Applications and Experiences of Quality Control to Surgical and Interventional Procedures

Biau, David; Dessolle, Lionel; Porcher, Raphaël

doi:10.5772/15877

Cited by 4 publications

(3 citation statements)

References 60 publications

(85 reference statements)

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“…SPC applications have been widely used in various production systems for monitoring processes and improving product quality [1], [19]. Statistical controls charts (also referred to as monitoring charts) are one of the most commonly used tools in SPC and have been extensively used in quality engineering as a monitoring tool to detect the presence of possible anomalies in the mean or variance of process measurements.…”

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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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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“…The primary utilization of control charts has focused on industrial quality control applications for many decades. Nowadays, the use of control charts has been broadened to many fields, including economics (Fris en (2009)), medicine (Biau et al (2011)), informatics (Park (2005)), and others. Shewhart control charts are very popular in SPC and can be utilized to successfully detect large variations in the process mean (Human et al (2010)).…”

Section: Ewma Statistical Control Schemementioning

confidence: 99%

PLS-based EWMA fault detection strategy for process monitoring

Harrou

Nounou

et al. 2015

Journal of Loss Prevention in the Process Industries

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“…Numerous control charts have been developed to monitor the mean of system measurements over time, and include the Shewhart chart, cumulative summation (CUSUM) chart (Page, 1954), and the EWMA chart (Crowder & Hamilton, 1992;Hawkins & Olwell, 1998;Hunter, 1986;Lucas & Saccucci, 1990). Nowadays, the application of control charts has been extended to many areas, such as economics, medicine, environment, and others (Biau, Dessolle, & Porcher, 2011;Frisén, 2009;Heffernan et al, 2004). The traditional Shewhart control charts are very popular in statistical process control.…”

Section: Ewma Control Schemementioning

confidence: 99%

Monitoring linear antenna arrays using an exponentially weighted moving average-based fault detection scheme

Harrou

Nounou

2014

Systems Science & Control Engineering

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The evolution of modern wireless communications systems has dramatically increased the demand for antenna arrays. An antenna array with certain radiation characteristics is often desired. However, the actual radiation pattern of an antenna array changes when faults are introduced in the array. In this paper a statistical fault detection methodology based on the exponentially weighted moving average (EWMA) control scheme is proposed to detect possible faulty radiation patterns in linear antenna arrays. The proposed method detects the faults based on deviation in the radiation pattern from the desired ones. The difference between synthesized radiation pattern obtained using the Minimax algorithm and the measured pattern can be used as an indicator about the existence or absence of faults. To assess the fault detection abilities of the EWMA control scheme, three case studies are considered, one involving a complete failure in one element in the array, one involving partial failure in two elements, and one involving degradation caused by random noise due to interference and other factors. The simulation results for all cases show the effectiveness of the proposed EWMA fault detection method.

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Applications and Experiences of Quality Control to Surgical and Interventional Procedures

Cited by 4 publications

References 60 publications

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

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

PLS-based EWMA fault detection strategy for process monitoring

Monitoring linear antenna arrays using an exponentially weighted moving average-based fault detection scheme

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