Outliers detection method of multiple measuring points of parameters in power plant units

Qi, Minfang; Fu, Zhongguang; Chen, Fei

doi:10.1016/j.applthermaleng.2015.04.008

Cited by 22 publications

(9 citation statements)

References 24 publications

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“…data gathered, the three time series are displayed in As Figure 1 aptly demonstrates, outliers accompanied our data collection. Outliers, those data that deviate significantly from the majority of observations, may be caused by different mechanisms in relation to normal data [21], [24]- [28]. These different mechanisms include, but are not limited to, factors such as sensor noise, process disturbance and instrument degradation.…”

Section: Methodsmentioning

confidence: 99%

“…This process, however, is hindered by the fact that the time series may present outliers. Johnson and Wichern [20] define an outlier as "an observation in a data set which appears to be inconsistent with the remainder of that set of data" [21]- [23], [25], [28]. Researches had been used different methodologies to detect the isolated and extremely peak values as PCA by [23], also using statistical procedures based on prior knowledge of the system that produces the data [21], [24]- [28].…”

Section: Introductionmentioning

confidence: 99%

“…Johnson and Wichern [20] define an outlier as "an observation in a data set which appears to be inconsistent with the remainder of that set of data" [21]- [23], [25], [28]. Researches had been used different methodologies to detect the isolated and extremely peak values as PCA by [23], also using statistical procedures based on prior knowledge of the system that produces the data [21], [24]- [28]. Others researches have been applied artificial intelligence and machine learning methodologies [22], [29]- [31].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Outliers and Imputing of Missing Values for Water Quality UV-VIS Absorbance Time Series

Plazas-Nossa

Angulo

Torres

2017

Ing.

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Context: The UV-Vis absorbance collection using online optical captors for water quality detection may yield outliers and/or missing values. Therefore, pre-processing to correct these anomalies is required to improve the analysis of monitoring data. The aim of this study is to propose a method to detect outliers as well as to fill-in the gaps in time series. Method: Outliers are detected using Winsorising procedure and the application of the Discrete Fourier Transform (DFT) and the Inverse of Fast Fourier Transform (IFFT) to complete the time series. Together, these tools were used to analyse a case study comprising three sites in Colombia ( (i) Bogotá D.C. Salitre-WWTP (Waste Water Treatment Plant), influent; (ii) Bogotá D.C. Gibraltar Pumping Station (GPS); and, (iii) Itagüí, San Fernando-WWTP, influent (Medellín metropolitan area) ) analysed via UV-Vis (Ultraviolet and Visible) spectra. Results: Outlier detection with the proposed method obtained promising results when window parameter values are small and self-similar, despite that the three time series exhibited different sizes and behaviours. The DFT allowed to process different length gaps having missing values. To assess the validity of the proposed method, continuous subsets (a section) of the absorbance time series without outlier or missing values were removed from the original time series obtaining an average 12 % error rate in the three testing time series. Conclusions: The application of the DFT and the IFFT using the 10 % most important harmonics of useful values, can be advantageous for its later use in different applications, specifically for time series of water quality and quantity in urban sewer systems. One potential application would be the analysis of dry weather affecting rainy seasons, a feature achieved by detecting values that correspond to unusual behaviour in a time series. Additionally, the results hint at the potential of the method in correcting other hydrologic time series.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection of Outliers and Imputing of Missing Values for Water Quality UV-VIS Absorbance Time Series

Plazas-Nossa

Angulo

Torres

2017

Ing.

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“…used robust regression analysis for structural health monitoring data which was capable of being unaffected by outliers. [10] used a modified Grubb's method for outlier detection based on the median and median absolute deviation applied in multiple measuring points parameters. indicated that the modified Grubb's method was more efficient and robust than the original Grubb's approach.…”

Section: Introductionmentioning

confidence: 99%

Detecting Outliers in the Simple Linear Regression for Children Affected with Leukemia in Mosul City

Thanoon¹,

Alrazzaq²

2022

Tikrit J. Pure Sci.

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In this study, the problem of outlier detection in “linear regression” analysis is studied using the “median” and “mean” “absolute deviation” about the median. “The mean and standard deviation” are heavily affected by outliers Hence, the outlier detection techniques based on these measures may not correctly identify all outliers in a dataset. However, “the mean absolute deviation about the median”, in combination with the median is sufficiently robust in the presence of outliers and provides a better alternative. The conceptualized method was tested using leukemia patients data and the results indicate that the new method performed better than the methods based on the mean/ standard deviation combination. It is recommended that the median and “mean absolute deviation” about the median be used in detecting outliers in regression analysis due to their inherent potential for increasing the” goodness-of-fit of the “linear regression mode

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“…Noticeably, it is observed that accurate deformation forecasting of a foundation surface based on the LSSVM approach typically relies on involving a significant amount of influencing factors derived from adjacent single point, which therefore results in large working load in the data collection stage. To overcome this challenge, this article further investigates the potentials of LSSVM in foundation pit displacements monitoring and prediction by integrating multi-point measuring techniques, 20 which considers multiple measuring points with close proximity as one measurement to offset the internal effects of adjacent single point. With the aims to elucidate the algorithms and parameters that are widely employed in previous LSSVM applications, and to eventually move toward formulating multi-point least squares support vector machine (MP-LSSVM) approach, this article presents the fundamentals of LSSVM in section “Overview of LSSVM” and elaborates on the proposed methodology in section “Methodology.” In section “Case study,” this article quantitatively evaluates the formulated MP-LSSVM approach against traditional single-point least squares support vector machine (SP-LSSVM) and finally concludes the key findings of implementing MP-LSSVM in foundation pit displacements monitoring and prediction in section “Discussion and conclusion.”…”

Section: Introductionmentioning

confidence: 99%

Foundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement

Liu

et al. 2018

Structural Health Monitoring

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Foundation pit displacement is a critical safety risk for both building structure and people lives. The accurate displacement monitoring and prediction of a deep foundation pit are essential to prevent potential risks at early construction stage. To achieve accurate prediction, machine learning methods are extensively applied to fulfill this purpose. However, these approaches, such as support vector machines, have limitations in terms of data processing efficiency and prediction accuracy. As an emerging approach derived from support vector machines, least squares support vector machine improve the data processing efficiency through better use of equality constraints in the least squares loss functions. However, the accuracy of this approach highly relies on the large volume of influencing factors from the measurement of adjacent critical points, which is not normally available during the construction process. To address this issue, this study proposes an improved least squares support vector machine algorithm based on multi-point measuring techniques, namely, multi-point least squares support vector machine. To evaluate the effectiveness of the proposed multi-point least squares support vector machine approach, a real case study project was selected, and the results illustrated that the multi-point least squares support vector machine approach on average outperformed single-point least squares support vector machine in terms of prediction accuracy during the foundation pit monitoring and prediction process.

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Outliers detection method of multiple measuring points of parameters in power plant units

Cited by 22 publications

References 24 publications

Detection of Outliers and Imputing of Missing Values for Water Quality UV-VIS Absorbance Time Series

Detection of Outliers and Imputing of Missing Values for Water Quality UV-VIS Absorbance Time Series

Detecting Outliers in the Simple Linear Regression for Children Affected with Leukemia in Mosul City

Foundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement

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