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

Liqun Hou, Junteng Hao, Yongguang Ma, Neil Bergmann


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%.


Industrial wireless sensor networks (IWSNs); fault diagnosis; wavelet transform; support vector machine; Industrial Internet of Things (IIoT)

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International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
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