RESEARCH PAPER
Geometric approach to machine exploitation efficiency : modelling and assessment
1
Silesian University of Technology, Faculty Organization and Management, ul. Roosevelta 26, 41-800 Zabrze, Poland
Publication date: 2022-03-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2022;24(1):114-122
HIGHLIGHTS
- The analysis of selected exploitation measures in discrete production processes is presented.
- A new approach to OEE modelling is proposed, based on a geometric interpretation of the time dependent components.
- The verification of this developed model is carried out with the use of analytical and simulation tools.
- The proposed method allows for the real-time mapping of the variability of the exploitation efficiency.
- There is a significant difference to the classical static approach of such an assessment
KEYWORDS
ABSTRACT
This article presents a new approach to the exploitation assessment of machines and devices. A key aspect of this approach is the construction of the assessment model based on the geometric representation of measures associated with each other, which covers the full specifics of the exploitation process. This approach is successfully implemented by the Overall Equipment Effectiveness (OEE) model, which is fully susceptible to the geometric modelling process due to the three-way system of assessed exploitation aspects. The result of this approach is the vectored OEE model and its interpretation in terms of time series of changes in values of components. Methods of determining vector calculus measures were developed, including the second-order tensor and gradient. This is the subject of the variability of the reliability conditions of machines or production processes. It allows for the realisation of an exploitation assessment based on dynamic changes in the values of their components in the time domain. This is a significant difference to the classical static approach to such an assessment. The developed new geometric OEE model was confirmed by verification tests using the LabView software, based on two parallel data sets obtained with analytical and simulation methods using the FlexSim software.
REFERENCES (37)
1.
Agrawal OP, Xu YF. Generalized vector calculus on convex domain. Communications In Nonlinear Science And Numerical Simulation 2015; 23(1-3): 129-140, http://doi.org/10.1016/j.cnsns....
2.
Aratujo I.G, Gomes F.M, Pereira F.M. Application of Monte Carlo’s method to predict the overall equipment effectiveness index of a cellulose machine. Sistemas & Gestao 2020; 15(1): 25-37, https://doi.org/10.20985/1980-....
3.
Bengtsson M. Using a game-based learning approach in teaching overall equipment effectiveness. Journal of Quality in Maintenance Engineering 2019; 26(3): 489-507, https://doi.org/10.1108/JQME-0....
4.
Campbell J., Jardine A., McGlynn J. Asset Management Excellence. Optimizing Equipment Life-Cycle Decisions. CRC Press, Boca Raton: 2011.
5.
Corrales NGL, Lambán MP, Korner MEH, Royo J. Overall equipment effectiveness: systematic literature review and overview of different approaches. Applied Sciences-Basel 2020, 10(18): 6469, https://doi.org/10.3390/app101....
6.
Duda S, Kawlewski K, Gembalczyk G. Concept of the system for control over keeping up the movement of a crane. Solid State Phenomena 2015, 220-221: 339-344, https://doi.org/10.4028/www.sc....
7.
EL Mazgualdi C, Masrour T, El Hassani I, Khdoudi A. Machine learning for KPIs prediction: a case study of the overall equipment effectiveness within the automotive industry. Soft Computing 2021; 25: 2891-2909, https://doi.org/10.1007/s00500....
8.
Farahani Ameneh, Tohidi Hamid, Shoja Ahmad. Optimization of overall equipment effectiveness with integrated modeling of maintenance and quality. Engineering Letters 2020; 28(2): 400-405.
9.
Gola A, Nioczym A. Application of OEE coefficient for manufacturing lines reliability improvement. Proceedings of the International Conference on Management Science and Management Innovation (MSMI 2017), Book Series: Advances in Economics Business and Management Research 2017; 31: 189-194.
10.
Hall P, Muller HG, Yao F. Estimation of functional derivatives. Annals of Statistics 2009; 37(6A): 3307-3329, https://doi.org/10.1214/09-AOS....
11.
Janik A, Ryszko A. Mapping the field of Industry 4.0 based on bibliometric analysis. VISION 2020: Sustainable Economic Development and Application of Innovation Management, Proceedings of the 32nd Conference of the International-Business-Information-Management-Association (IBIMA) 2018: 6316-6330.
12.
Jaqin C, Rozak A, Purba HH. Case study in increasing overall equipment effectiveness on progressive press machine using plan-do-check-act cycle. International Journal of Engineering, 2020; 33(1): 2245-2251, https://doi.org/10.5829/ije.20....
13.
Jasiulewicz-Kaczmarek M, Antosz K, Wyczolkowski R, Mazurkiewicz D, Sun B, Qian, C. Application of MICMAC, fuzzy AHP, and fuzzy TOPSIS for evaluation of the maintenance factors affecting sustainable manufacturing. Energies 2021; 14(5): 1436, http://doi.org/10.3390/en14051....
14.
Jasiulewicz–Kaczmarek M, Antosz K. Żywica 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, http://doi.org/10.17531/ein.20....
15.
Kalisch M, Przystalka P, Timofiejczuk A. Genetic optimization of meta-learning schemes for context-based fault detection. Advances In Technical Diagnostics, Book Series: Applied Condition Monitoring 2018; 10: 287-297, http://doi.org/10.1007/978-3-3....
16.
Kozlowski E, Mazurkiewicz D, Zabinski T, Prucnal S, Sep J. Machining sensor data management for operation-level predictive model. Expert Systems with Applications 2020; 159: 113600, https://doi.org/10.1016/j.eswa....
17.
Lohgheswary N, Nopiah ZM, Aziz AA, Zakaria E. Achievement of course outcome in vector calculus pre-test questions. Journal of Fundamental and Applied Sciences 2017; 9(5): 394-403, http://doi.org/10.4314/jfas.v9....
18.
Loska A.: Variant assessment of exploitation policy of selected companies managing technical network systems. Management Systems in Production Engineering 2015; 3(19): 179-188, http://doi.org/10.12914/MSPE-1....
19.
Luscinski S, Ivanov V. A simulation study of industry 4.0 factor on the ontology on flexibility with using FlexSim (R) software. Management and Production Engineering Review 2020; 11(3): 74-83, http://doi.org/10.24425/mper.2....
20.
Mostyn V, Huczala D, Moczulski W, Timofiejczuk A. Dimensional optimization of the robotic arm to reduce energy consumption. MM Science Journal 2020: 3745-3753, https://doi.org/10.17973/MMSJ.....
21.
Nakajima S.: Introduction to TPM. Total Productive Maintenance. Productivity Press, Portland: 1988.
22.
Narayan V.: Effective Maintenance Management: Risk and Reliability Strategies for Optimizing Performance. Industrial Press Inc., New York: 2003.
23.
Niebel W.B.: Engineering Maintenance Management, second edition. Marcel Dekker Inc., New York: 1994.
24.
Paszkowski W.: The assessment of acoustic effects of exploited road vehicles with the use of subjective features of sound. Eksploatacja i Niezawodność – Maintenance and Reliability 2019; 21(3): 522-529, 9, http://dx.doi.org/10.17531/ein....
25.
Pater J, Basara D, Stadnicka D. Influence of temperature based process parameter compensation on process efficiency and productivity. Technologia i Automatyzacja Montażu 2021; 2(7): 44-51, http://doi.org/10.15199/160.20....
26.
Peters R.W.: Maintenance Benchmarking and Best Practices: A Profit - and Customer - Centered Approach, McGraw-Hill, New York: 2006.
28.
Productivity Press Development Team: OEE for Operators. Productivity Press Inc., New York: 1999.
29.
Shehzad A, Zahoor S, Sarfraz S, Shehab E. Implementation of TPM in a process industry: a case study from Pakistan. Advances in Manufacturing Technology XXXII, Book Series: Advances in Transdisciplinary Engineering 2018; 8: 511-516, http://doi.org/10.3233/978-1-6....
30.
Singh J, Singh H, Sharma V. Success of TPM concept in a manufacturing unit - a case study. International Journal of Productivity and Performance Management 2018; 67(3): 536-549, https://doi.org/10.1108/IJPPM-....
31.
Smith J.: The KPI Book. Insight Training & Development Limited, Stoubridge: 2001.
32.
Stecula K, Brodny J, Palka D. Analysis of reasons for unplanned stoppages of machines in the example of the longwall shearer. Innovations In Science And Education, CBU International Conference Proceedings 2017; 5: 1210-1214, http://doi.org/10.12955/cbup.v....
33.
Supriatna A, Singgih ML, Widodo E, Kurniati N. Overall equipment effectiveness evaluation of maintenance strategies for rented equipment. International Journal of Technology 2020; 11(3): 619-630, https://doi.org/10.14716/ijtec....
35.
Swiderski A, Borucka A, Grzelak M, Gil L. Evaluation of machinery readiness using semi-Markov processes. Applied Sciences-Basel 2020; 10(4): 1541, http://doi.org/ 10.3390/app10041541.
36.
Wang YR, Chen AN. Production logistics simulation and optimization of industrial enterprise based on FlexSim. International Journal of Simulation Modelling 2016; 15(4): 732-741, http://doi.org/10.2507/IJSIMM1....
37.
Wiremann T. Developing performance indicators for managing maintenance (second edition). Industrial Press, New York: 2005.
CITATIONS (8):
1.
The Theory of Exploitation as a Support for Management Accounting in an Enterprise
Przemysław Drożyner, Stanisław Młynarski
Sustainability
Przemysław Drożyner, Stanisław Młynarski
Sustainability
2.
Innovations in Mechanical Engineering II
Małgorzata Jasiulewicz-Kaczmarek, Katarzyna Antosz
Małgorzata Jasiulewicz-Kaczmarek, Katarzyna Antosz
3.
Application of Lean Six Sigma for sustainable maintenance: case study
Katarzyna Antosz, Małgorzata Jasiulewicz-Kaczmarek, Robert Waszkowski, Jose Machado
IFAC-PapersOnLine
Katarzyna Antosz, Małgorzata Jasiulewicz-Kaczmarek, Robert Waszkowski, Jose Machado
IFAC-PapersOnLine
4.
Seasonal Methods of Demand Forecasting in the Supply Chain as Support for the Company’s Sustainable Growth
Anna Borucka
Sustainability
Anna Borucka
Sustainability
7.
Share
RELATED ARTICLE
| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
