Monitoring autocorrelated processes using variable sampling schemes at fixed‐times

Zou, Changliang; Wang, Zhaojun; Tsung, Fugee

doi:10.1002/qre.867

Cited by 29 publications

(19 citation statements)

References 28 publications

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“…The most typical approach is to fit appropriate time‐series models to the observations and then charting the residuals. Recently, this method was applied by Costa and Claro, Lin and Chou, Zou et al , and Costa and Machado …”

Section: Adaptive Shewhart Control Chartsmentioning

confidence: 99%

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Adaptive Control Charts: Recent Developments and Extensions

Psarakis

2015

Quality & Reliability Eng

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Statistical quality control is a very useful tool in the hands of business in order to improve their products quality. The control charts are the most widely used and the most effective tool of the statistical quality control. In this paper, the substantial recent developments in the design of the adaptive control charts, focusing on the univariate control charts, which allow some of their parameters to change during production, are presented.

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Section: Adaptive Shewhart Control Chartsmentioning

confidence: 99%

“…Zou et al suggest a variable sampling scheme at fixed times, including the VSI and VSSI schemes, to enhance the efficiency of the

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chart for autocorrelated process.…”

Section: Adaptive Shewhart Control Chartsmentioning

confidence: 99%

Adaptive Control Charts: Recent Developments and Extensions

Psarakis

2015

Quality & Reliability Eng

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“…In particular, two categories of methods can be identified [7]. The former and widely used one consists in applying traditional control charts to uncorrelated residuals computed as error terms of an appropriate time series model fitted to observed data (Harris and Ross [8], Apley and Shi [9], Zou et al [10]). The latter stream, instead, consists of methods directly working on the original data points ( [6], [11] and model free approaches, e.g.…”

Section: Multivariate Spc With Auto-correlatedmentioning

confidence: 99%

The Use of Adaptive PCA-Based Condition Monitoring Methods in Machining Processes

Grasso

Colosimo

Moroni

2012

Volume 4: Advanced Manufacturing Processes; Biomedical Engineering; Multiscale Mechanics of Biological Tissues; Sciences, Engin

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In different manufacturing applications the assessment of the health conditions of a machine tool, together with the quality and stability of the process, requires the capability of dealing with response variables described in terms of profile data. In the frame of in-process monitoring of sensor signals this is the case, for instance, of monitoring either series production of large lots of parts or machining processes characterized by cyclic signals, where both the condition of the machine components and the final quality of the worked piece may be correlated with the stability of repeating signal profiles in time. However, as far as real time data acquisition is concerned, and when measurements are performed with high sampling frequency, data are likely to be auto-correlated, and hence it is of fundamental importance to develop adaptive monitoring tools robust with respect to non-steady state conditions. The paper deals with the utilization of profile monitoring approaches for in-process monitoring of manufacturing operations and investigates their applicability to the problem of monitoring auto-correlated signals. In particular Principal Component Analysis (PCA) is applied in combination with an adaptive approach based on a moving time window for continuously revise the reference model is evaluated and discussed.A real case study is used to test the performances of the method: the task is to detect tool chipping and breakage in end milling operations by means of real-time monitoring of cutting force signals. The evolution of tool wear imposes a trend in observed signals which leads to the need for an adaptive approach to properly isolate the breakage event from the slow pattern change due to wear mechanism.

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“…The economic design was then treated by Bai and Lee 11 . To enhance the efficiency of theX control chart for autocorrelated processes, Zou et al 12 proposed using a VSI scheme. The economically designed X chart operating with samples of variable sizes was studied by Park and Reynolds 13 .…”

Section: Introductionmentioning

confidence: 99%

Bayesian np control charts with adaptive sample size for finite production runs

Kooli¹,

Limam²

2008

Quality & Reliability Eng

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The design of a control chart requires the specification of three decision variables, namely the sample size, n, the sampling interval, h, and the action limit under which the process must be stopped for potential repair. In this paper, the Bayesian attribute control chart, namely the np chart for short run production, using a variable sample size is discussed. In a simulated experiment, optimal solutions of the static np chart, the basic Bayesian np chart, and the Bayesian scheme with adaptive sample size are presented. Results of the empirical study show that varying the sample size leads to more cost savings compared with the other two approaches. In order to detect how the input parameters affect decision variables, a regression analysis is conducted. It is obtained that the benefits of using the basic Bayesian np chart and the Bayesian chart with adaptive sample size instead of the static scheme are affected by the length of the production run.

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Monitoring autocorrelated processes using variable sampling schemes at fixed‐times

Cited by 29 publications

References 28 publications

Adaptive Control Charts: Recent Developments and Extensions

Adaptive Control Charts: Recent Developments and Extensions

The Use of Adaptive PCA-Based Condition Monitoring Methods in Machining Processes

Bayesian np control charts with adaptive sample size for finite production runs

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