The Experts below are selected from a list of 64329 Experts worldwide ranked by ideXlab platform
M J Fuente - One of the best experts on this subject based on the ideXlab platform.
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fault detection based on Time Series Modeling and multivariate statistical process control
Chemometrics and Intelligent Laboratory Systems, 2018Co-Authors: A Sanchezfernandez, Francisco Javie Alda, G I Sainzpalmero, Jose Manuel Enitez, M J FuenteAbstract:Abstract Monitoring complex industrial plants is a very important task in order to ensure the management, reliability, safety and maintenance of the desired product quality. Early detection of abnormal events allows actions to prevent more serious consequences, improve the system's performance and reduce manufacturing costs. In this work, a new methodology for fault detection is introduced, based on Time Series models and statistical process control (MSPC). The proposal explicitly accounts for both dynamic and non-linearity properties of the system. A dynamic feature selection is carried out to interpret the dynamic relations by characterizing the auto- and cross-correlations for every variable. After that, a Time-Series based model framework is used to obtain and validate the best descriptive model of the plant (either linear o non-linear). Fault detection is based on finding anomalies in the temporal residual signals obtained from the models by univariate and multivariate statistical process control charts. Finally, the performance of the method is validated on two benchmarks, a wastewater treatment plant and the Tennessee Eastman Plant. A comparison with other classical methods clearly demonstrates the over performance and feasibility of the proposed monitoring scheme.
Alan Colman - One of the best experts on this subject based on the ideXlab platform.
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an approach to software reliability prediction based on Time Series Modeling
Journal of Systems and Software, 2013Co-Authors: Ayman Amin, Lars Grunske, Alan ColmanAbstract:Reliability is the key factor for software system quality. Several models have been introduced to estimate and predict reliability based on results of software testing activities. Software Reliability Growth Models (SRGMs) are considered the most commonly used to achieve this goal. Over the past decades, many researchers have discussed SRGMs' assumptions, applicability, and predictability. They have concluded that SRGMs have many shortcomings related to their unrealistic assumptions, environment-dependent applicability, and questionable predictability. Several approaches based on non-parametric statistics, Bayesian networks, and machine learning methods have been proposed in the literature. Based on their theoretical nature, however, they cannot completely address the SRGMs' limitations. Consequently, addressing these shortcomings is still a very crucial task in order to provide reliable software systems. This paper presents a well-established prediction approach based on Time Series ARIMA (Autoregressive Integrated Moving Average) Modeling as an alternative solution to address the SRGMs' limitations and provide more accurate reliability prediction. Using real-life data sets on software failures, the accuracy of the proposed approach is evaluated and compared to popular existing approaches.
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an automated approach to forecasting qos attributes based on linear and non linear Time Series Modeling
Automated Software Engineering, 2012Co-Authors: Ayman Amin, Lars Grunske, Alan ColmanAbstract:Predicting future values of Quality of Service (QoS) attributes can assist in the control of software intensive systems by preventing QoS violations before they happen. Currently, many approaches prefer Autoregressive Integrated Moving Average (ARIMA) models for this task, and assume the QoS attributes' behavior can be linearly modeled. However, the analysis of real QoS datasets shows that they are characterized by a highly dynamic and mostly nonlinear behavior to the extent that existing ARIMA models cannot guarantee accurate QoS forecasting, which can introduce crucial problems such as proactively triggering unrequired adaptations and thus leading to follow-up failures and increased costs. To address this limitation, we propose an automated forecasting approach that integrates linear and nonlinear Time Series models and automatically, without human intervention, selects and constructs the best suitable forecasting model to fit the QoS attributes' dynamic behavior. Using real-world QoS datasets of 800 web services we evaluate the applicability, accuracy, and performance aspects of the proposed approach, and results show that the approach outperforms the popular existing ARIMA models and improves the forecasting accuracy by on average 35.4%.
A Sanchezfernandez - One of the best experts on this subject based on the ideXlab platform.
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fault detection based on Time Series Modeling and multivariate statistical process control
Chemometrics and Intelligent Laboratory Systems, 2018Co-Authors: A Sanchezfernandez, Francisco Javie Alda, G I Sainzpalmero, Jose Manuel Enitez, M J FuenteAbstract:Abstract Monitoring complex industrial plants is a very important task in order to ensure the management, reliability, safety and maintenance of the desired product quality. Early detection of abnormal events allows actions to prevent more serious consequences, improve the system's performance and reduce manufacturing costs. In this work, a new methodology for fault detection is introduced, based on Time Series models and statistical process control (MSPC). The proposal explicitly accounts for both dynamic and non-linearity properties of the system. A dynamic feature selection is carried out to interpret the dynamic relations by characterizing the auto- and cross-correlations for every variable. After that, a Time-Series based model framework is used to obtain and validate the best descriptive model of the plant (either linear o non-linear). Fault detection is based on finding anomalies in the temporal residual signals obtained from the models by univariate and multivariate statistical process control charts. Finally, the performance of the method is validated on two benchmarks, a wastewater treatment plant and the Tennessee Eastman Plant. A comparison with other classical methods clearly demonstrates the over performance and feasibility of the proposed monitoring scheme.
Ayman Amin - One of the best experts on this subject based on the ideXlab platform.
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an approach to software reliability prediction based on Time Series Modeling
Journal of Systems and Software, 2013Co-Authors: Ayman Amin, Lars Grunske, Alan ColmanAbstract:Reliability is the key factor for software system quality. Several models have been introduced to estimate and predict reliability based on results of software testing activities. Software Reliability Growth Models (SRGMs) are considered the most commonly used to achieve this goal. Over the past decades, many researchers have discussed SRGMs' assumptions, applicability, and predictability. They have concluded that SRGMs have many shortcomings related to their unrealistic assumptions, environment-dependent applicability, and questionable predictability. Several approaches based on non-parametric statistics, Bayesian networks, and machine learning methods have been proposed in the literature. Based on their theoretical nature, however, they cannot completely address the SRGMs' limitations. Consequently, addressing these shortcomings is still a very crucial task in order to provide reliable software systems. This paper presents a well-established prediction approach based on Time Series ARIMA (Autoregressive Integrated Moving Average) Modeling as an alternative solution to address the SRGMs' limitations and provide more accurate reliability prediction. Using real-life data sets on software failures, the accuracy of the proposed approach is evaluated and compared to popular existing approaches.
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an automated approach to forecasting qos attributes based on linear and non linear Time Series Modeling
Automated Software Engineering, 2012Co-Authors: Ayman Amin, Lars Grunske, Alan ColmanAbstract:Predicting future values of Quality of Service (QoS) attributes can assist in the control of software intensive systems by preventing QoS violations before they happen. Currently, many approaches prefer Autoregressive Integrated Moving Average (ARIMA) models for this task, and assume the QoS attributes' behavior can be linearly modeled. However, the analysis of real QoS datasets shows that they are characterized by a highly dynamic and mostly nonlinear behavior to the extent that existing ARIMA models cannot guarantee accurate QoS forecasting, which can introduce crucial problems such as proactively triggering unrequired adaptations and thus leading to follow-up failures and increased costs. To address this limitation, we propose an automated forecasting approach that integrates linear and nonlinear Time Series models and automatically, without human intervention, selects and constructs the best suitable forecasting model to fit the QoS attributes' dynamic behavior. Using real-world QoS datasets of 800 web services we evaluate the applicability, accuracy, and performance aspects of the proposed approach, and results show that the approach outperforms the popular existing ARIMA models and improves the forecasting accuracy by on average 35.4%.
G I Sainzpalmero - One of the best experts on this subject based on the ideXlab platform.
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fault detection based on Time Series Modeling and multivariate statistical process control
Chemometrics and Intelligent Laboratory Systems, 2018Co-Authors: A Sanchezfernandez, Francisco Javie Alda, G I Sainzpalmero, Jose Manuel Enitez, M J FuenteAbstract:Abstract Monitoring complex industrial plants is a very important task in order to ensure the management, reliability, safety and maintenance of the desired product quality. Early detection of abnormal events allows actions to prevent more serious consequences, improve the system's performance and reduce manufacturing costs. In this work, a new methodology for fault detection is introduced, based on Time Series models and statistical process control (MSPC). The proposal explicitly accounts for both dynamic and non-linearity properties of the system. A dynamic feature selection is carried out to interpret the dynamic relations by characterizing the auto- and cross-correlations for every variable. After that, a Time-Series based model framework is used to obtain and validate the best descriptive model of the plant (either linear o non-linear). Fault detection is based on finding anomalies in the temporal residual signals obtained from the models by univariate and multivariate statistical process control charts. Finally, the performance of the method is validated on two benchmarks, a wastewater treatment plant and the Tennessee Eastman Plant. A comparison with other classical methods clearly demonstrates the over performance and feasibility of the proposed monitoring scheme.
