Cloud-based data-intensive framework towards fault diagnosis in large-scale petrochemical plants

Huo, Zhiqiang and Mukherjee, Mithun and Shu, Lei and Chen, Yuanfang and Zhou, Zhangbing (2016) Cloud-based data-intensive framework towards fault diagnosis in large-scale petrochemical plants. In: Wireless Communications and Mobile Computing Conference (IWCMC), 2016 International, 5 - 9 September 2016, Paphos, Cyprus.

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Abstract

Industrial Wireless Sensor Networks (IWSNs) are expected to offer promising monitoring solutions to meet the demands of monitoring applications for fault diagnosis in large-scale petrochemical plants, however, involves heterogeneity and Big Data problems due to large amounts of sensor data with high volume and velocity. Cloud Computing is an outstanding approach which provides a flexible platform to support the addressing of such heterogeneous and data-intensive problems with massive computing, storage, and data-based services. In this paper, we propose a Cloud-based Data-intensive Framework (CDF) for on-line equipment fault diagnosis system that facilitates the integration and processing of mass sensor data generated from Industrial Sensing Ecosystem (ISE). ISE enables data collection of interest with topic-specific industrial monitoring systems. Moreover, this approach contributes the establishment of on-line fault diagnosis monitoring system with sensor streaming computing and storage paradigms based on Hadoop as a key to the complex problems. Finally, we present a practical illustration referred to this framework serving equipment fault diagnosis systems with the ISE.

Keywords:Cloud computing, Monitoring, Fault diagnosis, Wireless sensor networks, Internet of things, Petrochemicals, Computational modeling
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
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ID Code:26412
Deposited On:01 Mar 2017 12:01

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