Novel industrial wireless sensor networks for machine condition monitoring and fault diagnosis

Hou, Liqun and Bergmann, Neil W. (2012) Novel industrial wireless sensor networks for machine condition monitoring and fault diagnosis. IEEE Transactions on Instrumentation and Measurement, 61 10: 2787-2798. doi:10.1109/TIM.2012.2200817

Author Hou, Liqun
Bergmann, Neil W.
Title Novel industrial wireless sensor networks for machine condition monitoring and fault diagnosis
Journal name IEEE Transactions on Instrumentation and Measurement   Check publisher's open access policy
ISSN 0018-9456
Publication date 2012-10
Sub-type Article (original research)
DOI 10.1109/TIM.2012.2200817
Volume 61
Issue 10
Start page 2787
End page 2798
Total pages 12
Place of publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Collection year 2013
Language eng
Abstract This paper proposes a novel industrial wireless sensor network (IWSN) for industrial machine condition monitoring and fault diagnosis. In this paper, the induction motor is taken as an example of monitored industrial equipment due to its wide use in industrial processes. Motor stator current and vibration signals are measured for further processing and analysis. On-sensor node feature extraction and on-sensor fault diagnosis using neural networks are then investigated to address the tension between the higher system requirements of IWSNs and the resource-constrained characteristics of sensor nodes. A two-step classifier fusion approach using Dempster-Shafer theory is also explored to increase diagnosis result quality. Four motor operating conditionsnormal without load, normal with load, loose feet, and mass imbalanceare monitored to evaluate the proposed system. Experimental results show that, compared with raw data transmission, on-sensor fault diagnosis could reduce payload transmission data by 99%, decrease node energy consumption by 97%, and prolong node lifetime from 106 to 150 h, an increase of 43%. The final fault diagnosis results using the proposed classifier fusion approach give a result certainty of at least 97.5%. To leverage the advantages of on-sensor fault diagnosis, another system operating mode is explored, which only transmits the fault diagnosis result when a fault happens or at a fixed interval. For this mode, the node lifetime reaches 73 days if sensor nodes transmit diagnosis results once per hour.
Keyword Condition monitoring
Data fusion
Fault diagnosis
Induction motors
Industrial wireless sensor networks (IWSNs)
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article number 6215047

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2013 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 33 times in Thomson Reuters Web of Science Article | Citations
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Created: Fri, 09 Nov 2012, 12:36:37 EST by Professor Neil Bergmann on behalf of School of Information Technol and Elec Engineering