Proactive Maintenance Model Using Reinforcement Learning Algorithm in Rubber Industry

Chandran, Senthil; Sudhakarapandian, R.

doi:10.3390/pr10020371

Cited by 10 publications

(4 citation statements)

References 23 publications

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“…One approach to implementing proactive maintenance is to use RL algorithms to develop predictive models that can be used to identify potential equipment failures before they occur. A research group in India, Senthil and Pandian (2022), conducted an investigation into the enhancement of availability of a curing machine deployed in the rubber company in India. Critical component identification in curing machines is necessary to prevent rapid failure followed by subsequent repairs that extend curing machine downtime.…”

Section: Predictive/prescription Maintenancementioning

confidence: 99%

Review of synergy between machine learning and first principles models for asset integrity management

Cai,

Fang,

Daida

et al. 2023

Front. Chem. Eng.

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The chemical process industry (CPI) accumulated a rich data asset through industrial 4.0. There is a strong drive to develop and utilize effective approaches for process performance prediction and improvement, process control, sensor development, asset management, etc. The synergy between machine learning and first principles models can bring new insights and add tremendous value to the CPI. This paper reviews various applications of the synergies towards asset integrity management. An overview of some related commercial software packages are also provided.

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Section: Predictive/prescription Maintenancementioning

confidence: 99%

Review of synergy between machine learning and first principles models for asset integrity management

Cai,

Fang,

Daida

et al. 2023

Front. Chem. Eng.

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show abstract

“…Although cost is essential to production management, other factors cannot be ignored. Therefore, many scholars have also researched maintenance decision-making, considering equipment availability [48,49], reliability [50], and so on. To improve the universality of the proposed model, this paper comprehensively considered the three optimization targets of the maintenance cost, availability, and reliability of systems.…”

Section: Comprehensive Optimization Targetmentioning

confidence: 99%

Data-Driven Predictive Maintenance Policy Based on Dynamic Probability Distribution Prediction of Remaining Useful Life

Xie,

Xue,

Zhang

et al. 2023

Machines

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As the reliability, availability, maintainability, and safety of industrial equipment have become crucial in the context of intelligent manufacturing, there are increasing expectations and requirements for maintenance policies. Compared with traditional methods, data-driven Predictive Maintenance (PdM), a superior approach to equipment and system maintenance, has been paid considerable attention by scholars in this field due to its high applicability and accuracy with a highly reliable quantization basis provided by big data. However, current data-driven methods typically provide only point estimates of the state rather than quantification of uncertainty, impeding effective maintenance decision-making. In addition, few studies have conducted further research on maintenance decision-making based on state predictions to achieve the full functionality of PdM. A PdM policy is proposed in this work to obtain the continuous probability distribution of system states dynamically and make maintenance decisions. The policy utilizes the Long Short-Term Memory (LSTM) network and Kernel Density Estimation with a Single Globally-optimized Bandwidth (KDE-SGB) method to dynamic predicting of the continuous probability distribution of the Remaining Useful Life (RUL). A comprehensive optimization target is introduced to establish the maintenance decision-making approach acquiring recommended maintenance time. Finally, the proposed policy is validated through a bearing case study, indicating that it allows for obtaining the continuous probability distribution of RUL centralized over a range of ±10 sampling cycles. In comparison to the other two policies, it could reduce the maintenance costs by 24.49~70.02%, raise the availability by 0.46~1.90%, heighten the reliability by 0.00~27.50%, and promote more stable performance with various maintenance cost and duration. The policy has offered a new approach without priori hypotheses for RUL prediction and its uncertainty quantification and provided a reference for constructing a complete PdM policy integrating RUL prediction with maintenance decision-making.

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“…However, as fast as DRL is enhancing its capabilities to master its application in games, the gap between its real-world, safety-critical systems is becoming wider. There are comparatively few studies, where DRL has been implemented on safety-critical industrial cases, some of which are presented by Rodríguez et al (2022); Senthil and Sudhakara Pandian (2022); Spielberg, Gopaluni and Loewen (2017); Shin, Badgwell, Liu and Lee (2019).…”

Section: Related Work and Contributionmentioning

confidence: 99%

Interpretable Input-Output Hidden Markov Model-Based Deep Reinforcement Learning for the Predictive Maintenance of Turbofan Engines

Abbas

Chasparis

Kelleher

2022

Lecture Notes in Computer Science

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An open research question in deep reinforcement learning is how to focus the policy learning of key decisions within a sparse domain. This paper emphasizes combining the advantages of inputoutput hidden Markov models and reinforcement learning towards interpretable maintenance decisions. We propose a novel hierarchical-modeling methodology that, at a high level, detects and interprets the root cause of a failure as well as the health degradation of the turbofan engine, while, at a low level, it provides the optimal replacement policy. It outperforms the baseline performance of deep reinforcement learning methods applied directly to the raw data or when using a hidden Markov model without such a specialized hierarchy. It also provides comparable performance to prior work, however, with the additional benefit of interpretability.

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Proactive Maintenance Model Using Reinforcement Learning Algorithm in Rubber Industry

Cited by 10 publications

References 23 publications

Review of synergy between machine learning and first principles models for asset integrity management

Review of synergy between machine learning and first principles models for asset integrity management

Data-Driven Predictive Maintenance Policy Based on Dynamic Probability Distribution Prediction of Remaining Useful Life

Interpretable Input-Output Hidden Markov Model-Based Deep Reinforcement Learning for the Predictive Maintenance of Turbofan Engines

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