Fault Diagnosis of Rotary Machines Using Deep Convolutional Neural Network with Wide Three Axis Vibration Signal Input
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Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lučića Street 5, 10002 Zagreb, Croatia
2
Department of Thermal Technology, University of Technology and Humanities in Radom, Stasieckiego Street 54, 26600 Radom, Poland
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(14), 4017; https://doi.org/10.3390/s20144017
Received: 2 July 2020
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Revised: 8 July 2020
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Accepted: 14 July 2020
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Published: 19 July 2020
(This article belongs to the Special Issue Deep Learning, Artificial Neural Networks and Sensors for Fault Diagnosis)
Fault diagnosis is considered as an essential task in rotary machinery as possibility of an early detection and diagnosis of the faulty condition can save both time and money. This work presents developed and novel technique for deep-learning-based data-driven fault diagnosis for rotary machinery. The proposed technique input raw three axes accelerometer signal as high definition 1D image into deep learning layers which automatically extract signal features, enabling high classification accuracy. Unlike the researches carried out by other researchers, accelerometer data matrix with dimensions 6400 × 1 × 3 is used as input for convolutional neural network training. Since convolutional neural networks can recognize patterns across input matrix, it is expected that wide input matrix containing vibration data should yield good classification performance. Using convolutional neural networks (CNN) trained model, classification in one of the four classes can be performed. Additionally, number of kernels of CNN is optimized using grid search, as preliminary studies show that alternating number of kernels impacts classification results. This study accomplished the effective classification of different rotary machinery states using convolutional artificial neural network for classification of raw three axis accelerometer signal input.
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Keywords:
maintenance; rotary machinery; fault diagnosis; convolutional neural network; classification
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MDPI and ACS Style
Kolar, D.; Lisjak, D.; Pająk, M.; Pavković, D. Fault Diagnosis of Rotary Machines Using Deep Convolutional Neural Network with Wide Three Axis Vibration Signal Input. Sensors 2020, 20, 4017. https://doi.org/10.3390/s20144017
AMA Style
Kolar D, Lisjak D, Pająk M, Pavković D. Fault Diagnosis of Rotary Machines Using Deep Convolutional Neural Network with Wide Three Axis Vibration Signal Input. Sensors. 2020; 20(14):4017. https://doi.org/10.3390/s20144017
Chicago/Turabian StyleKolar, Davor, Dragutin Lisjak, Michał Pająk, and Danijel Pavković. 2020. "Fault Diagnosis of Rotary Machines Using Deep Convolutional Neural Network with Wide Three Axis Vibration Signal Input" Sensors 20, no. 14: 4017. https://doi.org/10.3390/s20144017
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