Estimation and Optimization for System Availability Under Preventive Maintenance | Zendy

Yashpal Singh Raghav | Zendy; Mradula | Zendy; Rahul Varshney | Zendy; Umar Muhammad Modibbo | Zendy; Abdullah Ali H. Ahmadini | Zendy; Irfan Ali | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Estimation and Optimization for System Availability Under Preventive Maintenance

Author(s) -

Yashpal Singh Raghav,

Mradula,

Rahul Varshney,

Umar Muhammad Modibbo,

Abdullah Ali H. Ahmadini,

Irfan Ali

Publication year - 2022

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2022.3204394

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Engineers design systems to be reliable and work to fulfil their missions without failure for a specific period. However, the system components deteriorate with time and lead to its failures. A frequent system failure increases the management costs, hence posing a challenge to decision-makers. Therefore, for the avoidance of frequent system failures, preventive maintenance is necessary. The objective of any manufacturing firm is to maximize profit and minimize costs. The interval for preventive maintenance can be optimized if the system’s availability is maximized and its cost function minimized. This study evaluates the availability and cost function for a continuous operating series-parallel system under a fixed time environment. A multiobjective model is formulated to maximize the availability and minimize the cost function of the system. The study illustrated a numerical example and solved using goal programming (GP), fuzzy goal programming (FGP), genetic algorithm (GA), and particle swarm optimization (PSO) techniques. The results are compared using a robust statistical test and, the PSO proves to be better. A simulation study was carried out further to evaluate the availability and cost function using R and MATLAB packages.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research