Reinforcement Learning for Scheduling of Maintenance

Knowles, Michael; Baglee, David; Wermter, Stefan

doi:10.1007/978-0-85729-130-1_31

Cited by 21 publications

(13 citation statements)

References 30 publications

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“…In this state of maintenance, maintenance time is not estimated. The maintenance schedule [11] for preventive maintenance is prepared under the status of reliability. This schedule is compared with the datasheets of components in the curing machine.…”

Section: Rl-based Intelligent Diagnosismentioning

confidence: 99%

“…Reliability analysis in industry entails checking the availability of machines where several metrics are implemented via an RLA [10] to improve reliability. High reliability improvements [11] of machines has reduced the time required for scheduled maintenance when reinforcement learning (RL) must be used. In long-term maintenance, rewards generated by the sequential actions of a machine is described as decision-based.…”

Section: Introductionmentioning

confidence: 99%

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Proactive Maintenance Model Using Reinforcement Learning Algorithm in Rubber Industry

Chandran¹,

Sudhakarapandian

2022

Processes

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This paper presents an investigation into the enhancement of availability of a curing machine deployed in the rubber industry, located in Tamilnadu in India. Machine maintenance is a major task in the rubber industry, due to the demand for product. Critical component identification in curing machines is necessary to prevent rapid failure followed by subsequent repairs that extend curing machine downtime. A reward in the Reinforcement Learning Algorithm (RLA) prevents frequent downtime by improving the availability of the curing machine at time when unscheduled long-term maintenance would interfere with operation, due to the occurrence of unspecified failure to a critical component. Over time, depreciation and degradation of components in a machine are unavoidable, as is shown in the present investigation through intelligent assessment of the lifespan of components. So far, no effective methodology has been implemented in a real-time maintenance environment. RLAs seem to be a more effective application when it is based on intelligent assessment, which encompasses the failure and repair rate used to calculate availability in an automated environment. Training of RLAs is performed to evaluate overall equipment efficiency (OEE) in terms of availability. The availability of a curing machine in the form of state probability is modeled in first-order differential-difference equations. RLAs maximize the rate of availability of the machine. Preventive maintenance (PM) rate for four modules of 16 curing machines is expressed in a transition diagram, using transition rate. Transition rate indicates the degree of PM and unplanned maintenance rates that defines the total availability of the four modules. OEE is expressed in terms of the availability of curing machines, which is related to performance and quality. The results obtained by RLA are promising regarding short-term and long-term efficiencies of OEE, which are 95.19% and 83.37%, respectively.

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Section: Rl-based Intelligent Diagnosismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Proactive Maintenance Model Using Reinforcement Learning Algorithm in Rubber Industry

Chandran¹,

Sudhakarapandian

2022

Processes

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“…RL has been typically used to find an optimal schedule for maintenance to optimise the cost. Some of the studies have also considered the effect of degradation on cost [2]. For example, a four‐state MDP has been used to model CBM for multi‐component systems with individual reparable components.…”

Section: Literature Reviewmentioning

confidence: 99%

Reinforcement learning for optimal policy learning in condition‐based maintenance

Adsule¹,

Kulkarni²,

Tewari³

2020

IET Collaborative Intelligent Manufacturing

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“…Most of these works (e.g. [9][10][11][12][13][14]) base their maintenance decisions on just diagnostic information.…”

Section: Literature Reviewmentioning

confidence: 99%

“…425.4 5 a rc t 10 420. 9 10 a rc t 7 427.8 6 a rc t 15 421. 5 11 a rc t 17 428.4 Figure 7 shows the cost components C m+i (a, t) 6 and C r (a, t) for the different maintenance strategies.…”

Section: Component-level Optimizationmentioning

confidence: 99%

Timely condition-based maintenance planning for multi-component systems

Verbert

Schutter

Babuška

2017

Reliability Engineering & System Safety

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Last-minute maintenance planning is often undesirable, as it may cause downtime during operational hours, may require rescheduling of other activities, and does not allow to optimize the management of spare parts, material, and personnel. In spite of the aforementioned drawbacks of last-minute planning, most existing methods plan maintenance activities at the last minute. In this paper, we propose a new strategy for timely maintenance planning in multi-component systems. As a first step, we determine for each system component independently the most appropriate maintenance planning strategy. This way, the maintenance decisions can be tailored to the specific situations. For example, conservative maintenance decisions can be taken when the risk tolerance is low, and maintenance decisions can be made timely when we can accurately predict future degradation behavior. In the second step, we optimize the maintenance plan at the system level. Here, we account for economic and structural dependence with the aim to profit from spreading or combining various maintenance activities. The applicability of the method is demonstrated on a railway case. It is shown how the different cost functions (e.g. costs of maintenance, downtime, and failure) influence the maintenance decisions.

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Reinforcement Learning for Scheduling of Maintenance

Cited by 21 publications

References 30 publications

Proactive Maintenance Model Using Reinforcement Learning Algorithm in Rubber Industry

Proactive Maintenance Model Using Reinforcement Learning Algorithm in Rubber Industry

Reinforcement learning for optimal policy learning in condition‐based maintenance

Timely condition-based maintenance planning for multi-component systems

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