Digital Signal Processing and Artificial Intelligence for Automatic Learning

DLINE Journals portal

Home

New Journals

Browse Journals

Journal Prices

For Authors

Print ISSN:
Online ISSN: 2583-5009

About DSPAI
	DLINE Portal Home Home Aims & Scope Editorial Board Current Issue Next Issue Previous Issue Sample Issue Upcoming Conferences Self-archiving policy Alert Services Be a Reviewer Publisher Paper Submission Subscription Contact us

How To Order
	Order Online Price Information Request for Complimentary Print Copy

For Authors
	Guidelines for Contributors Online Submission Statement of Ethics and Responsibilities Review Policies Transfer of Copyright Archiving Policy Call for Papers Author Rights

RELATED JOURNALS

Journal of Digital Information Management (JDIM)

International Journal of Computational Linguistics Research (IJCL)

International Journal of Web Application (IJWA)

Digital Signal Processing and Artificial Intelligence for Automatic Learning

An Approach Based on Time-Series and Neural Networks for Safety Railway Incident Prediction

An Approach Based on Time-Series and Neural Networks for Safety Railway Incident Prediction
Rime Team-Networking, Modeling and e-Learning Team- Masi Laboratory- Engineering 3S Research Center-Mohammadia School of Engineers (EMI) Mohammed V University in Rabat Morocco

Abstract: Every day, thousands of people travel by train, not escaping the leniency of unforeseen events, the fluidity of the rail network being able to be disrupted by equipment breakdowns. Therefore, predictive maintenance is relevant and necessary to help anticipate these breakdowns and thus act against any mechanical, electrical, or technical constraints or obstacles that could disrupt or prevent the normal circulation of trains. This process is carried out using artificial intelligence approaches and various machine learning and deep learning models. This article will implement an approach that combines two essential concepts: time series and neural networks. We will start with the univariate analysis of the number of failures per day using a range of machine learning and deep learning algorithms, namely LSTM, BiLSTM, GRU, and SVR. The results show that we manage to minimize the prediction error; for example, with the GRU model, we get an RMSE of 0.487, but with increasing data, we get an RMSE of 0.463. Moreover, the problem encountered is the detection of peaks; the models cannot detect outliers, hence the use of the SVR model, which gives better coordination between the test data and the predicted data, with a gamma value of 0.03. Then, we tested the VAR model and the LSTM with several outputs; the latter gives satisfactory results with an accuracy rate of 92% and an RMSE of 0.006. Finally, we address the problem of classification of the nature of failures. We used several machine learning algorithms, such as SVM, KNN, Random Forest, then tested a method of Ã¢â‚¬Å“Ensemble Learning,Ã¢â‚¬Â the Vote. In the latter, we combined the three algorithms used previously, which increased the accuracy of the test to 61.73%.Ã‚Â

Keywords: Railway, LSTM, BiLSTM, GRU, SVR, VAR, SVM, KNN, Failure Prediction, PdM An Approach Based on Time-Series and Neural Networks for Safety Railway Incident Prediction

DOI:https://doi.org/10.6025/dspaial/2022/1/1/11-27

Full_Text PDF 1.08 MB Download: 37 times

References:

[1] Carvalho, T.P., Soares, F.A.A.M.N., Vita, R., Francisco, RdP., Basto, J.P. & Alcalá, S.G.S. (2019) A systematic literature review of machine learning methods applied to predictive maintenance. Computers and Industrial Engineering, 137, 106024 [DOI: 10.1016/j.cie.2019.106024].
[2] Kaffash, S., Nguyen, A.T. & Zhu, J. (2021) Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis. International Journal of Production Economics, 231, 107868 [DOI: 10.1016/j.ijpe.2020.107868].
[3] Botte, M. & D’Acierno, L. (2018) Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms. Urban Rail Transit, 4, 163–197 [DOI: 10.1007/s40864-018-0090-8].
[4] Brownlee, J. Deep Learning for Time Series Forecasting, p. 574.
[5] Plaud, A., Nguifo, E. & Charreyron, J. Classification des séries temporelles multivariées par l’usage de Mgrams, p. 12.
[6] Bashir, F. & Wei, H. (2018) Handling missing data in multivariate time series using a vector autoregressive model-imputation (VAR-IM) algorithm. Neurocomputing, 276, 23–30 [DOI: 10.1016/j.neucom.2017.03.097].
[7] M.S. says (2021). LSTM | Introduction to LSTM | Long Short Term Memor », Analytics Vidhya, mars 16. https://www.analyticsvidhya.com/blog/2021/03/introduction-to-long-short-term-memory-lstm/ (consulté le juin 29, 2021).
[8] Sun, Q., Jankovic, M.V., Bally, L. & Mougiakakou, S.G. (2018) Predicting blood glucose with an LSTM and bi-LSTM based deep neural network. In: 14th Symposium on Neural Networks and Applications (NEUREL), Belgrade, November 2018, pp. 1–5 [DOI: 10.1109/NEUREL.2018.8586990].
[9] Shen, G., Tan, Q., Zhang, H., Zeng, P. & Xu, J. (2018) Deep learning with gated recurrent unit networks for financial sequence predictions. Procedia Computer Science, 131, 895–903 [DOI: 10.1016/j.procs.2018.04.298].
[10] Barbour, W., Martinez Mori, J.C., Kuppa, S. & Work, D.B. (2018) Prediction of arrival times of freight traffic on US railroads using support vector regression, [août 2018]. Transportation Research Part C, 93, 211–227 [DOI: 10.1016/j.trc.2018.05.019].
[11] Hua, G., Zhu, L., Wu, J., Shen, C., Zhou, L. & Lin, Q. (2020) Blockchain-based federated learning for intelligent control in heavy haul railway. IEEE Access, 8, 176830–176839 [DOI: 10.1109/ACCESS.2020.3021253].
[12] Wu, Y., Ianakiev, K. & Govindaraju, V. (2002). Improved k-nearest neighbor classiÿcation. Pattern Recognition, 8.
[13] Jiang, C., Huang, P., Lessan, J., Fu, L. & Wen, C. (2019) Forecasting primary delay recovery of high-speed railway using multiple linear regression, supporting vector machine, artificial neural network, and random forest regression. Canadian Journal of Civil Engineering, 46, 353–363 [DOI: 10.1139/cjce-2017-0642].
[14] Brownlee, J. (2020). How to develop voting ensembles with python. Machine Learning Mastery. https://machinelearningm astery.com/voting-ensembles-with-python/ (consulté le juill. 01, 2021).
[15] Huang, P., Wen, C., Fu, L., Peng, Q. & Tang, Y. (2020) A deep learning approach for multi-attribute data: A study of train delay prediction in railway systems. Information Sciences, 516, 234–253 [DOI: 10.1016/j.ins.2019.12.053].
[16] Wen et al. “2020-A Predictive Model of Train Delays on a Railway li.pdf.
[17] F. Francis et M. Mohan. ARIMA Model Based Real Time Trend Analysis for Predictive Maintenance, in (2019) 3rd Inter national conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, juin 2019, pp. 735–739 [DOI: 10.1109/ICECA.2019.8822191].
[18] Ordóñez, C., Sánchez Lasheras, F., Roca-Pardiñas, J. & Juez, F.JdC. (2019) A hybrid Arima–SVM model for the study of the remaining useful life of aircraft engines. Journal of Computational and Applied Mathematics, 346, 184–191 [DOI: 10.1016/j.cam.2018.07.008].

DLINE Journals portal