Name
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 an energy 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%. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3991/ijoe.v15i08.10314 | INTERNATIONAL JOURNAL OF ONLINE AND BIOMEDICAL ENGINEERING |
Keywords | Field | DocType |
Industrial wireless sensor networks (IWSNs), fault diagnosis, wavelet transform, support vector machine, Industrial Internet of Things (IIoT) | Data processing,Wireless,Efficient energy use,Support vector machine,Real-time computing,Feature extraction,Engineering,Wireless sensor network,Energy consumption,Embedded system,Wavelet | Journal |
Volume | Issue | Citations |
15 | 8 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liqun Hou | 1 | 63 | 5.66 |
| Junteng Hao | 2 | 0 | 0.34 |
| Yongguang Ma | 3 | 0 | 0.34 |
| Neil W. Bergmann | 4 | 454 | 70.23 |