Predictive Maintenance Model for IIoT-based Manufacturing: A Transferable Deep Reinforcement Learning Approach. Ong, K. S. H., Wang, W., Hieu, N. Q., Niyato, D., & Friedrichs, T. IEEE Internet of Things Journal, 2022. Conference Name: IEEE Internet of Things Journaldoi abstract bibtex The Industrial Internet of Things (IIoT) is crucial for accurately assessing the state of complex equipment in order to perform predictive maintenance (PdM) successfully. However, existing IIoT-based PdM frameworks do not consider the influence of various practical yet complex system factors, such as the real-time production states, machine health, and maintenance manpower resources. For this reason, we propose a generic PdM optimization framework to assist maintenance teams in prioritizing and resolving maintenance task conflicts under real-world manufacturing conditions. Specifically, the PdM framework aims to jointly optimize edge-based machine network uptime and allocation of manpower resources in a stochastic IIoT-enabled manufacturing environment using the model-free Deep Reinforcement Learning (DRL) methods. Since DRL requires a significant amount of training data, we propose and demonstrate the use of Transfer Learning (TL) method to assist DRL in learning more efficiently by incorporating expert demonstrations, termed TL with demonstrations (TLD). TLD reduces training wall-time by 58% compared to baseline methods, and we conduct numerous experiments to illustrate the performance, robustness, and scalability of TLD. Finally, we discuss the general benefits and limitations of the proposed TL method, which are not well addressed in the existing literature but could be beneficial to both researchers and industry practitioners.
@article{ong_predictive_2022,
title = {Predictive {Maintenance} {Model} for {IIoT}-based {Manufacturing}: {A} {Transferable} {Deep} {Reinforcement} {Learning} {Approach}},
issn = {2327-4662},
shorttitle = {Predictive {Maintenance} {Model} for {IIoT}-based {Manufacturing}},
doi = {10.1109/JIOT.2022.3151862},
abstract = {The Industrial Internet of Things (IIoT) is crucial for accurately assessing the state of complex equipment in order to perform predictive maintenance (PdM) successfully. However, existing IIoT-based PdM frameworks do not consider the influence of various practical yet complex system factors, such as the real-time production states, machine health, and maintenance manpower resources. For this reason, we propose a generic PdM optimization framework to assist maintenance teams in prioritizing and resolving maintenance task conflicts under real-world manufacturing conditions. Specifically, the PdM framework aims to jointly optimize edge-based machine network uptime and allocation of manpower resources in a stochastic IIoT-enabled manufacturing environment using the model-free Deep Reinforcement Learning (DRL) methods. Since DRL requires a significant amount of training data, we propose and demonstrate the use of Transfer Learning (TL) method to assist DRL in learning more efficiently by incorporating expert demonstrations, termed TL with demonstrations (TLD). TLD reduces training wall-time by 58\% compared to baseline methods, and we conduct numerous experiments to illustrate the performance, robustness, and scalability of TLD. Finally, we discuss the general benefits and limitations of the proposed TL method, which are not well addressed in the existing literature but could be beneficial to both researchers and industry practitioners.},
journal = {IEEE Internet of Things Journal},
author = {Ong, Kevin Shen Hoong and Wang, Wenbo and Hieu, Nguyen Quang and Niyato, Dusit and Friedrichs, Thomas},
year = {2022},
note = {Conference Name: IEEE Internet of Things Journal},
keywords = {Industrial Internet of Things, Industrial Internet of Things (IIoT), Maintenance engineering, Manufacturing, Production, Resource management, Task analysis, Transfer learning, decision support, deep reinforcement learning., predictive maintenance, resource management, transfer learning},
pages = {1--1},
}
Downloads: 0
{"_id":"4JqYQwgRTTF7WmwsJ","bibbaseid":"ong-wang-hieu-niyato-friedrichs-predictivemaintenancemodelforiiotbasedmanufacturingatransferabledeepreinforcementlearningapproach-2022","author_short":["Ong, K. S. H.","Wang, W.","Hieu, N. Q.","Niyato, D.","Friedrichs, T."],"bibdata":{"bibtype":"article","type":"article","title":"Predictive Maintenance Model for IIoT-based Manufacturing: A Transferable Deep Reinforcement Learning Approach","issn":"2327-4662","shorttitle":"Predictive Maintenance Model for IIoT-based Manufacturing","doi":"10.1109/JIOT.2022.3151862","abstract":"The Industrial Internet of Things (IIoT) is crucial for accurately assessing the state of complex equipment in order to perform predictive maintenance (PdM) successfully. However, existing IIoT-based PdM frameworks do not consider the influence of various practical yet complex system factors, such as the real-time production states, machine health, and maintenance manpower resources. For this reason, we propose a generic PdM optimization framework to assist maintenance teams in prioritizing and resolving maintenance task conflicts under real-world manufacturing conditions. Specifically, the PdM framework aims to jointly optimize edge-based machine network uptime and allocation of manpower resources in a stochastic IIoT-enabled manufacturing environment using the model-free Deep Reinforcement Learning (DRL) methods. Since DRL requires a significant amount of training data, we propose and demonstrate the use of Transfer Learning (TL) method to assist DRL in learning more efficiently by incorporating expert demonstrations, termed TL with demonstrations (TLD). TLD reduces training wall-time by 58% compared to baseline methods, and we conduct numerous experiments to illustrate the performance, robustness, and scalability of TLD. Finally, we discuss the general benefits and limitations of the proposed TL method, which are not well addressed in the existing literature but could be beneficial to both researchers and industry practitioners.","journal":"IEEE Internet of Things Journal","author":[{"propositions":[],"lastnames":["Ong"],"firstnames":["Kevin","Shen","Hoong"],"suffixes":[]},{"propositions":[],"lastnames":["Wang"],"firstnames":["Wenbo"],"suffixes":[]},{"propositions":[],"lastnames":["Hieu"],"firstnames":["Nguyen","Quang"],"suffixes":[]},{"propositions":[],"lastnames":["Niyato"],"firstnames":["Dusit"],"suffixes":[]},{"propositions":[],"lastnames":["Friedrichs"],"firstnames":["Thomas"],"suffixes":[]}],"year":"2022","note":"Conference Name: IEEE Internet of Things Journal","keywords":"Industrial Internet of Things, Industrial Internet of Things (IIoT), Maintenance engineering, Manufacturing, Production, Resource management, Task analysis, Transfer learning, decision support, deep reinforcement learning., predictive maintenance, resource management, transfer learning","pages":"1–1","bibtex":"@article{ong_predictive_2022,\n\ttitle = {Predictive {Maintenance} {Model} for {IIoT}-based {Manufacturing}: {A} {Transferable} {Deep} {Reinforcement} {Learning} {Approach}},\n\tissn = {2327-4662},\n\tshorttitle = {Predictive {Maintenance} {Model} for {IIoT}-based {Manufacturing}},\n\tdoi = {10.1109/JIOT.2022.3151862},\n\tabstract = {The Industrial Internet of Things (IIoT) is crucial for accurately assessing the state of complex equipment in order to perform predictive maintenance (PdM) successfully. However, existing IIoT-based PdM frameworks do not consider the influence of various practical yet complex system factors, such as the real-time production states, machine health, and maintenance manpower resources. For this reason, we propose a generic PdM optimization framework to assist maintenance teams in prioritizing and resolving maintenance task conflicts under real-world manufacturing conditions. Specifically, the PdM framework aims to jointly optimize edge-based machine network uptime and allocation of manpower resources in a stochastic IIoT-enabled manufacturing environment using the model-free Deep Reinforcement Learning (DRL) methods. Since DRL requires a significant amount of training data, we propose and demonstrate the use of Transfer Learning (TL) method to assist DRL in learning more efficiently by incorporating expert demonstrations, termed TL with demonstrations (TLD). TLD reduces training wall-time by 58\\% compared to baseline methods, and we conduct numerous experiments to illustrate the performance, robustness, and scalability of TLD. Finally, we discuss the general benefits and limitations of the proposed TL method, which are not well addressed in the existing literature but could be beneficial to both researchers and industry practitioners.},\n\tjournal = {IEEE Internet of Things Journal},\n\tauthor = {Ong, Kevin Shen Hoong and Wang, Wenbo and Hieu, Nguyen Quang and Niyato, Dusit and Friedrichs, Thomas},\n\tyear = {2022},\n\tnote = {Conference Name: IEEE Internet of Things Journal},\n\tkeywords = {Industrial Internet of Things, Industrial Internet of Things (IIoT), Maintenance engineering, Manufacturing, Production, Resource management, Task analysis, Transfer learning, decision support, deep reinforcement learning., predictive maintenance, resource management, transfer learning},\n\tpages = {1--1},\n}\n\n","author_short":["Ong, K. S. H.","Wang, W.","Hieu, N. Q.","Niyato, D.","Friedrichs, T."],"key":"ong_predictive_2022","id":"ong_predictive_2022","bibbaseid":"ong-wang-hieu-niyato-friedrichs-predictivemaintenancemodelforiiotbasedmanufacturingatransferabledeepreinforcementlearningapproach-2022","role":"author","urls":{},"keyword":["Industrial Internet of Things","Industrial Internet of Things (IIoT)","Maintenance engineering","Manufacturing","Production","Resource management","Task analysis","Transfer learning","decision support","deep reinforcement learning.","predictive maintenance","resource management","transfer learning"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":["industrial internet of things","industrial internet of things (iiot)","maintenance engineering","manufacturing","production","resource management","task analysis","transfer learning","decision support","deep reinforcement learning.","predictive maintenance","resource management","transfer learning"],"search_terms":["predictive","maintenance","model","iiot","based","manufacturing","transferable","deep","reinforcement","learning","approach","ong","wang","hieu","niyato","friedrichs"],"title":"Predictive Maintenance Model for IIoT-based Manufacturing: A Transferable Deep Reinforcement Learning Approach","year":2022}