Designing variable sampling plans based on lifetime performance index under failure censoring reliability tests

Abstract

In this paper, we develop two novel variable reliability acceptance sampling (RAS) plans referred to repetitive group sampling (RGS) and resubmitted sampling (RS), for failure censoring reliability tests by considering information obtained during life test using lifetime performance index (LPI) under Weibull distribution assumption for item lifetime. Performances of the proposed plans are compared with the single sampling (SS) plan through real case studies which show the great performances of proposed plans in terms of minimization of the average failure number of tested items.

Keywords:

• censoring data
• lifetime
• life testing plan
• quality control
• reliability test
• reliability sampling plan

Additional information

Funding

This work was supported by Natural Sciences and Engineering Research Council of Canada through the NSERC Discovery Grant RGPIN 2019 06966.

Notes on contributors

Hasan Rasay

Hasan Rasay is an assistant professor of industrial engineering at Kermanshah University of Technology, Kermanshah, Iran. He received his MS and PhD from Yazd University , Iran, in industrial engineering. His main research interests are quality and reliability engineering and operation management.

Farnoosh Naderkhani

Farnoosh Naderkhani received her PhD in Mechanical and Industrial Engineering (MIE) department from University of Toronto (UofT). She is currently an Assistant Professor with Concordia Institute for Information System Engineering (CIISE) at Concordia University. Prior to joining CIISE, she was a Post-doctoral Fellow at H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. She has been actively involved in different areas of quality control and maintenance management. In particular, her main research domain is quality control and maintenance management with special focus on stochastic modeling; Condition-based Maintenance (CBM); Quality Assurance; Optimal Control of Partially Observable Processes-Systems; Advanced Machine Learning (ML) for Fault Diagnostic and Prognostics; Supply Chain Management, and; Reliability and Residual Life Prediction.

Amir Mohammad Golmohammadi

Amir Mohammad Golmohammadi is received his BSc, MSc and PhD degrees in Industrial Engineering from University of Kurdistan, Islamic Azad University, and Yazd University in Iran, respectively. He has worked for few years in industry and presented his research in several international journals and conferences. His current research interests include Facility Design & Planning, Cellular Manufacturing Systems and Meta-heuristic Algorithms. He has published 6 books and several articles in journals and conferences, such as Fuzzy Information and Engineering, Journal of Industrial Engineering & Production Research, International Journal of Production Research, Journal of Industrial and Systems Engineering, Journal of Engineering, Design and Technology, and International Journal of Energy Sector Management.