High quality process monitoring using a class of inter-arrival time distributions of the renewal process. (April 2016)

• Record Type: Journal Article
• Authors: Ali, Sajid; Pievatolo, Antonio
• Is Part Of: Computers & industrial engineering. Volume 94(2016)
• Page Start: 45
• Page End: 62
• Publication Date: 2016-04
• Subjects: High quality process -- Time-between-events -- Renewal process -- Weibull distribution -- Average run length -- Process monitoring; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2016.01.012
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: We introduce a new control chart based on the renewal process. Any lifetime distribution for TBE can be assumed using the renewal process. We introduce a parametric class of continuous distributions for the TBE. We assess the Weibull control chart performance by different performance measures. We also discuss the effect of parameter estimation on the ARL. Abstract: For high-quality processes where the defect rate is very low, e.g., parts per million (ppm), time-between-events (TBE) control charts have several advantages over the ordinary control charts. Most existing TBE control charts are based on the homogeneous Poisson process assumption, so that the distribution of TBE can only be exponential. However, the exponential distribution is not suitable in many applications, especially when the failure rate is not constant. In this article, we introduce a new TBE control chart, based on the renewal process, where the distribution of the TBE belongs to a parametric class of absolutely continuous distributions, which includes some well-known and commonly used lifetime distributions, i.e., exponential, Rayleigh, Weibull, Burr type XII, Pareto and Gompertz. The control structure of the proposed chart is derived analytically in general and numerical examples are presented for the Weibull distribution, due to its relevance in reliability. The performance of the proposed control charts is evaluated in terms of some standard useful measures, including average run length (ARL), the standard deviation of run length, the coefficient of variation of run length, expected quality loss (EQL) and relative ARL (RARL). The effect of parameter estimation, using both maximum likelihood and Bayesian methods, is also discussed. This study also presents an illustrative example and four case studies to highlight the practical aspects of the new TBE chart.