Journal Vol – 19 No -1, January 2024

Abstract:

The reliability parameters of a Mathematical model are analyzed for a system with three identical units and a standby. In this study, the primary unit is considered more important due to its high cost and working in two types of degraded conditions before a complete malfunction. Under the concept of preventive maintenance, the states of deterioration are reversed. The working of the system under two different efficiencies is discussed. The reliability of the Mathematical model, depending on the availability and working time, has been optimized using the Mathematical tool “Genetic Algorithm”. The optimum values of all parameters based on the exponential distribution are considered to optimize the reliability, and thus provide maximum benefits to the industry. Sensitivity analysis of the availability and the working time is carried out to understand the effects of changing parameters. Graphical and tabular analyses are presented to discuss the results and to draw conclusions about the system’s behavior.

Keywords:

deteriorated state,genetic algorithm,malfunction rate,preventive maintenance,regenerative point graphical technique,sensitivity analysis,

Refference:

I. Bhunia, A.K. and Sahoo, L. (2011). Genetic algorithm based reliability optimization in interval environment. In: Nedjah, N., dos Santos Coelho, L., Mariani, V.C., de Macedo Mourelle, L. (eds) Innovative Computing Methods and Their Applications to Engineering Problems. Studies in Computational Intelligence, 357. Springer, Berlin, Heidelberg.

II. Kumar, J., Bansal, S.A., Mehta, M. and Singh, H. (2020). Reliability analysis in process industries – an overview. GIS Sci J., 7(5), 151-168.
III. Kumari, K. and Poonia, M.S. (2023). Availability optimization of cylinder block in cast iron manufacturing plant using GA. European Chemical Bulletin, 12(4), 17784-17792.

IV. Kumari, S., Khurana, P. and Singla, S. (2022). Behaviour and profit analysis of a thresher plant under steady state. International Journal of System Assurance Engineering and Management, 13, 166-171.

V. Kumari, S., Singla, S. and Khurana, P. (2022). Particle swarm optimization for constrained cost reliability of rubber plant. Life Cycle Reliability and Safety Engineering, 11(3), 273-277.

VI. Malik, S., Verma, S., Gupta, A., Sharma, G. and Singla, S. (2022). Performability evaluation, validation and optimization for the steam generation system of a coal-fired thermal power plant. MethodsX, 9.

VII. Naithani, A., Khanduri, S. and Gupta, S. (2022). Stochastic analysis of main unit with two non-identical replaceable sub-units working with partial failure. International Journal of System Assurance Engineering and Management, 13, 1467-1473.

VIII. Naithani, A., Parashar, B., Bhatia, P.K. and Taneja G. (2013). Cost benefit analysis of a 2-out-of-3 induced draft fans system with priority for operation to cold standby over working at reduced capacity. Advanced Modelling and Optimization, 15(2), 499-509.

IX. Sachdeva, K., Taneja, G, and Manocha, A. (2022). Sensitivity and economic analysis of an insured system with extended conditional warranty. Reliability: Theory and Applications, 17, 24–31.

X. Singla, S. and Dhawan, P. (2022). Mathematical analysis of regenerative point graphical technique (RPGT). Mathematical Analysis and its Contemporary Applications, 4(4), 49-56.

XI. Taj, S. Z. and Rizwan, S. M. (2022). Reliability analysis of a 3-unit parallel system with single maintenance facility. Advanced Mathematical Models & Applications, 7(1), 93-103.

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Abstract:

The present paper deals with the lacunary interpolation problem called the mixed values problem or (0, 3; 0, 2) problem for which known data points are function values at all the points, third derivatives at even knots, and second derivatives at odd knots of the unit interval I = [0,1]. For this problem, we obtained an interpolating function. The paper is divided into two parts, where we have shown that the spline function exists and is convergent.

Keywords:

Lacunary interpolation,spline functions,Taylor expansion,modulus of continuity,error bounds,convergence of function,

Refference:

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Abstract:

The usage of nature-inspired meta-heuristic algorithms is increasing due to their simplicity and versatility. These algorithms are widely used in numerous domains, especially in scientific fields such as operations research, computer science, artificial intelligence, and mathematics. Based on the core principles of exploration and exploitation, they provide flexible problem-solving abilities. This study presents a novel method to improve the effectiveness of the War Strategy Optimization (WSO) algorithm for optimization issues. The suggested approach combines the WSO technique with the Harmony Search (HS) algorithm, resulting in a hybrid algorithm called H-WSO. The aim is to enhance the overall optimization performance by leveraging the capabilities of both algorithms through the integration of swarm intelligence approaches.     In order to assess the effectiveness of the recently suggested H-WSO algorithm, a set of experiments was carried out on 50 benchmark test functions. These functions included both unimodal and multimodal functions and spanned across different dimensions. The findings from these studies clearly showed a notable enhancement in the efficiency of the H-WSO algorithm when compared to the original WSO algorithm. Various metrics were utilized to evaluate the effectiveness of the proposed algorithm, including the optimal fitness function value (Mean), Standard Deviation (St.d), and Median. The H-WSO algorithm regularly shows higher efficiency than the WSO algorithm, making it a promising and practical approach for addressing complicated optimization challenges

Keywords:

Meta-heuristic Algorithms,War Strategy Optimization algorithm,Harmony Search algorithm,Hybrid method,

Refference:

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Abstract:

In this study, this paper presents a new method for linearizing thermocouple data using Python and compares the performance of higher-order polynomial models in achieving linearization. It involves fitting a non-linear model to the thermocouple data using the curve fit function from Python and then calculating the linearized temperature values using the optimized parameters. The paper also presents a comparative analysis of different polynomial models, ranging from 3rd to 12th order, and evaluates their performance in achieving linearization. The results show that higher-order polynomial models generally perform better than lower-order models in achieving linearization, but also have a higher risk of overfitting. The paper concludes that the presented method provides an effective way of linearizing thermocouple data using Python and that the choice of polynomial model should be carefully considered based on the data characteristics and the desired level of accuracy.

Keywords:

Sensor,Linearization,curve fitting,non-linearity,Thermocouple,Python,

Refference:

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