Next Article in Journal
Deep Learning-Based Localization for UWB Systems
Previous Article in Journal
Slicing the Core Network and Radio Access Network Domains through Intent-Based Networking for 5G Networks
Previous Article in Special Issue
FPGA Acceleration of CNNs-Based Malware Traffic Classification
Open AccessArticle

Frequency Occurrence Plot-Based Convolutional Neural Network for Motor Fault Diagnosis

1
Department of Electrical Engineering, University of San Jose-Recoletos, Cebu City 6000, Philippines
2
Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(10), 1711; https://doi.org/10.3390/electronics9101711
Received: 21 August 2020 / Revised: 12 October 2020 / Accepted: 13 October 2020 / Published: 18 October 2020
(This article belongs to the Special Issue Application of Electronic Devices on Intelligent System)
A novel motor fault diagnosis using only motor current signature is developed using a frequency occurrence plot-based convolutional neural network (FOP-CNN). In this study, a healthy motor and four identical motors with synthetically applied fault conditions—bearing axis deviation, stator coil inter-turn short circuiting, a broken rotor strip, and outer bearing ring damage—are tested. A set of 150 three-second sampling stator current signals from each motor fault condition are taken under five artificial coupling loads (0, 25%, 50%, 75% and 100%). The sampling signals are collected and processed into frequency occurrence plots (FOPs) which later serve as CNN inputs. This is done first by transforming the time series signals into its frequency spectra then convert these into two-dimensional FOPs. Fivefold stratified sampling cross-validation is performed. When motor load variations are considered as input labels, FOP-CNN predicts motor fault conditions with a 92.37% classification accuracy. It precisely classifies and recalls bearing axis deviation fault and healthy conditions with 99.92% and 96.13% f-scores, respectively. When motor loading variations are not used as input data labels, FOP-CNN still satisfactorily predicts motor condition with an 80.25% overall accuracy. FOP-CNN serves as a new feature extraction technique for time series input signals such as vibration sensors, thermocouples, and acoustics. View Full-Text
Keywords: fault diagnosis; frequency occurrence plot; convolutional neural network; motor loading; current signal; FOP-CNN; bearing fault; short circuit fault fault diagnosis; frequency occurrence plot; convolutional neural network; motor loading; current signal; FOP-CNN; bearing fault; short circuit fault
Show Figures

Figure 1

  • Externally hosted supplementary file 1
    Doi: http://dx.doi.org/10.21227/77da-c563
    Link: http://ieee-dataport.org/1447
    Description: The dataset has 150 three-second sampling motor current signals from each synthetically-prepared motors. There are five motors with respective fault condition - bearing axis deviation (F1), stator coil inter-turn short circuit (F2), rotor broken strip (F3), outer bearing ring damage (F4), and healthy (H). The motors are run under five coupling loads - 0, 25, 50, 75, and 100%. The sampling signals are collected and processed into frequency occurrence plots (FOPs). Each image has a label, for example F2_L50_130, where F2 is the fault condition, L50 is the coupling load condition. and 130 is the index of motor current signal. A total of 3,750 FOPs which can be used for motor fault diagnosis through image recognition problem.
MDPI and ACS Style

Piedad, E.J.; Chen, Y.-T.; Chang, H.-C.; Kuo, C.-C. Frequency Occurrence Plot-Based Convolutional Neural Network for Motor Fault Diagnosis. Electronics 2020, 9, 1711.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop