Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Enhanced feature extraction method for motor fault diagnosis using low-quality vibration data from wireless sensor networks
AU - Shu, Q.
AU - Lu, S.
AU - Xia, M.
AU - Ding, J.
AU - Niu, J.
AU - Liu, Y.
PY - 2020/1/15
Y1 - 2020/1/15
N2 - Wireless sensor networks (WSNs), which are usually powered by batteries, have been extensively used in condition monitoring and fault diagnosis of motors. To extend the battery service life, the length of the acquired and transmitted signal should be short and the sampling resolution should be reduced. In this case, the motor signal quality is low, which affects the fault diagnosis accuracy. To address this issue, this study proposes an enhanced feature extraction method for motor fault diagnosis using low-quality vibration signals acquired from a battery-powered WSN node. First, the vibration signal is converted to an image using a wavelet synchrosqueezed transform technique. Second, the constructed image is enhanced using a histogram equalization. Finally, the enhanced image is inputted into a convolutional neural network (CNN) model, and the motor fault type can be recognized from the CNN output. The effectiveness and efficiency of the proposed method are validated by comparing its performance in the brushless direct motor test rig with the performance of several traditional methods. The relationship between the fault diagnosis accuracy and WSN performances is investigated and discussed. The proposed method shows potential applications for remote motor fault diagnosis using the low-quality vibration signal acquired from a WSN node with limited battery capacity.
AB - Wireless sensor networks (WSNs), which are usually powered by batteries, have been extensively used in condition monitoring and fault diagnosis of motors. To extend the battery service life, the length of the acquired and transmitted signal should be short and the sampling resolution should be reduced. In this case, the motor signal quality is low, which affects the fault diagnosis accuracy. To address this issue, this study proposes an enhanced feature extraction method for motor fault diagnosis using low-quality vibration signals acquired from a battery-powered WSN node. First, the vibration signal is converted to an image using a wavelet synchrosqueezed transform technique. Second, the constructed image is enhanced using a histogram equalization. Finally, the enhanced image is inputted into a convolutional neural network (CNN) model, and the motor fault type can be recognized from the CNN output. The effectiveness and efficiency of the proposed method are validated by comparing its performance in the brushless direct motor test rig with the performance of several traditional methods. The relationship between the fault diagnosis accuracy and WSN performances is investigated and discussed. The proposed method shows potential applications for remote motor fault diagnosis using the low-quality vibration signal acquired from a WSN node with limited battery capacity.
KW - CNN
KW - histogram equalization
KW - low-quality vibration data
KW - motor fault diagnosis
KW - WSN
KW - WSST
KW - Condition monitoring
KW - Convolutional neural networks
KW - Electric batteries
KW - Equalizers
KW - Extraction
KW - Failure analysis
KW - Feature extraction
KW - Graphic methods
KW - Image enhancement
KW - Sensor nodes
KW - Effectiveness and efficiencies
KW - Feature extraction methods
KW - Histogram equalizations
KW - Motor fault
KW - Sampling resolution
KW - Vibration data
KW - Wireless sensor network (WSNs)
KW - Fault detection
U2 - 10.1088/1361-6501/ab5cca
DO - 10.1088/1361-6501/ab5cca
M3 - Journal article
VL - 31
JO - Measurement Science and Technology
JF - Measurement Science and Technology
SN - 0957-0233
IS - 4
M1 - 045016
ER -