Preprint Article Version 1 This version is not peer-reviewed

IWSNs with On-sensor Data Processing for Machine Fault Diagnosis

Version 1 : Received: 29 October 2018 / Approved: 29 October 2018 / Online: 29 October 2018 (12:51:44 CET)

How to cite: Hou, L.; Hao, J.; Ma, Y.; Bergmann, N. IWSNs with On-sensor Data Processing for Machine Fault Diagnosis. Preprints 2018, 2018100683 (doi: 10.20944/preprints201810.0683.v1). Hou, L.; Hao, J.; Ma, Y.; Bergmann, N. IWSNs with On-sensor Data Processing for Machine Fault Diagnosis. Preprints 2018, 2018100683 (doi: 10.20944/preprints201810.0683.v1).

Abstract

Machine fault diagnosis systems need to collect and transmit dynamic monitoring signals, like vibration and current signals, 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. To address this tension when implementing machine fault diagnosis applications in IIoT, this paper proposes a novel 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%.

Subject Areas

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

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