Edge Intelligence for Data Handling and Predictive Maintenance in IIOT
نویسندگان
چکیده
The use of IoT has become pervasive and devices are common in many domains. Industrial (IIoT) utilises sensors to monitor machines environments ensure optimal performance equipment processes. Predictive Maintenance (PM) which monitors the health determine probable failure components is one IIoT technique receiving attention lately. To achieve effective PM, massive amounts data collected, processed ultimately analysed by Machine Learning (ML) algorithms. Traditionally transmit their readings cloud for processing modelling. Handling transmitting between infrastructure a cost. Edge Computing (EC) both intermediate nodes can process provides opportunities reduce transmission costs increase speed. This article examines PM discusses how where be analysed. Initially, this presents sampling reduction techniques. These techniques allow amount transmitted but there potential accuracy trade-offs when ML algorithms utilise reduced datasets. An alternative approach move closer transmission. There three main that EC paradigm perform on intermediary nodes. categorized according occurs: Device Edge, Cloud (Federated Learning). In addition exploring traditional approaches, these state-of-the-art examined benefits weaknesses presented. A novel architecture demonstrate utilized also proposed.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2021
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2021.3069137