IWSNs with On-Sensor Data Processing for Energy Efficient Machine Fault Diagnosis

Cadernos de Campo: Revista de Ciências Sociais

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Title IWSNs with On-Sensor Data Processing for Energy Efficient Machine Fault Diagnosis
Creator Hou, Liqun; North China Electric Power University
Hao, Junteng; North China Electric Power University
Ma, Yongguang; North China Electric Power University
Bergmann, Neil; The University of Queensland
Subject Industrial wireless sensor networks (IWSNs); fault diagnosis; wavelet transform; support vector machine; Industrial Internet of Things (IIoT)
Description Machine fault diagnosis systems need to collect and transmit dynamic signals, like vibration and current, at high-speed. However, industrial wireless sensor networks (IWSNs) and Industrial Internet of Things (IIoT) are generally based on low-speed wireless protocols, such as ZigBee and IEEE802.15.4. Large amounts of transmission data will increase the energy consumption and shorten the lifetime of energy-constrained IWSN nodes as well.To address these tensions when implementing machine fault diagnosis applications in IWSNs, this paper proposes anenergy efficient IWSN with on-sensor data processing. On-sensor wavelet transforms using four popular mother wavelets are explored for fault feature extraction, while an on-sensor support vector machine classifier is investigated for fault diagnosis. The effectiveness of the presented approach is evaluated by a set of experiments using motor bearing vibration data. The experimental results show that compared with raw data transmission, the proposed on-sensor fault diagnosis method can reduce the payload transmission data by 99.95%, and reduce the node energy consumption by about 10%, while the fault diagnosis accuracy of the proposed approach reaches 98%.
Publisher kassel university press GmbH
Date 2019-05-14
Type info:eu-repo/semantics/article

Format application/pdf
Identifier https://online-journals.org/index.php/i-joe/article/view/10314
Source International Journal of Online and Biomedical Engineering (iJOE); Vol 15, No 08 (2019); pp. 42-61
Language eng
Relation https://online-journals.org/index.php/i-joe/article/view/10314/5655
Rights Copyright (c) 2019 Liqun Hou, Junteng Hao, Yongguan Ma, Neil Bergmann

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