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Application of machine learning and rough set theory in lean maintenance decision support system development
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Rzeszow University of Technology, Faculty of Mechanical Engineering and Aeronautics, Powstańców Warszawy 8, 35-959 Rzeszów, Poland
Poznan University of Technology, Faculty of Management Engineering, Prof. Rychlewskiego 2, 60-965 Poznan, Poland
Beihang University (BUAA), School of Automation Science and Electrical Engineering, 37 Xueyuan Road, Beijing, 100191, China
Publication date: 2021-12-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2021;23(4):695-708
  • A review of lean maintenance importance in manufacturing.
  • A approach with rough set theory and decision tree.
  • Rough set theory with different types of algorithms selected for predictive models.
  • The classification model for lean maintenance implementation assessment.
Lean maintenance concept is crucial to increase the reliability and availability of maintenance equipment in the manufacturing companies. Due the elimination of losses in maintenance processes this concept reduce the number of unplanned downtime and unexpected failures, simultaneously influence a company’s operational and economic performance. Despite the widespread use of lean maintenance, there is no structured approach to support the choice of methods and tools used for the maintenance function improvement. Therefore, in this paper by using machine learning methods and rough set theory a new approach was proposed. This approach supports the decision makers in the selection of methods and tools for the effective implementation of Lean Maintenance.
Amin MA, Alam MR, Alidrisi H, Karim MA. A fuzzy-based leanness evaluation model for manufacturing organisations. Production Planning & Control 2021; 32(11): 959-974,
Antony J, Psomas E, Garza-Reyes JA, Hines P. Practical implications and future research agenda of lean manufacturing: a systematic literature review. Production Planning & Control 2020:1-37,
Antosz K, Mazurkiewicz D, Kozłowski E, Sęp J, Żabiński T. Machining Process Time Series Data Analysis with a Decision Support Tool. In: Machado J., Soares F., Trojanowska J., Ottaviano E. (eds) Innovations in Mechanical Engineering. icieng 2021. Lecture Notes in Mechanical Engineering. Springer, Cham. 2021; 14-27,
Antosz K, Paśko Ł, Gola A. The use of intelligent systems to support the decision-making process in Lean Maintenance management IFAC PAPERSONLINE 2019; 52(10): 148-153,
Antosz K. Metodyka modelowania oceny i doskonalenia koncepcji Lean Maintenance, OW PRZ, Rzeszow, 2019.
Arlinghaus JC, Knizkov S. Lean Maintenance and Repair Implementation - A Cross-Case Study of Seven Automotive Service Suppliers. Procedia CIRP 2020; 93: 955-964,
Arrascue-Hernandez G, Cabrera-Brusil J, Chavez-Soriano P, Raymundo-Ibañez C, Perez M. LEAN maintenance model based on change management allowing the reduction of delays in the production line of textile SMEs in Peru. In IOP Conference Series: Materials Science and Engineering 2020; 796(1): 012017,
Arslankaya S, Atay H. Maintenance management and lean manufacturing practices in a firm which produces dairy products. Procedia-Social and Behavioral Sciences 2015; 207: 214-224,
Aucasime-Gonzales P, Tremolada-Cruz S, Chavez-Soriano P, Dominguez F, Raymundo C. Waste Elimination Model Based on Lean Manufacturing and Lean Maintenance to Increase Efficiency in the Manufacturing Industry. In IOP Conference Series: Materials Science and Engineering IOP Publisher 2020; 999(1): 012013,
Ball P, Lunt P. Lean eco-efficient innovation in operations through the maintenance organisation. International Journal of Production Economics 2020; 219: 405-415,
Baluch NH, Che Sobry A, Shahimi M. TPM and Lean maintenance-A Critical Review Interdisciplinary. Journal of Contemporary Research in Business 20124 (2): 850-857.
Barnard A. Lean Reliability Engineering. In INCOSE International Symposium 2014; 24(s1): 13-23,
Bar-or A, Schuster A, Wolff R, Keren D. Decision tree induction in high dimensional hierarchically distributed databases. In Proceedings SI-AM International Data Mining Conference Newport Beach CA 2005; 466-470,
Bertolini M, Mezzogori D, Neroni M, Zammori F. Machine Learning for industrial applications: a comprehensive literature review. Expert Systems with Applications 2021: 114820,
Bhasin S. Prominent obstacles to lean. International Journal of Productivity and Performance Management 2011; 61(4): 403-425,
Bhuvanesh Kumar M, Parameshwaran R. Fuzzy integrated QFD FMEA framework for the selection of lean tools in a manufacturing organisation. Production Planning & Control 2018; 29(5): 403-417,
Bortolotti T, Boscari S, Danese P. Successful lean implementation: Organizational culture and soft lean practices. International Journal of Production Economics 2015; 160: 182-201,
Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and Regression Trees. Chapman & Hall New York 1984.
Bukowski L, Werbińska-Wojciechowska S. Using fuzzy logic to support maintenance decisions according to Resilience-Based Maintenance concept. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2021; 23 (2): 294-307,
Campagner A, Ciucci D, Hüllermeier E. Rough set-based feature selection for weakly labeled data. International Journal of Approximate Reasoning 2021; 136: 150-167,
Ceruti A, Marzocca P, Liverani A, Bil C. Maintenance in aeronautics in an industry 4 0 context: the role of augmented reality and additive manufacturing. Journal of Computational Design and Engineering 2019; 6(4): 516-526,
Chemweno P, Pintelon L, Muchiri PN, Van Horenbeek A. Risk assessment methodologies in maintenance decision making: A review of dependability modelling approaches. Reliability Engineering & System Safety 2018; 173: 64-77,
Chen C, Wang C, Lu N, Jiang B, Xing Y. A data-driven predictive maintenance strategy based on accurate failure prognostics. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2021; 23 (2): 387-394,
Chowdary BV, Ojha K, Alexander A. Improvement of refinery maintenance and mechanical services: application of lean manufacturing principles. International Journal of Collaborative Enterprise 2018; 6(1): 20-36,
Ciano MP, Pozzi R, Rossi T, Strozzi F. How IJPR has Addressed 'Lean': A Literature Review Using Bibliometric Tools. International Journal of Production Research 2019; 57 (15-16): 5284-5317,
Damián M, Chambilla M, Viacava G, Eyzaguirre J, Raymundo C. Lean Service Model for Maintenance Management Using a Linear Programming Approach. In 2021 10th International Conference on Industrial Technology and Management (ICITM) 2021; 25-30,
Dekker R. Applications of maintenance optimisation models: A review and analysis. Reliability Engineering and System Safety 1996; 51: 229-240,
Drożyner P. The impact of the implementation of management system on the perception of role and tasks of maintenance services and effectiveness of their functioning. Journal of Quality in Maintenance Engineering 2021; 27(2): 430-450,
Duran O, Capaldo A, Acevedo PAD. Lean maintenance applied to improve maintenance efficiency in thermoelectric power plants. Energies 2017; 10(10): 1-22,
Epler I, Sokolović V, Milenkov M, Bukvić M. Application of lean tools for improved effectiveness in maintenance of technical systems for special purposes. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2017; 19 (4): 615–623,
Fawcelt T. An introduction to ROC analysis. Pattern Recogn Lett 2006; 27: 861-874,
Gao R, Wang L, Teti R, Dornfeld D, Kumara S, Mori M, Helu M. Cloud-enabled Prognosis for Manufacturing. CIRP Annals-Manufacturing Technology 2015; 64(2): 749-772,
Gaur J, Goel AK, Rose A, Bhushan B. Emerging trends in machine learning. In 2019 2nd International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) IEEE 2019; 1: 881-885,
Godara S, Singh R. Evaluation of predictive machine learning techniques as expert systems in medical diagnosis. Indian Journal of Science and Technology 2016; 9(10): 1-14,
Gupta S, Gupta P, Parida A. Modeling lean maintenance metric using incidence matrix approach. International Journal of System Assurance Engineering and Management 2017; 8(4): 799-816,
Henríquez-Alvarado F, Luque-Ojeda V, Macassi-Jauregui I, Alvarez JM, Raymundo-Ibañez C. Process optimization using lean manufacturing to reduce downtime: Case study of a manufacturing SME in Peru. Paper presented at the ACM International Conference Proceeding Series 2019; 261-265.
Holgado M, Macchi M, Evans S. Exploring the impacts and contributions of maintenance function for sustainable manufacturing. International Journal of Production Research 2020; 58(23): 7292-7310,
Jasiulewicz-Kaczmarek M, Antosz K, Wyczółkowski R, Mazurkiewicz D, Sun B, Qian C, Ren Y. Application of MICMAC, Fuzzy AHP, and Fuzzy TOPSIS for Evaluation of the Maintenance Factors Affecting Sustainable Manufacturing. Energies 2021; 14(5): 1436,
Jasiulewicz-Kaczmarek M, Antosz K, Zywica P, Mazurkiewicz D, Sun B, Ren Y. Framework of machine criticality assessment with criteria interactions. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2021; 23(2): 207-220,
Jasiulewicz-Kaczmarek M, Saniuk A. How to Make Maintenance Processes More Efficient Using Lean Tools?. In International Conference on Applied Human Factors and Ergonomics, Cham 2018; 9-20,
Jasiulewicz-Kaczmarek M, Żywica P, Gola A. Fuzzy set theory driven maintenance sustainability performance assessment model: A multiple criteria approach. Journal of Intelligent Manufacturing 2021; 32(5): 1497-1515,
Jing S, Niu Z,Chang PC. The application of VIKOR for the tool selection in lean management. Journal of Intelligent Manufacturing 2019; 30(8): 2901-2912,
Kammerer K. Hoppenstedt B. Pryss R. Stokler S. Allgaier J. Reichert M. Anomaly detections for manufacturing systems based on sensor data-insights into two challenging real-world production settings. Sensors 2019; 19(24): 5370,
Khazravi N, Alavi SM. A New Method To Feature Selection In Rough Fuzzy Set Theory Based On Degree Of Separation Turkish. Journal of Computer and Mathematics Education (TURCOMAT) 2021; 12(14): 1889-1897.
Kovács G. Combination of Lean Value-Oriented Conception and Facility Layout Design for Even More Significant Efficiency Improvement and Cost Reduction. International Journal of Production Research 2020; 58 (10): 2916-2936,
Kuhnle A, Jakubik J, Lanza G. Reinforcement learning for opportunistic maintenance optimization. Production Engineering 2018; 13(1): 33-41,
Kumar MB, Parameshwaran R. A comprehensive model to prioritise lean tools for manufacturing industries: a fuzzy FMEA AHP and QFD-based approach. International Journal of Services and Operations Management 2020; 37(2): 170-196,
Larose DT. Discovering knowledge from data Introduction to data mining. Scientific publisher PWN Warsaw 2013,
Lundgren C, Skoogh A, Bokrantz J. Quantifying the effects of maintenance-a literature review of maintenance models. Procedia CIRP 2018; 72: 1305-1310,
Ma J, Atef M, Nada S, Nawar A. Certain Types of Covering-Based Multigranulation -Fuzzy Rough Sets with Application to Decision-Making Complexity 2020; Article ID 6661782,
Macchi M, Roda I, Fumagalli L. On the Advancement of Maintenance Management Towards Smart Maintenance in Manufacturing. IFIP International Conference on Advances in Production Management Systems (APMS) Hamburg Germany Springer, Cham. 2017; 383-390,
Marksberry P. The Modern Theory of the Toyota production System: A Systems Inquiry of the world's most emulated and profitable management system. CSR Press, Taylor & Francis Group, New York 2013.
Marttonen-Arola S, Baglee D, Kinnunen SK, Holgado M. Introducing Lean into Maintenance Data Management: A Decision Making Approach. In: Liyanage J Amadi-Echendu J Mathew J (eds) Engineering Assets and Public Infrastructures in the Age of Digitalization Lecture Notes in Mechanical Engineering Springer Cham 2020; https://doi org/10 1007/978-3-030-48021-9_28.
Matyas K, Nemeth T, Kovacs K, Glawar R. A procedural approach for realizing prescriptive maintenance planning in manufacturing industries. CIRP Annals 2017; 66(1): 461-464,
McCarthy D, Rich N. Lean TPM A Blueprint for Change. Elsevier Butterworth-Heinemann 2004,,.
Mosyurchak A, Veselkov V, Turygin A, Hammer M. Prognosis of behaviour of machine tool spindles their diagnostics and maintenance. MM Science Journal 2017: 2100-2104,,
Mouzani IA , Bouami DRISS. The integration of lean manufacturing and lean maintenance to improve production efficiency. International Journal of Mechanical and Production Engineering Research and Development 2019; 9(1): 601-612,
Muchiri P, Pintelon L, Gelders L, Martin H. Development of maintenance function performance measurement framework and indicators. International Journal of Production Economics 2011; 131(1): 295-302,
Mumani AA, Magableh GM, Mistarihi MZ. Decision making process in lean assessment and implementation: a review. Management Review Quarterly 2021; 1-40,
Ndhaief N, Nidhal R , Hajji A, Bistorin O. Environmental issue in an integrated production and maintenance control of unreliable manufacturing/remanufacturing systems. International Journal of Production Research 2020; 58(14): 4182-4200,
Nowotarski P, Pasławski J, Dallasega P. Multi-Criteria Assessment of Lean Management Tools Selection in Construction. Archives of Civil Engineering 2021; 711-726.
Pasko Ł, Setlak G. Badanie jakości predykcyjnej segmentacji rynku. Zeszyty Naukowe Politech Śląskiej Seria Informatyka 2016; 37: 83-97.
Pawlak Z, Polkowski L, Skowron A. Rough set theory. KI 2001; 15(3): 38-39,
Pawlak Z. Rough Sets: Theoretical Aspects of Reasoning about Data Theory and Decision. Library D: Springer Netherlands 1991,
Peres RS, Barata J, Leitao P, Garcia G. Multistage quality control using machine learning in the automotive industry. IEEE Access 2019; 7: 79908-79916,
Phogat S, Gupta AK. Theoretical analysis of JIT elements for implementation in the maintenance sector of Indian industries. International Journal of Productivity and Quality Management 2018; 25(2): 212-224,
Pinto GFL, Silva FJG, Campilho RDSG, Casais R B, Fernandes A J, Baptista A. Continuous improvement in maintenance: a case study in the automotive industry involving Lean tools. Procedia Manufacturing 2019; 38: 1582-1591,
Pombal , Ferreira LP, Sá J C, Pereira MT, Silva FJG. Implementation of lean methodologies in the management of consumable materials in the maintenance workshops of an industrial company. Procedia Manufacturing 2019; 38: 975-982,
Qu J, Bai X, Gu J , Taghizadeh-Hesary F, Lin J. Assessment of Rough Set Theory in Relation to Risks Regarding Hydraulic Engineering Investment Decisions. Mathematics 2020; 8(8): 1308,
Ramos E, Mesia R, Alva C, Miyashiro R. Applying lean maintenance to optimize manufacturing processes in the supply chain: A Peruvian print company case. International Journal of Supply Chain Management 2020; 9: 264-281.
Ravikumar S, Ramachandran KI, Sugumaran V. Machine learning approach for automated visual inspection of machine components. Expert systems with applications 2011; 38(4): 3260-3266,
Rødseth H, Schjølberg P. Data-driven predictive maintenance for green manufacturing In Proceedings of the 6th international workshop of advanced manufacturing and automation. Advances in Economics Business and Management Research Atlantis Press 2016; 36-41.
Sakthi Nagaraj T, Jeyapaul R, Vimal KEK, Mathiyazhagan K. Integration of human factors and ergonomics into lean implementation: ergonomic-value stream map approach in the textile industry. Production Planning & Control 2019; 30(15): 1265-1282,
Saxena A, Saad A. Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems. Applied Soft Computing 2007; 7(1): 441-454, ,
Shanmuganathan VK, Haran AP, Gayathri N. Condition monitoring maintenance of aero-engines through LUMS-A method for the implementation of Lean tools. Measurement 2015; 73: 226-230,
Shou W, Wang J, Wu P, Wang X. Lean management framework for improving maintenance operation: development and application in the oil and gas industry. Production Planning & Control 2021; 32(7):585-602,
Shou W, Wang J, Wu P, Wang X. Value adding and non-value adding activities in turnaround maintenance process: classification validation and benefits. Production Planning & Control 2020; 31(1): 60-77,
Sidhu SS, Singh K, Ahuja IS. An empirical investigation of maintenance practices for enhancing manufacturing performance in small and medium enterprises of northern India. Journal of Science and Technology Policy Management 2021;
Simões JM, Gomes CF, Yasin MM. Changing role of maintenance in business organisations: measurement versus strategic orientation. International Journal of Production Research 2016; 54(11): 3329-3346,
Singh AK, Vinodh S, Vimal KEK. Application of Grey based decision making approach for lean tool selection. In 5th International & 26th All India Manufacturing Technology Design and Research Conference (AIMTDR 2014) December 12th-14th 2014 IIT Guwahati Assam India 2014.
Skowron A, Dutta S. Rough sets: past present and future. Natural computing 2018; 17(4): 855-876,
Smith R , Hawkins B. Lean maintenance; reduce cost improve quality and increase market share. Elsevier Butterworth-Heinemann 2004.
Sokolova M, Lapalme GA. Systematic analysis of performance measures for classification tasks. Information Process Management 2009; 45: 427-437,
Świderski A, Borucka A, Grzelak M, Gil L. Evaluation of Machinery Readiness Using Semi-Markov Processes. Applied Sciences 2020; 10(4):1541,
Thawkar A, Tambe P, Deshpande V. A reliability centred maintenance approach for assessing the impact of maintenance for availability improvement of carding machine. International Journal of Process Management and Benchmarking 2018; 8(3): 318-339,
Tortorella GL, Fogliatto FS, Cauchick-Miguel PA, Kurnia S, Jurburg D. Integration of Industry 4 0 technologies into Total Productive Maintenance practices. International Journal of Production Economics 2021; 240: 108224,
Traini E, Bruno G, D'antonio G, Lombardi F. Machine learning framework for predictive maintenance in milling. IFAC-PapersOnLine 2019; 52(13): 177-182,
Van Horenbeek A, Kellens K, Pintelon L, Duflou JR. Economic and environmental aware maintenance optimization. Procedia CIRP 2014, 15: 343-348,
Velmurugan RS, Dhingra T. Maintenance Strategy Selection and its Impact in Maintenance Function: a Conceptual Framework. International Journal of Operations & Production Management 2015: 35 (12): 1622-1661,
Welte R, Estler M, Lucke D. A Method for Implementation of Machine Learning Solutions for Predictive Maintenance in Small and Medium Sized Enterprises. Procedia CIRP 2020; 93: 909-914,
Wu Z, Xu J , Xu Z. A multiple attribute group decision making framework for the evaluation of lean practices at logistics distribution centres. Annals of Operations Research 2016; 247(2): 735-757,
Ylipää T, Skoogh A, Bokrantz J, Gopalakrishnan M. Identification of maintenance improvement potential using OEE assessment. International Journal of Productivity and Performance Management 2017; 66(1): 126-143,
Zhang C, Wang C, Chen Q. Design of Lean Maintenance Process for Ball Screw Actuator. In 2019 IEEE 1st International Conference on Civil Aviation Safety and Information Technology (ICCASIT) IEEE 2019; 425-430,
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