A Comparative Study of CUSUM and EWMA Charts: Detection of Incipient Drifts in a Mutlivariate Framework

Boudaoud, Nassim; Cherfi, Zohra

doi:10.1080/08982110500251295

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…Examples are Hunter [9]; and Ye, Vilbert and Chen [10]. Numerous comparative studies are also presented in [11], [12]. In this respect, various tools, techniques, approaches and their applications in different fields have been developed [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Fast and Real-Time Sensor-Fault Detection using Shannon’s Entropy

Alsaleh

Daass

Pomorski

et al. 2021

2021 5th International Conference on Control and Fault-Tolerant Systems (SysTol)

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In this paper, we aim at improving the change detection techniques by introducing an adaptive thresholding with a sliding time window. In particular, a real-time optimal sliding time window length is implemented without any preliminary learning step as required in conventional sensorfault detection methods. Based on Shannon's entropy, our method improves the change detection techniques using an adaptive thresholding. The technique can be applied by any change detection technique based on the generalized likelihood ratio (GLR). To validate the robustness of our approach, two commonly used change detection techniques are considered: the cumulative sum (Cusum) and the exponentially weighted Moving average (EWMA) control charts. Experimental validation is experimentally shown considering real data in the context of collaborative mobile robots. In addition, this experiment leads to a fault-tolerant fusion methodology based on the use of an extended Kalman filter (EKF).

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Section: Introductionmentioning

confidence: 99%

Fast and Real-Time Sensor-Fault Detection using Shannon’s Entropy

Alsaleh

Daass

Pomorski

et al. 2021

2021 5th International Conference on Control and Fault-Tolerant Systems (SysTol)

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“…Therefore, EWMA-based charts are an appropriate monitoring scheme to be adopted when dealing with individual observations [14]. According to the literature, EWMA is one of the most frequently used control charts for monitoring auto-correlated processes because of its flexibility and sensitivity to small shifts [28], which is presented in more details next. In the application presented later in Sections 5, the Shewhart and CUSUM charts are used as a benchmark for anomaly-detection using time series modeling.…”

Section: Spc Overviewmentioning

confidence: 99%

Seasonal ARMA-based SPC charts for anomaly detection: Application to emergency department systems

et al. 2016

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Monitoring complex production systems is primordial to ensure management, reliability and safety as well as maintaining the desired product quality. Early detection of emergent abnormal behaviour in monitored systems allows pre-emptive action to prevent more serious consequences, to improve system operations and to reduce manufacturing and/or service costs. This study reports the design of a new methodology for the detection of abnormal situations based on the integration of time-series analysis models and statistical process control (SPC) tools for the joint development of a monitoring system to help supervising of the behaviour of emergency department services (EDs). The monitoring system developed is able to provide early alerts in the event of abnormal situations. The seasonal autoregressive moving average (SARMA)-based exponentially weighted moving average (EWMA) anomaly detection scheme proposed was successfully applied to the practical data collected from the database of the paediatric emergency department (PED) at Lille regional hospital centre, France. The method developed utilizes SARMA as a modelling framework and EWMA for anomaly detection. The EWMA control chart is applied to the uncorrelated residuals obtained from the SARMA model. The detection results of the EWMA chart are compared with two other commonly applied residual-based tests: a Shewhart individuals chart and a Cumulative Sum (CUSUM) control chart.

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“…It is shown that it performs consistently better than the T 2 chart. Boudaoud and Cherfi [11] propose a new statistic for monitoring multivariate trend processes. They focus on the choices of more sensitive statistics than the classical Hotelling T 2 statistic.…”

Section: Introductionmentioning

confidence: 99%

Multivariate SPC for total inertial tolerancing

Pillet

Abdelhakim

Pairel

et al. 2013

Int. J. Metrol. Qual. Eng.

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Abstract. This paper presents a joint use of the T 2 chart and total inertial tolerancing for process control. Here, we will show an application of these approaches in the case of the machining of mechanical workpieces using a cutting tool. When a cutting tool in machining impacts different manufactured dimensions of the workpiece, there is a correlation between these parameters when the cutting tool has maladjustment due to bad settings. Thanks to total inertial steering, the correlation structure is known. This paper shows how T 2 charts allow one to take this correlation into account when detecting the maladjustment of the cutting tool. Then the total inertial steering approach allows one to calculate the value of tool offsets in order to correct this maladjustment. We will present this approach using a simple theoretical example for ease of explanation.

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A Comparative Study of CUSUM and EWMA Charts: Detection of Incipient Drifts in a Mutlivariate Framework

Cited by 4 publications

References 5 publications

Fast and Real-Time Sensor-Fault Detection using Shannon’s Entropy

Fast and Real-Time Sensor-Fault Detection using Shannon’s Entropy

Seasonal ARMA-based SPC charts for anomaly detection: Application to emergency department systems

Multivariate SPC for total inertial tolerancing

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