Cloud-based Data-intensive Framework towards fault diagnosis in large-scale petrochemical plants

Abstract

Abstract-Industrial Wireless Sensor Networks (IWSNs) are expected to offer promising monitoring solutions to meet the demands of monitoring applications for fault diagnosis in largescale 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.

Digital Twin—Cyber Replica of Physical Things: Architecture, Applications and Future Research Directions

“…The first scenario is the detection of fault tolerance and device health, which can help smart manufacturing systems to avoid outages by predicting device failures and rerouting workflows in the production process. In particular, cloud-based frameworks that are designed to handle large amounts of data utilize embedded sensor systems in manufacturing devices in order to monitor system health, leading to the creation of predictive models for avoidance procedures [83]. In the example of a robotic gripper in a manufacturing production line, Redelinghuys et al [82] investigated anomaly detection in the gripper closing speed for detecting via pressure sensor to detect faults and degradation that could lead to failure and considered utilizing the result to reroute production traffic to another assembly line.…”

The Architecture of Mass Customization-Social Internet of Things System: Current Research Profile

“…On one hand, through offline batch processing [118][119][120], deep learning can mine valuable information from massive data to predict the reliability of equipment. On the other hand, through real-time stream processing [121][122][123], deep learning can resolve the complex and changeable operating conditions in order to monitor the equipment in real-time, thus ensuring timely maintenance. In both cases, deep learning methods extract fault feature representations and combine them with data-and knowledge-driven approaches.…”

A Reference Architecture and Model for Sensor Data Warehousing

“…An example of this can be found in Huo et alia [5], where the authors present a framework for faultdiagnosis that employs an enterprise DW as an integrated repository for monitoring data, but do not explain the specific data model adopted. Scriney et alia [6] proposes a methodology to obtain multidimensional data cubes starting from XML and JSON data sources.…”

“…For example, example frameworks for streaming processing include Apache Storm 4 , Apache Spark [16], and Apache Flink 5 . Spark and Flink can also take on batch processing tasks.…”