Application of Deep Learning Method for Condition Monitoring and Fault Diagnosis from Vibration Data in Bearings

Yıl 2022, Cilt: 10 Sayı: 2, 346 - 365, 01.06.2022

Yunus Emre Karabacak , Nurhan Gürsel Özmen

https://doi.org/10.36306/konjes.1049489

Öz

Since bearings are machine elements that are frequently used in several industry due to their load carrying capacity, they are subjected to wear or breakage such as adhesion, abrasion and creep under overloading conditions. For this reason, condition monitoring and fault detection are an important issue for sustainability, high performance and reliability. Feature selection is a difficult task, hence, some features may change due to changing working conditions. Therefore, in this study, convolutional neural networks (ESA), which is a deep learning method in which features are determined by internal dynamics, are used for the detection of healthy bearings (SR) and bearing failures (outer ring failure-AR1, inner ring failure-AR2, rolling element failure-AR3). In order to train ESA approaches with different architectures, spectrograms of vibration signals using Short-Time Fourier Transform were obtained. The results of GoogleNet, ResNet-50, EfficientNet-B0 and AlexNet approaches that are trained with spectograms are comparatively examined. It has been seen that ESAs with complex architectures (GoogleNet, ResNet-50, EfficientNet-B0 ) detect failures with 100% accuracy and AlexNet with 90% accuracy, but it has been observed that the training time increases as the network structure changes and the number of layers increases. It is observed that the results of the study are far better than the similar papers in the literature. As a result, it is seen that the convolutional neural network method with different approaches provides high classification accuracy in the most basic bearing fault detection and is a promising method for fault diagnosis.

Anahtar Kelimeler

Fault detection, Bearings, Deep Learning, Condition Monitoring, Vibration

RULMANLARDA TİTREŞİM VERİLERİNDEN DURUM İZLEME VE ARIZA TEŞHİSİ İÇİN DERİN ÖĞRENME YÖNTEMİNİN UYGULANMASI

Öz

Rulmanlar, yük taşıma kapasiteleri nedeniyle endüstride pek çok alanda sıklıkla kullanılan makine elemanları olduklarından, aşırı yükleme durumlarında adhezyon, abrazyon ve sürünme gibi aşınma türlerine ya da kırılmalara maruz kalabilirler. Bu nedenle, rulmanlarda durum izlemesi yapılması ve arızaların teşhis edilmesi, sürdürülebilirlik, yüksek performans ve güvenlik açılarından önemli bir husustur. Arıza türlerinin ayırt edilmesinde belirleyici özniteliklerin seçilmesi, farklı çalışma koşullarında bir takım öznitelikler de değişebildiğinden zor bir süreçtir. Bu nedenle, bu çalışmada sağlıklı rulmanların (SR) ve rulman arızalarının (dış bilezik arızası-AR1, iç bilezik arızası-AR2, yuvarlanma arızası-AR3) tespiti için özniteliklerin içsel dinamiklerle belirlendiği derin öğrenme yöntemi olan olan evrişimli sinir ağları (ESA) kullanılmıştır. Birbirinden farklı mimarilere sahip ESA yaklaşımlarını eğitmek için Kısa Zamanlı Fourier Dönüşümü uygulanan titreşim sinyallerinin spektrogramları elde edilmiştir. Spektogram verileri ile eğitilen GoogleNet, ResNet-50, EfficientNet-B0 ve AlexNet yaklaşımlarının sonuçları karşılaştırmalı olarak incelenmiştir. Karmaşık mimariye sahip ESA’ların (GoogleNet, ResNet-50, EfficientNet-B0 ) arızaları %100 doğrulukla, AlexNet’in ise %90 doğrulukla tespit ettiği görülmüştür, ancak ağ yapısı değiştikçe ve katman saysı arttıkça eğitim süresinin de uzadığı görülmüştür. Elde edilen sonuçların literatürdeki çalışmaların sonuçlarından üstün olduğu gözlenmiştir. Sonuç olarak, farklı yaklaşımlara sahip evrişimli sinir ağları yönteminin en temel rulman arıza tespitinde yüksek sınıflandırma doğruluğu sağladığı ve arıza teşhisi için umut vadeden bir yöntem olduğu görülmektedir.

Anahtar Kelimeler

Arıza teşhisi, Rulmanlar, Derin Öğrenme, Durum İzleme, Titreşim

Kaynakça

• Al Shorman, O., Irfan, M., Saad, N., Zhen, D., Haider, N., Glowacz, A., AlShorman, A., 2020, “A review of artificial intelligence methods for condition monitoring and fault diagnosis of rolling element bearings for induction motor”, Shock and Vibration, Cilt 2020.
• Amarnath, M., Sugumaran, V., Kumar, H., 2013, “Exploiting sound signals for fault diagnosis of bearings using decision tree”, Measurement, Cilt 46, Sayı 3, ss. 1250-1256.
• Caesarendra, W., Tjahjowidodo, T., 2017, “A review of feature extraction methods in vibration-based condition monitoring and its application for degradation trend estimation of low-speed slew bearing”, Machines, Cilt 5, Sayı 4, ss. 21.
• Cakir, M., Guvenc, M. A., Mistikoglu, S., 2021, “The experimental application of popular machine learning algorithms on predictive maintenance and the design of IoT based condition monitoring system”, Computers & Industrial Engineering, Cilt 151, ss. 106948.
• Chen, Z., Gryllias, K., Li, W., 2019, “Mechanical fault diagnosis using convolutional neural networks and extreme learning machine”, Mechanical systems and signal processing, Cilt 133, ss. 106272.
• Chen, Z., Li, C., Sanchez, R. V., 2015, “Gearbox fault identification and classification with convolutional neural networks”, Shock and Vibration, Cilt 2015.
• Cheng, H., Kong, X., Chen, G., Wang, Q., Wang, R., 2021, “Transferable convolutional neural network based remaining useful life prediction of bearing under multiple failure behaviors”, Measurement, Cilt 168, ss. 108286.
• Choudhary, A., Shimi, S. L., Akula, A., 2018, “Bearing fault diagnosis of induction motor using thermal imaging”, 2018 International Conference on Computing, Power and Communication Technologies (GUCON), ss. 950-955, IEEE.
• Correa, J. C. A. J., Guzman, A. A. L., 2020, Mechanical Vibrations and Condition Monitoring, Academic Press. CWRU Rulman Veri Merkezi web sitesi. http://csegroups.case.edu/bearingdatacenter)(14.12.2022)
• Dong, S., Luo, T., Zhong, L., Chen, L., Xu, X., 2017, “Fault diagnosis of bearing based on the kernel principal component analysis and optimized k-nearest neighbour model”, Journal of Low Frequency Noise, Vibration and Active Control, 36(4), 354-365.
• Duan, Z., Wu, T., Guo, S., Shao, T., Malekian, R., Li, Z., 2018, “Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review”, The International Journal of Advanced Manufacturing Technology, 96(1), 803-819.
• Elforjani, M., Shanbr, S., 2017, “Prognosis of bearing acoustic emission signals using supervised machine learning”, IEEE Transactions on industrial electronics, 65(7), 5864-5871.
• Eren, B., Guvenc, M. A., Mistikoglu, S., 2021, “Artificial intelligence applications for friction stir welding: A review”, Metals and Materials International, 27(2), 193-219.
• Geitner, F. K., Bloch, H. P., 2012, Chapter 3 machinery component failure analysis, Machinery failure analysis and troubleshooting 4th edn. Butterworth-Heinemann, Oxford, 87-293.
• Goyal, D., Choudhary, A., Pabla, B. S., Dhami, S. S., 2020, “Support vector machines based non-contact fault diagnosis system for bearings”, Journal of Intelligent Manufacturing, 31(5), 1275-1289.
• Guo, S., Zhang, B., Yang, T., Lyu, D., Gao, W., 2019, “Multitask convolutional neural network with information fusion for bearing fault diagnosis and localization”, IEEE Transactions on Industrial Electronics, 67(9), 8005-8015.
• Gupta, P., Pradhan, M. K., 2017, “Fault detection analysis in rolling element bearing: A review”, Materials Today: Proceedings, 4(2), 2085-2094.
• Hao, X., Zheng, Y., Lu, L., Pan, H., 2021, “Research on Intelligent Fault Diagnosis of Rolling Bearing Based on Improved Deep Residual Network”, Applied Sciences, 11(22), 10889.
• Hase, A., 2020, “Early detection and identification of fatigue damage in thrust ball bearings by an acoustic emission technique”, Lubricants, 8(3), 37.
• Hoang, D. T., Kang, H. J., 2019, “A motor current signal-based bearing fault diagnosis using deep learning and information fusion”, IEEE Transactions on Instrumentation and Measurement, 69(6), 3325-3333.
• Hoang, D. T., Kang, H. J., 2019, “Rolling element bearing fault diagnosis using convolutional neural network and vibration image”, Cognitive Systems Research, 53, 42-50.
• Huang, W., Cheng, J., Yang, Y., Guo, G., 2019, “An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis”, Neurocomputing, 359, 77-92.
• Islam, M. M., Kim, J. M., 2019, “Automated bearing fault diagnosis scheme using 2D representation of wavelet packet transform and deep convolutional neural network”, Computers in Industry, 106, 142-153.
• Jeon, H., Jung, Y., Lee, S., Jung, Y., 2020, “Area-Efficient Short-Time Fourier Transform Processor for Time–Frequency Analysis of Non-Stationary Signals”, Applied Sciences, 10(20), 7208.
• Jing, L., Zhao, M., Li, P., Xu, X., 2017, “A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox”, Measurement, 111, 1-10.
• Karabacak, Y. E., Gürsel Özmen, N., Gümüşel, L., 2020, “Worm gear condition monitoring and fault detection from thermal images via deep learning method”, Mintenance and Reliability, 22(3).
• Karabacak, Y. E., Özmen, N. G., 2021, “Common Spatial Pattern-based Feature Extraction and Worm Gear Fault Detection through Vibration and Acoustic Measurements”, Measurement, 110366.
• Karabacak, Y. E., Özmen, N. G., Gümüşel, L., 2022, “Intelligent worm gearbox fault diagnosis under various working conditions using vibration, sound and thermal features”, Applied Acoustics, 186, 108463.
• Kim, S., An, D., Choi, J. H., 2020, “Diagnostics 101: A Tutorial for Fault Diagnostics of Rolling Element Bearing Using Envelope Analysis in MATLAB”, Applied Sciences, 10(20), 7302.
• Kumar, A., Vashishtha, G., Gandhi, C. P., Zhou, Y., Glowacz, A., Xiang, J., 2021, “Novel convolutional neural network (NCNN) for the diagnosis of bearing defects in rotary machinery”, IEEE Transactions on Instrumentation and Measurement, 70, 1-10.
• Kumar, A., Zhou, Y., Gandhi, C. P., Kumar, R., Xiang, J., 2020, “Bearing defect size assessment using wavelet transform based Deep Convolutional Neural Network (DCNN)”, Alexandria Engineering Journal, 59(2), 999-1012.
• Li, H., Huang, J., Ji, S., 2019, “Bearing fault diagnosis with a feature fusion method based on an ensemble convolutional neural network and deep neural network”, Sensors, 19(9), 2034.
• Li, X., Zhang, W., Ding, Q., 2019, “Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction”, Reliability engineering & system safety, 182, 208-218.
• Liu, Z., Zhang, L., Carrasco, J., 2020, “Vibration analysis for large-scale wind turbine blade bearing fault detection with an empirical wavelet thresholding method”, Renewable Energy, 146, 99-110.
• Ma, P., Zhang, H., Fan, W., Wang, C., Wen, G., Zhang, X., 2019, “A novel bearing fault diagnosis method based on 2D image representation and transfer learning-convolutional neural network”, Measurement Science and Technology, 30(5), 055402.
• Malla, C., Panigrahi, I., 2019, “Review of condition monitoring of rolling element bearing using vibration analysis and other techniques”, Journal of Vibration Engineering & Technologies, 7(4), 407-414.
• Matlab, Derin Öğrenme Araç Kutusu, https://www.mathworks.com/, 20.02.2020.
• Mohd Ghazali, M. H., Rahiman, W., 2021, “Vibration Analysis for Machine Monitoring and Diagnosis: A Systematic Review”, Shock and Vibration, Article ID 9469318.
• Nguyen-Schäfer, H., 2016, “Contact stresses in rolling bearings”, Computational Design of Rolling Bearings, ss. 47-61, Springer, Cham.
• O’Lmasov Ahadjon Akramjon, O. G., 2020, “New approaches in the diagnosis and monitoring of rotor oscillations using shaft sensors”, Science and Education, 1(1), 158-166.
• Peng, Y., Cai, J., Wu, T., Cao, G., Kwok, N., Zhou, S., Peng, Z., 2019, “Online wear characterisation of rolling element bearing using wear particle morphological features”, Wear, 430, 369-375.
• Randall, R. B., 2021, “Vibration-based condition monitoring: industrial, automotive and aerospace applications”, John Wiley & Sons.
• Ranjan, R., Ghosh, S. K., Kumar, M., 2020, “Fault diagnosis of journal bearing in a hydropower plant using wear debris, vibration and temperature analysis: A case study”, Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 234(3), 235-242.
• Rezaei, A., Dadouche, A., Wickramasinghe, V., Dmochowski, W., 2011, “A comparison study between acoustic sensors for bearing fault detection under different speed and load using a variety of signal processing techniques”, Tribology Transactions, 54(2), 179-186.
• Sadoughi, M., Hu, C., 2019, “Physics-based convolutional neural network for fault diagnosis of rolling element bearings”, IEEE Sensors Journal, 19(11), 4181-4192.
• Schwendemann, S., Amjad, Z., Sikora, A., 2021, “A survey of machine-learning techniques for condition monitoring and predictive maintenance of bearings in grinding machines”, Computers in Industry, 125, 103380.
• Shao, H., Xia, M., Han, G., Zhang, Y., Wan, J., 2020, “Intelligent fault diagnosis of rotor-bearing system under varying working conditions with modified transfer convolutional neural network and thermal images”, IEEE Transactions on Industrial Informatics, 17(5), 3488-3496.
• Sharma, V., Parey, A., 2017, “Frequency domain averaging based experimental evaluation of gear fault without tachometer for fluctuating speed conditions”, Mechanical Systems and Signal Processing, 85, 278-295.
• She, D., Jia, M., 2019, “Wear indicator construction of rolling bearings based on multi-channel deep convolutional neural network with exponentially decaying learning rate”, Measurement, 135, 368-375.
• Skf, Rulman Kataloğu, https://www.skf.com/, 19.02.2022.
• Smith, W. A., Randall, R. B., 2015, “Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study”, Mechanical systems and signal processing, 64, 100-131.
• Sun, M., Song, Z., Jiang, X., Pan, J., Pang, Y., 2017, “Learning pooling for convolutional neural network”, Neurocomputing, 224, 96-104.
• Sun, J., Yan, C., Wen, J., 2017, “Intelligent bearing fault diagnosis method combining compressed data acquisition and deep learning”, IEEE Transactions on Instrumentation and Measurement, 67(1), 185-195.
• Usamentiaga, R., Venegas, P., Guerediaga, J., Vega, L., Molleda, J., Bulnes, F. G., 2014, “Infrared thermography for temperature measurement and non-destructive testing”, Sensors, 14(7), 12305-12348.
• Vashisht, R. K., Peng, Q., 2018, “Crack detection in the rotor ball bearing system using switching control strategy and Short Time Fourier Transform”, Journal of Sound and Vibration, 432, 502-529.
• Wu, Y., Zhao, R., Jin, W., He, T., Ma, S., Shi, M., 2021, “Intelligent fault diagnosis of rolling bearings using a semi-supervised convolutional neural network”, Applied Intelligence, 51(4), 2144-2160.
• Xiong, S., Zhou, H., He, S., Zhang, L., Xia, Q., Xuan, J., Shi, T., 2020, “A novel end-to-end fault diagnosis approach for rolling bearings by integrating wavelet packet transform into convolutional neural network structures”, Sensors, 20(17), 4965.
• Xu, Z., Li, C., Yang, Y., 2021, “Fault diagnosis of rolling bearings using an improved multi-scale convolutional neural network with feature attention mechanism”, ISA transactions, 110, 379-393.
• Yamashita, R., Nishio, M., Do, R. K. G., Togashi, K., 2018, “Convolutional neural networks: an overview and application in radiology”, Insights into imaging, 9(4), 611-629.
• Yoo, Y., Baek, J. G., 2018, “A novel image feature for the remaining useful lifetime prediction of bearings based on continuous wavelet transform and convolutional neural network”, Applied Sciences, 8(7), 1102.
• Zhang, W., Li, C., Peng, G., Chen, Y., Zhang, Z., 2018, “A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load”, Mechanical Systems and Signal Processing, 100, 439-453.
• Zhang, X., Zhou, J., 2013, “Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines”, Mechanical Systems and Signal Processing, 41(1-2), 127-140.
• Zhang, Y., Xing, K., Bai, R., Sun, D., Meng, Z., 2020, “An enhanced convolutional neural network for bearing fault diagnosis based on time–frequency image”, Measurement, 157, 107667.
• Zhao, D., Wang, T., Chu, F., 2019, “Deep convolutional neural network based planet bearing fault classification”, Computers in Industry, 107, 59-66.
• Zhu, J., Chen, N., Peng, W., 2018, “Estimation of bearing remaining useful life based on multiscale convolutional neural network”, IEEE Transactions on Industrial Electronics, 66(4), 3208-3216.
• Zhu, Z., Peng, G., Chen, Y., Gao, H., 2019, “A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis”, Neurocomputing, 323, 62-75.