Developing an adaptable sequential probability ratio test applicable for lifetime analysis of different continuous distributions

ABSTRACT

It is commonly discussed by the quality/reliability engineers that acceptance sampling plans and life testing schemes designed using the concept of sequential probability ratio test (SPRT) can effectively decrease the cost and time of the experiment/inspection. Nevertheless, reaching a procedure to perform an SPRT-based life test usually involves some computations which are not simple and straightforward, even for simple forms of statistical distributions. Moreover, for each statistical distribution describing the model of the lifetime data, different specialized computations should be performed to propose a procedure for the life test. To address these shortcomings, we develop a novel life test according to the SPRT of the Bernoulli/binomial distribution, which can be simply, straightforwardly and effectively adapted for life testing of different continuous distributions. Our adaptable SPRT abbreviated to ASPRT is first designed considering the Weibull distribution and is then extended for gamma and other continuous distributions. In order to evaluate the performance, ASPRT is applied to different real-world and simulated data sets. To better prove the efficiency, it is also compared with a benchmark SPRT on different data sets. Both computational and comparative results demonstrate that ASPRT is able to effectively and efficiently conduct the life testing of different continuous distributions.

KEYWORDS:

• Lifetime
• lot
• batch
• hypothesis test
• reliability
• life testing
• acceptance sampling plans

Highlights

• A novel lifetime testing scheme using the concept of sequential probability ratio test (SPRT) is designed.

• Unlike the existing similar SPRTs, the proposed approach has a simple and straightforward procedure.

• It has an adaptable structure which enables it to be simply utilized for life testing of different continuous distributions.

• It is applied to both real-world benchmark and simulated synthetic data sets.

• The computational and comparative results demonstrate the effectiveness and efficiency of the proposed approach, with respect to different evaluation measures.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

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.

Esmaeil Alinezhad

Esmaeil Alinezhad is an assistant professor for Industrial Engineering at Shiraz University of Technology. He is received his PhD, MSc, and BSc degrees in Industrial Engineering from Tarbiat Modares University, Yazd University, and Isfahan University of Technology in Iran, respectively. He has been involved in many different areas related to mathematical modeling and applied data mining. In particular, his current research interests focus on mathematical modeling and optimization, social network analysis, applied data mining and machine learning, production and scheduling models, design and optimization of multi-criteria decision-making models, Multivariate analysis, etc.