A modified CUSUM control chart for monitoring industrial processes

Muhammad, Faisal; Zafar, Raja Fawad; Abbas, Nasir; Mahmood, Tahir

doi:10.1002/qre.2307

Cited by 34 publications

(42 citation statements)

References 11 publications

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“…The proposed chart triggers signal at 158th sample to onward. The total numbers of samples points noted out‐of‐control by proposed chart are 43(158, 159,…, 200) while chart proposed by Faisal et al only detects 30 sample points (171, 172,…, 200) on this same data. From Figure , it can be clearly observed that proposed chart identifies the shift earlier as compared with the chart developed by Faisal et al The output of this example is in accordance with the results of the proposed charts discussed in Sections and .…”

Section: Real Life Applicationmentioning

confidence: 98%

An enhanced approach for the progressive mean control charts

Abbas

Nazir

Akhtar

et al. 2018

Quality & Reliability Eng

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To maintain and improve the quality of the processes, control charts play an important role for reduction of variation. To detect large shifts in the process parameters, Shewhart control charts are commonly applied but for small shifts, exponentially weighted moving averages (EWMA), cumulative sum (CUSUM), double exponentially weighted moving average (DEWMA), double CUSUM, moving average (MA), double moving average (DMA), and progressive mean (PM) control charts, are used. This study proposes double progressive mean (DPM) and optimal DPM control charts to enhance the performance of the PM chart. As the proposed DPM control charts use information sequentially, hence their performance is compared with natural competitors EWMA, CUSUM, DEWMA, double CUSUM, MA, DMA, and PM control charts. Run length and its different properties are evaluated to compare the performance of the proposed charts and counterparts. Results reveal that proposed optimal DPM outperforms the other charts. An example related to voltage on fixed capacitance level is also provided to illustrate the proposed charts.

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Section: Real Life Applicationmentioning

confidence: 98%

An enhanced approach for the progressive mean control charts

Abbas

Nazir

Akhtar

et al. 2018

Quality & Reliability Eng

Self Cite

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“…Another memory type well‐known structure is a CUSUM control chart (cf. Faisal et al). A CUSUM‐3 chart was proposed by Saghaei et al, which depends on three CUSUM statistics given below:

\begin{matrix} lefttrue \\ For intercept : \{\begin{matrix} center \\ {CUSUM}_{I (k)}^{+} = max [0, {\overset{A}{true}}_{0 k}^{*} - (A_{0}^{*} - k_{I}^{* +}) + {CUSUM}_{I (k - 1)}^{+}] \\ {CUSUM}_{I (k)}^{-} = max [0, (A_{0}^{*} - k_{I}^{* -}) - {\overset{A}{true}}_{0 k}^{*} + {CUSUM}_{I (k - 1)}^{-}] \end{matrix}, \\ For Slope : \{\begin{matrix} center \\ {CUSUM}_{S (k)}^{+} = max [0, {\overset{A}{true}}_{1 k}^{*} - (A_{1}^{*} - k_{S}^{* +}) + {CUSUM}_{S (k - 1)}^{+}] \\ {CUSUM}_{S (k)}^{-} = max [0, (A_{1}^{*} - k_{S}^{* -}) - {\overset{A}{true}}_{1 k}^{*} + {CUSUM}_{S (k - 1)}^{-}] \end{matrix}, \\ For error variance : \{\begin{matrix} center \\ {CUSUM}_{E (k)}^{+} \end{matrix} \end{matrix}

…”

Section: Simple Linear Profilesmentioning

confidence: 99%

“…38 Another memory type well-known structure is a CUSUM control chart (cf. Faisal et al 39 ). A CUSUM-3 chart was proposed by Saghaei et al, 22 which depends on three CUSUM statistics given below:…”

Section: Existing Structures For Simple Linear Profilesmentioning

confidence: 99%

On improved monitoring of linear profiles under modified successive sampling

Riaz

Mahmood

2019

Quality & Reliability Eng

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Control charts are often used for the monitoring of quality characteristics of interest. There may exist some other characteristics that are associated with the main quality characteristic. A model that quantifies the relationship between them is termed as a profile, for instance, the relation between charge and capacitance. The monitoring of the main variable that is linearly related with an associated explanatory variable is termed as simple linear profiles' monitoring. It is a common practice to use simple random sampling (SRS) for profile monitoring. This study intends to enhance profile monitoring by considering modified successive sampling (MSS) approach. The performance of the existing and the proposed schemes are evaluated using the well‐known metrics average run length. The comparative analysis revealed that the proposed structure outperforms the existing ones in terms of efficiency. A real application from electrical engineering is used to show the implementation of our proposal in practice.

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“…The Shewhart based structures are useful to detect a large amount of shift in the process parameter while for the detection of small to moderate changes, EWMA and CUSUM charts were used (cf. Faisal, et al [35]). Instead of these charts, Abbas, et al [36] proposed a method, which is compatible for all type of shifts (i.e., small, moderate and large) and referred to the assorted_3 chart.…”

Section: Introductionmentioning

confidence: 99%

An Improved Control Chart for Monitoring Linear Profiles and its Application in Thermal Conductivity

2020

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In most of the manufacturing processes, we encounter different quality characteristics of a product and process. These characteristics can be categorized into two kinds; study variables (variable of interest) and the supporting/explanatory variables. Sometime, a linear relationship might exist between the study and supporting variable, which is called simple linear profiles. This study focuses on the simple linear profiles under assorted control charting approach to detect the large, moderate and small disturbances in the process parameters. The evaluation of the proposed assorted method is assessed by using numerous performance measures, for instance, average run length, relative average run length, extra and sequential extra quadratic losses. A comparative analysis of the proposal is also carried out with some existing linear profile methods including Shewhart_3, Hotelling's 2 , EWMA_3, EWMA/R and CUSUM_3 charts. Finally, a real-life application of the proposed assorted chart is presented to monitor thermal management of diamondcopper composite.

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A modified CUSUM control chart for monitoring industrial processes

Cited by 34 publications

References 11 publications

An enhanced approach for the progressive mean control charts

An enhanced approach for the progressive mean control charts

On improved monitoring of linear profiles under modified successive sampling

An Improved Control Chart for Monitoring Linear Profiles and its Application in Thermal Conductivity

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