TY - GEN
T1 - IIoT-based Predictive Maintenance for Oil and Gas Industry
AU - Jia, Zhiyang
AU - Wang, Jihe
AU - Deng, Cheng
N1 - Publisher Copyright:
© 2022 Association for Computing Machinery.
PY - 2022/10/21
Y1 - 2022/10/21
N2 - The Industrial Internet of Things (IIoT) promises to provide an expanded awareness of field assets and equipment, access to data from across locations, and actionable insights for maximizing operational performance and safety of the oil and gas industry. Using automation and machine learning, with the application of predictive maintenance, efficiencies can be boosted and problems can be mitigated sooner and more effectively. The proposed system is mainly based on the data collection, processing, analysis, and modeling of an enormous number of historical and real-time data generated during the operation of the equipment on the edge side. The data-driven predictive maintenance used machine learning models and deep learning models to predict the remaining useful life (RUL). Bi-LSTM based prediction model has been trained on the cloud, and deployed onto the edge devices. The predictive maintenance process includes data acquisition, data processing, training of machine learning model, equipment health assessment, remaining useful life prediction, strategy formulation, and strategy execution. The predictive maintenance solution driven by the IIoT helps oil and gas companies make predictions before equipment failures have a significant impact on their company's safety level and profits to improve asset reliability and promote cost savings.
AB - The Industrial Internet of Things (IIoT) promises to provide an expanded awareness of field assets and equipment, access to data from across locations, and actionable insights for maximizing operational performance and safety of the oil and gas industry. Using automation and machine learning, with the application of predictive maintenance, efficiencies can be boosted and problems can be mitigated sooner and more effectively. The proposed system is mainly based on the data collection, processing, analysis, and modeling of an enormous number of historical and real-time data generated during the operation of the equipment on the edge side. The data-driven predictive maintenance used machine learning models and deep learning models to predict the remaining useful life (RUL). Bi-LSTM based prediction model has been trained on the cloud, and deployed onto the edge devices. The predictive maintenance process includes data acquisition, data processing, training of machine learning model, equipment health assessment, remaining useful life prediction, strategy formulation, and strategy execution. The predictive maintenance solution driven by the IIoT helps oil and gas companies make predictions before equipment failures have a significant impact on their company's safety level and profits to improve asset reliability and promote cost savings.
KW - IIot
KW - Long short-term memory
KW - Oil and Gas Industry
KW - Predictive maintenance
UR - http://www.scopus.com/inward/record.url?scp=85150483154&partnerID=8YFLogxK
U2 - 10.1145/3573428.3573503
DO - 10.1145/3573428.3573503
M3 - Conference contribution
AN - SCOPUS:85150483154
T3 - ACM International Conference Proceeding Series
SP - 432
EP - 436
BT - Proceedings of 2022 6th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2022
PB - Association for Computing Machinery
T2 - 6th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2022
Y2 - 21 October 2022 through 23 October 2022
ER -