Current issue
Online First
Archive
Instructions for Authors
About the Journal
Contact
1/2018 vol. 63
Stats
CC-BY 4.0
Get citation
ORIGINAL PAPER
A method to assess reliability of seasonally operated machines using fuzzy logic principles
P. Rybacki 1
,
 
Z. Grześ 1
 
 
 
More details
Hide details
1
Uniwersytet Przyrodniczy w Poznaniu Instytut Inżynierii Biosystemów ul. Wojska Polskiego 50, 60-637 Poznań, Poland
 
 
Journal of Research and Applications in Agricultural Engineering 2018;63(1):79-91
 
Article (PDF)
References (38)
 
KEYWORDS
machine reliability
machine operation
AHP
fuzzy logic
technical servicing
 
ABSTRACT
The aim of this study was to develop an original method, which would objectively quantify reliability of seasonally operated machines. The method uses an algebraic deduction model and fuzzy logic algorithms facilitating simulation studies. The assessment of machine reliability provides the index of reliability IR, which is based on a set of adopted criteria. An additional objective for the authors was to empirically verify the developed method based on seasonally operated agricultural machines. Values of index of reliability IR fall within the range of 0.647-0.725, depending on the type of tested machines. In accordance with the adopted criteria of linguistic synthesis the obtained values of the index IR indicate high reliability of tested machines.
REFERENCES (38)
1.
Catuneanu V.M., Mihalache A.N.: Reliability Fundamentals. Amsterdam: Elsevier, 1989.
Google Scholar
 
 
2.
Chen S.M.: Fuzzy system reliability analysis using fuzzy number arithmetic co-operations. Fuzzy Sets and Systems, 1994, 64, 31-38.
Google Scholar
 
 
3.
Chen G., Pham T.T.: Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems. CRC Press, 2001.
Google Scholar
 
 
4.
Elsayed E.A.: Reliability Engineering. Addison Wesley Longman, 1996.
Google Scholar
 
 
5.
Emblemsvag J., Tonning L.: Decision support in selecting maintenance organization. Journal of Quality in Maintenance Engineering, 2003, 9(1), 11-24.
Google Scholar
 
 
6.
Fedrizzi M., Kacprzyk J., Roubens M.: Interactive Fuzzy Optimization. Springer - Verlag, Berlin/New York, 1991.
Google Scholar
 
 
7.
Grabisch M.: Fuzzy integral in multicriteria decision making. Fuzzy Sets and Systems, 1995, Vol. 69(3), 279-298.
Google Scholar
 
 
8.
Huang H.Z., Zuo M.J., Sun Z.Q.: Bayesian reliability analysis for fuzzy lifetime data. Fuzzy Sets and Systems, 2006, 157, 1674-1686.
Google Scholar
 
 
9.
Ivezic D., Tanasijevic M., Ignjatovic D.: Fuzzy Approach to Dependability Performance Evaluation. Quality and Reliability Engineering International, 2008, Vol. 24(7), 779-792.
Google Scholar
 
 
10.
Juściński S., Piekarski W.: The farm vehicles operation in the aspect of the structure of demand for maintenance inspections. Maintenance and Reliability, 2010, 1, 59-68.
Google Scholar
 
 
11.
Kapur K.C., Lamberson L.R.: Reliability in Engineering Design. New York: John Wiley & Sons, 1977.
Google Scholar
 
 
12.
Khanlari A., Mohammadi K., Sohrabi B.: Prioritizing equipments for preventive maintenance (PM) activities using fuzzy rules. Computers and Industrial Engineering, 2008, 54(2), 169-184.
Google Scholar
 
 
13.
Kowalczyk A.: Modelowanie rozmyte w strategii podejmowania decyzji grupowych w hydroinżynierii. Rozprawa doktorska. Politechnika Krakowska, 2007.
Google Scholar
 
 
14.
Kowalczyk A.: Uwzględnienie rang decydentów w procesie podejmowania decyzji. Woda Środowisko Obszary Wiejskie. Wydawnictwo Instytutu Melioracji i Użytków Zielonych w Falentach, 2009, 9, 2(26), 107-123.
Google Scholar
 
 
15.
Osowski S.: Sieci neuronowe w ujęciu algebraicznym. Wydawnictwo Naukowo-Techniczne, Warszawa, 1996.
Google Scholar
 
 
16.
Liu S.L, Shi Y.M., Chai J.: Reliability evaluation of system based on the fusion of Kullback information. Science Technology and Engineering, 2006, 20, 3339-3341.
Google Scholar
 
 
17.
Liu Y., Huang H.Z.: Reliability assessment for fuzzy multistate systems. International Journal of Systems Science, 2010, 41, 365-379.
Google Scholar
 
 
18.
Macha E.: Niezawodność maszyn. Wydawnictwo Politechniki Opolskiej. 2001, 237.
Google Scholar
 
 
19.
Mamdani E.H.: Advances in the linguistic synthesis of fuzzy controllers. International Journal of Man-Machine Studies, 1976, Vol. 8, 669-678.
Google Scholar
 
 
20.
Mamdani E.H.: Applications of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Transactions on Computers, 1977, Vol. 26(12), 1182-1191.
Google Scholar
 
 
21.
Piegat A.: Modelowanie i sterowanie rozmyte. Akademicka Oficyna Wydawnicza EXIT. Warszawa, 1999.
Google Scholar
 
 
22.
Rybacki P.: Investigation of the decision-making process of service station selection for agricultural tractors with the assistance of the AHP method. Journal of Research and Applications in Agricultural Engineering, 2011, Vol. 56(2), 126-130.
Google Scholar
 
 
23.
Saaty T.L.: A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 1977, Vol. 15(3), 234-281.
Google Scholar
 
 
24.
Saaty T.L.: Axiomatic foundation of the analytic hierarchy process. Management Science, 1986, Vol. 32(7), 841-855.
Google Scholar
 
 
25.
Saaty T L.: Deriving the AHP 1-9 Scale from First Principles ISAHP 2001, Berne – Switzerland, 2001, 397-402.
Google Scholar
 
 
26.
Saraswat S., Yadava G.S.: An over view on reliability, availability, maintainability and supportability (RAMS) engineering. International Journal of Quality and Reliability Management, 2008, 25(3), 330-344.
Google Scholar
 
 
27.
Savoia M.: Structural reliability analysis through fuzzy number approach, with application to stability. Computers and Structures, 2002, 80, 1087-1102.
Google Scholar
 
 
28.
Shiraishi N., Furuta H.: Reliability analysis based on fuzzy probability. J. Energy. Mech., 1983, 109, 1445-1459.
Google Scholar
 
 
29.
Szmidt E., Kacprzyk J.: Distances between intuitionistic fuzzy sets. Fuzzy Sets Syst., 2000, 114, 505-518.
Google Scholar
 
 
30.
Tanasijević M., Ivezić D., Jovančić P., Ignjatović D., Bugarić U.: Dependability assessment of open-pit mines equipment – study on the bases of fuzzy algebra rules. Eksploatacja i Niezawodność – Maintenance and Reliability, 2013, 15(1), 66-74.
Google Scholar
 
 
31.
Tao J., Zhang Y.A., Chen X., Ming Z.: Bayesowski model wzrostu niezawodności oparty na dynamicznych parametrach rozkładu. Eksploatacja i Niezawodność – Maintenance and Reliability, 2010, 2, 13-16.
Google Scholar
 
 
32.
Wang J., Yang J.B., Sen P.: Safety analyses and synthesis using fuzzy sets and evidential reasoning. Reliability Engineering and System Safety, 1995, 47(2), 103-118.
Google Scholar
 
 
33.
Wang Z.L., Li Y.F., Huang H.Z., Yu L.: Reliability analysis of structure for fuzzy safety state. Intelligent Automation and Soft Computing, 2012, 16(3), 215-242.
Google Scholar
 
 
34.
Ważyńska-Fiok K., Jaźwiński J.: Niezawodność systemów technicznych. PWN, Warszawa, 1990.
Google Scholar
 
 
35.
Wu W.J., Huang H.Z., Wang Z.L., Li Y.F., Pang Y.: Reliability analysis of mechanical vibration component using fuzzy sets theory. Eksploatacja i Niezawodność - Maintenance and Reliability, 2012, 14(2), 130-134.
Google Scholar
 
 
36.
Zadeh L.A.: Fuzzy sets. Information and Control, 1965, Vol. 8(3), 338-353.
Google Scholar
 
 
37.
Zadeh L.A.: Fuzzy Logic. Computer, 1988, Vol. 1(4), 83-93.
Google Scholar
 
 
38.
Zadeh L.A.: Knowledge representation in fuzzy logic. IEEE Transactions on Knowledge and Data Engineering, 1989, Vol. 1, 89-100.
Google Scholar
 
 
Share
Send by email
RELATED ARTICLE
Forecasting of the daily demand for natural gas in rural households using the methods of artificial intelligence. Part II. Forecasting using fuzzy logic
Indexes
 
Keywords index
Topics index
Authors index
eISSN:2719-423X
ISSN:1642-686X
Journals System - logo
© 2006-2025 Journal hosting platform by Bentus
Scroll to top