Condition monitoring and fault diagnosis of motor bearings using undersampled vibration signals from a wireless sensor network

Wireless sensor networks (WSNs) which consist of miscellaneous sensors are used frequently in monitoring vital equipment. Benefiting from the development of data mining technologies, the massive data generated by sensors facilitate condition monitoring and fault diagnosis. However, too much data inc...

Full description

Saved in:
Bibliographic Details
Published in:Journal of sound and vibration 2018-02, Vol.414, p.81-96
Main Authors: Lu, Siliang, Zhou, Peng, Wang, Xiaoxian, Liu, Yongbin, Liu, Fang, Zhao, Jiwen
Format: Article
Language:English
Subjects:
Bandpass filters
Bandpass sampling
Bearings
Computer simulation
Condition monitoring
Data mining
Energy consumption
Energy storage
Fault diagnosis
Motor bearing
Motors
Offshore drilling rigs
Offshore platforms
Remote sensors
Sampled data
Sensors
Signal processing
Undersampled signal
Vibration analysis
Vibration monitoring
Vibration signal analysis
Wind power
Wireless networks
Wireless sensor network
Wireless sensor networks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Wireless sensor networks (WSNs) which consist of miscellaneous sensors are used frequently in monitoring vital equipment. Benefiting from the development of data mining technologies, the massive data generated by sensors facilitate condition monitoring and fault diagnosis. However, too much data increase storage space, energy consumption, and computing resource, which can be considered fatal weaknesses for a WSN with limited resources. This study investigates a new method for motor bearings condition monitoring and fault diagnosis using the undersampled vibration signals acquired from a WSN. The proposed method, which is a fusion of the kurtogram, analog domain bandpass filtering, bandpass sampling, and demodulated resonance technique, can reduce the sampled data length while retaining the monitoring and diagnosis performance. A WSN prototype was designed, and simulations and experiments were conducted to evaluate the effectiveness and efficiency of the proposed method. Experimental results indicated that the sampled data length and transmission time of the proposed method result in a decrease of over 80% in comparison with that of the traditional method. Therefore, the proposed method indicates potential applications on condition monitoring and fault diagnosis of motor bearings installed in remote areas, such as wind farms and offshore platforms.
ISSN:0022-460X
1095-8568
DOI:10.1016/j.jsv.2017.11.007