Rotating shaft fault prediction using convolutional neural network : a preliminary study

• Autorzy: Kolar Davor , Lisjak Dragutin , Pająk Michał

Identyfikatory

DOI

10.2478/kones-2019-0060

• Języki publikacji: EN

Abstrakty

EN

One of the most important subsystems of the vehicles and machines operating currently in industry and transportation are the rotating subsystems. During the operation, due to the forcing factors influence, the technical state of them is changing and the failure can occur. Fault diagnosis is maintenance task considered as an essential in such subsystems, since possibility of an early detection and diagnosis of the faulty condition can save both time and money. To do this the analysis of the subsystems vibrations is performed. The identified technical state should be considered in a context of the ability and different inability states. Therefore, the first step of the diagnostic procedure is the ability and different inability states identification. Traditional data-driven techniques of fault diagnosis require signal processing for feature extraction, as they are unable to work with raw signal data, consequently leading to need for both expert knowledge and human work. The emergence of deep learning architectures in condition-based maintenance promises to ensure high performance fault diagnosis while lowering necessity for expert knowledge and human work. This article presents authors initial research in deep learning-based data-driven fault diagnosis of rotating subsystems. The proposed technique input raw three-axis accelerometer signal as high-definition image into deep learning layers, which automatically extract signal features, enabling high classification accuracy.

Słowa kluczowe

EN

condition-based maintenance; rotating systems; fault diagnosis; convolutional neural networks

• Wydawca: Łukasiewicz Research Network - Institute of Aviation ; European Science Society of Powertrain and Transport KONES Poland ; Wydawnictwo Instytutu Technicznego Wojsk Lotniczych
• Czasopismo: Journal of KONES
• Rocznik: 2019
• Tom: Vol. 26, No. 3
• Strony: 75--81
• Opis fizyczny: Bibliogr. 12 poz., rys.

Twórcy

autor

Kolar Davor

• University of Zagreb Faculty of Mechanical Engineering and Naval Architecture Ivana Lučića Street 5, 10002 Zagreb, Croatia tel.: [phone]

autor

Lisjak Dragutin

• University of Zagreb Faculty of Mechanical Engineering and Naval Architecture Ivana Lučića Street 5, 10002 Zagreb, Croatia tel.: [phone]

autor

Pająk Michał

• University of Technology and Humanities in Radom Department of Thermal Technology Stasieckiego Street 54, 26-600 Radom, Poland tel.: [phone]

Bibliografia

• [1] Sinha, J. K., Elbhbah, K., A future possibility of vibration based condition monitoring of rotating machines, Mechanical Systems and Signal Processing, Vol. 34, No. 1-2, pp. 231-240, 2013.
• [2] Yan, J., Machinery prognostics and prognosis oriented maintenance management. Singapore: John Wiley & Sons Singapore Pte. Ltd, 2015.
• [3] Chan, T.-H., Jia, K., Gao, S., Lu, J., Zeng, Z., Ma, Y., PCANet: A Simple Deep Learning Baseline for Image Classification?, IEEE Transactions on Image Processing, Vol. 24, No. 12, pp. 5017-5032, 2015.
• [4] He, K., Zhang, X., Ren, S., Sun, J., Deep Residual Learning for Image Recognition, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, USA, Las Vegas, NV 2016.
• [5] Huang, P.-S., Kim, M., Hasegawa-Johnson, M., Smaragdis, P., Deep learning for monaural speech separation, in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1562-1566, Florence, Italy 2014.
• [6] Noda, K., Yamaguchi, Y., Nakadai, K., Okuno, H. G., Ogata, T., Audio-visual speech recognition using deep learning, Applied Intelligence, Vol. 42, No. 4, pp. 722-737, 2015.
• [7] Schmidhuber, J., “Deep learning in neural networks: An overview,” Neural Networks, Vol. 61, pp. 85-117, 2015.
• [8] Shaheryar, A., Yin, X.-C., Yousuf, W., Robust Feature Extraction on Vibration Data under Deep-Learning Framework: An Application for Fault Identification in Rotary Machines, International Journal of Computer Applications, Vol. 167, No. 4, pp. 37-45, 2017.
• [9] Li, S., Liu, G., Tang, X., Lu, J., Hu, J., An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis, Sensors, Vol. 17, No. 8, p. 1729, 2017.
• [10] Shao, S., Sun, W., Wang, P., Gao, R. X., Yan, R., Learning features from vibration signals for induction motor fault diagnosis, pp. 71-76, 2016.
• [11] Le Cun, Y., Bengio, Y., Hinton, G., Deep learning, Nature, Vol. 521, No. 7553, pp. 436-444, 2015.
• [12] Van der Maaten, L., Hinton, G., Visualizing non-metric similarities in multiple maps, Machine Learning, Vol. 87, No. 1, pp. 33-55, 2012.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).