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Robust-MBDL: A Robust Multi-branch Deep Learning Based Model for Remaining Useful Life Prediction and Operational Condition Identification of Rotating Machines

Published 12 Sep 2023 in cs.LG and cs.AI | (2309.06157v2)

Abstract: In this paper, a Robust Multi-branch Deep learning-based system for remaining useful life (RUL) prediction and condition operations (CO) identification of rotating machines is proposed. In particular, the proposed system comprises main components: (1) an LSTM-Autoencoder to denoise the vibration data; (2) a feature extraction to generate time-domain, frequency-domain, and time-frequency based features from the denoised data; (3) a novel and robust multi-branch deep learning network architecture to exploit the multiple features. The performance of our proposed system was evaluated and compared to the state-of-the-art systems on two benchmark datasets of XJTU-SY and PRONOSTIA. The experimental results prove that our proposed system outperforms the state-of-the-art systems and presents potential for real-life applications on bearing machines.

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References (48)
  1. Hybrid deep neural network model for remaining useful life estimation. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 3872–3876. IEEE, 2019.
  2. A data-driven model for milling tool remaining useful life prediction with convolutional and stacked lstm network. Measurement, 154:107461, 2020.
  3. Prognostics of rolling element bearings with the combination of paris law and reliability method. In 2017 Prognostics and System Health Management Conference (PHM-Harbin), pages 1–6, 2017.
  4. Effective and efficient dropout for deep convolutional neural networks. arXiv preprint arXiv:1904.03392, 2019.
  5. A deep learning-based two-stage prognostic approach for remaining useful life of rolling bearing. Applied Intelligence, 52(5):5880–5895, 2022.
  6. Accurate prediction of machining cycle times and feedrates with deep neural networks using bilstm. Journal of Manufacturing Systems, 2023.
  7. A deep learning framework for univariate time series prediction using convolutional lstm stacked autoencoders. In 2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA), pages 1–6. IEEE, 2019.
  8. Remaining useful life prediction and challenges: A literature review on the use of machine learning methods. Journal of Manufacturing Systems, 63:550–562, 2022.
  9. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
  10. A bidirectional lstm prognostics method under multiple operational conditions. IEEE Transactions on Industrial Electronics, 66(11):8792–8802, 2019.
  11. A novel deep convolutional neural network-bootstrap integrated method for rul prediction of rolling bearing. Journal of Manufacturing Systems, 61:757–772, 2021.
  12. Bi-lstm-based two-stream network for machine remaining useful life prediction. IEEE Transactions on Instrumentation and Measurement, 71:1–10, 2022.
  13. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
  14. 1d convolutional neural networks and applications: A survey. Mechanical systems and signal processing, 151:107398, 2021.
  15. Automatic features extraction using autoencoder in intrusion detection system. In 2018 International Conference on Electrical Engineering and Computer Science (ICECOS), pages 219–224, 2018.
  16. Machinery health prognostics: A systematic review from data acquisition to rul prediction. Mechanical systems and signal processing, 104:799–834, 2018.
  17. An improved exponential model for predicting remaining useful life of rolling element bearings. IEEE Transactions on Industrial Electronics, 62(12):7762–7773, 2015.
  18. Stochastic prognostics for rolling element bearings. Mechanical Systems and Signal Processing, 14(5):747–762, 2000.
  19. Physics-informed meta learning for machining tool wear prediction. Journal of Manufacturing Systems, 62:17–27, 2022.
  20. Feature extraction based on morlet wavelet and its application for mechanical fault diagnosis. Journal of sound and vibration, 234(1):135–148, 2000.
  21. Network in network. arXiv preprint arXiv:1312.4400, 2013.
  22. Data super-network fault prediction model and maintenance strategy for mechanical product based on digital twin. Ieee Access, 7:177284–177296, 2019.
  23. A hybrid predictive maintenance approach for cnc machine tool driven by digital twin. Robotics and Computer-Integrated Manufacturing, 65:101974, 2020.
  24. A novel approach for automatic acoustic novelty detection using a denoising autoencoder with bidirectional lstm neural networks. In 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP), pages 1996–2000. IEEE, 2015.
  25. Pronostia: An experimental platform for bearings accelerated degradation tests. In IEEE International Conference on Prognostics and Health Management, pages 1–8, 2012.
  26. The fast Fourier transform. Springer, 1982.
  27. A time-restricted self-attention layer for asr. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 5874–5878. IEEE, 2018.
  28. A review of activation function for artificial neural network. In 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pages 281–286. IEEE, 2020.
  29. Asymmetric loss functions for deep learning early predictions of remaining useful life in aerospace gas turbine engines. In 2020 International Joint Conference on Neural Networks (IJCNN), pages 1–7, 2020.
  30. Sebastian Ruder. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747, 2016.
  31. A survey of the advancing use and development of machine learning in smart manufacturing. Journal of manufacturing systems, 48:170–179, 2018.
  32. A systematic review of machine learning algorithms for prognostics and health management of rolling element bearings: fundamentals, concepts and applications. Measurement Science and Technology, 32(1):012001, 2020.
  33. Combined cnn-lstm network for state-of-charge estimation of lithium-ion batteries. IEEE Access, 7:88894–88902, 2019.
  34. Review of tdnn (time delay neural network) architectures for speech recognition. In 1991 IEEE International Symposium on Circuits and Systems (ISCAS), pages 582–585, 1991.
  35. Characterized bioelectric signals by means of neural networks and wavelets to remotely control a human-machine interface. Sensors, 19(8):1923, 2019.
  36. Attention is all you need. Advances in neural information processing systems, 30, 2017.
  37. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of machine learning research, 11(12), 2010.
  38. A hybrid prognostics approach for estimating remaining useful life of rolling element bearings. IEEE Transactions on Reliability, 69(1):401–412, 2018.
  39. A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests. Journal of Manufacturing Science and Engineering, 139(7):071018, 2017.
  40. Sacgnet: A remaining useful life prediction of bearing with self-attention augmented convolution gru network. Lubricants, 10(2):21, 2022.
  41. 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, 2018.
  42. Prognostics based on stochastic degradation process: The last exit time perspective. IEEE Transactions on Reliability, 70(3):1158–1176, 2021.
  43. Multivariate time series missing data imputation using recurrent denoising autoencoder. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 760–764. IEEE, 2019.
  44. A wiener process model with dynamic covariate for degradation modeling and remaining useful life prediction. IEEE Transactions on Reliability, 72(1):214–223, 2022.
  45. Hybrid sparse convolutional neural networks for predicting manufacturability of visual defects of laser powder bed fusion processes. Journal of Manufacturing Systems, 62:835–845, 2022.
  46. Generalized cross entropy loss for training deep neural networks with noisy labels. Advances in neural information processing systems, 31, 2018.
  47. An attention-based deep learning approach for inertial motion recognition and estimation in human-robot collaboration. Journal of Manufacturing Systems, 67:97–110, 2023.
  48. Isolation of an eleven-atom polydentate carbon-chain chelate obtained by cycloaddition of a cyclic osmium carbyne with an alkyne. Angewandte Chemie, 130(12):3208–3211, 2018.

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