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Mathematical Problems in Engineering
Volume 2013, Article ID 712028, 8 pages
http://dx.doi.org/10.1155/2013/712028
Research Article

A Self-Learning Sensor Fault Detection Framework for Industry Monitoring IoT

1State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China
2Information Center of Guangdong Power Grid Corporation, China Southern Power Grid, Guangzhou 510620, China
3Beijing Guotie Huachen Communication & Infomation Technology Co., Ltd., Beijing 10070, China
4Beijing Electronic Science and Technology Institute, Beijing 100070, China

Received 8 June 2013; Accepted 3 August 2013

Academic Editor: Zhongmei Zhou

Copyright © 2013 Yu Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Many applications based on Internet of Things (IoT) technology have recently founded in industry monitoring area. Thousands of sensors with different types work together in an industry monitoring system. Sensors at different locations can generate streaming data, which can be analyzed in the data center. In this paper, we propose a framework for online sensor fault detection. We motivate our technique in the context of the problem of the data value fault detection and event detection. We use the Statistics Sliding Windows (SSW) to contain the recent sensor data and regress each window by Gaussian distribution. The regression result can be used to detect the data value fault. Devices on a production line may work in different workloads and the associate sensors will have different status. We divide the sensors into several status groups according to different part of production flow chat. In this way, the status of a sensor is associated with others in the same group. We fit the values in the Status Transform Window (STW) to get the slope and generate a group trend vector. By comparing the current trend vector with history ones, we can detect a rational or irrational event. In order to determine parameters for each status group we build a self-learning worker thread in our framework which can edit the corresponding parameter according to the user feedback. Group-based fault detection (GbFD) algorithm is proposed in this paper. We test the framework with a simulation dataset extracted from real data of an oil field. Test result shows that GbFD detects 95% sensor fault successfully.