Delay in Statistical Control of Systems with Wear

Dohnal, Gejza

doi:10.1002/qre.1265

Cited by 3 publications

(2 citation statements)

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“…Some types of CCs, which are superior in detecting small shifts in the mean, eg, CUSUM or EWMA, consider such weak signals in a certain sense. In some cases, namely, when the monitored process is subject to a slowly varying change or to a continuous change (instead of abrupt changes), the violation of stationarity can, due to some indications, be observed even if the measured signal lies within the control limits.…”

Section: Introductionmentioning

confidence: 99%

Weak signal detection in SPC

Dohnal

2019

Appl Stoch Models Bus & Ind

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Changes in the behavior of dynamic systems are detected based on changes in the monitored quantities or their characteristics. This detection usually takes place by monitoring the time evolution of a variable and detecting the change at the time when a predetermined threshold is exceeded. This threshold is determined on the basis of the detection scheme requirements, in particular, the probability of false alarms and the detection rate for the actual change. In some cases, however, a change does not come suddenly, but certain "hints" in the system behavior can be observed that may indicate a future change. For example, an increasing frequency of outliers can result in a sudden permanent change in the signal. The occurrence of some "unusual" frequencies often indicates an imminent change. For example, an increasing correlation value indicates an undesired process status. Detection of these "subliminal" hints can often improve the characteristics of the detection scheme, especially the detection rate for the actual change. In this paper, we will deal with the detection of weak signals in statistical process monitoring using a control chart with adaptive control limits. KEYWORDSadaptive control, ARL, control chart, limits overrun, SPC, weak signal Appl Stochastic Models Bus Ind. 2020;36:225-236.wileyonlinelibrary.com/journal/asmb

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Section: Introductionmentioning

confidence: 99%

Weak signal detection in SPC

Dohnal

2019

Appl Stoch Models Bus & Ind

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“…The most widely used methods for online change detecting have been developed within the Statistical Process Control (SPC) framework . These methods are typically used for controlling the quality during a manufacturing process (see, eg, the works of Montgomery, Kenett and Zacks, and Dohnal).…”

Section: Introductionmentioning

confidence: 99%

Novelty detection based on learning entropy

Dohnal

Bukovský

2019

Appl Stoch Models Bus & Ind

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The Approximate Individual Sample Learning Entropy is based on incremental learning of a predictor truex˜false(k+hfalse)=ϕfalse(boldxfalse(kfalse),boldwfalse), where x(k) is an input vector of a given size at time k, w is a vector of weights (adaptive parameters), and h is a prediction horizon. The basic assumption is that, after the underlying process x changes its behavior, the incrementally learning system will adapt the weights w to improve the predictor truex˜. Our goal is to detect a change in the behavior of the weight increment process. The main idea of this paper is based on the fact that weight increments △w(k), where △w(k) = w(k + 1) − w(k), create a weakly stationary process until a change occurs. Once a novelty behavior of the underlying process x(k) occurs, the process △w(k) changes its characteristics (eg, the mean or variation). We suggest using convenient characteristics of △w(k) in a multivariate detection scheme (eg, the Hotelling's T2 control chart).

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On Assessing the Performance of Sequential Procedures for Detecting a Change

Kenett

Pollak

2012

Quality & Reliability Eng

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The literature on statistical process control has focused mostly on the average run length (ARL) to an alarm, as a performance criterion of sequential schemes. When the process is in control, this is the ARL to false alarm, generally denoted by ARL0, and represents the in‐control operating characteristic of the procedure. The ARL from the occurrence of a change to its detection represents an out‐of‐control operating characteristic and is typically embodied by ARL1, the ARL to detection assuming that the change occurs at the very start of surveillance. However, these indices do not tell the whole story, and at times they are not defined well by a single number. We review the role of various operating characteristics in assessing performance of sequential procedures in comparison with ARL0 and ARL1. Copyright © 2012 John Wiley & Sons, Ltd.

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Delay in Statistical Control of Systems with Wear

Cited by 3 publications

References 20 publications

Weak signal detection in SPC

Weak signal detection in SPC

Novelty detection based on learning entropy

On Assessing the Performance of Sequential Procedures for Detecting a Change

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