Optimal Cusum Control Chart for Censored Reliability Data with Log-logistic Distribution

Aiming at the situation in some Chinese auto companies that the workload of body welding quality inspection is high and the sample size is extremely small, a brand-new CUSUM Control Chart for variance monitoring is proposed in the paper to realize the effective quality control in body welding variance, whose principle is to use variance statistics based on Queensberry transformation Φ-1(G((n-1) St2/σ02)) to monitor infinitely small variances in the process of body welding. Evaluation instance results show that, compared with traditional CUSUM control chart, EWMA control chart and weighted CUSUM control chart, the proposed CUSUM control chart based on variance monitoring is more sensitive to the abnormal variation fluctuation and can detect the abnormity of quality variation earlier.

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Bootstrap-based maximum multivariate CUSUM control chart

Quality Technology & Quantitative Management ◽

10.1080/16843703.2018.1535765 ◽

2018 ◽

Vol 17 (1) ◽

pp. 52-74 ◽

Cited By ~ 4

Author(s):

Hidayatul Khusna ◽

Muhammad Mashuri ◽

Muhammad Ahsan ◽

Suhartono Suhartono ◽

Dedy Dwi Prastyo

Keyword(s):

Control Chart ◽

Cusum Control Chart

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Impact of Measurement Error on Mixed EWMA-CUSUM Control Chart

Scientia Iranica ◽

10.24200/sci.2020.53453.3244 ◽

2020 ◽

Vol 0 (0) ◽

pp. 0-0

Author(s):

Muhammad Noor-ul-Amin

Keyword(s):

Measurement Error ◽

Control Chart ◽

Cusum Control Chart

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X̅ and R control charts based on marshall-olkin inverse log-logistic distribution for positive skewed data

African Journal of Pure and Applied Sciences ◽

10.33886/ajpas.v1i1.167 ◽

2020 ◽

Vol 1 (1) ◽

pp. 9-16

Author(s):

O. L. Aako ◽

J. A. Adewara ◽

K. S Adekeye ◽

E. B. Nkemnole

Keyword(s):

Control Chart ◽

Control Charts ◽

Average Run Length ◽

Logistic Distribution ◽

Skewed Data ◽

Process Data ◽

And Performance ◽

Set Up ◽

Control Limits ◽

Normally Distributed

The fundamental assumption of variable control charts is that the data are normally distributed and spread randomly about the mean. Process data are not always normally distributed, hence there is need to set up appropriate control charts that gives accurate control limits to monitor processes that are skewed. In this study Shewhart-type control charts for monitoring positively skewed data that are assumed to be from Marshall-Olkin Inverse Loglogistic Distribution (MOILLD) was developed. Average Run Length (ARL) and Control Limits Interval (CLI) were adopted to assess the stability and performance of the MOILLD control chart. The results obtained were compared with Classical Shewhart (CS) and Skewness Correction (SC) control charts using the ARL and CLI. It was discovered that the control charts based on MOILLD performed better and are more stable compare to CS and SC control charts. It is therefore recommended that for positively skewed data, a Marshall-Olkin Inverse Loglogistic Distribution based control chart will be more appropriate.

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