Efficient Data Reduction at the Edge of Industrial Internet of Things for PMSM Bearing Fault Diagnosis
Open accessAbstract
An efficient data reduction algorithm is designed and implemented on an industrial Internet of Things (IIoT) node for permanent magnet synchronous motor (PMSM) bearing fault diagnosis in variable speed conditions. Leakage flux and vibration signals are, respectively, acquired by a magnetic sensor and an accelerometer on the IIoT node in a noninvasive manner. These two signals are processed and mixed on the IIoT and transmitted to a server. The received signal is separated, the cumulative rotation angle is calculated, and the vibration signal is resampled for bearing fault identification. The proposed method can reduce about 95% of the transmission data while maintaining sufficient precision in bearing fault diagnosis in comparison to a traditional method. The proposed method based on edge computing reduces the power consumption, and hence it is suitable to use on a battery-supplied IIoT node for remote PMSM condition monitoring and fault diagnosis.
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In [118], an efficient algorithm implemented in an industrial IoT node can reduce about 95% of transmission data compared to the traditional method.
“The proposed method can reduce about 95% of the transmission data compared to the traditional method, resulting in reduced power consumption in the battery-powered IoT node.”
From Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges (2022) · cites this paper as [118]
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“Qian et al.[21] designed an edge computing-based fault diagnosis system with a hardware embedded for rotating machines, and the system can perform the procedure including data collection, feature extraction, and fault classification just within just 0.25s. Wang et al.[22] proposed a multi-sensor signal fusion algorith…”
BackgroundInfluentialSelf-citationA Scientific Model to Support Industrial Data Management Process Using Virtualized Environments
Pioli, L., Thomé, T. G., Nunes, J. X. M., de Macedo, D. D. J., de L., P. C. R., & Dantas, M. A. R. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-89899-1_15
Fusion Interpolation Codec in Wireless Sensor Network for Mechanical Vibration Monitoring
Fu, H., Deng, L., Tang, B., & Zhu, K. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3449934
Research on Internet of Things Signals Acquisition and Processing based on Edge Computation
Zhang, X., Qiu, W., Yang, H., & Hou, A. (2024) · International Conference on Computer Engineering and Intelligent Control (ICCEIC) · doi:10.1109/icceic64099.2024.10775418
“In these scenarios, Internet of Things devices collect substantial quantities of data in real time through a variety of sensors and transmit it to a central server or cloud for processing [1] .”
A Comparison of Induction Motor Fault Detection Using Single and Double-Channel Phase Currents
Wright, R., Fajri, P., Fu, X., & Asrari, . (2024) · IEEE Energy Conversion Congress and Exposition (ECCE) · doi:10.1109/ecce55643.2024.10861226
Li, W., Du, Y., Hu, J., & Wang, Z. (2026) · IEEE International Electrical and Energy Conference (CIEEC) · doi:10.1109/cieec69682.2026.11571947
“Permanent magnet synchronous motors (PMSM) are widely used in applications such as electric vehicles, industrial automation, and household appliances [1], [2], [3].”
CryoFormer-KANNet: An End-to-End Cloud–Edge Fault Diagnosis Framework for Semiconductor Cryopumps
Wang, Y., Zhu, J.-H., Wang, X.-D., Ao, D.-C., & Wu, M. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3712941
Self-citation
- Authors
- Wang, X., Lu, S., Huang, W., Wang, Q., Zhang, S., & Xia, M.
- Affiliations
- University of Science and Technology of China (CN)
- Anhui University (CN)
- Chongqing University (CN)
- Lancaster University (GB)
- Venue
- IEEE Transactions on Instrumentation and Measurement
- Published
- 2021
- Pages
- 1-1
- ISSN
- 0018-9456 (ISSN-L)
- 1557-9662
- OpenAlex
- W3120595564
- Cited by
- 65
- References
- 34
- Citations by year
- 2026: 2 · 2025: 13 · 2024: 14 · 2023: 18 · 2022: 14 · 2021: 4
- FWCI
- 5.56
- Type
- journal article
- Language
- en
- Open access
- green
- eprints.lancs.ac.uk
- Licence
- Locations
- IEEE Transactions on Instrumentation and Measurement (publishedVersion)
- Lancaster EPrints (Lancaster University) (acceptedVersion)
- Funders
- Open Project Program of the State Key Laboratory of Mechanical Transmissions of Chongqing University (SKLMT-KFKT-201812)
- National Natural Science Foundation of China (52075002)
- Open Project Program of the Traction Power State Key Laboratory of Southwest Jiaotong University (TPL1905)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Motor Design and Analysis
- Keywords
- data reduction, edge computing, Industrial Internet of Things, permanent magnet synchronous motor, bearing fault diagnosis, vibration signals, signal separation, resampling, variable speed operation, energy consumption reduction, battery-powered sensor nodes
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