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

Abstract

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%.