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Reinforcement learning for predictive maintenance: a systematic technical review

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Abstract

The manufacturing world is subject to ever-increasing cost optimization pressures. Maintenance adds to cost and disrupts production; optimized maintenance is therefore of utmost interest. As an autonomous learning mechanism reinforcement learning (RL) is increasingly used to solve complex tasks. While designing an optimal, model-free RL solution for predictive maintenance (PdM) is an attractive proposition, there are several key steps and design elements to be considered—from modeling degradation of the physical equipment to creating RL formulations. In this article, we survey how researchers have applied RL to optimally predict maintenance in diverse forms—from early diagnosis to computing a “health index” to directly suggesting a maintenance action. Contributions of this article include developing a taxonomy for PdM techniques in general and one specifically for RL applied to PdM. We discovered and studied unique techniques and applications by applying \(tf-idf\) (a text mining technique). Furthermore, we systematically studied how researchers have mathematically formulated RL concepts and included some detailed case-studies that help demonstrate the complete flow of applying RL to PdM. Finally, in Sect. 14, we summarize the insights for researchers, and for the industrial practitioner we lay out a simple approach for implementing RL for PdM.

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Notes

  1. Sinha (2021) reports 12.3 B devices as of 2021 and estimates 27.0 B to be connected by 2025.

  2. As of July-2022, combined over Scopus, Web of Science and IEEE Xplore.

  3. The R Project for Statistical Computing: https://www.r-project.org/.

  4. A variant of PM is “opportunistic” maintenance; triggered when a machine fails before \(\tau\) and is administered CM and all other machines receive PM.

  5. For certain RL problems, for example when the reward is stochastic, this definition may not strictly apply. For the problem to be solvable, it is sufficient if the expected value is a function of s, a, and \(s^{\prime}\).

  6. Not all RL algorithms are value based; some estimate the return over long trajectories governed by policies (Swazinna et al. 2022; Schaefer et al. 2007). However, in general, they all seek the Bellman optimality condition.

  7. The Bellman equation is designed to consider all state-action pairs. In case of partial, sampled data, using it as a surrogate objective for value prediction, is a poor choice (Fujimoto et al. 2022).

  8. As of 09-Feb-2023, the search “‘reinforcement learning’ AND ‘predictive maintenance’ AND (PID OR MPC)”, did not return any results on the Scopus and Web Of Science databases.

  9. This statistical model is different from the MDP “model” we refer to in the model-based/model-free RL.

  10. This assists in stabilizing learning in neural-networks.

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Acknowledgements

We would like to sincerely thank the Reviewers. Their valuable comments and suggestions helped us improve the technical quality of the manuscript.

Funding

This work was supported by Symbiosis Institute of Technology.

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  1. Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, 412115, Maharashtra, India

    Rajesh Siraskar, Satish Kumar, Shruti Patil, Arunkumar Bongale & Ketan Kotecha

  2. Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune, 412115, Maharashtra, India

    Satish Kumar, Shruti Patil & Ketan Kotecha

  3. Birlasoft Ltd., Pune, 411057, Maharashtra, India

    Rajesh Siraskar

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Appendices

Appendix 1: Acronyms and notations

See Table 11.

Table 11 Acronyms and notations
Full size table

Appendix 2: Tables—applications, algorithms and evaluation data-sets

See Tables 12, 13, 14, 15 and 16.

Table 12 Industrial applications (RQ-3)
Full size table
Table 13 Algorithms applied by researchers (standard and their variants)
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Table 14 Specialized and novel algorithms applied by researchers
Full size table
Table 15 MDP model formulation
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Table 16 Environments and data-set used for evaluation
Full size table

Appendix 3: \({tf-idf}\) weighting

The \({tf-idf}\) scheme assigns a weight to each term in a document using Eq. (30), that is low when the term is infrequent in a document or occurs in many documents and is high when the term occurs many times within a small sub-set of documents.

$$\begin{aligned} tf-idf(t, d) = tf(t, d) \times idf(t), \end{aligned},$$
(30)

where t is a term within the document-set d; and the inverse document frequency, idf, is computed using (31)

$$\begin{aligned} idf(t) = log \left[ \frac{n}{df(t)} \right] + 1 \end{aligned}$$
(31)

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Siraskar, R., Kumar, S., Patil, S. et al. Reinforcement learning for predictive maintenance: a systematic technical review. Artif Intell Rev 56, 12885–12947 (2023). https://doi.org/10.1007/s10462-023-10468-6

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