Documents dont l'auteur est "Ouali, Mohamed-Salah"

Mohamed-Salah OualiDépartement de mathématiques et de génie industrielVoir sa fiche dans le Répertoire des Expertises de Polytechnique Montréal

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Aoudjit, H., Ouali, M.-S., & Yacout, S. (2007). Modélisation et analyse de sensibilité d'un processus de dégradation stochastique composé. Revues JESA, 41(9-10). Non disponible

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Ben Slimene, M., & Ouali, M.-S. (juin 2022). Anomaly Detection Method of Aircraft System using Multivariate Time Series Clustering and Classification Techniques [Résumé]. 10th IFAC Triennial Conference on Manufacturing Modelling, Management and Control (MIM 2022), Nantes, FRANCE. Publié dans IFAC PapersOnLine, 55(10). Lien externe

Burlet-Vienney, D., Belmekki, T., Chinniah, Y. A., Aucourt, B., & Ouali, M.-S. (octobre 2018). Analysis and prevention of serious and fatal accidents caused by mobile equipment in Québec [Communication écrite]. 9th International Conference on the Safety of Industrial Automated Systems (SIAS 2018), Nancy, France. Non disponible

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Elhefnawy, M., Ouali, M.-S., Ragab, A., & Amazouz, M. (2023). Fusion of heterogeneous industrial data using polygon generation & deep learning. Results in Engineering, 19, 11 pages. Disponible

Elhefnawy, M., Ragab, A., & Ouali, M.-S. (2022). Fault classification in the process industry using polygon generation and deep learning. Journal of Intelligent Manufacturing, 33(5), 1531-1544. Lien externe

Elhefnawy, M., Ouali, M.-S., & Ragab, A. (2022). Multi-output regression using polygon generation and conditional generative adversarial networks. Expert Systems With Applications, 203, 14 pages. Lien externe

Elhefnawy, M., Ragab, A., & Ouali, M.-S. (2022). Polygon generation and video-to-video translation for time-series prediction. Journal of Intelligent Manufacturing, 34(1), 261-279. Lien externe

Elsheikh, A., Yacout, S., Ouali, M.-S., & Shaban, Y. (2020). Failure time prediction using adaptive logical analysis of survival curves and multiple machining signals. Journal of Intelligent Manufacturing, 31(2), 403-415. Lien externe

Elsheikh, A., Yacout, S., & Ouali, M.-S. (2018). Bidirectional handshaking LSTM for remaining useful life prediction. Neurocomputing, 323(148-156), 148-156. Lien externe

Elbadiry, A. H., Bassetto, S., & Ouali, M.-S. (2016). Study of similarity analysis methods for aviation system failures. IEEE Aerospace and Electronic Systems Magazine, 31(6), 12-22. Lien externe

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Ghasemi, A., Yacout, S., & Ouali, M.-S. (2010). Evaluating the reliability function and the mean residual life for equipment with unobservable states. IEEE Transactions on Reliability, 59(1), 45-54. Lien externe

Ghasemi, A., Yacout, S., & Ouali, M.-S. (2010). Parameter Estimation Methods for Condition-Based Maintenance With Indirect Observations. IEEE Transactions on Reliability, 59(2), 426-439. Lien externe

Ghasemi, A., Yacout, S., & Ouali, M.-S. (octobre 2007). Optimal inspection period and replacement policy for CBM with imperfect information using PHM [Communication écrite]. World Congress on Engineering and Computer Science, San Francisco (California). Lien externe

Ghasemi, A., Yacout, S., & Ouali, M.-S. (2008). Optimal strategies for non-costly and costly observations in condition based maintenance. IAENG International Journal of Applied Mathematics, 38(2), 9-9. Lien externe

Ghasemi, A., Yacout, S., & Ouali, M.-S. (2007). Optimal Condition Based Maintenance With Imperfect Information and the Proportional Hazards Model. International Journal of Production Research, 45(4), 989-1012. Lien externe

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Jocelyn, S., Ouali, M.-S., & Chinniah, Y. A. (2018). Estimation of probability of harm in safety of machinery using an investigation systemic approach and Logical Analysis of Data. Safety Science, 105, 32-45. Lien externe

Jocelyn, S., Chinniah, Y. A., Ouali, M.-S., & Yacout, S. (2017). Application of Logical Analysis of Data to Machinery-Related Accident Prevention Based on Scarce Data. Reliability Engineering & System Safety, 159, 223-236. Lien externe

Jocelyn, S., Ouali, M.-S., & Chinniah, Y. A. (2017). Improving machinery-related risk identification and estimation with accident reporting and logical analysis of data. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 61(1), 1659-1663. Lien externe

Jocelyn, S., Ouali, M.-S., & Chinniah, Y. A. (octobre 2017). Updating machine related risk with accident reporting and logical analysis of data [Communication écrite]. 61st Annual Meeting of Human Factors and Ergonomics Society (HFES 2017), Austin, TX, USA. Non disponible

Jocelyn, S., Chinniah, Y. A., Ouali, M.-S., & Yacout, S. (mai 2016). Application of logical analysis of data to prevent machinery-related accidents [Communication écrite]. Industrial and Systems Engineering Research Conference (ISERC 2016), Anaheim, CA, USA. Non disponible

Jocelyn, S., Chinniah, Y. A., & Ouali, M.-S. (2016). Contribution of dynamic experience feedback to the quantitative estimation of risks for preventing accidents: A proposed methodology for machinery safety. Safety Science, 88, 64-75. Lien externe

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Lamghabbar, A., Yacout, S., & Ouali, M.-S. (2004). Concurrent Optimization of the Design and Manufacturing Stages of Product Development. International Journal of Production Research, 42(21), 4495-4512. Lien externe

Lambhabbar, A., Yacout, S., & Ouali, M.-S. (janvier 2002). Concurrent optimisation of design and manufacturing phases of QFD, CROS-SCRO [Communication écrite]. 44th Annual Conference, MIT, Toronto. Non disponible

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Mikhail, M., Ouali, M.-S., & Yacout, S. (2024). A data-driven methodology with a nonparametric reliability method for optimal condition-based maintenance strategies. Reliability Engineering and System Safety, 241, 109668 (11 pages). Lien externe

Meango, T. J.-M., & Ouali, M.-S. (2020). Failure interaction model based on extreme shock and Markov processes. Reliability Engineering and System Safety, 197, 11 pages. Lien externe

Mikhail, M., Yacout, S., & Ouali, M.-S. (octobre 2019). Optimal preventive maintenance strategy using reinforcement learning [Communication écrite]. 4th North American International Conference on Industrial Engineering and Operations Management, Toronto, ON (9 pages). Lien externe

Meango, T. J.-M., & Ouali, M.-S. (2018). Failure interaction models for multicomponent systems: a comparative study. SN Applied Sciences, 1(1). Lien externe

Malki, Z., Ait-Kadi, D., & Ouali, M.-S. (2015). Age replacement policies for two-component systems with stochastic dependence. Journal of Quality in Maintenance Engineering, 21(3), 346-357. Lien externe

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Nadim, K., Ouali, M.-S., Ghezzaz, H., & Ragab, A. (2023). Learn-to-supervise: Causal reinforcement learning for high-level control in industrial processes. Engineering Applications of Artificial Intelligence, 126, 106853 (29 pages). Lien externe

Nadim, K., Ragab, A., & Ouali, M.-S. (2022). Data-driven dynamic causality analysis of industrial systems using interpretable machine learning and process mining. Journal of Intelligent Manufacturing, 34(1), 57-83. Lien externe

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Ouali, M.-S., Aoudjit, H., & Audet, C. (2014). Replacement Scheduling of a Fleet of Hydroelectric Generators: A Case Study. International Journal of Performability Engineering, 10(6), 615-630. Lien externe

Ouali, M.-S., Tadj, L., Yacout, S., & Ait-Kadi, D. (2011). A survey of replacement models with minimal repair. Dans Replacement Models with Minimal Repair (p. 3-100). Lien externe

Ouali, M.-S., Aoudjit, H., & Audet, C. (2003). Optimisation des stratégies de maintenance intégration à la production. Journal Européen des Systèmes Automatisés, 37(5), 587-605. Non disponible

Ouali, M.-S., & Yacout, S. (2003). Optional preventive replacement policy for two-component system. Journal of Decision Systems, 12(1), 11-20. Lien externe

Ouali, M.-S., Rezg, N., & Xiaolan, X. (avril 2001). Maintenance préventive et optimisation des flux d'un système de production [Communication écrite]. Conference francophone de modelisation et de simulation (MOSIM 2001), Troyes, France. Publié dans Journal européen des systèmes automatisés, 36(1). Non disponible

Ouali, M.-S., Rezg, N., & Xie, X. (avril 2001). Maintenance préventive et optimisation des flux d'une ligne de production [Communication écrite]. MOSIM '01 : 3e conférence francophone de modélisation et simulation, Troyes, FRA. Non disponible

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Ragab, A., Elhefnawy, M., & Ouali, M.-S. (janvier 2022). Artificial Intelligence-Based Survival Analysis For Industrial Equipment Performance Management [Communication écrite]. 68th Annual Reliability and Maintainability Symposium (RAMS 2022), Tucson, AZ, USA (7 pages). Lien externe

Ragab, A., El Koujok, M., Ghezzaz, H., Amazouz, M., Ouali, M.-S., & Yacout, S. (2019). Deep understanding in industrial processes by complementing human expertise with interpretable patterns of machine learning. Expert Systems With Applications, 122, 388-405. Lien externe

Ragab, A., Yacout, S., Ouali, M.-S., & Osman, H. (2019). Prognostics of multiple failure modes in rotating machinery using a pattern-based classifier and cumulative incidence functions. Journal of Intelligent Manufacturing, 30(1), 255-274. Lien externe

Ragab, A., de Carné de Carnavalet, X., Yacout, S., & Ouali, M.-S. (2017). Face recognition using multi-class Logical Analysis of Data. Pattern Recognition and Image Analysis, 27(2), 276-288. Lien externe

Ragab, A., Yacout, S., Ouali, M.-S., & Osman, H. (2017). Pattern-based prognostic methodology for condition-based maintenance using selected and weighted survival curves. Quality and Reliability Engineering International, 33(8), 1753-1772. Lien externe

Ragab, A., Yacout, S., & Ouali, M.-S. (janvier 2016). Remaining useful life prognostics using pattern-based machine learning [Communication écrite]. Annual Reliability and Maintainability Symposium (RAMS 2016), Tucson, AZ (7 pages). Lien externe

Ragab, A., Yacout, S., & Ouali, M.-S. (janvier 2015). Interpretable pattern-based machine learning for condition-based maintenance [Communication écrite]. 61st Annual Reliability and Maintainability Symposium (RAMS 2015), Palm Harbor, Florida (6 pages). Non disponible

Ragab, A., Yacout, S., Ouali, M.-S., & Osman, H. (janvier 2015). Multiple failure modes prognostics using logical analysis of data [Communication écrite]. Annual Reliability and Maintainability Symposium (RAMS 2015), Palm Harbor, FL, USA (7 pages). Lien externe

Ragab, A., Ouali, M.-S., Yacout, S., & Osman, H. (mai 2014). Condition-based maintenance prognostics using logical analysis of data [Communication écrite]. IIE Annual Conference and Expo 2014, Montréal, Québec. Non disponible

Ragab, A., Ouali, M.-S., Yacout, S., & Osman, H. (2014). Remaining useful life prediction using prognostic methodology based on logical analysis of data and Kaplan-Meier estimation. Journal of Intelligent Manufacturing, 27(5), 943-958. Lien externe

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Soualhi, M., El Koujok, M., Nguyen, K. T. P., Medjaher, K., Ragab, A., Ghezzaz, H., Amazouz, M., & Ouali, M.-S. (2021). Adaptive prognostics in a controlled energy conversion process based on long- and short-term predictors. Applied Energy, 283, 116049 (14 pages). Lien externe

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Tadj, L., Ouali, M.-S., Yacout, S., & Ait-Kadi, D. (2011). Replacement models with minimal repair. Lien externe

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Waghen, K., & Ouali, M.-S. (2022). A data-driven fault tree for a time causality analysis in an aging system. Algorithms, 15(6), 19 pages. Lien externe

Waghen, K., & Ouali, M.-S. (2021). Multi-level interpretable logic tree analysis: A data-driven approach for hierarchical causality analysis. Expert Systems With Applications, 178, 12 pages. Lien externe

Waghen, K., & Ouali, M.-S. (2019). Interpretable logic tree analysis: A data-driven fault tree methodology for causality analysis. Expert Systems With Applications, 136, 376-391. Lien externe

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Yacout, S., & Ouali, M.-S. (janvier 2019). Using Artificial Intelligence for Block Maintenance of Pavement Segments with Similar Degradation [Communication écrite]. 65th Annual Reliability and Maintainability Symposium (RAMS 2019), Orlando, Florida. Lien externe

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Ziani, R., Ouali, M.-S., & Artiba, A. (2014). Sensibility of Bayesian inference methods for reliability prediction of ageing systems, case of Diesel locomotives. International Journal of Production Research, 52(14), 4142-4155. Lien externe