A side‐sensitive synthetic coefficient of variation chart. (19th January 2021)

• Record Type: Journal Article
• Authors: Yeong, Wai Chung; Lee, Ping Yin; Lim, Sok Li; Khaw, Khai Wah; Khoo, Michael Boon Chong
• Is Part Of: Quality and reliability engineering international. Volume 37:Number 5(2021)
• Page Start: 2014
• Page End: 2033
• Publication Date: 2021-01-19
• Subjects: average run length -- coefficient of variation -- control charts -- expected average run length -- process monitoring and control -- statistical quality control -- synthetic chart -- side‐sensitive; Reliability (Engineering) -- Periodicals; Quality control -- Periodicals; High technology -- Periodicals; 620.00452
• DOI: 10.1002/qre.2843
• Languages: English
• ISSNs: 0748-8017

Abstract

Abstract: Control charts for monitoring the coefficient of variation (γ) are useful for processes with an inconsistent mean (μ) and a standard deviation (σ) which changes with μ, by monitoring the consistency in the ratio σ over μ. The synthetic‐γ chart is one of the charts proposed to monitor γ, and its attractiveness lie in waiting until a second point to fall outside the control limits before a decision is made. However, existing synthetic‐γ charts do not differentiate between the points falling outside the upper control limit ( UCL ) and lower control limit ( LCL ). Hence, this paper proposes a side‐sensitive synthetic‐γ chart, where successive nonconforming samples must either fall above the UCL or below the LCL . Formulae to compute the average run length ( ARL ), the standard deviation of the run length ( SDRL ) and expected average run length ( EARL ) are derived using the Markov chain approach, and the algorithms to obtain the optimal charting parameters are proposed. Subsequently, the optimal charting parameters, ARL, SDRL and EARL values for various numerical examples are shown. Comparisons show that the side‐sensitive synthetic‐γ chart consistently outperforms the existing synthetic‐γ chart, especially for small shifts. The proposed chart also consistently outperforms the Shewhart‐γ chart, while showing comparable or better performance than the Exponentially Weighted Moving Average (EWMA) chart for most shift sizes, except for very small shifts. Finally, this paper shows the implementation of the proposed chart on an industrial example.