Kinetic study of an in situ inhibited polymerization using robust M-statistics

Habibi, Ali

doi:10.1016/j.cherd.2008.06.011

Cited by 2 publications

(2 citation statements)

References 27 publications

(33 reference statements)

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“…In the theoretical study of robustness and resistance, M-estimation, introduced by Huber (1964), is the simplest approach to use, both computationally and theoretically. It has been widely used in civil engineering, aerospace, marine vessels, 20,25–27 and many other areas.…”

Section: Robust Statisticsmentioning

confidence: 99%

An online anomaly recognition and early warning model for dam safety monitoring data

Lü

et al. 2019

Structural Health Monitoring

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Anomaly recognition and early warning of monitoring data are of great significance in the field of modern dam safety management. Multidimensional least-squares regression model with the Pauta criterion is a well-known traditional method, but it is easy to misjudge the normal value and miss the outliers. Thereby, an online robust recognition and early warning model combining robust statistics and confidence interval is proposed to detect outliers. The threshold [Formula: see text] is set based on the derived confidence interval [Formula: see text] and the scale estimator [Formula: see text] (derived from the location M-estimator). Monitoring data obtained from a gravity dam and a rockfill dam were taken as examples to demonstrate the robust recognition and early warning model. The results show that the proposed method can effectively improve the reliability of anomaly recognition and early warnings, which is valuable in engineering applications.

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Section: Robust Statisticsmentioning

confidence: 99%

An online anomaly recognition and early warning model for dam safety monitoring data

Lü

et al. 2019

Structural Health Monitoring

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“…To solve the problem of missing judgment in identifying data sequences with more outliers using the LSR method, robust estimators [23][24][25] were introduced into the model construction of dam safety monitoring anomaly recognition and the setting of early warning thresholds. Touati et al coupled variance component estimation with robust estimation to process outliers in dam data to obtain reliable recognition.…”

Section: Introductionmentioning

confidence: 99%

A self-matching model for online anomaly recognition of safety monitoring data in dams

Zhang

Lü

et al. 2022

Structural Health Monitoring

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The online anomaly recognition of real-time dam safety monitoring data, such as deformation and seepage data from the automatic sensing instruments (e.g., the osmometer and the multi-point displacement meter), has the premise of ensuring data reliability, and it is also one of the core functional modules of online dam safety monitoring. To compensate for the limitation of a single method to identify outliers and further improve the reliability and the rapidity of the anomaly recognition of dam safety monitoring data, a self-matching model based on data-types for online anomaly recognition (SMM) was proposed in this paper. Based on a detailed classification of dam safety monitoring data sequences, this article describes a comparison and analysis of the applicability of a statistical regression model based on the least-squares regression (LSR) model and the online robust recognition and early warning (RREW) model for different datatype sequences. For the single-step-type sequences and normal-type sequences with low fitting accuracy, which could not be completely identified by the two models above, an improved cloud model recognition method based on the diurnal variation rate (ICM) was proposed to compensate for the limitations. Finally, the SMM was determined, that is, the LSR model was used for the multi-point-outlier-type and normal-type sequences with high fitting accuracy, the RREW model method was used for the double-step-type and oscillatory-type sequences, and the ICM method was used for the single-step-type sequences and normal-type sequences with low fitting accuracy. The engineering application of the Dadu River Basin showed that this method effectively solved the problems of low calculation efficiency and a 2% misjudgment rate when using the RREW model alone, and this method greatly improved the accuracy and timeliness of the anomaly recognition of dam safety monitoring data, so it had important theoretical significance and engineering application value.

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Kinetic study of an in situ inhibited polymerization using robust M-statistics

Cited by 2 publications

References 27 publications

An online anomaly recognition and early warning model for dam safety monitoring data

An online anomaly recognition and early warning model for dam safety monitoring data

A self-matching model for online anomaly recognition of safety monitoring data in dams

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