutation (GA-A) that enables a more effective evolution of the predictive group maintenance policy. Application of this model on a case study of a two-bridge network made of 23 different components shows a potential 11.27% reduction in maintenance cost, highlighting the model’s practical significance.This research was funded by the Engineering and Physical Sciences Research Council (UK) and Innovate UK through the Innovation and Knowledge Centre for Smart Infrastructure and Construction (Grant EP/N021614/1). This work was partially supported by Talent recruitment Funds of Tsinghua University grant NO.113052