Method of Machining Centre Sliding System Fault Detection using Torque Signals and Autoencoder

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 Search in Google Scholar

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 Search in Google Scholar

3 Archenti A, Laspas T. Accuracy and Performance Analysis of Machine Tools. Precision Manufacturing. 2019;215–44. https://doi.org/10.1007/978-981-10-4938-5_7 Search in Google Scholar

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 Search in Google Scholar

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 Computer. 2020;2(1):72–92. https://doi.org/10.33969/jiec.2020.21006 Search in Google Scholar

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 Search in Google Scholar

7 Alghassi A, Yu Z, Farbiz F. Machine Performance Monitoring and Fault Classification using Vibration Frequency Analysis. 2020 Prognostics and Health Management Conference (PHM-Besançon). 2020 May; https://doi.org/10.1109/PHM-Besancon49106.2020.00009 Search in Google Scholar

8 Józwik J, Kuric I, Sága M, Lonkwic P. Diagnostics of CNC Machine Tools in Manufacturing Process with Laser Interferometer Technology. Manufacturing Technology. 2014 Mar 1;14(1):23–30. https://doi.org/10.21062/ujep/x.2014/a/1213-2489/mt/14/1/23 Search in Google Scholar

9 Winarno A, Prayoga BT, Hendaryanto IA. Linear Motion Error Evaluation 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 Search in Google Scholar

10 Józwik J, Wac-Włodarczyk A, Michałowska J, Kłoczko EngM. Monitoring 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 Search in Google Scholar

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 Search in Google Scholar

12 Sun WH, Yeh SS. Using the Machine Vision Method to Develop an On-machine Insert Condition Monitoring System for Computer Numerical Control Turning Machine Tools. Materials. 2018 Oct 14;11(10):1977. https://doi.org/10.3390/ma11101977 Search in Google Scholar

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 Search in Google Scholar

14 Zhou ZD, Gui L, Tan YG, Liu MY, Liu Y, Li RY. Actualities and Development of Heavy-Duty CNC Machine Tool Thermal Error Monitoring Technology. Chinese Journal of Mechanical Engineering. 2017 Jul 25;30(5):1262–81. https://doi.org/10.1007/s10033-017-0166-5 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

19 Aouabdi S, Taibi M, Bouras S, Boutasseta N. Using multi-scale entropy and principal component analysis to monitor gears degradation 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 Search in Google Scholar

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 Search in Google Scholar

21 Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging. Chinese Journal of Aeronautics. 2020 Feb 1;33(2):427–38. https://doi.org/10.1016/j.cja.2019.08.014 Search in Google Scholar

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 Search in Google Scholar

23 Nayana BR, Geethanjali P. Analysis of Statistical Time-Domain Features Effectiveness in Identification of Bearing Faults From Vibration Signal. IEEE Sensors Journal. 2017 Sep 1;17(17):5618–25. https://doi.org/10.1109/JSEN.2017.2727638 Search in Google Scholar

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 Search in Google Scholar

25 Zhang X, Zhang Q, Tan L, Xu G. Running state detection and performance 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 Search in Google Scholar

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 Search in Google Scholar

27 Zhang W, Wang Z, Li X. Blockchain-based decentralized federated transfer learning methodology for collaborative machinery fault diagnosis. Reliability Engineering & System Safety. 2023 Jan; 229: 108885. https://doi.org/10.1016/j.ress.2022.108885 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

31 Alghassi A. Generalized Anomaly Detection Algorithm Based on Time Series Statistical Features. Intelligent Systems Reference Library. 2021;177–200. https://doi.org/10.1007/978-3-030-67270-6_7 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

34 Provotar OI, Linder YM, Veres MM. Unsupervised Anomaly Detection in Time Series Using LSTM-Based Autoencoders. 2019 IEEE International Conference on Advanced Trends in Information Theory (ATIT). 2019 Dec; https://doi.org/10.1109/ATIT49449.2019.9030505 Search in Google Scholar

35 Bampoula X, Siaterlis G, Nikolakis N, Alexopoulos K. A Deep Learning 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 Search in Google Scholar

36 Ahmad S, Styp-Rekowski K, Nedelkoski S, Kao O. Autoencoder-based Condition Monitoring and Anomaly Detection Method for Rotating Machines. 2020 IEEE International Conference on Big Data (Big Data). 2020 Dec 10; https://doi.org/10.1109/BigData50022.2020.9378015 Search in Google Scholar

37 Lei Le, Andrew Patterson, Martha White. Supervised autoencoders: Improving generalization performance with unsupervised regularizers. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3-8 Dec 2018, Montréal, Canada 2018: 107-117. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Systems. 2022 Dec;33(12):7842–52. https://doi.org/10.1109/TNNLS.2021.3088685 Search in Google Scholar

41 Jais IKM, Ismail AR, Nisa SQ. Adam Optimization Algorithm for Wide and Deep Neural Network. Knowledge Engineering and Data Science. 2019 Jun 23;2(1):41. https://doi.org/10.17977/um018v2i12019p41-46 Search in Google Scholar