Risk-based maintenance assessment in the manufacturing industry: minimisation of suboptimal prioritisation

• Autorzy: Ratnayake R. M. Chandima , Antosz K.

Identyfikatory

DOI

10.1515/mper-2017-0005

• Języki publikacji: EN

Abstrakty

EN

Manufacturing firms continuously strive to increase the efficiency and effectiveness in the maintenance management processes. Focus is placed on eliminating the unexpected failures which cause unnecessary costs and the production losses. Risk-based maintenance (RBM) strategies enable to address the above through the identification of probability and consequences of potential failures whilst providing a way for prioritisation of maintenance actions based on the risk of possible failures. Such prioritisations enable to identify the optimal maintenance strategy, intervals of maintenance tasks, and optimal level of spare parts inventory. However, the risk assessment activities are performed with the support of a risk matrix. Suboptimal classifications and/or prioritisations arise due to the inherent nature of the risk matrix. This is caused by the fact that there are no means to incorporate actual circumstances at the boundary of the input ranges or at the levels of linguistic data and risk categories. In this paper, a risk matrix is first developed in collaboration with one of the manufacturing firms in Poland. Then, it illustrates the use of fuzzy logic for minimisation of suboptimal prioritisation and/or classifications using a fuzzy inference system (FIS) together with illustrative membership functions and a rule base. Finally, an illustrative risk assessment is also demonstrated to illustrate the methodology.

Słowa kluczowe

EN

classification; fuzzy logic; manufacturing firms; risk based maintenance; risk matrix

• Wydawca: Production Engineering Committee of the Polish Academy of Sciences ; Polish Association for Production Management
• Czasopismo: Management and Production Engineering Review
• Rocznik: 2017
• Tom: Vol. 8, No. 1
• Strony: 38--45
• Opis fizyczny: Bibliogr. 16 poz., rys., tab., wykr.

Twórcy

autor

Ratnayake R. M. Chandima

• University of Stavanger, Faculty of Science and Technology, Department of Mechanical and Structural Engineering and Materials Science, Norway

autor

Antosz K.

• Rzeszow University of Technology, Department of Manufacturing and Production Engineering, Al. Powstancow Warszawy 8, 35-959 Rzeszow, Poland

Bibliografia

• [1] Parida A., Kumar U., Maintenance productivity and performance measurement, Handbook of maintenance management and engineering, Springer-Verlag London Limited, part I, 17-41, 2009.
• [2] Duffuaa S.O., Daya M.B., Turnaround maintenance in petrochemical industry: Practices and suggested improvements, J. Qual. in Maint. Eng., 10, 184-190, 2004.
• [3] Daya M.B., Duffuaa S.O., Raouf A., Knezevic J., Ait-Kadi D., Handbook of maintenance management and engineering, Springer-Verlag London Limited, 2009.
• [4] Daya M.B., Duffuaa S.O., Maintenance and Quality: The Missing Link, J. Qual. in Maint. Eng., 1, 20-26, 1995.
• [5] Wenchi S., Wang J., Wang X., Chong H.Y., An application of value stream mapping for turnaround maintenance in oil and gas industry: Case study and lessions learned, in: Raid´en A.B., Aboagye-Nimo E. [Eds.], Proc. 31st Annual ARCOM Conference, 7-9 September 2015, Association of Researchers in Construction Management, Lincoln, 813-822, 2015.
• [6] Kurniati N., Yeh R.H., Lin J., Quality Inspection and Maintenance: The Framework of Interaction. Industrial Engineering and Service Science, IESS 2015 Proced. Manuf., 4, 244-251, 2015.
• [7] Ratnayake R.M.C., Stadnicka D., Antosz K., Deriving an Empirical Model for Machinery Prioritization: Mechanical Systems Maintenance, Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Bangkok, 1442-1447, 2013.
• [8] Ratnayake R.M.C., Plant Systems and Equipment Maintenance: Use of Fuzzy Logic for Criticality Assessment in NORSOK Standard Z-008, Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Bangkok, 1468-1472, 2013.
• [9] Ratnayake R.M.C., KBE Development for Criticality Classification of Mechanical Equipment: A Fuzzy Expert System, Int. J. Dis. and Risk. Red., 9, 84-98, 2014.
• [10] Piegat A., Modelling and fuzzy control [in Polish: Modelowanie i sterowanie rozmyte], Akademicka Oficyna Wydawnicza EXIT, Warszawa, 1999.
• [11] Stadnicka D., Antosz K., Ratnayake R.M.C., Prioritization of Maintenance Tasks: Development of an Empirical Formula for Machine Classification, Saf. Sci., 63, 34-41, 2014.
• [12] Tay K.M, Lim C.P., On the use of fuzzy inference techniques in assessment models: part II: industrial applications’, Fuz. Opti. and Dec. Mak., 3, 283-302, 2008.
• [13] Ratnayake R.M.C., Knowledge based engineering approach for subsea pipeline systems’ FFR assessment: A fuzzy expert system, The TQM Journal, 28, 40-61, 2016.
• [14] Hameed I.A., Using Gaussian membership functions for improving the reliability and robustness of students’ evaluation systems, Expert Systems with Applications, 38, 6, 7135-7142, 2011.
• [15] Mathworks, Fuzzy inference system modelling: Gaussian combination membership function, 2014, available: http://www.mathworks.se/help/fuzzy/gauss2mf.html, last accessed 5th June 2016.
• [16] Matlab, MATLAB 7.12.0 (R2014b), Fuzzy logic Toolbox, 1984-2014 The MathWorks, Inc., 2014.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).