Application control chart concepts of designing a pre-alarm system in the nuclear power plant control room

Hwang, Sheue‐Ling; Lin, Jhih-Tsong; Liang, Guo-Feng; Yau, Yi-Jan; Yenn, Tzu-Chung; Hsu, Chih-Chao

doi:10.1016/j.nucengdes.2008.07.011

Cited by 51 publications

(16 citation statements)

References 13 publications

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“…The Shewhart control chart performs well for the detection of large process shifts, whereas CUSUM and EWMA control charts are well known for their ability to quickly detect small process shifts. Although first proposed for the manufacturing industry, control charts have now been applied in a wide variety of disciplines, such as nuclear engineering (Hwang et al ), education (Wang and Liang), and analytical laboratories (Masson and Abbasi).…”

Section: Introductionmentioning

confidence: 99%

Nonparametric Progressive Mean Control Chart for Monitoring Process Target

Abbasi

Miller

Riaz

2012

Quality & Reliability Eng

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Nonparametric control charts are widely used when the parametric distribution of the quality characteristic of interest is questionable. In this study, we proposed a nonparametric progressive mean control chart, namely the nonparametric progressive mean chart, for efficient detection of disturbances in process location or target. The proposed chart is compared with the recently proposed nonparametric exponentially weighted moving average and nonparametric cumulative sum charts using different run length characteristics such as the average run length, standard deviation of the run length, and the percentile points of the run length distribution. The comparisons revealed that the proposed chart outperformed recent nonparametric exponentially weighted moving average and nonparametric cumulative sum charts, in terms of detecting the shifts in process target. A real life example concerning the fill heights of soft drink beverage bottles is also provided to illustrate the application of the proposed nonparametric control chart. Copyright © 2012 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

Nonparametric Progressive Mean Control Chart for Monitoring Process Target

Abbasi

Miller

Riaz

2012

Quality & Reliability Eng

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“…In [32], a novel HyBUTLA algorithm for learning the behavior of hybrid (discrete and continuous) systems was used for identification of behavior models of industrial production processes. A prealarm system where alarms are generated to alert system operators of probable future changes in the system was proposed in [33].…”

Section: Related Workmentioning

confidence: 99%

FN-DFE: Fuzzy-Neural Data Fusion Engine for Enhanced Resilient State-Awareness of Hybrid Energy Systems

Wijayasekara

Linda

Manic

et al. 2014

IEEE Trans. Cybern.

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Resiliency and improved state-awareness of modern critical infrastructures, such as energy production and industrial systems, is becoming increasingly important. As control systems become increasingly complex, the number of inputs and outputs increase. Therefore, in order to maintain sufficient levels of state-awareness, a robust system state monitoring must be implemented that correctly identifies system behavior even when one or more sensors are faulty. Furthermore, as intelligent cyber adversaries become more capable, incorrect values may be fed to the operators. To address these needs, this paper proposes a fuzzy-neural data fusion engine (FN-DFE) for resilient state-awareness of control systems. The designed FN-DFE is composed of a three-layered system consisting of: 1) traditional threshold based alarms; 2) anomalous behavior detector using self-organizing fuzzy logic system; and 3) artificial neural network-based system modeling and prediction. The improved control system state-awareness is achieved via fusing input data from multiple sources and combining them into robust anomaly indicators. In addition, the neural network-based signal predictions are used to augment the resiliency of the system and provide coherent state-awareness despite temporary unavailability of sensory data. The proposed system was integrated and tested with a model of the Idaho National Laboratory's hybrid energy system facility known as HYTEST. Experiment results demonstrate that the proposed FN-DFE provides timely plant performance monitoring and anomaly detection capabilities. It was shown that the system is capable of identifying intrusive behavior significantly earlier than conventional threshold-based alarm systems.

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“…The applications of control charts are not limited to the manufacturing industry. They are being utilized in different fields of life, like nuclear engineering (cf Hwang et al), health care (cf Woodall), and many others. When the quality characteristic of interest has measurable trait, variable control charts are applied.…”

Section: Introductionmentioning

confidence: 99%

On designing an efficient control chart to monitor fraction nonconforming

Abbas

Nazir

Akhtar

et al. 2019

Quality & Reliability Eng

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Process control measures are mostly applied in production and manufacturing industries. The most important tool used in these disciplines is control chart. In manufacturing and production processes, when the quality characteristic of interest cannot be directly measured, it becomes essential to apply attribute control charts. To monitor fraction nonconforming of the output, quality practitioners mostly prefer p‐chart. In this article, a new progressive mean (PM) control chart is being proposed for monitoring drift in proportion of nonconforming products. The design evaluations of the proposed chart are made and compared through different properties of run length distribution, such as average run length (ARL), standard deviation of run length (SDRL), and some percentile points. The performance of the proposed chart is assessed under zero‐state and steady‐state scenarios. The proposed PM chart is compared with p‐chart, moving average (MA) chart, optimal CUSUM chart, modified exponentially weighted moving average (EWMA) chart, and runs rules p‐charts for monitoring fraction nonconforming. The proposed chart spots efficiently sustained disturbances in the process as compared with their existing counterparts. Two illustrative examples are also provided; one from real‐life application of nonconforming bearing and seal assemblies data and the other from simulated data for the implementation of PM chart.

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Application control chart concepts of designing a pre-alarm system in the nuclear power plant control room

Cited by 51 publications

References 13 publications

Nonparametric Progressive Mean Control Chart for Monitoring Process Target

Nonparametric Progressive Mean Control Chart for Monitoring Process Target

FN-DFE: Fuzzy-Neural Data Fusion Engine for Enhanced Resilient State-Awareness of Hybrid Energy Systems

On designing an efficient control chart to monitor fraction nonconforming

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