A generic model of the information and decisional chain using Machine Learning based assistance in a manufacturing context
| dc.citation.epage | 1036 | |
| dc.citation.issue | 4 | |
| dc.citation.journalTitle | Математичне моделювання та комп'ютинг | |
| dc.citation.spage | 1023 | |
| dc.contributor.affiliation | Політехнічний університет Верхньої Франції | |
| dc.contributor.affiliation | Університет Мухаммеда V у Рабаті | |
| dc.contributor.affiliation | University Polytechnique des Hauts-de-France | |
| dc.contributor.affiliation | Mohammed V University in Rabat | |
| dc.contributor.author | Маллук, І. | |
| dc.contributor.author | Б. Абу Ель Мажд | |
| dc.contributor.author | Саллез, Ю. | |
| dc.contributor.author | Mallouk, I. | |
| dc.contributor.author | B. Abou el Majd | |
| dc.contributor.author | Sallez, Y. | |
| dc.coverage.placename | Львів | |
| dc.date.accessioned | 2025-03-10T09:22:02Z | |
| dc.date.created | 2023-02-28 | |
| dc.date.issued | 2023-02-28 | |
| dc.description.abstract | У наш час виробники повинні мати справу з величезною міжнародною конкуренцією і постійно вдосконалювати свої показники. У цьому контексті для виробничих систем використовуються декілька основних підходів, а саме: CBM (обслуговування на основі стану), PHM (прогнозування та керування станом) і PLM (керування життєвим циклом продукції) для підтримки і підвищення їхньої доступності, надійності і продуктивності. Це означає, що дані про експлуатаційне використання виробничого обладнання повинні бути доступними для всіх зацікавлених сторін через ефективні інформаційні ланцюги. Однак, незважаючи на велику кількість даних, зацікавлені сторони повинні отримувати допомогу в прийнятті рішень. Ця стаття має на меті запропонувати загальну архітектуру, яка моделює ланцюжок інформації та рішень від цільової системи до відповідних зацікавлених сторін, допомагаючи їм у прийнятті рішень. Запропонована загальна архітектура проілюстрована прикладом використання на основі алгоритму LSTM (Long Short-Term Memory) в контексті керування енергоспоживанням для парку мобільних роботів. | |
| dc.description.abstract | Nowadays, manufacturers must deal with huge international competition and continually improve their performances. In this context, several essential approaches namely CBM (Condition-based maintenance), PHM (Prognostics and Health Management), and PLM (Product Lifecycle Management) are used for manufacturing systems to maintain and increase their availability, reliability and performance. This implies that operational usage data of the manufacturing equipment must then be made available to all stakeholders concerned through efficient informational chains. However confronted with a large amount of data, the stakeholders must be assisted in their decision-making. This paper aims to propose a generic architecture that models the information and decision chain from the target system to the relevant stakeholders by assisting them in their decision-making. The proposed generic architecture is illustrated by a use case based on the LSTM (Long Short-Term Memory) algorithm in the context of energy management for a fleet of mobile robots. | |
| dc.format.extent | 1023-1036 | |
| dc.format.pages | 14 | |
| dc.identifier.citation | Mallouk I. A generic model of the information and decisional chain using Machine Learning based assistance in a manufacturing context / I. Mallouk, B. Abou el Majd, Y. Sallez // Mathematical Modeling and Computing. — Lviv Politechnic Publishing House, 2023. — Vol 10. — No 4. — P. 1023–1036. | |
| dc.identifier.citationen | Mallouk I. A generic model of the information and decisional chain using Machine Learning based assistance in a manufacturing context / I. Mallouk, B. Abou el Majd, Y. Sallez // Mathematical Modeling and Computing. — Lviv Politechnic Publishing House, 2023. — Vol 10. — No 4. — P. 1023–1036. | |
| dc.identifier.doi | doi.org/10.23939/mmc2023.04.1023 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/64083 | |
| dc.language.iso | en | |
| dc.publisher | Видавництво Львівської політехніки | |
| dc.publisher | Lviv Politechnic Publishing House | |
| dc.relation.ispartof | Математичне моделювання та комп'ютинг, 4 (10), 2023 | |
| dc.relation.ispartof | Mathematical Modeling and Computing, 4 (10), 2023 | |
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| dc.relation.references | [25] Mallouk I., Berger T., Abou El Majd B., Sallez Y. A Proposal to Model the Monitoring Architecture of a Complex Transportation System. International Workshop on Service Orientation in Holonic and MultiAgent Manufacturing. SOHOMA 2020: Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future. 532–542 (2021). | |
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| dc.relation.references | [37] Goebel K., Saha B., Saxena A., Celaya J. R., Christophersen J. P. Prognostics in battery health management. IEEE Instrumentation & Measurement Magazine. 11 (4), 33–40 (2008). | |
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| dc.relation.references | [40] Wang C., Lu N., Wang S., Cheng Y., Jiang B. Dynamic long short-term memory neural-network-based indirect remaining-useful-life prognosis for satellite Lithium-ion battery. Applied Sciences. 8 (11), 2078 (2018). | |
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| dc.relation.references | [42] Siami-Namini S., Tavakoli N., Namin A. S. The Performance of LSTM and BiLSTM in Forecasting Time Series. 2019 IEEE International Conference on Big Data (Big Data). 3285–3292 (2019). | |
| dc.relation.references | [43] Hochreiter S., Schmidhuber J. Long Short-Term Memory. Neural Computation. 9 (8), 1735–1780 (1997). | |
| dc.relation.references | [44] Prajapati A., Bechtel J., Ganesan S. Condition based maintenance: A survey. Journal of Quality in Maintenance Engineering. 18 (4), 384–400 (2012). | |
| dc.relation.references | [45] Ait Lhadj Lamin S., Raghib A., Abou El Majd B. Robust multi-objective optimization for solving the RFID network planning problem. Mathematical Modeling and Computing. 8 (4), 616–626 (2021). | |
| dc.relation.references | [46] Chemlal Y., Azouazi M. Implementing quality assurance practices in teaching machine learning in higher education. Mathematical Modeling and Computing. 10 (3), 660–667 (2023). | |
| dc.relation.references | [47] Jin H., Song Q., Hu X. Auto-Keras: An efficient neural architecture search system. KDD ’19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1946–1956 (2019). | |
| dc.relation.referencesen | [1] Basselot V., Berger T., Sallez Y. Information chain modeling from product to stakeholder in the use phase – Application to diagnoses in railway transportation. Manufacturing Letters. 20, 22–26 (2019). | |
| dc.relation.referencesen | [2] Kiritsis D. Closed-loop PLM for intelligent products in the era of the Internet of things. Computer-Aided Design. 43 (5), 479–501 (2011). | |
| dc.relation.referencesen | [3] Merkert J., Mueller M., Hubl M. A survey of the application of machine learning in decision support systems. ECIS 2015 Completed Research Papers. 133 (2015). | |
| dc.relation.referencesen | [4] Parnell G. S., Driscoll P. J., Henderson D. L. Decision Making in Systems Engineering and Managmeent. John Wiley & Sons, Inc. (2011). | |
| dc.relation.referencesen | [5] Bosse E., Solaiman B. Fusion of information and analytics: a discussion on potential methods to cope with uncertainty in complex environments (big data and IoT). International Journal of Digital Signals and Smart Systems. 2 (4), 279–316 (2018). | |
| dc.relation.referencesen | [6] Murty K. G., Kim W.-J. An iDMSS Based on Bipartite Matching and Heuristics for Rental Bus Allocation. Intelligent Decision-making Support Systems. 219–235 (2006). | |
| dc.relation.referencesen | [7] Wallace W. A., De Balogh F. Decision Support Systems for Disaster Management. 45, 134–146 (1985). | |
| dc.relation.referencesen | [8] Glasspool D. W., Fox J., Castillo F. D., Monaghan V. E. L. Interactive decision support for medical planning. Conference on Artificial Intelligence in Medicine in Europe. AIME 2003: Artificial Intelligence in Medicine. 335–339 (2003). | |
| dc.relation.referencesen | [9] Ayodele T. O. Types of Machine Learning Algorithms. New Advances in Machine Learning (2010). | |
| dc.relation.referencesen | [10] Soofi A. A., Awan A. Classification Techniques in Machine Learning: Applications and Issues. Journal of Basic & Applied Sciences. 13, 459–465 (2017). | |
| dc.relation.referencesen | [11] Maulud D. H., Abdulazeez A. M. A Review on Linear Regression Comprehensive in Machine Learning. Journal of Applied Science and Technology Trends. 1 (4), 140–147 (2020). | |
| dc.relation.referencesen | [12] Ezugwu A. E., Ikotun A. M., Oyelade O. O., Abualigah L., Agushaka J. O., Eke C. I., Akinyelu A. A. A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Engineering Applications of Artificial Intelligence. 110, 104743 (2022). | |
| dc.relation.referencesen | [13] Van der Maaten L., Postma E., van den Herik J. Dimensionality Reduction: A Comparative Review Dimensionality Reduction: A Comparative Review. Tilburg centre for Creative Computing, Tilburg University (2009). | |
| dc.relation.referencesen | [14] Barua L., Zou B., Zhou Y. Machine learning for international freight transportation management: A comprehensive review. Research in Transportation Business & Management. 34, 100453 (2020). | |
| dc.relation.referencesen | [15] Power D. J. Using Big Data for analytics and decision support. Journal of Decision Systems. 23 (2), 222–228 (2014). | |
| dc.relation.referencesen | [16] Gugulothu N., TV V., Malhotra P., Vig L., Agarwal P., Shroff G. Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks. Preprint arXiv:1709.01073 (2021). | |
| dc.relation.referencesen | [17] Khumprom P., Yodo N. A data-driven predictive prognostic model for lithium-ion batteries based on a deep learning algorithm. Energies. 12 (4), 660 (2019). | |
| dc.relation.referencesen | [18] Jamshidi P., Camara J., Schmerl B., Kaestner C., Garlan D. Machine learning meets quantitative planning: Enabling self-adaptation in autonomous robots. 2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). 39–50 (2019). | |
| dc.relation.referencesen | [19] Mosallam A., Medjaher K., Zerhouni N. Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction. Journal of Intelligent Manufacturing. 27 (5), 1037–1048 (2016). | |
| dc.relation.referencesen | [20] Kulkarni K., Devi U., Sirighee A., Hazra J., Rao P. Predictive Maintenance for Supermarket Refrigeration Systems Using only Case Temperature Data. 2018 Annual American Control Conference (ACC). 4640–4645 (2018). | |
| dc.relation.referencesen | [21] Uhlmann E., Pontes R. P., Geisert C., Hohwieler E. Cluster identification of sensor data for predictive maintenance in a Selective Laser Melting machine tool. Procedia Manufacturing. 24, 60–65 (2018). | |
| dc.relation.referencesen | [22] Krishna K. M., Kannadaguli P. IoT based CNC machine condition monitoring system using machine learning techniques. 2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT). 61–65 (2020). | |
| dc.relation.referencesen | [23] Morariu C., Morariu O., R˘aileanu S., Borangiu T. Machine learning for predictive scheduling and resource allocation in large scale manufacturing systems. Computers in Industry. 120, 103244 (2020). | |
| dc.relation.referencesen | [24] Sallez Y., Berger T., Deneux D., Trentesaux D. The lifecycle of active and intelligent products: The augmentation concept. International Journal of Computer Integrated Manufacturing. 23 (10), 905–924 (2010). | |
| dc.relation.referencesen | [25] Mallouk I., Berger T., Abou El Majd B., Sallez Y. A Proposal to Model the Monitoring Architecture of a Complex Transportation System. International Workshop on Service Orientation in Holonic and MultiAgent Manufacturing. SOHOMA 2020: Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future. 532–542 (2021). | |
| dc.relation.referencesen | [26] Koestler A. Beyond Atomism and Holism the Concept of the Holon. Perspectives in Biology and Medicine. 13 (2), 131–154 (1970). | |
| dc.relation.referencesen | [27] Koestler A. The Ghost in the Machine. New York, Macmillan (1968). | |
| dc.relation.referencesen | [28] Indriago C., Cardin O., Rakoto N., Castagna P., Chac`on E. H2CM: A holonic architecture for flexible hybrid control systems. Computers in Industry. 77, 15–28 (2016). | |
| dc.relation.referencesen | [29] Cardin O., Derigent W., Trentesaux D. Evolution of holonic control architectures towards Industry 4.0: A short overview. IFAC-PapersOnLine. 51 (11), 1243–1248 (2018). | |
| dc.relation.referencesen | [30] Le Mortellec A., Clarhaut J., Sallez Y., Berger T., Trentesaux D. Embedded holonic fault diagnosis of complex transportation systems. Engineering Applications of Artificial Intelligence. 26 (1), 227–240 (2013). | |
| dc.relation.referencesen | [31] Murphy K. P. Machine Learning: A Probabilistic Perspective. MIT Press (2012). | |
| dc.relation.referencesen | [32] Mbuli J., Nouiri M., Trentesaux D., Baert D. Root causes analysis and fault prediction in intelligent transportation systems: coupling unsupervised and supervised learning techniques. 2019 International Conference on Control, Automation and Diagnosis (ICCAD). 1–6 (2019). | |
| dc.relation.referencesen | [33] Musa A. B. A comparison of ℓ1-regularizion, PCA, KPCA and ICA for dimensionality reduction in logistic regression. International Journal of Machine Learning and Cybernetics. 5, 861–873 (2014). | |
| dc.relation.referencesen | [34] Zhang C., Ma Y. Ensemble Machine Learning: Methods and Applications. Springer, New York (2012). | |
| dc.relation.referencesen | [35] Huotari M., Arora S., Malhi A., Fr¨amling K. Comparing seven methods for state-of-health time series prediction for the lithium-ion battery packs of forklifts. Applied Soft Computing. 111, 107670 (2021). | |
| dc.relation.referencesen | [36] He X., Zhao K., Chu X. AutoML: A survey of the state-of-the-art. Knowledge-Based Systems. 212, 106622 (2021). | |
| dc.relation.referencesen | [37] Goebel K., Saha B., Saxena A., Celaya J. R., Christophersen J. P. Prognostics in battery health management. IEEE Instrumentation & Measurement Magazine. 11 (4), 33–40 (2008). | |
| dc.relation.referencesen | [38] Mishra M., Martinsson J., Rantatalo M., Goebel K. Bayesian hierarchical model-based prognostics forlithium-ion batteries. Reliability Engineering & System Safety. 172, 25–35 (2018). | |
| dc.relation.referencesen | [39] Qu J., Liu F., Ma Y., Fan J. A Neural-Network-Based Method for RUL Prediction and SOH Monitoring of Lithium-Ion Battery. IEEE Access. 7, 87178–87191 (2019). | |
| dc.relation.referencesen | [40] Wang C., Lu N., Wang S., Cheng Y., Jiang B. Dynamic long short-term memory neural-network-based indirect remaining-useful-life prognosis for satellite Lithium-ion battery. Applied Sciences. 8 (11), 2078 (2018). | |
| dc.relation.referencesen | [41] Chung J., Gulcehre C., Cho K., Bengio Y. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. Preprint arXiv:1412.3555 (2014). | |
| dc.relation.referencesen | [42] Siami-Namini S., Tavakoli N., Namin A. S. The Performance of LSTM and BiLSTM in Forecasting Time Series. 2019 IEEE International Conference on Big Data (Big Data). 3285–3292 (2019). | |
| dc.relation.referencesen | [43] Hochreiter S., Schmidhuber J. Long Short-Term Memory. Neural Computation. 9 (8), 1735–1780 (1997). | |
| dc.relation.referencesen | [44] Prajapati A., Bechtel J., Ganesan S. Condition based maintenance: A survey. Journal of Quality in Maintenance Engineering. 18 (4), 384–400 (2012). | |
| dc.relation.referencesen | [45] Ait Lhadj Lamin S., Raghib A., Abou El Majd B. Robust multi-objective optimization for solving the RFID network planning problem. Mathematical Modeling and Computing. 8 (4), 616–626 (2021). | |
| dc.relation.referencesen | [46] Chemlal Y., Azouazi M. Implementing quality assurance practices in teaching machine learning in higher education. Mathematical Modeling and Computing. 10 (3), 660–667 (2023). | |
| dc.relation.referencesen | [47] Jin H., Song Q., Hu X. Auto-Keras: An efficient neural architecture search system. KDD ’19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1946–1956 (2019). | |
| dc.rights.holder | © Національний університет “Львівська політехніка”, 2023 | |
| dc.subject | виробництво | |
| dc.subject | прийняття рішень | |
| dc.subject | прогнозування та керування здоров’ям (PHM) | |
| dc.subject | довга короткочасна пам’ять. (LSTM) | |
| dc.subject | manufacturing | |
| dc.subject | decision-making | |
| dc.subject | prognostic and health management (PHM) | |
| dc.subject | long short-term memory (LSTM) | |
| dc.title | A generic model of the information and decisional chain using Machine Learning based assistance in a manufacturing context | |
| dc.title.alternative | Загальна модель інформації та ланцюга прийняття рішень з використанням допомоги на основі машинного навчання у виробничому контексті | |
| dc.type | Article |
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