The Experts below are selected from a list of 18687 Experts worldwide ranked by ideXlab platform
Shen Yin - One of the best experts on this subject based on the ideXlab platform.
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Recent Advances in key-performance-indicator Oriented Prognosis and Diagnosis With a MATLAB Toolbox: DB-KIT
IEEE Transactions on Industrial Informatics, 2019Co-Authors: Yuchen Jiang, Shen YinAbstract:Process safety, system reliability, and product quality are becoming increasingly essential in the modern industry. As a result, prognosis and fault diagnosis of the complex systems have gained a substantial amount of research attention. In order to evaluate the influence of the detected faults to systems' behavior, there is a pressing need to design prognosis and diagnosis systems oriented to the key-performance-indicators (KPIs). Dedicated to this requirement, we have recently developed a MATLAB toolbox data based key-performance-indicator oriented fault detection toolbox (DB-KIT), which realizes a series of effective algorithms, to provide a systematic and illustrative material to the peer researchers. This paper investigates the recent advances in the multivariate statistical analysis based approaches. Formulations based on the optimization problems are proposed to better clarify the ideas behind different solutions and to study them in a unified data-driven framework. Theoretical fundamentals of some selected algorithms in the DB-KIT are elaborated. Moreover, new evaluation results on dataset defects are presented, which compare the algorithms' robustness and demonstrate the power of DB-KIT. The open-source code and the demonstrative simulations can be regarded as baseline and resources for innovation research, comparative studies, and educational purposes.
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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, Guang Wang, Jianfang Jiao, Pengxing Guo, 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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Recent results on key performance indicator oriented fault detection using the DB-KIT toolbox
IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society, 2017Co-Authors: Yuchen Jiang, Shen YinAbstract:A new MATLAB toolbox DB-KIT was recently developed for the design and implementation of fault diagnosis systems. For the purpose of key performance indicator (KPI) oriented fault detection, over the past few years, a series of test statistics and the corresponding thresholds were derived based on the modified data structures originating from the existing multivariate statistical analysis tools. These data-driven approaches are numerically reliable, efficient and of high fault detection performance. Especially, the false alarm rates (FARs) under KPI-unrelated fault scenarios are suppressed with great efforts, which is the central task of the KPI-oriented fault detection problem. DB-KIT was firstly introduced at the 2016 IEEE Industrial Electronics Conference, and the initial results on algorithm efficiency and fault detection performance were reported in Comparison of KPI related fault detection algorithms using a newly developed MATLAB toolbox: DB-KIT with simulation tests on the Tennessee Eastman Process benchmark. This paper reports more recent results on a widely used numerical test and on a close-loop configured three-stage hot rolling mill process to reveal the performance of the algorithms at the extreme faulty conditions, and demonstrates the performance under the plant-wide performance supervised framework.
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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.
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a novel scheme for key performance indicator prediction and diagnosis with application to an industrial hot strip mill
IEEE Transactions on Industrial Informatics, 2013Co-Authors: Steven X Ding, Shen Yin, Haiyang Hao, Kaixiang Peng, Bo ShenAbstract:In this paper, a data-driven scheme of key performance indicator (KPI) prediction and diagnosis is developed for complex industrial processes. For static processes, a KPI prediction and diagnosis approach is proposed in order to improve the prediction performance. In comparison with the standard partial least squares (PLS) method, the alternative approach significantly simplifies the computation procedure. By means of a data-driven realization of the so-called left coprime factorization (LCF) of a process, efficient KPI prediction, and diagnosis algorithms are developed for dynamic processes, respectively, with and without measurable KPIs. The proposed KPI prediction and diagnosis scheme is finally applied to an industrial hot strip mill, and the results demonstrate the effectiveness of the proposed scheme.
Kaixiang Peng - One of the best experts on this subject based on the ideXlab platform.
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a novel key performance indicator oriented hierarchical monitoring and propagation path identification framework for complex industrial processes
Isa Transactions, 2020Co-Authors: Jie Dong, Kaixiang PengAbstract:Abstract As the first protective layer for modern complex industrial processes, process monitoring and fault diagnosis (PM-FD) systems play a vital role in ensuring product quality, overall equipment effectiveness and process safety, which have recently become one of the hotspots both in academic research and practical application domains. Different from previous frameworks, this paper dedicates on industrial practices and theoretical methods for hierarchical monitoring and propagation path identification of key performance indicator (KPI) oriented faults in complex industrial processes, which can not only help field engineers to timely and purposefully keep track of the state of the process, but also help them to take appropriate remedial actions to remove the abnormal behaviors from the process. For these purposes, firstly, a new data-driven gap metric approach is proposed for monitoring KPI oriented faults in the block level. Then, Bayesian fusion is implemented to form monitoring decisions from the plant-wide level. After that, a neural network architecture-based Granger causality analysis method is developed for propagation path identification of KPI oriented faults. Finally, the proposed methods are validated in Tennessee Eastman process, where detailed simulation processes are presented and better performance is shown compared with the existing approaches.
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hierarchical monitoring and root cause diagnosis framework for key performance indicator related multiple faults in process industries
IEEE Transactions on Industrial Informatics, 2019Co-Authors: Jie Dong, Kaixiang Peng, Chuanfang ZhangAbstract:In actual production processes, the occurrence probability of multiple faults is much higher than that of a single fault, which will affect the process industry operating performance and final products quality. This paper is concerned with industrial practices and theoretical approaches for detection and location of key performance indicator (KPI) related multiple faults in process industries. First, a new KPI-related multiple fault monitoring scheme is addressed from the subprocess level based on the developed correlation-based canonical variable analysis model. Then, Bayesian fusion is implemented to form the final monitoring decisions from the plantwide level. After that, a tensor subspace analysis-based discriminant analysis method is proposed for locating the root causes, which will help field engineers to take correction actions and recover the process operations. Finally, the application to a typical industry process, i.e., hot strip mill process, is given to demonstrate the performance and effectiveness of the proposed methods with real industrial data.
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a comparison and evaluation of key performance indicator based multivariate statistics process monitoring approaches
Journal of Process Control, 2015Co-Authors: Kai Zhang, Haiyang Hao, Zhiwe Che, Steve X Ding, Kaixiang PengAbstract:Abstract In this paper, the key performance indicator (KPI)-based multivariate statistical process monitoring and fault diagnosis (PM-FD) methods for linear static processes are surveyed and evaluated using the multivariate statistics framework. Based on their computational characteristics, the possible methods will be broadly classified into three categories: direct, linear regression-based, and PLS-based. The three categories are respectively presented in the first part, then the comparison study in aspects of their interconnections, geometric properties, and computational costs are shown, and finally their performance for PM-FD of KPIs is evaluated using a new evaluation index called expected detection delay, where a numerical case and the Tennessee Eastman process are used to provide a demonstration of the evaluation result.
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a novel scheme for key performance indicator prediction and diagnosis with application to an industrial hot strip mill
IEEE Transactions on Industrial Informatics, 2013Co-Authors: Steven X Ding, Shen Yin, Haiyang Hao, Kaixiang Peng, Bo ShenAbstract:In this paper, a data-driven scheme of key performance indicator (KPI) prediction and diagnosis is developed for complex industrial processes. For static processes, a KPI prediction and diagnosis approach is proposed in order to improve the prediction performance. In comparison with the standard partial least squares (PLS) method, the alternative approach significantly simplifies the computation procedure. By means of a data-driven realization of the so-called left coprime factorization (LCF) of a process, efficient KPI prediction, and diagnosis algorithms are developed for dynamic processes, respectively, with and without measurable KPIs. The proposed KPI prediction and diagnosis scheme is finally applied to an industrial hot strip mill, and the results demonstrate the effectiveness of the proposed scheme.
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.
Ecky George - One of the best experts on this subject based on the ideXlab platform.
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assessment of steroid use as a key performance indicator in inflammatory bowel disease analysis of data from 2385 uk patients
Alimentary Pharmacology & Therapeutics, 2019Co-Authors: Christia P Selinge, Gareth Parkes, Ash Assi, Jimmy K Limdi, Hele Ludlow, Pritash Patel, Melissa A Smith, Santosh Saluke, Zandile Ndlovu, Ecky GeorgeAbstract:BACKGROUND Patients with IBD are at risk of excess corticosteroids. AIMS To assess steroid excess in a large IBD cohort and test associations with quality improvement and prescribing. METHODS Steroid exposure was recorded for outpatients attending 19 centres and associated factors analysed. Measures taken to avoid excess were assessed. RESULTS Of 2385 patients, 28% received steroids in the preceding 12 months. 14.8% had steroid excess or dependency. Steroid use was significantly lower at 'intervention centres' which participated in a quality improvement programme (exposure: 23.8% vs 31.0%, P < .001; excess 11.5% vs 17.1%, P < .001). At intervention centres, steroid use fell from 2015 to 2017 (steroid exposure 30.0%-23.8%, P = .003; steroid excess 13.8%-11.5%, P = .17). Steroid excess was judged avoidable in 50.7%. Factors independently associated with reduced steroid excess in Crohn's disease included maintenance with anti-TNF agents (OR 0.61 [95% CI 0.24-0.95]), treatment in a centre with a multi-disciplinary team (OR 0.54 [95% CI 0.20-0.86]) and treatment at an intervention centre (OR 0.72 [95% CI 0.46-0.97]). Treatment with 5-ASA in CD was associated with higher rates of steroid excess (OR 1.72 [95% CI 1.24-2.09]). In ulcerative colitis (UC), thiopurine monotherapy was associated with steroid excess (OR 1.97 [95% CI 1.19-3.01]) and treatment at an intervention centre with less steroid excess (OR 0.72 [95% CI 0.45-0.95]). CONCLUSIONS This study validates steroid assessment as a meaningful quality measure and provides a benchmark for this performance indicator in a large cohort. A programme of quality improvement was associated with lower steroid use.
Yuchen Jiang - One of the best experts on this subject based on the ideXlab platform.
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Recent Advances in key-performance-indicator Oriented Prognosis and Diagnosis With a MATLAB Toolbox: DB-KIT
IEEE Transactions on Industrial Informatics, 2019Co-Authors: Yuchen Jiang, Shen YinAbstract:Process safety, system reliability, and product quality are becoming increasingly essential in the modern industry. As a result, prognosis and fault diagnosis of the complex systems have gained a substantial amount of research attention. In order to evaluate the influence of the detected faults to systems' behavior, there is a pressing need to design prognosis and diagnosis systems oriented to the key-performance-indicators (KPIs). Dedicated to this requirement, we have recently developed a MATLAB toolbox data based key-performance-indicator oriented fault detection toolbox (DB-KIT), which realizes a series of effective algorithms, to provide a systematic and illustrative material to the peer researchers. This paper investigates the recent advances in the multivariate statistical analysis based approaches. Formulations based on the optimization problems are proposed to better clarify the ideas behind different solutions and to study them in a unified data-driven framework. Theoretical fundamentals of some selected algorithms in the DB-KIT are elaborated. Moreover, new evaluation results on dataset defects are presented, which compare the algorithms' robustness and demonstrate the power of DB-KIT. The open-source code and the demonstrative simulations can be regarded as baseline and resources for innovation research, comparative studies, and educational purposes.
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Recent results on key performance indicator oriented fault detection using the DB-KIT toolbox
IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society, 2017Co-Authors: Yuchen Jiang, Shen YinAbstract:A new MATLAB toolbox DB-KIT was recently developed for the design and implementation of fault diagnosis systems. For the purpose of key performance indicator (KPI) oriented fault detection, over the past few years, a series of test statistics and the corresponding thresholds were derived based on the modified data structures originating from the existing multivariate statistical analysis tools. These data-driven approaches are numerically reliable, efficient and of high fault detection performance. Especially, the false alarm rates (FARs) under KPI-unrelated fault scenarios are suppressed with great efforts, which is the central task of the KPI-oriented fault detection problem. DB-KIT was firstly introduced at the 2016 IEEE Industrial Electronics Conference, and the initial results on algorithm efficiency and fault detection performance were reported in Comparison of KPI related fault detection algorithms using a newly developed MATLAB toolbox: DB-KIT with simulation tests on the Tennessee Eastman Process benchmark. This paper reports more recent results on a widely used numerical test and on a close-loop configured three-stage hot rolling mill process to reveal the performance of the algorithms at the extreme faulty conditions, and demonstrates the performance under the plant-wide performance supervised framework.
