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Predictive Maintenance Based on Identity Resolution and Transformers in IIoT

Authors :
Zhibo Qi
Lei Du
Ru Huo
Tao Huang
Source :
Future Internet, Vol 16, Iss 9, p 310 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

The burgeoning development of next-generation technologies, especially the Industrial Internet of Things (IIoT), has heightened interest in predictive maintenance (PdM). Accurate failure forecasting and prompt responses to downtime are essential for improving the industrial efficiency. Traditional PdM methods often suffer from high false alarm rates and inefficiencies in complex environments. This paper introduces a predictive maintenance framework using identity resolution and a transformer model. Devices receive unique IDs via distributed identifiers (DIDs), followed by a state awareness model to assess device health from sensor signals. A sequence prediction model forecasts future signal sequences, which are then used with the state awareness model to determine future health statuses. Combining these predictions with unique IDs allows for the rapid identification of facilities needing maintenance. Experimental results show superior performance, with 99% accuracy for the state awareness model and a mean absolute error (MAE) of 0.062 for the sequence prediction model, underscoring the effectiveness of the framework.

Details

Language :
English
ISSN :
19995903
Volume :
16
Issue :
9
Database :
Directory of Open Access Journals
Journal :
Future Internet
Publication Type :
Academic Journal
Accession number :
edsdoj.8545b80e7c6246519cbba7843372e5f5
Document Type :
article
Full Text :
https://doi.org/10.3390/fi16090310