The state of the textile machines can be evaluated by monitoring different parameters, such as vibration and temperature, so their maintenance can be performed a short time before the failure. However, the evolution of each monitored parameter can conduct to different decisions regarding the maintenance actions. Within this framework, fuzzy logic was proposed for planning the predictive maintenance activities of the textile machines. A fuzzy decision making system was developed and its effectiveness was illustrated for the planning of the predictive maintenance of a sewing machine needle. The results of the case study show fuzzy logic as a proactive approach to perform when needed maintenance activities of textile machines.
AbdullahS. and AminN., Analysis of S-box image encryption based on generalized fuzzy soft expert set, Nonlinear Dynamics79 (2015), 1679–1692.
2.
AhmadR. and KamaruddinS., An overview of time-based and condition-based maintenance in industrial application, Computers & Industrial Engineering63(1) (2012), 135–149.
3.
Al-NajjarB., On establishing cost-effective condition-based maintenance: Exemplified for vibration-based maintenance in case companies, Journal of Quality in Maintenance Engineering18(4) (2012), 401–416.
4.
AzadehA., EbrahimipourV. and BavarP., A fuzzy inference system for pump failure diagnosis to improve maintenance process: The case of a petrochemical industry, Expert Systems With Applications37(1) (2010), 627–639.
5.
BabanM., BabanC.F. and BlagaF.S., Maintenance planning of cold plastic deformation tools using fuzzy logic, Eksploatacja i Niezawodnosc-Maintenance and Reliability3 (2010), 21–26.
6.
BagavathiappanS., LahiriB.B., SaravananT., PhilipJ. and JayakumarT., Infrared thermography for condition monitoring–A review, Infrared Physics & Technology60 (2013), 35–55.
7.
CatarinoA., RochaA., MonteiroJ.L. and SoaresF., A System For Knitting Process Monitoring And Fault Detection OnWeft Circular Knitting Machines, World Textile Conference –4th AUTEX Conference, Roubaix, 2004, pp. 1–6.
8.
CatuneanuV.M. and MihalacheA.N., Reliability Fundamentals, Elsevier, Amsterdam, 1989.
9.
GuptaK.N., Vibration –A tool for machine diagnostics and condition monitoring, Sadhana22(3) (1997), 393–410.
10.
HussainS., Fuzzy information system for condition based maintenance of gearbox, Journal of Intelligent & Fuzzy Systems, Preprint (2015). doi: 10.3233/IFS-141530
11.
IsermanR., Fault-diagnosis applications: Model-based condition monitoring: Actuators, drives, machinery, plants, sensors, and fault-tolerant systems, Springer, Berlin, Heidelberg, New York, 2011.
12.
JangJ.-S.R., SunC.T. and MizutaniE., Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice-Hall, Upper Saddle River, NJ, 1997.
13.
JardineA.K.S., LinD. and BanjevicD., A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mechanical Systems and Signal Processing20(7) (2006), 1483–1510.
14.
KhanlariA., MohammadiK. and SohrabiB., Prioritizing equipments for preventive maintenance (PM) activities using fuzzy rules, Computers & Industrial Engineering54(2) (2008), 169–184.
15.
KothamasuR., HuangS.H. and VerDuinW.H., System health monitoring and prognostics-a review of current paradigms and practices, The International Journal of Advanced Manufacturing Technology28(9-10) (2006), 1012–1024.
16.
KumarE.V. and ChaturvediS.K., Prioritization of maintenance tasks on industrial equipment for reliability, A fuzzy approach International Journal of Quality & Reliability Management28(1) (2011), 109–126.
17.
KumarE.V., ChaturvediS.K. and DeshpandéA.W., Maintenance of industrial equipment. Degree of certainty with fuzzy modelling using predictive maintenance, International Journal of Quality & Reliability Management26(2) (2009), 196–211.
18.
LuK.Y. and SyC.-C., A real-time decision-making of maintenance using fuzzy agent, Expert Systems With Applications36(2) (2009), 2691–2698.
19.
LuZ. and SunY.C., Research on maintainability evaluation model based on fuzzy theory, Chinese Journal of Aeronautics20(5) (2007), 402–407.
20.
MbaD. and Rao RajB.K.N, Development of acoustic emission technology for condition monitoring and diagnosis of rotating machines: Bearings, pumps, gearboxes, engines, and rotating structures, The Shock and Vibration Digest38(1) (2006), 3–16.
21.
MobleyR.K., An introduction to predictive maintenance, Butterworth-Heinemann, second edition, 2002.
22.
PrajapatiA., BechtelJ. and GanesanS., Condition based maintenance: A survey, Journal of Quality in Maintenance Engineering18(4) (2012), 384–400.
23.
JayaswalPratesh, WadhwaniA.K. and MulchandaniK.B., Machine fault signature analysis, International Journal of Rotating Machinery2008 (2008), 1–11.
24.
PuschT., CherifC., FarooqA., WittenbergS., HoffmannR. and TschöpeC., Early fault detection at textile machines with the help of structure-borne sound analysis, Melliand China6 (2009), S53–S56.
25.
QiH.S., AlzaabiR.N., WoodA.S. and JaniM., A fuzzy criticality assessment system of process equipment for optimised maintenance management, International Journal of Computer Integrated Manufacturing28(1) (2015), 112–125.
26.
RossT., Fuzzy Logic with Engineering Applications, John Wiley & Sons Ltd.,
second edition, 2004.
27.
SergakiA. and KalaitzakisK., A fuzzy knowledge based method for maintenance planning in a power system, Reliability Engineering and System Safety77 (2002), 19–30.
28.
SivanandamS.N., SumathiS. and DeepaS.N., Introduction to Fuzzy Logic using MATLAB, Springer, 2006.
29.
TorunT.K. and MarmaraliA., Online fault detection system for circular knitting machines, Tekstil ve Konfeksiyon21(2) 2011, 164–170.
30.
Van RensselarJ., Vibration analysis: The other half of the equation, Tribology & Lubrication Technology67(8) (2011), 38–48.
31.
VeldmanJ., WortmannH. and KlingenbergW., Typology of condition based maintenance, Journal of Quality in Maintenance Engineering17(2) (2011), 183–202.
32.
VenkatasubramanianV., RengaswamyR. and KavuriS.N., A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies, Computers and Chemical Engineering27(3) (2003), 313–326.
33.
VenkatasubramanianV., RengaswamyR., YinK. and KavuriS.N., A review of process fault detection and diagnosis Part I: Quantitative model-based methods, Computers and Chemical Engineering27(3) (2003), 293–311.
34.
VenkatasubramanianV., RengaswamyR., YinK. and KavuriS.N., A review of process fault detection and diagnosis: Part III: Process history based methods, Computers and Chemical Engineering27(3) (2003), 327–346.
