Topic
Preventive maintenance
About: Preventive maintenance is a research topic. Over the lifetime, 6760 publications have been published within this topic receiving 95158 citations.
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TL;DR: In this paper, the authors present a new methodology for risk-based maintenance, which comprises three main modules: risk estimation module, risk evaluation module, and maintenance planning module, which helps management in making correct decisions concerning investment in maintenance or related field.
Abstract: The overall objective of the maintenance process is to increase the profitability of the operation and optimize the total life cycle cost without compromising safety or environmental issues. Risk assessment integrates reliability with safety and environmental issues and therefore can be used as a decision tool for preventive maintenance planning. Maintenance planning based on risk analysis minimizes the probability of system failure and its consequences (related to safety, economic, and environment). It helps management in making correct decisions concerning investment in maintenance or related field. This will, in turn, result in better asset and capital utilization. This paper presents a new methodology for risk-based maintenance. The proposed methodology is comprehensive and quantitative. It comprises three main modules: risk estimation module, risk evaluation module, and maintenance planning module. Details of the three modules are given. A case study, which exemplifies the use of methodology to a heating, ventilation and air-conditioning (HVAC) system, is also discussed.
384 citations
TL;DR: The most common maintenance and repair strategy is "fix it when it breaks" as discussed by the authors, which can provide relatively high equipment reliability, but it tends to do so at excessive cost (higher scheduled downtimes).
Abstract: The oldest and most common maintenance and repair strategy is “fix it when it breaks”. The appeal of this approach is that no analysis or planning is required. The problems with this approach include the occurrence of unscheduled downtime at times that may be inconvenient, perhaps preventing accomplishment of committed production schedules. Unscheduled downtime has more serious consequences in applications such as aircraft engines. These problems provide motivation to perform maintenance and repair before the problem arises. The simplest approach is to perform maintenance and repair at pre-established intervals, defined in terms of elapsed or operating hours. This strategy can provide relatively high equipment reliability, but it tends to do so at excessive cost (higher scheduled downtimes). A further problem with time-based approaches is that failures are assumed to occur at specific intervals.
378 citations
TL;DR: In this article, reliability models are built for a service producing system which works intermittently, is subject to wear, and can be improved through maintenance actions like cleaning, lubrication, realignment, etc.
Abstract: Maintenance of goods producing systems is undertaken on the principle of minimum cost whereas the maintenance of service producing systems is done on the principle of operational reliability. High pressure boilers, elevator ropes, aeroplane engines, subway tunnels, suspension bridges, air conditioning and transportation networks are examples of such systems. In this paper, reliability models are built for a service producing system which works intermittently, is subject to wear, and can be improved through maintenance actions like cleaning, lubrication, realignment, etc.-short of replacement. Finally an application is shown and maintenance scheduling is developed through an example.
354 citations
TL;DR: This paper considers a continuously monitored multi-component system and uses a Genetic Algorithm for determining the optimal degradation level beyond which preventive maintenance has to be performed and considers a predictive model describing the evolution of the degrading system based on the use of Monte Carlo simulation.
352 citations
TL;DR: The manufacturing big data method used for active preventive maintenance has the potential to accelerate implementation of Industry 4.0.
Abstract: Industry 4.0 has become more popular due to recent developments in cyber-physical systems, big data, cloud computing, and industrial wireless networks. Intelligent manufacturing has produced a revolutionary change, and evolving applications, such as product lifecycle management, are becoming a reality. In this paper, we propose and implement a manufacturing big data solution for active preventive maintenance in manufacturing environments. First, we provide the system architecture that is used for active preventive maintenance. Then, we analyze the method used for collection of manufacturing big data according to the data characteristics. Subsequently, we perform data processing in the cloud, including the cloud layer architecture, the real-time active maintenance mechanism, and the offline prediction and analysis method. Finally, we analyze a prototype platform and implement experiments to compare the traditionally used method with the proposed active preventive maintenance method. The manufacturing big data method used for active preventive maintenance has the potential to accelerate implementation of Industry 4.0.
341 citations