The Industrial IoT era has benefitted immensely from increased data collection and processing, which enables Industry 4.0 and predictive maintenance. Predictive maintenance tries to predict when different types of failures will occur. Prescriptive maintenance goes further by optimizing the maintenance decisions, i.e. what to do and when to do it. This paper considers a distributed factory environment, and firstly, proposes a model-based approach to prescriptive maintenance using the Partially Observable Markov Decision Process (POMDP) framework. A particle filter algorithm performs online estimation of a POMDP model to enable it to adapt to each machine over time. The POMDP is then solved using the point based value iteration (PBVI) method to obtain maintenance decisions. Next, we present a model-free approach to prescriptive maintenance using the Deep Q-Network (DQN) reinforcement learning method to obtain maintenance decisions. The POMDP and DQN methods are implemented on GPU-accelerated edge computing nodes and their performance in terms of reward and downtime are compared.


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    Titel :

    Model-based and Model-free Prescriptive Maintenance on Edge Computing Nodes


    Beteiligte:
    Tham, Chen-Khong (Autor:in) / Sharma, Naman (Autor:in) / Hu, Jingrui (Autor:in)


    Erscheinungsdatum :

    01.06.2023


    Format / Umfang :

    4306116 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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