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A novel bearing fault detection approach using a convolutional neural network

  • Tolga Aydın

    Dr. Tolga Aydın received a MSc and PhD degree in Computer Engineering from Bilkent University, Türkiye. Dr. Aydın is an Asst. Prof. of Computer Engineering at Ataturk University, Türkiye. His research area includes Algorithms, Artificial Intelligence, Machine Learning and Data Mining.

         , Ebru Erdem

    Ebru Erdem received an MSc in Computer Engineering from Ataturk University, Türkiye. Erdem is a Research Assistant in Computer Engineering at Ataturk University, Türkiye. Her research area includes Computer Science, Algorithms, Distributed Systems, Artificial Intelligence, Engineering and Technology.

         , Burak Erkayman

    Assoc. Prof. Dr. Burak Erkayman received a MSc in Industrial Engineering from Yıldız Technical University and a PhD in Industrial Engineering from Sakarya University, Türkiye, respectively. Dr. Erkayman is an Assoc. Prof. of Industrial Engineering at Ataturk University, Türkiye. His research area includes Information Systems, Operations Management and Simulation. His current teaching interest is focused on Enterprise Resource Planning, Business Intelligence, and Decision Support Systems. His academic publications have appeared in some indexed journals.

     ORCID logo EMAIL logo     , Mustafa Engin Kocadağistan

    Dr. Mustafa Engin Kocadağistan, born in 1965, works at the University of Ataturk, Faculty of Engineering, Department of Metallurgy and Materials Engineering, Erzurum, Türkiye. He graduated in Mining Engineering from the Technical University of İstanbul, Türkiye, in 1989. He received his MSc and PhD degrees from Ataturk University, Erzurum, Türkiye, in 2015. He studied open-pit chrome mining, bioleaching and hydrometallurgy techniques, solid-state welding methods, boron ores, and nature restoration in open-pit mining.

         and Tanju Teker

    Prof. Dr. Tanju Teker, born in 1971, works at the University of Sivas Cumhuriyet, Faculty of Technology, Department of Manufacturing Engineering, Sivas, Türkiye. He graduated in Metallurgy Education from Gazi University, Ankara, Türkiye, in 1997. He received his MSc and PhD degrees from Firat University, Elazig, Türkiye, in 2004 and 2010, respectively. He studied metal coating techniques, fusion and solid-state welding methods, casting, and wear.
Published/Copyright: January 30, 2024
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Materials Testing
From the journal Materials Testing Volume 66 Issue 4

Abstract

Bearing fault detection is an important part of mechanical equipment and rotating machinery. Bearing failure should be detected early because it can lead to property and safety losses. This study proposes convolutional neural network (CNN) based models for bearing fault detection. Since the main advantages of the proposed methods apply to different types of warehouse data, failure can be detected in a short time and applied directly to raw data. These new models achieve comparable or better performance compared to the existing models in the literature. Although the structure of the proposed models is simpler and the number of parameters used is smaller, these new models achieve successful empirical results. Data sets from CWRU and IMS were used to test the models. This study compares the proposed models with the existing models in the literature. It also compares the new models with the machine learning algorithms and obtains better empirical results.

Keywords: bearing material properties; CNN; deep learning; fault detection; machine learning
Corresponding author: Burak Erkayman, Department of Industrial Engineering, Atatürk Üniversitesi, Erzurum, Türkiye, E-mail:

About the authors

Tolga Aydın

Dr. Tolga Aydın received a MSc and PhD degree in Computer Engineering from Bilkent University, Türkiye. Dr. Aydın is an Asst. Prof. of Computer Engineering at Ataturk University, Türkiye. His research area includes Algorithms, Artificial Intelligence, Machine Learning and Data Mining.

Ebru Erdem

Ebru Erdem received an MSc in Computer Engineering from Ataturk University, Türkiye. Erdem is a Research Assistant in Computer Engineering at Ataturk University, Türkiye. Her research area includes Computer Science, Algorithms, Distributed Systems, Artificial Intelligence, Engineering and Technology.

Burak Erkayman

Assoc. Prof. Dr. Burak Erkayman received a MSc in Industrial Engineering from Yıldız Technical University and a PhD in Industrial Engineering from Sakarya University, Türkiye, respectively. Dr. Erkayman is an Assoc. Prof. of Industrial Engineering at Ataturk University, Türkiye. His research area includes Information Systems, Operations Management and Simulation. His current teaching interest is focused on Enterprise Resource Planning, Business Intelligence, and Decision Support Systems. His academic publications have appeared in some indexed journals.

Mustafa Engin Kocadağistan

Dr. Mustafa Engin Kocadağistan, born in 1965, works at the University of Ataturk, Faculty of Engineering, Department of Metallurgy and Materials Engineering, Erzurum, Türkiye. He graduated in Mining Engineering from the Technical University of İstanbul, Türkiye, in 1989. He received his MSc and PhD degrees from Ataturk University, Erzurum, Türkiye, in 2015. He studied open-pit chrome mining, bioleaching and hydrometallurgy techniques, solid-state welding methods, boron ores, and nature restoration in open-pit mining.

Tanju Teker

Prof. Dr. Tanju Teker, born in 1971, works at the University of Sivas Cumhuriyet, Faculty of Technology, Department of Manufacturing Engineering, Sivas, Türkiye. He graduated in Metallurgy Education from Gazi University, Ankara, Türkiye, in 1997. He received his MSc and PhD degrees from Firat University, Elazig, Türkiye, in 2004 and 2010, respectively. He studied metal coating techniques, fusion and solid-state welding methods, casting, and wear.

  1. Research ethics: Not applicable.

  2. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: None declared.

  5. Data availability: The raw data can be obtained on request from the corresponding author.

References

[1] X. Chen, B. Zhang, and D. Gao, “Bearing fault diagnosis base on multi-scale CNN and LSTM model,” J. Intell. Manuf., vol. 32, no. 1, pp. 971–987, 2021, https://doi.org/10.1007/s10845-020-01600-2.Search in Google Scholar

[2] R. Magar, L. Ghule, J. Li, Y. Zhao, and A. B. Farimani, “FaultNet: a deep convolutional neural network for bearing fault classification,” IEEE Access, vol. 9, no. 1, pp. 25189–25199, 2021, https://doi.org/10.1109/access.2021.3056944.Search in Google Scholar

[3] A. Shenfield and M. Howarth, “A novel deep learning model for the detection and identification of rolling element-bearing faults,” Sensors, vol. 2, no. 18, p. 5112, 2020, https://doi.org/10.3390/s20185112.Search in Google Scholar PubMed PubMed Central

[4] A. Zhang, S. Li, Y. Cui, W. Yang, R. Dong, and J. Hu, “Limited data rolling bearing fault diagnosis with few-shot learning,” IEEE Access, vol. 7, no. 1, pp. 110895–110904, 2019, https://doi.org/10.1109/access.2019.2934233.Search in Google Scholar

[5] A. Karaduman, H. Lekesiz and A. R. Yildiz, “Minimization of release bearing load loss in a clutch system for high-speed rotations using the differential evolution algorithm,” Mater. Test., vol. 64, no. 11, pp. 1627–1635, 2022, https://doi.org/10.1515/mt-2022-0111.Search in Google Scholar

[6] H. Abderazek, A. R. Yildiz, and S. M. Sait, “Optimization of constrained mechanical design problems using the equilibrium optimization algorithm,” Mater. Test., vol. 63, no. 6, pp. 552–559, 2021, https://doi.org/10.1515/mt-2020-0092.Search in Google Scholar

[7] N. Sabangban, N. Panagant, S. Bureerat, K. Wansasueb, S. Kuma, A. R. Yildiz, and N. Pholdee, “Simultaneous aerodynamic and structural optimisation of a low-speed horizontal-axis wind turbine blade using metaheuristic algorithms,” Mater. Test., vol. 65, no. 5, pp. 699–714, 2023, https://doi.org/10.1515/mt-2022-0308.Search in Google Scholar

[8] S. Balli and F. Sen, “Performance evaluation of artificial neural networks for identification of failure modes in composite plates,” Mater. Test., vol. 63, no. 6, pp. 565–570, 2021, https://doi.org/10.1515/mt-2020-0094.Search in Google Scholar

[9] Y. Chen, G. Peng, C. Xie, W. Zhang, C. Li, and S. Liu, “ACDIN: bridging the gap between artificial and real bearing damages for bearing fault diagnosis,” Neurocomputing, vol. 294, no. 1, pp. 61–71, 2018, https://doi.org/10.1016/j.neucom.2018.03.014.Search in Google Scholar

[10] X. Ding and Q. He, “Energy-fluctuated multiscale feature learning with deep convnet for intelligent spindle bearing fault diagnosis,” IEEE Trans. Instrum. Meas., vol. 66, no. 1, pp. 1926–1935, 2017, https://doi.org/10.1109/tim.2017.2674738.Search in Google Scholar

[11] S. Guo, T. Yang, W. Gao, C. Zhang, and Y. Zhang, “An intelligent fault diagnosis method for bearings with variable rotating speed based on pythagorean spatial pyramid pooling CNN,” Sensors, vol. 18, no. 11, p. 3857, 2018, https://doi.org/10.3390/s18113857.Search in Google Scholar PubMed PubMed Central

[12] J. Pan, Y. Zi, J. Chen, Z. Zhou, and B. Wang, “LiftingNet, A novel deep learning network with layerwise feature learning from noisy mechanical data for fault classification,” IEEE Trans. Ind. Electron., vol. 65, no. 1, pp. 4973–4982, 2017, https://doi.org/10.1109/tie.2017.2767540.Search in Google Scholar

[13] W. Zhang, C. Li, G. Peng, Y. Chen, and Z. Zhang, “A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load,” Mech. Syst. Signal Process., vol. 100, pp. 439–453, 2018, https://doi.org/10.1016/j.ymssp.2017.06.022.Search in Google Scholar

[14] Z. Zilong and Q. Wei, “Intelligent fault diagnosis of rolling bearing using one-dimensional multi-scale deep convolutional neural network based health state classification,” in 2018 IEEE 15th International conference on networking, sensing and control (ICNSC), 2018, pp. 1–6.10.1109/ICNSC.2018.8361296Search in Google Scholar

[15] L. Wen, X. Li, L. Gao, and Y. Zhang, “A new convolutional neural network-based data-driven fault diagnosis method,” IEEE Trans. Ind. Electron., vol. 65, no. 1, pp. 5990–5998, 2017, https://doi.org/10.1109/tie.2017.2774777.Search in Google Scholar

[16] M. Xia, T. Li, L. Xu, L. Liu, and C. W. De Silva, “Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks,” IEEE/ASME Trans. Mechatron., vol. 23, no. 1, pp. 101–110, 2017, https://doi.org/10.1109/tmech.2017.2728371.Search in Google Scholar

[17] R. Liu, G. Meng, B. Yang, C. Sun, and X. Chen, “Dislocated time series convolutional neural architecture: an intelligent fault diagnosis approach for electric machine,” IEEE Trans. Ind. Inf., vol. 13, no. 1, pp. 1310–1320, 2016, https://doi.org/10.1109/tii.2016.2645238.Search in Google Scholar

[18] C. A. Yi, Y. L. Wang, H. Y. Lai, Y. W. Chen, and C. Y. Yang, “Bearing fault diagnosis with deep learning models,” in 2020 International Conference on Image Processing and Robotics (ICIP), 2020, pp. 1–6.10.1109/ICIP48927.2020.9367335Search in Google Scholar

[19] J. Lee, H. Qiu, G. Yu, and J. Lin, and Rexnord Technical Services, Data from: Bearing Data Set, IMS, University of Cincinnati, NASA Ames Prognostics Data Repository, 2007. Available at: http://ti.arc.nasa.gov/project/prognostic-datarepository.Search in Google Scholar

[20] B. Sahoo, Data from: Data-Driven Machinery Fault Diagnosis, 2016. Available at: https://biswajitsahoo1111.github.io/cbm_codes_open.Search in Google Scholar

[21] L. A. Pinedo-Sánchez, D. A. Mercado-Ravell, and C. A. Carballo-Monsivais, “Vibration analysis in bearings for failure prevention using CNN,” J. Braz. Soc. Mech. Sci. Eng., vol. 42, no. 1, pp. 1–17, 2020, https://doi.org/10.1007/s40430-020-02711-w.Search in Google Scholar

[22] Y. Hu and X. Li, “Bearing and gearbox data for fault diagnostics application,” Mendeley Data, vol. 1, no. 1, p. 1, 2019, https://doi.org/10.17632/fkp3nn4tp7.1.Search in Google Scholar

[23] P. S. Addison, The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance, Oxfordshire, CRC Press, 2017.Search in Google Scholar

[24] L. Breiman, “Bagging predictors,” Mach. Learn., vol. 24, no. 1, pp. 123–140, 1996, https://doi.org/10.1007/bf00058655.Search in Google Scholar

[25] V. N. Vapnik, “Adaptive and learning systems for signal processing communications, and control,” Stat. Learn. Theory, vol. 1, no. 1, pp. 45–57, 1998, https://doi.org/10.1007/bf00058655.Search in Google Scholar

[26] J. Wang, D. Wang, S. Wang, W. Li, and K. SongWang, “Fault diagnosis of bearings based on multi-sensor information fusion and 2D convolutional neural network,” IEEE Access, vol. 9, no. 1, pp. 23717–23725, 2021, https://doi.org/10.1109/access.2021.3056767.Search in Google Scholar

[27] N. Riaz, S. I. A. Shah, and F. Rehman, “An intelligent hybrid scheme for identification of faults in industrial ball screw linear motion systems,” IEEE Access, vol. 9, no. 1, pp. 35136–35150, 2021, https://doi.org/10.1109/access.2021.3062496.Search in Google Scholar

[28] S. Zhang, F. Ye, B. Wang, and T. G. Habetler, “Semi-supervised bearing fault diagnosis and classification using variational autoencoder-based deep generative models,” IEEE Sens. J., vol. 21, no. 1, pp. 6476–6486, 2020, https://doi.org/10.1109/jsen.2020.3040696.Search in Google Scholar

[29] A. Khorram, M. Khalooei, and M. Rezghi, “End-to-end CNN+ LSTM deep learning approach for bearing fault diagnosis,” Appl. Intell., vol. 51, no. 1, pp. 736–751, 2021, https://doi.org/10.1007/s10489-020-01859-1.Search in Google Scholar

[30] X. Li, Y. Hu, M. Li, and J. Zheng, “Fault diagnostics between different type of components: a transfer learning approach,” Appl. Soft Comput., vol. 86, no. 1, p. 105950, 2020, https://doi.org/10.1016/j.asoc.2019.105950.Search in Google Scholar

[31] S. Ma, W. Liu, W. Cai, Z. Shang, and G. Liu, “Lightweight deep residual CNN for fault diagnosis of rotating machinery based on depthwise separable convolutions,” IEEE Access, vol. 7, no. 1, pp. 57023–57036, 2019, https://doi.org/10.1109/access.2019.2912072.Search in Google Scholar

[32] Z. Zhuang, H. Lv, J. Xu, Z. Huang, and W. Qin, “A deep learning method for bearing fault diagnosis through stacked residual dilated convolutions,” Appl. Sci., vol. 9, no. 9, p. 2019, 1823, https://doi.org/10.3390/app9091823.Search in Google Scholar

[33] M. Sohaib and J. M. Kim, “Fault diagnosis of rotary machine bearings under inconsistent working conditions,” IEEE Trans. Instrum. Meas., vol. 69, no. 1, pp. 3334–3347, 2019, https://doi.org/10.1109/tim.2019.2933342.Search in Google Scholar

[34] J. W. Oh and J. Jeong, “Convolutional neural network and 2-D image based fault diagnosis of bearing without retraining,” in Proceedings of the 2019 3rd International Conference on Compute and Data Analysis, 2019, pp. 134–138.10.1145/3314545.3314563Search in Google Scholar

[35] W. Mao, Y. Liu, L. Ding, and Y. Li, “Imbalanced fault diagnosis of rolling bearing based on generative adversarial network: a comparative study,” IEEE Access, vol. 7, pp. 9515–9530, 2019, https://doi.org/10.1109/access.2018.2890693.Search in Google Scholar

[36] W. Zhang, F. Zhang, W. Chen, Y. Jiang, and D. Song, “Fault state recognition of rolling bearing based fully convolutional network,” Comput. Sci. Eng., vol. 21, no. 1, pp. 55–63, 2018, https://doi.org/10.1109/mcse.2018.110113254.Search in Google Scholar

[37] W. Qian, S. Li, J. Wang Zenghui, Z. An, and X. Jiang, “An intelligent fault diagnosis framework for raw vibration signals: adaptive overlapping convolutional neural network,” Meas. Sci. Technol., vol. 29, no. 1, p. 095009, 2018, https://doi.org/10.1088/1361-6501/aad101.Search in Google Scholar

[38] S. Haidong, J. Hongkai, L. Ying, and L. Xingqiu, “A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders,” Mech. Syst. Signal Process., vol. 102, no. 1, pp. 278–297, 2018, https://doi.org/10.1016/j.ymssp.2017.09.026.Search in Google Scholar

[39] H. Shao, H. Jiang, X. Li, and S. Wu, “Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine,” Knowledge-Based Syst., vol. 140, no. 1, pp. 1–14, 2018, https://doi.org/10.1016/j.knosys.2017.10.024.Search in Google Scholar

[40] W. Mao, S. Tian, X. Liang, and J. He, “Online bearing fault diagnosis using support vector machine and stacked auto-encoder,” in 2018 IEEE International Conference on Prognostics and Health Management (ICPHM), 2018, pp. 1–7.10.1109/ICPHM.2018.8448775Search in Google Scholar

[41] H. Jiang, X. Li, H. Shao, and K. Zhao, “Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network,” Meas. Sci. Technol., vol. 29, no. 1, p. 065107, 2018, https://doi.org/10.1088/1361-6501/aab945.Search in Google Scholar

[42] L. Eren, “Bearing fault detection by one-dimensional convolutional neural networks,” Math. Probl. Eng., vol. 2017, no. 8617315, pp. 1–9, 2017, https://doi.org/10.1155/2017/8617315.Search in Google Scholar

[43] C. Lu, Z. Wang, and B. Zhou, “Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification,” Adv. Eng. Inf., vol. 32, no. 1, pp. 139–151, 2017, https://doi.org/10.1016/j.aei.2017.02.005.Search in Google Scholar

[44] S. Li, G. Liu, X. Tang, J. Lu, and J. Hu, “An ensemble deep convolutional neural network model with improved DS evidence fusion for bearing fault diagnosis,” Sensors, vol. 17, no. 8, p. 1729, 2017, https://doi.org/10.3390/s17081729.Search in Google Scholar PubMed PubMed Central

[45] C. Lu, Z. Y. Wang, W. L. Qin, and J. Ma, “Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification,” Signal Process., vol. 130, no. 1, pp. 377–388, 2017, https://doi.org/10.1016/j.sigpro.2016.07.028.Search in Google Scholar

[46] J. Sun, C. Yan, and J. Wen, “Intelligent bearing fault diagnosis method combining compressed data acquisition and deep learning,” IEEE Trans. Instrum. Meas., vol. 67, no. 1, pp. 185–195, 2017, https://doi.org/10.1109/tim.2017.2759418.Search in Google Scholar

[47] X. Guo, L. Chen, and C. Shen, “Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis,” Measurement, vol. 93, pp. 490–502, 2016, https://doi.org/10.1016/j.measurement.2016.07.054.Search in Google Scholar
Published Online: 2024-01-30
Published in Print: 2024-04-25

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Strain-life behavior of thick-walled nodular cast iron
  3. A novel bearing fault detection approach using a convolutional neural network
  4. Improved Gx40CrNi25-20 grade austenitic stainless steel
  5. Enhanced strength of (CoFeNiMn)100−xCrx (x = 5, 20, 35 at.%) high entropy alloys via formation of carbide phases produced from industrial-grade raw materials
  6. Modeling of thrust force and torque in drilling aluminum 7050
  7. Construction of amidinothiourea crosslinked graphene oxide membrane by multilayer self-assembly for efficient removal of heavy metal ions
  8. Effect of tool rotational speed on friction stir spot welds of AZ31B Mg alloy to AISI 304 stainless steel
  9. A new enhanced mountain gazelle optimizer and artificial neural network for global optimization of mechanical design problems
  10. Effect of particle volume fraction on wear behavior in Al–SiC MMC coated on DIN AlZnMgCu1.5 alloy
  11. Processing, microstructural characterization, and mechanical properties of deep cryogenically treated steels and alloys – overview
  12. Experimental and numerical investigation of patch effect on the bending behavior for hat-shaped carbon fiber composite beams
  13. Influence of water on microstructure and mechanical properties of a friction stir spot welded 7075-T651 Al alloy
  14. Effect of copper powder addition on the product quality of sintered stainless steels
  15. Mechanical and thermal properties of short banana fiber reinforced polyoxymethylene composite materials dependent on alkali treatment
https://doi.org/10.1515/mt-2023-0334
Keywords for this article
bearing material properties; CNN; deep learning; fault detection; machine learning

Articles in the same Issue

  1. Frontmatter
  2. Strain-life behavior of thick-walled nodular cast iron
  3. A novel bearing fault detection approach using a convolutional neural network
  4. Improved Gx40CrNi25-20 grade austenitic stainless steel
  5. Enhanced strength of (CoFeNiMn)100−xCrx (x = 5, 20, 35 at.%) high entropy alloys via formation of carbide phases produced from industrial-grade raw materials
  6. Modeling of thrust force and torque in drilling aluminum 7050
  7. Construction of amidinothiourea crosslinked graphene oxide membrane by multilayer self-assembly for efficient removal of heavy metal ions
  8. Effect of tool rotational speed on friction stir spot welds of AZ31B Mg alloy to AISI 304 stainless steel
  9. A new enhanced mountain gazelle optimizer and artificial neural network for global optimization of mechanical design problems
  10. Effect of particle volume fraction on wear behavior in Al–SiC MMC coated on DIN AlZnMgCu1.5 alloy
  11. Processing, microstructural characterization, and mechanical properties of deep cryogenically treated steels and alloys – overview
  12. Experimental and numerical investigation of patch effect on the bending behavior for hat-shaped carbon fiber composite beams
  13. Influence of water on microstructure and mechanical properties of a friction stir spot welded 7075-T651 Al alloy
  14. Effect of copper powder addition on the product quality of sintered stainless steels
  15. Mechanical and thermal properties of short banana fiber reinforced polyoxymethylene composite materials dependent on alkali treatment
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