Performance Analysis of Carded Silver System: A Case Study

1. INTRODUCTION

In worldwide competitive scenario, the industrialists are adopting new technologies to run the industries round the clock for reaching their targets and fulfillment of user‟s needs. The products should be available to consumers to their satisfaction at reasonable cost. To meet the increasing customer demand industries needs to run continuously without any failure. So the products should be available to consumers to their satisfaction at reasonable cost. Therefore, the reliability and availability are the main parameters during planning, designing and operation of industrial systems. The reliability and availability analysis is most desirable for longer working duration of industries to reduce the production cost. The present analysis can benefit industry in terms of lower maintenance and higher production rate. The need and application of reliability technology has been addressed by various researchers in the past. Elegbedeet al.[1] solved a multi-objective combinatorial optimization problem using GA and experiments plan methodology. Castro et al. [2] presented a methodology using GA for solving the engineering design problems. Khanduja et al. [3,4] proposed a mathematical modeling in distinct process plants using reliability and availability analysis technique.Azaronet al. [5] suggested a method for optimization reliability and evaluationby redundant systems for non-repairable dissimilar component cold stand system.Garg et al. [6] presented the availability of combed sliver yarn production system, an important functionary unit of yarn production plant. Kumar et al. [7] used genetic algorithm for performance optimization and mathematical modelling in CO2 cooling system of fertilizer plant. Garg et al. [8] analyzed the system behavior by utilizing the rough and imperfect data of the complex repairable system.Sharma et al. [9] described the GA approach for the availability optimization of refining unit of a sugar industry. A reliability mode using Markov approach proposed by Gowid et al [10] in LNG production plant for computation of time dependent system availability. Houet al. [11] discussed the availability evaluation of systems by means of random set theory. Kumar et al.[12]. Implemented the PSO technique to improve the availability of a repairable system in lectogen milk powder system plant and to optimize the availability of various sub-systems. Kumar et al.,[13] analyze the behaviour of multi-state repairable system of Towel Manufacturing System using G.A. Malik et. al. [14] had examined performance modeling for water flow system.

Carded silver production system comprises of three subsystems namely blow room, Carding machine, and Draw Frames. Cotton arrives in the mill in the form of hard pressed bales which consists of lot of impurities. After doing some manual operation of this hard pressed bales material with the purpose of improve the quality of yarn. After that this conditioned material is fed manually in to blow room where opening and cleaning of cotton takes place. next from the blow room small cotton tufts transfer to the all cards where cards individualize

is done for improving the luster and strength of carded silver .After that it is passed through draw frames for improving silver uniformity which after drawing operation goes for spinning operation.

• Failure/repair rates for every subsystem are exponentially distributed i.e. constant. • No simultaneous failures occur between subsystems/system. • The execution for a predetermined duration of a repaired unit is in the same class as new. • No further failure can occur when system is in failure state. • The capacity and nature of standby subsystems are same as the working subsystems. • All the subsystems are initially in good working state. • At any given time each subsystem has three states viz. working, reduced or failed. • System may operate in reduced capacity.

Steady state availability of the system is essential to be analysed by putting t and d/dt= 0 on equations (1) to (4) we get:

Table 4.1 Decision Matrix of Blow Room machine (B) ofCarded Silver Production System.

Table 4.1 represents the decision matrix for subsystem Blow Room machine (B).as failure rate varies of subsystem (B) from 0.006 to 0.0009(keeping other parameters constant) the availability drops by 2.75% & it gets improved by 4.24% with repair rate varies from 0.1 to 0.4. Table 4.2 represents the decision matrix for subsystem Carding machine (C).as failure rate varies of subsystem (D) from 0.002to 0.008 (keeping other parameters constant) the availability decreases by 1.91% & it gets improved by 3.37% with repair rate varies from 0.01 to 0.07. Table 4.3 represents the decision matrix for subsystem Draw frame machine(D) as failure rate

rate varies from 0.1 to 0.4.

The subsystem priorities have been decided on the basis of their effect on overall system availability .It is clear from table 4.4 Blow Room machine (B) is most critical subcomponent and assigned top priorities from view point of repair. Similarly Draw Frame Machine (D) second lastly Carding Machine (C) has minimum effect on system availability hence assigned last priority.

Genetic algorithm techniques have efficiently been used to achieve the quality solution for both constrained and unconstrained optimization programme.GA begins with set of solutions (represented by Chromosomes) called population. Solutions from one population are taken and used to form new population. This is motivated by hope that new population will be better than previous one. Solutions which are than selected to form new solution (offspring) are selected according to their fitness (the more suitable they are the more chances to reproduce).this is repeated until some conditions are satisfied.

Simulation is done for utmost population size those changes from 10 to 80. Here the Generation size is kept constant as 100. The most favorable value of system‟s availability is 98.53%, for which the finest probable combination of failure and repair

μ4=0.39999, 5=0.00609, μ5=0.79825, at population size 50 as given in table 1. Again, the simulation is made for maximum number of generation, varies from 50 to 400 with a step size of 50. Here, the population size is kept constant at 100. The optimum value of system‟s performance is 98.52%, for which the finest combination of failure and repair variable is 1 =0.006, μ1=0.4, 2 =0.005, μ2 =0.03998, 3=0.006, μ3 =0.69996, 4 =0.005, μ4 =0.03996,5=0.00630, μ5=0.79989 at generation rate 150as given in table 2.

1. Elegbede, C. and Abjallah, K. (2003). “Availability Allocation to Repairable Systems with Genetic Algorithms: A Multi-Objective Formulation”, Reliability Engineering and System Safety, Vol. 82, Issue 3, pp. 255-264. 2. Castro, H.F.D. and Cavalca, K.L. (2006). “Maintenance Resources Optimization Applied to a Manufacturing System”, Reliability Engineering and System Safety, Vol. 91, Issue 4, pp. 413-420. 3. Khanduja, R., Tewari, P.C. and Kumar, D. (2008). “Availability Analysis of Bleaching System in a Paper Plant”, Udyog Pragati, Vol. 32, No.1, pp. 24-29. 4. Khanduja, R., Tewari, P.C. and Chauhan, R.S. (2009). “Performance Analysis of Screening Unit in a Paper Plant using Genetic Algorithm”, Journal of Industrial and Systems Engineering, Vol. 3, No. 2, pp. 140-151. 5. Azaron, A., Perkgoz, C., Katagiri, H., Kato, K., and Sakawa, M. (2009). “Multi-Objective reliability optimization for dissimilar-unit cold-standby systems using a genetic algorithm”, Journal Computer Operation and Research, Vol. 36, pp. 1562-1571. 6. Garg, S., Singh, J. and Singh, D.V. (2010). “Mathematical Modeling and Performance Analysis of Combed Yarn Production System: Based on Few Data”, Journal of Applied Mathematical Modeling, Vol. 34, No. 11, pp. 3300–3308. Fertilizer Plant”, International Journal of Industrial Engineering Computation, Vol. 2, pp 689-695. 44. 8. Garg, H. and Sharma, S. P. (2013). “Reliability–Redundancy Allocation Problem of Pharmaceutical Plant”, Journal of Engineering Science and Technology, Vol. 8, pp. 190-18. 9. Gowid S, Dixon R and Ghani S (2014). “Optimization of Reliability and Maintenance of liquefaction system on LNG Terminal using Markov Modeling”, International Journal of Quality Reliability and Management Vol. 31(3), pp. 293-310. 70. 10. Sharma, S.P. and Vishwakarma, Y. (2014). “Availability optimization of refining system of sugar industry by Markov process and genetic algorithm”, International Conference on Reliability, optimization and Information Technology, India, pp. 29-33. 11. Hou Y., Sallak, M. and Schon, W. (2015). “Availability analysis of systems using random set theory”, IFAC-Papers Online, 48, pp. 1315–1320. 12. Kumar, M, Kumar, V, and Modgil, V. (2017). “Availability Analysis and Optimization of Lactogen Milk Powder Production System using PSO”, International Journal of Mechanical Engineering and Technology. Volume 8, Issue 11, November 2017, pp. 839–849. 13. Kumar, M,. Kumar, V, and Modgil, V. (2018). “Optimization of Availability of Towel Manufacturing System: A case study”, International Journal of Applied Engineering Research (IJAER) Volume 13, Number 12 (2018) pp. 10525-10534. 14. Malik S. and Tewari P. C. (2018). “Performance modeling and maintenance priorities decision for the water flow system of a coal-based thermal power plant”, International Journal of Quality & Reliability Management, 35, (4), pp. 996–1010.

Ph.D., Mechanical Engineering, UIET, MDU, Rohtak mannmukesh2006@gmail.com