Method of machining centre sliding system fault detection using torque signals and autoencoder

• Autorzy: Augustyn Damian , Fidali Marek

Identyfikatory

DOI

10.2478/ama-2023-0051

• Języki publikacji: EN

Abstrakty

EN

The sliding system of machining centres often causes maintenance and process problems. Improper operation of the sliding system can result from wear of mechanical parts and drives faults. To detect the faulty operation of the sliding system, measurements of the torque of its servomotors can be used. Servomotor controllers can measure motor current, which can be used to calculate motor torque. For research purposes, the authors used a set of torque signals from the machining centre servomotors that were acquired over a long period. The signals were collected during a diagnostic test programmed in the machining centre controller and performed once per day. In this article, a method for detecting anomalies in torque signals was presented for the condition assessment of the machining centre sliding systems. During the research, an autoencoder was used to detect the anomaly, and the condition was assessed based on the value of the reconstruction error. The results indicate that the anomaly detection method using an autoencoder is an effective solution for detecting damage to the sliding system and can be easily used in a condition monitoring system.

Słowa kluczowe

EN

condition monitoring; torque signal; machining centre; anomaly detection; autoencoder

• Wydawca: Oficyna Wydawnicza Politechniki Białostockiej
• Czasopismo: Acta Mechanica et Automatica
• Rocznik: 2023
• Tom: Vol. 17, no. 3
• Strony: 445--451
• Opis fizyczny: Bibliogr. 41 poz., rys., wykr.

Twórcy

autor

Augustyn Damian

• Department of Fundamentals of Machinery Design, Faculty of Mechanical Engineering, Silesian University of Technology, ul. Akademicka 2A, 44-100 Gliwice, Poland

• https://orcid.org/0000-0002-4896-4090

autor

Fidali Marek

• Department of Fundamentals of Machinery Design, Faculty of Mechanical Engineering, Silesian University of Technology, ul. Akademicka 2A, 44-100 Gliwice, Poland

• https://orcid.org/0000-0002-3667-5582

Bibliografia

• 1. Kim Y, Bae H, Kim S, Vachtsevanos G. Fault Diagnosis of AC Servo Motor with Current Signals Based on Wavelet Decomposition and Template Matching Methods. IFAC Proceedings Volumes. 2008;41(2):7239–44. https://doi.org/10.3182/20080706-5-kr-1001.01225
• 2. Lee WG, Lee JW, Hong MS, Nam S-H, Jeon Y, Lee MG. Failure Diagnosis System for a Ball-Screw by Using Vibration Signals. Shock and Vibration. 2015;2015:1–9. https://doi.org/10.1155/2015/435870
• 3. Archenti A, Laspas T. Accuracy and Performance Analysis of Ma-chine Tools. Precision Manufacturing. 2019;215–44. https://doi.org/10.1007/978-981-10-4938-5_7
• 4. Jamil N, Hassan MF, Lim SK, Yusoff AR. Predictive maintenance for rotating machinery by using vibration analysis. Journal of Mechanical Engineering and Sciences. 2021 Sep 19;15(3):8289–99. https://doi.org/10.15282/jmes.15.3.2021.07.0651
• 5. Polat K. The Fault Diagnosis based on Deep Long Short-Term Memory Model from the Vibration Signals in the Computer Numerical Control Machines. Journal of the Institute of Electronics and Com-puter. 2020;2(1):72–92. https://doi.org/10.33969/jiec.2020.21006
• 6. Desavale RG, Katiyar JK, Jagadeesha T. Vibrations Characteristics Analysis of Rotor-Bearings System Due to Surface Defects Based in CNC Machines. Recent Advances in Manufacturing, Automation, Design and Energy Technologies. 2021 Oct 12;705–10. https://doi.org/10.1007/978-981-16-4222-7_78
• 7. Alghassi A, Yu Z, Farbiz F. Machine Performance Monitoring and Fault Classification using Vibration Frequency Analysis. 2020 Prog-nostics and Health Management Conference (PHM-Besançon). 2020 May; https://doi.org/10.1109/PHM-Besancon49106.2020.00009
• 8. Józwik J, Kuric I, Sága M, Lonkwic P. Diagnostics of CNC Machine Tools in Manufacturing Process with Laser Interferometer Technolo-gy. Manufacturing Technology. 2014 Mar 1;14(1):23–30. https://doi.org/10.21062/ujep/x.2014/a/1213-2489/mt/14/1/23
• 9. Winarno A, Prayoga BT, Hendaryanto IA. Linear Motion Error Evalu-ation of Open-Loop CNC Milling Using a Laser Interferometer. Acta Mechanica et Automatica. 2022 Mar 24;16(2):124–9. https://doi.org/10.2478/ama-2022-0016
• 10. Józwik J, Wac-Włodarczyk A, Michałowska J, Kłoczko EngM. Moni-toring of the Noise Emitted by Machine Tools in Industrial Conditions. Journal of Ecological Engineering. 2018 Jan 1;19(1):83–93. https://doi.org/10.12911/22998993/79447
• 11. Madhusudana CK, Kumar H, Narendranath S. Fault Diagnosis of Face Milling Tool using Decision Tree and Sound Signal. Materials Today: Proceedings. 2018;5(5):12035–44. https://doi.org/10.1016/j.matpr.2018.02.178
• 12. Sun WH, Yeh SS. Using the Machine Vision Method to Develop an On-machine Insert Condition Monitoring System for Computer Nu-merical Control Turning Machine Tools. Materials. 2018 Oct 14;11(10):1977. https://doi.org/10.3390/ma11101977
• 13. Xing K, Liu X, Liu Z, Mayer JRR, Achiche S. Low-Cost Precision Monitoring System of Machine Tools for SMEs. Procedia CIRP. 2021;96:347–52. https://doi.org/10.1016/j.procir.2021.01.098
• 14. Zhou ZD, Gui L, Tan YG, Liu MY, Liu Y, Li RY. Actualities and De-velopment of Heavy-Duty CNC Machine Tool Thermal Error Monitor-ing Technology. Chinese Journal of Mechanical Engineering. 2017 Jul 25;30(5):1262–81. https://doi.org/10.1007/s10033-017-0166-5
• 15. Sudianto A, Jamaludin Z, Abdul Rahman AA, Novianto S, Muharrom F. Automatic Temperature Measurement and Monitoring System for Milling Process of AA6041 Aluminum Aloy using MLX90614 Infrared Thermometer Sensor with Arduino. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences. 2021 Apr 30;82(2):1–14. https://doi.org/10.37934/arfmts.82.2.114
• 16. Duro JA, Padget JA, Bowen CR, Kim HA, Nassehi A. Multi-sensor data fusion framework for CNC machining monitoring. Mechanical Systems and Signal Processing. 2016 Jan;66-67:505–20. https://doi.org/10.1016/j.ymssp.2015.04.019
• 17. Goli A, Tirkolaee EB, Weber GW. An Integration of Neural Network and Shuffled Frog-Leaping Algorithm for CNC Machining Monitoring. Foundations of Computing and Decision Sciences. 2021 Mar 1;46(1):27–42. https://doi.org/10.2478/fcds-2021-0003
• 18. Zou Z, Lin Y, Lin D, Gu F, Ball AD. Online Tool Condition Monitoring of CNC Turnings Based on Motor Current Signature Analysis. 2021 26th International Conference on Automation and Computing (ICAC). 2021 Sep 2; https://doi.org/10.23919/ICAC50006.2021.9594219
• 19. Aouabdi S, Taibi M, Bouras S, Boutasseta N. Using multi-scale entropy and principal component analysis to monitor gears degrada-tion via the motor current signature analysis. Mechanical Systems and Signal Processing. 2017 Jun;90:298–316. https://doi.org/10.1016/j.ymssp.2016.12.027
• 20. Choudhary A, Mian T, Fatima S. Convolutional neural network based bearing fault diagnosis of rotating machine using thermal images. Measurement. 2021 May;176:109196. https://doi.org/10.1016/J.MEASUREMENT.2021.109196
• 21. Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging. Chinese Journal of Aero-nautics. 2020 Feb 1;33(2):427–38. https://doi.org/10.1016/j.cja.2019.08.014
• 22. Janssens O, Loccufier M, Van Hoecke S. Thermal Imaging and Vibration-Based Multisensor Fault Detection for Rotating Machinery. IEEE Transactions on Industrial Informatics. 2019 Jan;15(1):434–44. https://doi.org/10.1109/TII.2018.2873175
• 23. Nayana BR, Geethanjali P. Analysis of Statistical Time-Domain Features Effectiveness in Identification of Bearing Faults From Vibra-tion Signal. IEEE Sensors Journal. 2017 Sep 1;17(17):5618–25. https://doi.org/10.1109/JSEN.2017.2727638
• 24. Moussa MA, Boucherma M, Khezzar A. A Detection Method for Induction Motor Bar Fault Using Sidelobes Leakage Phenomenon of the Sliding Discrete Fourier Transform. IEEE Transactions on Power Electronics. 2017 Jul;32(7):5560–72. https://doi.org/10.1109/TPEL.2016.2605821
• 25. Zhang X, Zhang Q, Tan L, Xu G. Running state detection and per-formance evaluation method for feed mechanism of numerical control machine. 2017 IEEE International Conference on Prognostics and Health Management (ICPHM). 2017 Jun; https://doi.org/10.1109/ICPHM.2017.7998332
• 26. Li X, Xu Y, Li N, Yang B, Lei Y. Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks. IEEE/CAA Journal of Automatica Sinica. 2023 Jan 1;10(1):121–34. https://doi.org/10.1109/JAS.2022.105935
• 27. Zhang W, Wang Z, Li X. Blockchain-based decentralized federated transfer learning methodology for collaborative machinery fault diag-nosis. Reliability Engineering & System Safety. 2023 Jan; 229: 108885. https://doi.org/10.1016/j.ress.2022.108885
• 28. Neupane D, Kim Y, Seok J, Hong J. CNN-Based Fault Detection for Smart Manufacturing. Applied Sciences. 2021 Dec 10;11(24):11732. https://doi.org/10.3390/APP112411732
• 29. Sun Y, Li S. Bearing fault diagnosis based on optimal convolution neural network. Measurement. 2022 Feb;190:110702. https://doi.org/10.1016/J.MEASUREMENT.2022.110702
• 30. Canbaz H, Polat K. Fault Detection of CNC Machines from Vibration Signals Using Machine Learning Methods. Artificial Intelligence and Applied Mathematics in Engineering Problems. 2020;365–74. https://doi.org/10.1007/978-3-030-36178-5_27
• 31. Alghassi A. Generalized Anomaly Detection Algorithm Based on Time Series Statistical Features. Intelligent Systems Reference Li-brary. 2021;177–200. https://doi.org/10.1007/978-3-030-67270-6_7
• 32. Korbicz J, Kowalczuk Z, Kościelny JM, Cholewa W, editors. Fault Diagnosis. Berlin, Heidelberg: Springer Berlin Heidelberg; 2004. https://doi.org/10.1007/978-3-642-18615-8
• 33. Augustyn D, Fidali M. Application of torque signal analysis of servo-motors to assess of support system condition of industrial machining centre. Applied Condition Monitoring. 2023
• 34. Provotar OI, Linder YM, Veres MM. Unsupervised Anomaly Detection in Time Series Using LSTM-Based Autoencoders. 2019 IEEE Inter-national Conference on Advanced Trends in Information Theory (ATIT). 2019 Dec; https://doi.org/10.1109/ATIT49449.2019.9030505
• 35. Bampoula X, Siaterlis G, Nikolakis N, Alexopoulos K. A Deep Learn-ing Model for Predictive Maintenance in Cyber-Physical Production Systems Using LSTM Autoencoders. Sensors [Internet]. 2021 Jan 1;21(3):972. https://doi.org/10.1109/ATIT49449.2019.9030505
• 36. Ahmad S, Styp-Rekowski K, Nedelkoski S, Kao O. Autoencoder-based Condition Monitoring and Anomaly Detection Method for Ro-tating Machines. 2020 IEEE International Conference on Big Data (Big Data). 2020 Dec 10; https://doi.org/10.1109/BigData50022.2020.9378015
• 37. Lei Le, Andrew Patterson, Martha White. Supervised autoencoders: Improving generalization performance with unsupervised regulariz-ers. In Advances in Neural Information Processing Systems 31: An-nual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3-8 Dec 2018, Montréal, Canada 2018: 107-117.
• 38. Tran DH, Nguyen VL, Nguyen H, Jang YM. Self-Supervised Learning for Time-Series Anomaly Detection in Industrial Internet of Things. Electronics. 2022 Jul 8;11(14):2146. https://doi.org/10.3390/electronics11142146
• 39. Serradilla O, Zugasti E, Ramirez de Okariz J, Rodriguez J, Zurutuza U. Adaptable and Explainable Predictive Maintenance: Semi-Supervised Deep Learning for Anomaly Detection and Diagnosis in Press Machine Data. Applied Sciences. 2021 Aug 11;11(16):7376. https://doi.org/10.3390/app11167376
• 40. Amjad RA, Liu K, Geiger BC. Understanding Neural Networks and Individual Neuron Importance via Information-Ordered Cumulative Ablation. IEEE Transactions on Neural Networks and Learning Sys-tems. 2022 Dec;33(12):7842–52. https://doi.org/10.1109/TNNLS.2021.3088685
• 41. Jais IKM, Ismail AR, Nisa SQ. Adam Optimization Algorithm for Wide and Deep Neural Network. Knowledge Engineering and Data Sci-ence. 2019 Jun 23;2(1):41. https://doi.org/10.17977/um018v2i12019p41-46

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).