The Experts below are selected from a list of 32220 Experts worldwide ranked by ideXlab platform
Okyay Kaynak - One of the best experts on this subject based on the ideXlab platform.
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Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis
IEEE Transactions on Industrial Electronics, 2015Co-Authors: Shen Yin, Xiangping Zhu, Okyay KaynakAbstract:Standard partial least squares (PLS) serves as a powerful tool for key performance indicator (KPI) monitoring in large-scale Process industry for last two decades. However, the standard approach and its recent modifications still encounter some problems for fault diagnosis related to KPI of the underlying Process. To cope with these difficulties, an improved PLS (IPLS) approach is presented in this paper. IPLS is able to decompose the Measurable Process variables into the KPI-related and unrelated parts, respectively. Based on it, the corresponding test statistics are designed to offer meaningful fault diagnosis information and thus, the corresponding maintenance actions can be further taken to ensure the desired performance of the systems. In order to demonstrate the effectiveness of the proposed approach, a numerical example and Tennessee Eastman (TE) benchmark Process are respectively utilized. It can be seen that the proposed approach shows satisfactory results not only for diagnosing KPI-related faults but also for its high fault detection rate.
Shen Yin - One of the best experts on this subject based on the ideXlab platform.
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Key performance indicator related fault detection based on modified KRR algorithm
2017 36th Chinese Control Conference (CCC), 2017Co-Authors: Wei Sun, Shen Yin, Jianfang Jiao, Pengxing Guo, Guang Wang, Chengyuan SunAbstract:In the handling of the nonlinear systems, Kernel Ridge Regression (KRR) has recently served as an effective method to deal with multicollinearity problem, but it has not been used to solve the problem of fault diagnosis problems. KRR is unable to ensure the safety and the reliability of industrial systems. While previous fault detection method still encounters some problems for key performance indicator (KPI) related fault diagnosis of the underlying Process. In order to compensate for these drawbacks, this paper proposes a new KPI-related fault detection algorithm, named Modified KRR (MKRR). MKRR can decompose accurately the Measurable Process variables into the KPI-related and KPI-unrelated parts and use corresponding test statistics to monitor them. The method can offer good performance in industrial systems. To prove the effectiveness of the proposed method, a nonlinear numerical example is used. The simulation results show that the proposed method performs better than traditional KPLS in KPI-related fault detection.
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Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis
IEEE Transactions on Industrial Electronics, 2015Co-Authors: Shen Yin, Xiangping Zhu, Okyay KaynakAbstract:Standard partial least squares (PLS) serves as a powerful tool for key performance indicator (KPI) monitoring in large-scale Process industry for last two decades. However, the standard approach and its recent modifications still encounter some problems for fault diagnosis related to KPI of the underlying Process. To cope with these difficulties, an improved PLS (IPLS) approach is presented in this paper. IPLS is able to decompose the Measurable Process variables into the KPI-related and unrelated parts, respectively. Based on it, the corresponding test statistics are designed to offer meaningful fault diagnosis information and thus, the corresponding maintenance actions can be further taken to ensure the desired performance of the systems. In order to demonstrate the effectiveness of the proposed approach, a numerical example and Tennessee Eastman (TE) benchmark Process are respectively utilized. It can be seen that the proposed approach shows satisfactory results not only for diagnosing KPI-related faults but also for its high fault detection rate.
Wolfgang Marquardt - One of the best experts on this subject based on the ideXlab platform.
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Detection of multivariable trends in measured Process quantities
Journal of Process Control, 2006Co-Authors: Folko Flehmig, Wolfgang MarquardtAbstract:Abstract This paper addresses the detection of a trend of a multivariable Process. In particular, many (Measurable) Process quantities are considered simultaneously to identify a polynomial trend of a given order. A novel method is suggested to detect such trends in noisy measurements. The start time and the duration of the multivariable trend are computed by means of an efficient multi-scale algorithm. The method can be employed in different contexts. For example, a multivariable trend with a given duration and an endpoint at current time can be determined in on-line applications, or an off-line search for multiple multivariable trends with arbitrary start times and durations in a historic archive of plant measurements can be carried out.
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Monitoring Trends of UnMeasurable Process Quantities
IFAC Proceedings Volumes, 2001Co-Authors: Folko Flehmig, Wolfgang MarquardtAbstract:Abstract In this paper, a methodology is suggested for analyzing the behavior of a chemical Process with respect to the extent in which trends in unMeasurable Process quantities can be identified through recognition of trends in Measurable Process quantities. The analysis is based on a linear approximation of the nonlinear dynamics. It is shown how the suggested methodology can be employed in order to design a trend monitoring system for the task of inferring trends in unMeasurable Process quantities from measurement trends. One key question for the successful design of such a system is the selection of appropriate measurements for which trend monitoring is performed on-line.
Hajo A. Reijers - One of the best experts on this subject based on the ideXlab platform.
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Transforming unstructured natural language descriptions into Measurable Process performance indicators using Hidden Markov Models
Information Systems, 2017Co-Authors: Jh Han Van Der Aa, Henrik Leopold, Adela Del-río-ortega, Manuel Resinas, Hajo A. ReijersAbstract:Monitoring Process performance is an important means for organizations to identify opportunities to improve their operations. The definition of suitable Process Performance Indicators (PPIs) is a crucial task in this regard. Because PPIs need to be in line with strategic business objectives, the formulation of PPIs is a managerial concern. Managers typically start out to provide relevant indicators in the form of natural language PPI descriptions. Therefore, considerable time and effort have to be invested to transform these descriptions into PPI definitions that can actually be monitored. This work presents an approach that automates this task. The presented approach transforms an unstructured natural language PPI description into a structured notation that is aligned with the implementation underlying a business Process. To do so, we combine Hidden Markov Models and semantic matching techniques. A quantitative evaluation on the basis of a data collection obtained from practice demonstrates that our approach works accurately. Therefore, it represents a viable automated alternative to an otherwise laborious manual endeavor.
Xiangping Zhu - One of the best experts on this subject based on the ideXlab platform.
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Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis
IEEE Transactions on Industrial Electronics, 2015Co-Authors: Shen Yin, Xiangping Zhu, Okyay KaynakAbstract:Standard partial least squares (PLS) serves as a powerful tool for key performance indicator (KPI) monitoring in large-scale Process industry for last two decades. However, the standard approach and its recent modifications still encounter some problems for fault diagnosis related to KPI of the underlying Process. To cope with these difficulties, an improved PLS (IPLS) approach is presented in this paper. IPLS is able to decompose the Measurable Process variables into the KPI-related and unrelated parts, respectively. Based on it, the corresponding test statistics are designed to offer meaningful fault diagnosis information and thus, the corresponding maintenance actions can be further taken to ensure the desired performance of the systems. In order to demonstrate the effectiveness of the proposed approach, a numerical example and Tennessee Eastman (TE) benchmark Process are respectively utilized. It can be seen that the proposed approach shows satisfactory results not only for diagnosing KPI-related faults but also for its high fault detection rate.