Nonparametric control scheme for monitoring phase II nonlinear profiles with varied argument values

Zhang, Yang; He, Zhen; Fang, Juntao; Zhang, Min

doi:10.3901/cjme.2012.03.587

Cited by 12 publications

(5 citation statements)

References 19 publications

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“…Changing the value of η can move the profile along the Y-axis but would not change the profile shape. Figure 1 shows some sample profiles from the IC and OC models, however, without error terms in Equations (14) and (15). For fixed δ, as η varies from À1.5 to 9, the OC profile will be apart (η < 0), tangential (η = 0), and intersectional (0 < η ≤ 9) to the IC profile.…”

Section: Performance Assessmentmentioning

confidence: 99%

“…[1][2][3] Based on the idea of fitting regression models, profile monitoring separates common-cause variability from special-cause variability over time. 4 Many methods in the literatures are developed to detect the changes in the mean profile and the noise in linear profiles [5][6][7][8][9][10] or nonlinear profiles, [11][12][13][14][15] and some other approaches are proposed for monitoring the functional variation of linear profiles 4,16,17 or nonlinear profiles. 18,19 Besides detecting above changes, Chipman et al 20 pointed out that one possible purpose in profile monitoring is detecting changes away from the 'normal' profile toward one of several prespecified 'bad' profiles.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the idea of fitting regression models, profile monitoring separates common‐cause variability from special‐cause variability over time . Many methods in the literatures are developed to detect the changes in the mean profile and the noise in linear profiles or nonlinear profiles, and some other approaches are proposed for monitoring the functional variation of linear profiles or nonlinear profiles …”

Section: Introductionmentioning

confidence: 99%

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A Score‐test‐based EWMA Control Chart for Detecting Prespecified Quadratic Changes in Linear Profiles

Zhang

et al. 2015

Quality & Reliability Eng

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In profile monitoring, some methods have been developed to detect the unspecified changes in the profiles. However, due to the characteristics of a system or process, several prespecified changes may happen in some statistical process control applications, such as the typical form errors in cylindrical surface manufacturing processes. Hence, statistical process control is important and challenging for detecting changes away from the ‘normal’ profile toward one of several prespecified ‘bad’ profiles. A novel score‐test‐based EWMA control chart is proposed to detect the prespecified quadratic changes in linear profiles. Numerical simulation results show that the proposed chart is effective and robust when the vertex varies along one direction, and, in most cases, performs better than alternative methods in detecting small to moderate shifts. Finally, an example is given to illustrate the implementation of the proposed control chart. Copyright © 2015 John Wiley & Sons, Ltd.

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Section: Performance Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Score‐test‐based EWMA Control Chart for Detecting Prespecified Quadratic Changes in Linear Profiles

Zhang

et al. 2015

Quality & Reliability Eng

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“…Liu et al . and Zhang et al . both discussed the allocation of the overall probability of a false alarm between two combined control charts by using an allocation factor.…”

Section: Phase II Multivariate Control Chartsmentioning

confidence: 99%

“…Williams et al . pointed out, ‘Once a baseline curve is found, some appropriate distance metric can be used to measure how “different” each individual curve is from the baseline.’ Different metrics have been proposed by Williams et al ., Vaghefi et al ., and Zhang et al . to analyze or monitor nonlinear profiles in Phase I or Phase II.…”

Section: Introductionmentioning

confidence: 99%

Control Charts for Monitoring Linear Profiles with Within-Profile Correlation Using Gaussian Process Models

Zhang

et al. 2013

Qual. Reliab. Engng. Int.

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Profile monitoring is the utilization of control charts for checking the stability of the quality of a product over time when the product quality is characterized by a function at each time point. Most existing control charts for monitoring profiles are based on the assumption that the observations within each profile are independent of each other, which is often invalid in practice. Successive measurements within profiles often exhibit spatial or serial correlation. This paper focuses on Phase II linear profile monitoring when within-profile data are correlated. A Gaussian process model is used to describe the within-profile correlation (WPC). Two Shewhart-type multivariate control charts are proposed to monitor the linear trend term and the WPC separately in Phase II. Our proposed approaches are compared with alternative methods through numerical simulations in which different in-control WPCs are considered. Simulation studies show that the proposed control charts are sensitive to changes in the linear trend term when the correlation is strong and effective in detecting large shifts in the WPC. Finally, an example is given to illustrate the implementation of our proposed control charts.

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Machine vision based quality control of free-form profiles in automatic cutting processes

Pacella

Grieco

Blaco

2017

Computers & Industrial Engineering

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Nonparametric control scheme for monitoring phase II nonlinear profiles with varied argument values

Cited by 12 publications

References 19 publications

A Score‐test‐based EWMA Control Chart for Detecting Prespecified Quadratic Changes in Linear Profiles

A Score‐test‐based EWMA Control Chart for Detecting Prespecified Quadratic Changes in Linear Profiles

Control Charts for Monitoring Linear Profiles with Within-Profile Correlation Using Gaussian Process Models

Machine vision based quality control of free-form profiles in automatic cutting processes

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