In order to
prevent possible casualties and economic loss, it is critical to accurate
prediction of the Remaining Useful Life (RUL) in rail prognostics health
management. However, the traditional neural networks is difficult to capture
the long-term dependency relationship of the time series in the modeling of the
long time series of rail damage, due to the coupling relationship of
multi-channel data from multiple sensors. Here, in this paper, a novel RUL
prediction model with an enhanced pulse separable convolution is used to solve
this issue. Firstly, a coding module based on the improved pulse separable
convolutional network is established to effectively model the relationship
between the data. To enhance the network, an alternate gradient back propagation
method is implemented. And an efficient channel attention (ECA) mechanism is
developed for better emphasizing the useful pulse characteristics. Secondly, an
optimized Transformer encoder was designed to serve as the backbone of the
model. It has the ability to efficiently understand relationship between the
data itself and each other at each time step of long time series with a full
life cycle. More importantly, the Transformer encoder is improved by
integrating pulse maximum pooling to retain more pulse timing characteristics.
Finally, based on the characteristics of the front layer, the final predicted
RUL value was provided and served as the end-to-end solution. The empirical
findings validate the efficacy of the suggested approach in forecasting the rail
RUL, surpassing various existing data-driven prognostication techniques.
Meanwhile, the proposed method also shows good generalization performance on
PHM2012 bearing data set.
References
[1]
Shang, P.P., Liu, X.B. and Ma, S. (2022) Study on Rail Service Life of Shuohuang Heavy Haul Railway. Railway Engineering, 62, 68-71+85.
[2]
Ni, J.N., Zhang, W., Liu, N.X., et al. (2022) Demand Forecast and Development Countermeasures of Heavy Haul Railway Transportation in the “14th Five Year” Plan. Railway Freight Transport, 40, 7-11+19.
[3]
He, B., Liu, L. and Zhang, D. (2021) Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review. Journal of Computing and Information Science in Engineering, 21, Article ID: 030801. https://doi.org/10.1115/1.4049537
[4]
Khazeiynasab, S.R. and Qi, J. (2021) Generator Parameter Calibration by Adaptive Approximate Bayesian Computation with Sequential Monte Carlo Sampler. IEEE Transactions on Smart Grid, 12, 4327-4338. https://doi.org/10.1109/TSG.2021.3077734
[5]
Muneer, A., Taib, S.M., Naseer, S., et al. (2021) Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis. Electronics, 10, Article 2453. https://doi.org/10.3390/electronics10202453
[6]
Wang, C. and Kou, P. (2020) Wind Speed Forecasts of Multiple Wind Turbines in a Wind Farm Based on Integration Model Built by Convolutional Neural Network and Simple Recurrent Unit. Transactions of China Electrotechnical Society, 35, 2723-2735.
[7]
Kang, S.Q., Xing, Y.Y., Wang, Y.J., et al. (2023) Rolling Bearing Life Prediction Based on Unsupervised Deep Modeltransfer. Acta Automatica Sinica, 49, 2627-2638.
[8]
Shifat, T.A., Yasmin, R. and Hur, J.W. (2021) A Data Driven RUL Estimation Framework of Electric Motor Using Deep Electrical Feature Learning from Current Harmonics and Apparent Power. Energies, 14, Article 3156. https://doi.org/10.3390/en14113156
[9]
Dong, S., Wang, P. and Abbas, K. (2021) A Survey on Deep Learning and Its Applications. Computer Science Review, 40, Article ID: 100379. https://doi.org/10.1016/j.cosrev.2021.100379
[10]
Janiesch, C., Zschech, P. and Heinrich, K. (2021) Machine Learning and Deep Learning. Electronic Markets, 31, 685-695. https://doi.org/10.1007/s12525-021-00475-2
[11]
Sherstinsky, A. (2020) Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network. Physica D: Nonlinear Phenomena, 404, Article ID: 132306. https://doi.org/10.1016/j.physd.2019.132306
[12]
Zhao, Z.H., Li, Q., Yang, S.P., et al. (2022) Research on Remaining Service Life Prediction Based on Bilstm and Attention Mechanism. Journal of Vibration and Shock, 41, 44-50+196.
[13]
Vaswani, A., Shazeer, N., Parmar, N., et al. (2017) Attention Is All You Need. Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, 4-9 December 2017, 6000-6010.
[14]
Mo, Y., Wu, Q., Li, X., et al. (2021) Remaining Useful Life Estimation via Transformer Encoder Enhanced by a Gated Convolutional Unit. Journal of Intelligent Manufacturing, 32, 1997-2006. https://doi.org/10.1007/s10845-021-01750-x
[15]
Zhang, Z., Song, W. and LI, Q. (2022) Dual-Aspect Self-Attention Based on Transformer for Remaining Useful Life Prediction. IEEE Transactions on Instrumentation and Measurement, 71, Article No. 2505711. https://doi.org/10.1109/TIM.2022.3160561
[16]
Chen, Z.H. (2023) Research on Residual Life Prediction Technology of Electromechanical Actuator Based on Multimodaltransformer. Acta Armamentarii, 44, 2920-2931. http://kns.cnki.net/kcms/detail/11.2176.TJ.20221024.1556.003.html
[17]
Maas, W. (1997) Networks of Spiking Neurons: The Third Generation of Neural Network Models. Neural Networks, 10, 1659-1671. https://doi.org/10.1016/S0893-6080(97)00011-7
[18]
Hong, C. and Wang, J. (2020) Short Term Load Forecasting Model Based on Pulse Neural Network. Proceedings of the CSU-EPSA, 32, 139-144.
[19]
Wang, B., Lei, Y., Li, N., et al. (2019) Deep Separable Convolutional Network Forremaining Useful Life Prediction of Machinery. Mechanical Systems and Signal Processing, 134, Article ID: 106330. https://doi.org/10.1016/j.ymssp.2019.106330
[20]
Mamalet, F. and Garcia, C. (2012) Simplifying Convnets for Fast Learning. International Conference on Artificial Neural Networks (ICANN), Lausanne, 11-14 September 2012, 58-65. https://doi.org/10.1007/978-3-642-33266-1_8
[21]
Kong, L., Min, Y., He, J., et al. (2022) Research on Steel Surface Defect Recognition Based on Pulse Neural Network. Packaging Engineering, 43, 13-22.
[22]
Zhang, C., Huang, C., He, J., et al. (2022) Defects Recognition of Train Wheelset Tread Based on Improved Spiking Neural Network. Chinese Journal of Electronics, 32, 941-954. https://doi.org/10.23919/cje.2022.00.162
[23]
Holden, A.V. (2013) Models of the Stochastic Activity of Neurones. In: Levin, S., Ed., Lecture Notes in Biomathematics, Vol. 12, Springer, Heidelberg.
[24]
Wang, Q., Wu, B., Zhu, P., et al. (2020) ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, 13-19 June 2020, 11534-11542. https://doi.org/10.1109/CVPR42600.2020.01155
[25]
LI, X., Wang, W., Hu, X., et al. (2020) Selective Kernel Networks. Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, 15-20 June 2019, 510-519. https://doi.org/10.1109/CVPR.2019.00060
[26]
Sengupta, A., Ye, Y., Wang, R., et al. (2019) Going Deeper in Spiking Neural Networks: VGG and Residual Architectures. Frontiers in Neuroscience, 13, Article 95. https://doi.org/10.3389/fnins.2019.00095
[27]
Wang, Y., Deng, L., Zheng, L., et al. (2012) Temporal Convolutional Network with Soft Thresholding and Attention Mechanism for Machinery Prognostics. Journal of Manufacturing Systems, 60, 512-526. https://doi.org/10.1016/j.jmsy.2021.07.008
[28]
Chen, Y.Y., Peng, G., Zhu, Z., et al. (2020) A Novel Deep Learning Method Based on Attention Mechanism for Bearing Remaining Useful Life Prediction, Applied Soft Computing, 86, Article ID: 105919. https://doi.org/10.1016/j.asoc.2019.105919
[29]
Li, X., Zhang, W., Ma, H., et al. (2020) Data Alignments in Machinery Remaining Useful Life Prediction Using Deep Adversarial Neural Networks, Knowledge-Based Systems, 197, Article ID: 105843. https://doi.org/10.1016/j.knosys.2020.105843
