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Noise removal in the presence of significant anomalies for Industrial IoT sensor data in manufacturing by Liu, Yuehua; Dillon, Tharam; Yu, Wenjin; Rahayu, Wenny; Mostafa, Fahed is a Computer Science article available to read on EtoBox.

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The emergence of the Industrial Internet of Things (IIoT) to enhance manufacturing and industrial processes allows data analysts to address significant problems such as predictive maintenance. For the purpose of accurate data analysis, cleansing noisy sensor data is one of the most fundamental and necessary steps. Without first removing the noise, the anomaly detection techniques are likely to give a large number of false positives. However using traditional outlier detection methods directly for such analysis is not appropriate as both noise and significant anomalies might exist in sensor data. This paper introduces the new challenges and proposes a novel solution to address the issue of removing noise while preserving anomalies in IIoT Data. It proposes an approach that measures both the rate of change and deviation to compute the noise score. It employs a sliding window technique to define the analysis unit of the contrast measure which is used in conjunction with statistical techniques. Extensive experiments demonstrate that the proposed approach outperforms the other state-of-the-art noise detection methods, providing a clean data set that preserves the anomalies on which one

Who reads Noise removal in the presence of significant anomalies for Industrial IoT sensor data in manufacturing?

Author

Liu, Yuehua; Dillon, Tharam; Yu, Wenjin; Rahayu, Wenny; Mostafa, Fahed

Publisher

Institute of Electrical and Electronics Engineers; Institute of Electrical and Electronics Engineers Inc.; Institute of Electrical and Electronics Engineers (IEEE) (ISSN 2327-4662)

Published

2020

Language

EN

Field

Computer Science (Physical Sciences)