Downloads

https://doi.org/10.62836/jcmea.v3i1.030108
Guojun Zhang, Zhou, T., & Yiqun Cai. (2023). CORAL-based Domain Adaptation Algorithm for Improving the Applicability of Machine Learning Models in Detecting Motor Bearing Failures. Journal of Computational Methods in Engineering Applications, 3(1), 1–17. https://doi.org/10.62836/jcmea.v3i1.030108
Volume 3, Issue 1, 2023

CORAL-based Domain Adaptation Algorithm for Improving the Applicability of Machine Learning Models in Detecting Motor Bearing Failures

Motor bearings are essential components in various industrial and transportation systems, vital for minimizing friction and enhancing machinery longevity. Failures in these bearings can lead to extensive machine downtime and significant repair costs, thereby emphasizing the need for effective predictive maintenance strategies. This paper focuses on leveraging advancements in Machine Learning (ML) and Artificial Intelligence (AI) to preemptively identify and rectify potential bearing failures, transitioning from traditional periodic maintenance to more efficient, condition-based approaches. We introduce a novel domain adaptation technique using Correlation Alignment (CORAL) to improve the accuracy of fault predictions across different operational settings. This method effectively minimizes the statistical disparities between training and operational data, enhancing the adaptability and effectiveness of predictive models. The results indicate that models equipped with domain adaptation outperform traditional models, particularly in their ability to generalize across diverse environments, thereby supporting more reliable and efficient predictive maintenance practices. This research contributes to the ongoing evolution of maintenance strategies in industrial settings, highlighting the potential of AI to transform traditional practices by reducing unexpected downtime and optimizing maintenance schedules.

motor beaing failtures; machine learning; domain adaptation

References

  1. Wittek E, Kriese M, Tischmacher H, et al. Capacitances and Lubricant Film Thicknesses of Motor Bearings under Different Operating Conditions. In Proceedings of the XIX International Conference on Electrical Machines-ICEM2010, Rome, Italy, 6–8 September 2010; pp. 1–6. DOI: https://doi.org/10.1109/ICELMACH.2010.5608142
  2. Devaney MJ, Eren L. Detecting Motor Bearing Faults. IEEE Instrumentation & Measurement Magazine 2004; 7(4): 30–50. DOI: https://doi.org/10.1109/MIM.2004.1383462
  3. Dai W. Evaluation and Improvement of Carrying Capacity of a Traffic System. Innovations in Applied Engineering and Technology 2022; 1–9. DOI: https://doi.org/10.58195/iaet.v1i1.001
  4. Yu L, Li J, Cheng S, et al. Secure Continuous Aggregation in Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems 2013; 25(3): 762–774. DOI: https://doi.org/10.1109/TPDS.2013.63
  5. Xiong S, Chen X, Zhang H. Deep Learning-Based Multifunctional End-to-End Model for Optical Character Classification and Denoising. Journal of Computational Methods in Engineering Applications 2023; 1–13. DOI: https://doi.org/10.62836/jcmea.v3i1.030103
  6. Wenjun D, Fatahizadeh M, Touchaei HG, et al. Application of Six Neural Network-Based Solutions on Bearing Capacity of Shallow Footing on Double-Layer Soils. Steel and Composite Structures2023; 49(2): 231–244.
  7. Du M, Liu N, Hu X. Techniques for Interpretable Machine Learning. Communications of the ACM 2019; 63(1): 68–77. DOI: https://doi.org/10.1145/3359786
  8. Xiong S, Zhang H, Wang M. Ensemble Model of Attention Mechanism-Based DCGAN and Autoencoder for Noised OCR Classification. Journal of Electronic & Information Systems 2022; 4(1): 33–41. DOI: https://doi.org/10.30564/jeis.v4i1.6725
  9. Ren P, Zhao Z, Yang Q. Exploring the Path of Transformation and Development for Study Abroad Consultancy Firms in China. Journal of Computational Methods in Engineering Applications 2023; 1–12.
  10. Lei J. Green Supply Chain Management Optimization Based on Chemical Industrial Clusters. Innovations in Applied Engineering and Technology 2022; 1–17. DOI: https://doi.org/10.62836/iaet.v1i1.003
  11. Li C, Tang Y.The Factors of Brand Reputation in Chinese Luxury Fashion Brands. Journal of Integrated Social Sciences and Humanities 2023; 1–14. DOI: https://doi.org/10.62836/jissh.v1i1.228
  12. Giger ML. Machine learning in Medical Imaging. Journal of the American College of Radiology 2018; 15(3): 512–520. DOI: https://doi.org/10.1016/j.jacr.2017.12.028
  13. Kononenko I. Machine Learning for Medical Diagnosis: History, State of the Art and Perspective. Artificial Intelligence in Medicine2001; 23(1): 89–109. DOI: https://doi.org/10.1016/S0933-3657(01)00077-X
  14. Richards BA, Lillicrap TP, Beaudoin P, et al. A Deep Learning Framework for Neuroscience. Nature Neuroscience2019; 22(11): 1761–1770. DOI: https://doi.org/10.1038/s41593-019-0520-2
  15. Farahani A, Voghoei S, Rasheed K, et al. A Brief Review of Domain Adaptation. In Advances in Data Science and Information Engineering, Proceedings of the ICDATA 2020 and IKE 2020, Las Vegas, NV, USA, 27–30 July 2020; Springer: Cham, Switzerland, 2021; pp. 877–894. DOI: https://doi.org/10.1007/978-3-030-71704-9_65
  16. Ben-David S, Blitzer J, Crammer K, et al. Analysis of Representations for Domain Adaptation. In Advances in Neural Information Processing Systems; MIT Press: Cambridge, MA, 2006; Volume 19. DOI: https://doi.org/10.7551/mitpress/7503.003.0022
  17. You K, Long M, Cao Z, et al. Universal Domain Adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition2019, Long Beach, CA, USA, 15–20 June 2019; pp. 2720–2729. DOI: https://doi.org/10.1109/CVPR.2019.00283
  18. Ganin Y, Lempitsky V. Unsupervised Domain Adaptation by Backpropagation. In Proceedings of the 32nd International Conference on Machine Learning, PMLR, Lille, France, 7–9 July 2015; pp. 1180–1189.
  19. Xiong S, Zhang H, Wang M, et al. Distributed Data Parallel Acceleration-Based Generative Adversarial Network for Fingerprint Generation. Innovations in Applied Engineering and Technology 2022; 1–12. DOI: https://doi.org/10.62836/iaet.2021.1003
  20. Murdoch WJ, Singh C, Kumbier K, et al. Definitions, Methods, and Applications in Interpretable Machine Learning. Proceedings of the National Academy of Sciences2 019; 116(44): 22071–22080. DOI: https://doi.org/10.1073/pnas.1900654116
  21. Hao Y, Chen Z, Jin J, et al. Joint Operation Planning of Drivers and Trucks for Semi-Autonomous Truck Platooning. Transportmetrica A: Transport Science 2023; 1–37. DOI: https://doi.org/10.1080/23249935.2023.2266041
  22. Wang C, Gan M, Zhu CA.Fault Feature Extraction of Rolling Element Bearings Based on Wavelet Packet Transform and Sparse Representation Theory. Journal of Intelligent Manufacturing 2018; 29: 937–951. DOI: https://doi.org/10.1007/s10845-015-1153-2
  23. Han S, Oh S, Jeong J. Bearing Fault Diagnosis Based on Multiscale Convolutional Neural Network Using Data Augmentation. Journal of Sensors 2021; 2021(1): 6699637. DOI: https://doi.org/10.1155/2021/6699637
  24. Zhang XP, Hu NQ, Hu L, et al. A Bearing Fault Diagnosis Method Based on Sparse Decomposition Theory. Journal of Central South University 2016; 23(8): 1961–1969. DOI: https://doi.org/10.1007/s11771-016-3253-3
  25. Sharma A, Amarnath M, Kankar PK. Nonlinear Dynamic Analysis of Defective Rolling Element Bearing Using Higuchi’s Fractal Dimension. Sādhanā 2019; 44(4): 76. DOI: https://doi.org/10.1007/s12046-019-1060-x
  26. Liu N, Gu H, Wei Y, et al. Performance Enhancement of AlGaN-Based Deep Ultraviolet Light-Emitting Diodes by Using Stepped and Super-Lattice n-Type Confinement Layer. Superlattices and Microstructures 2020; 141: 106492. DOI: https://doi.org/10.1016/j.spmi.2020.106492
  27. Feng Z, Xiong S, Cao D, et al. Hrs: A Hybrid Framework for Malware Detection. In Proceedings of the 2015 ACM International Workshop on International Workshop on Security and Privacy Analytics, San Antonio, TX, USA, 4 March 2015; pp. 19–26. DOI: https://doi.org/10.1145/2713579.2713585
  28. Pan S. A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering 2010; 22(10): 1345–1359. DOI: https://doi.org/10.1109/TKDE.2009.191
  29. Gopalan R, Li R, Chellappa R. Domain Adaptation for Object Recognition: An Unsupervised Approach. In Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain, 6–13 November 2011; pp. 999–1006. DOI: https://doi.org/10.1109/ICCV.2011.6126344
  30. Jhuo IH, Liu D, Lee DT, et al. Robust Visual Domain Adaptation with Low-Rank Reconstruction. In Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 16–21 June 2012; pp. 2168–2175. DOI: https://doi.org/10.1109/CVPR.2012.6247924
  31. Pan SJ, Tsang IW, Kwok JT, et al. Domain Adaptation via Transfer Component Analysis. IEEE Transactions on Neural Networks 2010; 22(2): 199–210. DOI: https://doi.org/10.1109/TNN.2010.2091281
  32. Gretton A, Borgwardt K, Rasch M, et al. A Kernel Method for the Two-Sample-Problem. Advances in Neural Information Processing Systems 2006; 19. DOI: https://doi.org/10.7551/mitpress/7503.003.0069
  33. Sun B, Saenko K. Deep Coral: Correlation Alignment for Deep Domain Adaptation. In Computer Vision—ECCV 2016 Workshops; Springer International Publishing: Cham, Switzerland, 2016; pp. 443–450. DOI: https://doi.org/10.1007/978-3-319-49409-8_35
  34. Song YY, Ying LU. Decision Tree Methods: Applications for Classification and Prediction. Shanghai Archives of Psychiatry 2015; 27(2): 130.
  35. Suthaharan S, Suthaharan S. Decision Tree Learning. In Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning; Springer: Boston, MA, USA, 2016; pp. 237–269. DOI: https://doi.org/10.1007/978-1-4899-7641-3_10
  36. Biau G, Scornet E. A Random Forest Guided Tour. Test 2016; 25: 197–227. DOI: https://doi.org/10.1007/s11749-016-0481-7
  37. Rigatti SJ. Random Forest. Journal of Insurance Medicine 2017; 47(1): 31–39. DOI: https://doi.org/10.17849/insm-47-01-31-39.1
  38. Peterson LE. K-Nearest Neighbor. Scholarpedia 2009; 4(2): 1883. DOI: https://doi.org/10.4249/scholarpedia.1883
  39. Dai W. Design of Traffic Improvement Plan for Line 1 Baijiahu Station of Nanjing Metro. Innovations in Applied Engineering and Technology 2023; 10. DOI: https://doi.org/10.58195/iaet.v2i1.133
  40. Zhao Z, Ren P, Tang M. Analyzing the Impact of Anti-Globalization on the Evolution of Higher Education Internationalization in China. Journal of Linguistics and Education Research 2022; 5(2): 15–31. DOI: https://doi.org/10.30564/jler.v5i2.5552
  41. Xiong S, Yu L, Shen H, et al. Efficient Algorithms for Sensor Deployment and Routing in Sensor Networks for Network-Structured Environment Monitoring. In Proceedings of the 2012 IEEE INFOCOM, Orlando, FL, USA, 25–30 March 2012; pp. 1008–1016. DOI: https://doi.org/10.1109/INFCOM.2012.6195455
  42. Feng Z, Cao D., Xiong S, et al. Method and Apparatus for File Identification. U.S. Patent No. 10284577, 7 May 2019.
  43. Xiong S, Li J, Li M, et al. Multiple Task Scheduling for Low-Duty-Cycled Wireless Sensor Networks. In Proceedings of the 2011 IEEE INFOCOM, Shanghai, China, 10–15 April 2011; pp. 1323–1331. DOI: https://doi.org/10.1109/INFCOM.2011.5934916
  44. Yu L, Li J, Cheng S, et al. Secure Continuous Aggregation via Sampling-Based Verification in Wireless Sensor Networks. In Proceedings of the 2011 IEEE INFOCOM, Shanghai, China, 10–15 April 2011; pp. 1763–1771. DOI: https://doi.org/10.1109/INFCOM.2011.5934974
  45. Li J, Xiong S. Efficient Pr-Skyline Query Processing and Optimization in Wireless Sensor Networks. Wireless Sensor Network 2010; 2(11): 838. DOI: https://doi.org/10.4236/wsn.2010.211101
  46. Wang Z, Shafieezadeh A. REAK: Reliability Analysis through Error Rate-Based Adaptive Kriging. Reliability Engineering & System Safety 2019; 182: 33–45. https://doi.org/10.1016/j.ress.2018.10.004. DOI: https://doi.org/10.1016/j.ress.2018.10.004
  47. Rahimi M, Wang Z, Shafieezadeh A, et al. An Adaptive Kriging-Based Approach with Weakly Stationary Random Fields for Soil Slope Reliability Analysis. In Proceedings of the 2019 Eighth International Conference on Case Histories in Geotechnical Engineering, Philadelphia, PA, USA, 24–27 March 2019; pp. 148–157. DOI: https://doi.org/10.1061/9780784482155.015
  48. Wang Z, Shafieezadeh A. ESC: An Efficient Error-Based Stopping Criterion for Kriging-Based Reliability Analysis Methods. Structural and Multidisciplinary Optimization 2019; 59: 1621–1637. https://doi.org/10.1007/s00158-018-2150-9. DOI: https://doi.org/10.1007/s00158-018-2150-9
  49. Wang Z, Shafieezadeh A. Reliability-Based Bayesian Updating Using Machine Learning. In Proceedings of the 13th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP13), Seoul, Korea, 26–30 May 2019.
  50. Wang Z, Shafieezadeh A. A Parallel Learning Strategy for Adaptive Kriging-Based Reliability Analysis Methods. In Proceedings of the 13th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP13), Seoul, Korea, 26–30 May 2019.
  51. Hur J, Wang Z, Shafieezadeh A, et al. Seismic Reliability Analysis of NPP’s Nonstructural Components Using Surrogate Models. Available online: https://s-space.snu.ac.kr/handle/10371/153449(accessed on 7 October 2024).
  52. Darestani YM, Wang Z, Shafieezadeh A. Wind Reliability of Transmission Line Models Using Kriging-Based Methods. In Proceedings of the 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP13, Seoul, Korea, 26–30 May 2019.
  53. Wang Z, Shafieezadeh A. Real-Time High-Fidelity Reliability Updating with Equality Information Using Adaptive Kriging. Reliability Engineering & System Safety 2020; 195: 106735. https://doi.org/10.1016/j.ress.2019.106735. DOI: https://doi.org/10.1016/j.ress.2019.106735
  54. Rahimi M, Wang Z, Shafieezadeh A, et al. Exploring Passive and Active Metamodeling-Based Reliability Analysis Methods for Soil Slopes: A New Approach to Active Training. International Journal of Geomechanics 2020; 20: 04020009. DOI: https://doi.org/10.1061/(ASCE)GM.1943-5622.0001613
  55. Zhang C, Wang Z, Shafieezadeh A. Value of Information Analysis via Active Learning and Knowledge Sharing in Error-Controlled Adaptive Kriging. IEEE Access 2020; 8: 51021–51034. https://doi.org/10.1109/ACCESS.2020.2980228. DOI: https://doi.org/10.1109/ACCESS.2020.2980228
  56. Wang Z, Shafieezadeh A. On Confidence Intervals for Failure Probability Estimates in Kriging-Based Reliability Analysis. Reliability Engineering & System Safety 2020; 196: 106758. https://doi.org/10.1016/j.ress.2019.106758. DOI: https://doi.org/10.1016/j.ress.2019.106758
  57. Wang Z, Shafieezadeh A. Highly Efficient Bayesian Updating Using Metamodels: An Adaptive Kriging-Based Approach. Structural Safety 2020; 84: 101915. https://doi.org/10.1016/j.strusafe.2019.101915. DOI: https://doi.org/10.1016/j.strusafe.2019.101915
  58. Zhang C, Wang Z, Shafieezadeh A. Error Quantification and Control for Adaptive Kriging-Based Reliability Updating with Equality Information. Reliability Engineering & System Safety 2021; 207: 107323. https://doi.org/10.1016/j.ress.2020.107323. DOI: https://doi.org/10.1016/j.ress.2020.107323
  59. Wang Z, Shafieezadeh A. Metamodel-Based Subset Simulation Adaptable to Target Computational Capacities: The Case for High-Dimensional and Rare Event Reliability Analysis. Structural and Multidisciplinary Optimization 2021; 64: 649–675. DOI: https://doi.org/10.1007/s00158-021-02864-9
  60. Song C, Wang Z, Shafieezadeh A, et al. BUAK-AIS: Efficient Bayesian Updating with Active Learning Kriging-Based Adaptive Importance Sampling. Computer Methods in Applied Mechanics and Engineering 2022; 391: 114578. https://doi.org/10.1016/j.cma.2022.114578. DOI: https://doi.org/10.1016/j.cma.2022.114578
  61. Zhao Y, Hu H, Song C, et al. Predicting Compressive Strength of Manufactured-Sand Concrete Using Conventional and Metaheuristic-Tuned Artificial Neural Network. Measurement 2022; 194: 110993. https://doi.org/10.1016/j.measurement.2022.110993. DOI: https://doi.org/10.1016/j.measurement.2022.110993
  62. Zhao Y, Wang Z. Subset Simulation with Adaptable Intermediate Failure Probability for Robust Reliability Analysis: An Unsupervised Learning-Based Approach. Structural and Multidisciplinary Optimization 2022; 65(6): 172. https://doi.org/10.1007/s00158-022-03260-7. DOI: https://doi.org/10.1007/s00158-022-03260-7
  63. Xiao X, Li Q, Wang Z. A Novel Adaptive Importance Sampling Algorithm for Bayesian Model Updating. Structural Safety 2022; 97: 102230. https://doi.org/10.1016/j.strusafe.2022.102230. DOI: https://doi.org/10.1016/j.strusafe.2022.102230
  64. Wang Z, Shafieezadeh A, Xiao X, et al. Optimal Monitoring Location for Tracking Evolving Risks to Infrastructure Systems: Theory and Application to Tunneling Excavation Risk. Reliability Engineering & System Safety 2022; 228: 108781. https://doi.org/10.1016/j.ress.2022.108781. DOI: https://doi.org/10.1016/j.ress.2022.108781
  65. Wang Z, Shafieezadeh A. Bayesian Updating with Adaptive, Uncertainty-Informed Subset Simulations: High-Fidelity Updating with Multiple Observations. Reliability Engineering & System Safety 2023; 230: 108901. https://doi.org/10.1016/j.ress.2022.108901. DOI: https://doi.org/10.1016/j.ress.2022.108901
  66. Cao M, Li Q, Wang Z. Reliability Updating with Equality Information Using Adaptive Kriging-Based Importance Sampling. Structural and Multidisciplinary Optimization 2023; 66: 76. https://doi.org/10.1007/s00158-023-03492-1. DOI: https://doi.org/10.1007/s00158-023-03492-1
  67. Ye X, Wu P, Liu A, et al. A Deep Learning-Based Method for Automatic Abnormal Data Detection: Case Study for Bridge Structural Health Monitoring. International Journal of Structural Stability and Dynamics 2023; 23(11): 2350131. https://doi.org/10.1142/S0219455423501316. DOI: https://doi.org/10.1142/S0219455423501316

Supporting Agencies

  1. Funding: Not applicable.