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Intelligent Fault Diagnosis of Rotary Machinery by Convolutional Neural Network with Automatic Hyper-Parameters Tuning Using Bayesian Optimization

1
Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lučića Street 5, 10002 Zagreb, Croatia
2
Faculty of Mechanical Engineering, University of Technology and Humanities in Radom, Stasieckiego Street 54, 26-600 Radom, Poland
*
Author to whom correspondence should be addressed.
Academic Editor: Ningyun Lu
Sensors 2021, 21(7), 2411; https://doi.org/10.3390/s21072411
Received: 23 February 2021 / Revised: 19 March 2021 / Accepted: 29 March 2021 / Published: 31 March 2021
(This article belongs to the Section Fault Diagnosis & Sensors)
Intelligent fault diagnosis can be related to applications of machine learning theories to machine fault diagnosis. Although there is a large number of successful examples, there is a gap in the optimization of the hyper-parameters of the machine learning model, which ultimately has a major impact on the performance of the model. Machine learning experts are required to configure a set of hyper-parameter values manually. This work presents a convolutional neural network based data-driven intelligent fault diagnosis technique for rotary machinery which uses model with optimized hyper-parameters and network structure. The proposed technique input raw three axes accelerometer signal as high definition 1-D data into deep learning layers with optimized hyper-parameters. Input is consisted of wide 12,800 × 1 × 3 vibration signal matrix. Model learning phase includes Bayesian optimization that optimizes hyper-parameters of the convolutional neural network. Finally, by using a Convolutional Neural Network (CNN) model with optimized hyper-parameters, classification in one of the 8 different machine states and 2 rotational speeds can be performed. This study accomplished the effective classification of different rotary machinery states in different rotational speeds using optimized convolutional artificial neural network for classification of raw three axis accelerometer signal input. Overall classification accuracy of 99.94% on evaluation set is obtained with the CNN model based on 19 layers. Additionally, more data are collected on the same machine with altered bearings to test the model for overfitting. Result of classification accuracy of 100% on second evaluation set has been achieved, proving the potential of using the proposed technique. View Full-Text
Keywords: rotary machinery; fault diagnosis; convolutional neural network; classification; hyper-parameters tuning; bayesian optimization rotary machinery; fault diagnosis; convolutional neural network; classification; hyper-parameters tuning; bayesian optimization
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MDPI and ACS Style

Kolar, D.; Lisjak, D.; Pająk, M.; Gudlin, M. Intelligent Fault Diagnosis of Rotary Machinery by Convolutional Neural Network with Automatic Hyper-Parameters Tuning Using Bayesian Optimization. Sensors 2021, 21, 2411. https://doi.org/10.3390/s21072411

AMA Style

Kolar D, Lisjak D, Pająk M, Gudlin M. Intelligent Fault Diagnosis of Rotary Machinery by Convolutional Neural Network with Automatic Hyper-Parameters Tuning Using Bayesian Optimization. Sensors. 2021; 21(7):2411. https://doi.org/10.3390/s21072411

Chicago/Turabian Style

Kolar, Davor, Dragutin Lisjak, Michał Pająk, and Mihael Gudlin. 2021. "Intelligent Fault Diagnosis of Rotary Machinery by Convolutional Neural Network with Automatic Hyper-Parameters Tuning Using Bayesian Optimization" Sensors 21, no. 7: 2411. https://doi.org/10.3390/s21072411

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