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An Analysis of the Performance of the SPRT Chart with Estimated parameters Under the Weibull Distribution 1

12th International Conference on Robotics Vision Signal processing and Power Applications

作者： Teoh, Jing Wei Teoh, Wei L. EI-Ghandour, Laila Chong, Zhi Lin Teh, Sin Yin Heriot Watt Univ Malaysia Putrajaya Malaysia Heriot Watt Univ Edinburgh Midlothian Scotland Univ Tunku Abdul Rahman Kampar Kampar Perak Malaysia Univ Sains Malaysia George Town Malaysia

ISBN: (数字)9789819990054

ISBN: (纸本)9789819990078;9789819990054;9789819990047

Most developments of the sequential probability ratio test (SPRT) control chart assume that the underlying process comes from a Normal distribution with known mean and standard deviation. Nevertheless, the true values of the process parameters are usually inaccessible in production settings, and they must be approximated from a set of Phase-I data. In certain areas, the process data can be positively skewed, which in turn affect the performance of control charts designed under the Normal distribution. In this paper, we provide a thorough analysis on the performances of the SPRT chart with estimated process parameters under the influence of Weibull distributed data. The unconditional properties of the expected value and standard deviation of the time to signal are evaluated using Monte Carlo simulation to facilitate comparisons between the Normal and Weibull distributions. Results show that both the in-control and out-of-control performances of the SPRT chart deteriorate when Weibull data are used. However, the optimal design of the SPRT chart with estimated process parameters seems to reverse the effect for large process mean shifts.

关键词： Average time to signal process parameter estimation Sequential probability ratio test Statistical process control Weibull distribution

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Optimal estimation and Feedforward Control of Strip-Longitudinal Hardness for Thickness Hunting Suppression of Tandem Cold Mill process 21st

Optimal Estimation and Feedforward Control of Strip-Longitud...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Lee, Hoyong Hur, Seungmin Woo, Kyungsik Lee, Dongjun Seoul Natl Univ Dept Mech & Aerosp Engn Seoul South Korea Seoul Natl Univ IAMD Seoul South Korea Hyundai Steel Co R&D Ctr Chungcheongnam Do South Korea

Following the recent trend of weight reduction in car industry, producing high quality cold-rolled AHSS (advanced high-strength steel) strip becomes important. Thickness hunting (or fluctuation) problem can be more prominent for this cold-rolled AHSS strip making, which can stem from the non-uniformity of hot-rolled strip and can severely degrade product quality. In this paper, we propose a novel framework to estimate the strip-longitudinal hardness of the TCM (tandem cold mill) process and its feedforward control to substantially reduce the thickness hunting, while fully incorporating the interconnected nature and sensing sparsity of the TCM process. In particular, our estimator consists of the following two complementary loops: 1) fast real-time hardness estimation loop, which optimally fuses the process model and sensing information;and 2) slower constant process-parameter estimation loop via optimization utilizing the nonlinear process model and (stored/measured) sensor data. Efficacy of the proposed estimation and control frameworks are then validated with high-precision TCM process physics simulator. Copyright (C) 2020 The Authors.

关键词： Feedforward control process parameter estimation sensor fusion steel manufacturing process unscented transformation

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Optimal estimation and Feedforward Control of Strip-Longitudinal Hardness for Thickness Hunting Suppression of Tandem Cold Mill process ⁎

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IFAC-PapersOnLine 2020年第2期53卷 11988-11995页

作者： Hoyong Lee Seungmin Hur Kyungsik Woo Dongjun Lee Department of Mechanical & Aerospace Engineering and IAMD Seoul National University Seoul Korea Hyundai Steel Company R&D Center Chungcheongnam-do Korea

Following the recent trend of weight reduction in car industry, producing high quality cold-rolled AHSS (advanced high-strength steel) strip becomes important. Thickness hunting (or fluctuation) problem can be more prominent for this cold-rolled AHSS strip making, which can stem from the non-uniformity of hot-rolled strip and can severely degrade product quality. In this paper, we propose a novel framework to estimate the strip-longitudinal hardness of the TCM (tandem cold mill) process and its feedforward control to substantially reduce the thickness hunting, while fully incorporating the interconnected nature and sensing sparsity of the TCM process. In particular, our estimator consists of the following two complementary loops: 1) fast real-time hardness estimation loop, which optimally fuses the process model and sensing information; and 2) slower constant process-parameter estimation loop via optimization utilizing the nonlinear process model and (stored/measured) sensor data. Efficacy of the proposed estimation and control frameworks are then validated with high-precision TCM process physics simulator.

关键词： Feedforward control process parameter estimation sensor fusion steel manufacturing process unscented transformation

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Plant Wide Oil and Gas Separation Plant Optimisation using Response Surface Methodology

Plant Wide Oil and Gas Separation Plant Optimisation using R...

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3rd IFAC Workshop on Automatic Control in Offshore Oil and Gas Production (OOGP)

作者： Andreasen, Anders Rasmussen, Kasper Ronn Mando, Matthias Ramboll Energy Studies & FEED Bavnehojvej 5 DK-6700 Esbjerg Denmark Aalborg Univ Dept Energy Technol Pontoppidanstr 101 DK-9220 Aalborg Denmark Juel & Kroyer Maintenance Engn Toldbodvej 1 DK-6700 Esbjerg Denmark

In this paper we will demonstrate Response Surface Methodology (RSM) applied to process simulation of an offshore oil and gas separation plant. By performing surrogate experiments according to Design of Experiment (DoE) and subsequent construction of multiple linear regression models for the chosen responses, the overall separation process is optimised in terms of power consumption under the constraints of quality specification of gas and oil export, respectively. The fluid treated in the separation plant is rich in NGL (Natural Gas Liquids) which causes challenges in meeting the export specifications. Further, the NGL causes increased condensate recycle in the compression system thereby increasing power consumption for the compressors as well as increased cooling for heat exchangers. Offshore the NGL is difficult to dispose, which makes fractionation unattractive and the NGL must be exported via the oil (partly stabilised) or via the gas (rich gas) or a combination of the two. Effectively, this violates the export specifications and the NGL must be extracted in on-shore facilities either at the oil or gas receiving facilities. By exploring different options, applying RSM, it is found that in terms of overall power consumption export of NGL with the gaseous products is more effective that exporting the NGL with the partly stabilised oil. The methodology used in the present paper can also be applied to existing production facilities as a generic optimisation tool. Due to the simplicity of the regression models mimicking the separation plant they can be coupled with the overall process control for potential on-line plant optimisation. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Industrial Production Systems Optimization problems Model reduction process Simulators process parameter estimation Regression Analysis Statistical design

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Optimal design of exponentially weighted moving average- (X)over-bar chart for the mean with estimated process parameters

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2017年第22期46卷 11077-11090页

作者： You, H. W. Khoo, Michael B. C. Lee, M. H. Castagliola, P. Saha, S. Univ Sains Malaysia Sch Math Sci George Town 11800 Malaysia Swinburne Univ Technol Fac Engn Comp & Sci Sarawak Campus Kuching Sarawak Malaysia Univ Nantes LUNAM Univ Nantes France IRCCyN UMR CNRS 6597 Nantes France

This paper proposes an optimal design of the exponentially weighted moving average (EWMA)-(X) over bar chart with estimated process parameters in terms of average run length (ARL) and standard deviation of the run length (SDRL), using a Markov chain. An optimal design enables the EWMA-(X) over bar chart with estimated process parameters to be optimally designed by minimizing the out-of-control ARL, in order to overcome the current limitation, where only the EWMA-(X) over bar chart with known process parameters is optimally designed. With the proposed procedure, the EWMA-(X) over bar chart with estimated process parameters can be designed to have a similar in-control performance on average to its known process parameters counterpart.

关键词： Exponentially weighted moving average Individual measurements Markov chain Optimization design process parameter estimation Subgroup data

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Kernel latent features adaptive extraction and selection method for multi-component non-stationary signal of industrial mechanical device

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NEUROCOMPUTING 2016年 216卷 296-309页

作者： Tang, Jian Liu, Zhuo Zhang, Jian Wu, Zhiwei Chai, Tianyou Yu, Wen Nanjing Univ Informat Sci & Technol Jiangsu Engn Ctr Network Monitoring Nanjing 210044 Peoples R China Northeaster Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China NUIST Sch Comp & Software Nanjing 210044 Jiangsu Peoples R China Beijing Univ Technol Coll Elect & Control Engn Beijing 100124 Peoples R China CINVESTAV IPN Dept Control Automat Av IPN 2508 Mexico City 07360 DF Mexico

Heavy key mechanical devices relate to production quality and quantity of complex industrial process directly. It is necessary to estimate some difficulty-to-measure process parameters inside these devices. Multi-component and non-stationary mechanical signals, such as vibration and acoustic ones, are always employed to model these process parameters indirectly. How to effective extract and select interesting information from these signals is the key step to build effective soft sensor model. In this paper, a new kernel latent features adaptive extraction and selection method is proposed. Ensemble empirical mode decomposition (EEMD) is used to decompose these mechanical signals into multiple time scales sub signals with different physical interpretations. These sub-signals are transformed to frequency spectra, and then kernel partial least squares (KPLS) algorithm is used to extract their kernel features. Integrated with mutual information (MI)-based feature selection method, a new define index is exploited to select the important sub-signals and their latent features adaptively. The shell vibration and acoustic signals of an experimental laboratory-scale ball mill in the mineral grinding process are used to validate the proposed approach. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Industrial mechanical device Multi-component non-stationary mechanical signal process parameter estimation Ensemble empirical mode decomposition (EEMD) Kernel partial least squares (KPLS) Feature extraction and feature selection

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Model-Predictive-Control (MPC) of Steam Trap Subcool in Steam-Assisted Gravity Drainage (SAGD) 9

Model-Predictive-Control (MPC) of Steam Trap Subcool in Stea...

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9th IFAC Symposium on Advanced Control of Chemical processes ADCHEM 2015

作者： Purkayastha, Sagar N. Gates, Ian D. Trifkovic, Milana Univ Calgary Calgary AB T2N 1N4 Canada

For thermal technologies for heavy oil and oil sand reservoir extraction, such as cyclic steam stimulation and steam-assisted gravity drainage (SAGD), suboptimal steam conformance leads to recovery factors between 25-50%. Although preliminary research using Proportional-Integral-Derivative (PID)) control in SAGD) operations has proved beneficial towards steam conformance, PID) control is responsive only to deviation from set-points and lacks constraint-handling capabilities. This results in suboptimal actuation signals that are sometimes unattainable. This paper summarizes research on a Model-Predictive-Controller (MPC) with proactive adjustments of steam injection rate. The steam injection rate was determined based on recursive parameter updates of a suitable tit Le varying dynamic model describing the implicit relationship between the subcool temperature difference and the input heat rate, to achieve optimal steam conformance. Furthermore, the steam injection rate was constrained such that the pressure with which the steam impinged on the formation, called well bottom hole pressure (BHP), was below the formation fracture pressure of 4500 kPa at all times. The real time control study was made possible by establishing a bidirectional communication link between the Computer Modelling Group (CMG) STARS (TM) and MATLAB/Simulink software. The three-dimensional heterogeneous reservoir model, developed in STARS (TM) acted as a virtual plant, and the MPC, developed in MATLAB/Simulink, acted as an onsite controller. Results show 35.7% improvement in oil recovery and a more efficient cumulative steam-to-oil ratio (cSOR) profile in comparison to the base case of steam injection at a constant BHP of 4000 kPa. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Automatic process control (closed loop) Predictive control Set-point control SISO process models process parameter estimation Recursive estimation Recursive least squares Constrained parameters Steam-Assisted Gravity Drainage

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Model-Predictive-Control (MPC) of Steam Trap Subcool in Steam-Assisted Gravity Drainage (SAGD)

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IFAC-PapersOnLine 2015年第8期48卷 539-544页

作者： Sagar N. Purkayastha Ian D. Gates Milana Trifkovic University of Calgary Calgary AB T2N 1N4 Canada

For thermal technologies for heavy oil and oil sand reservoir extraction, such as cyclic steam stimulation and steam-assisted gravity drainage (SAGD), suboptimal steam conformance leads to recovery factors between 25-50%. Although preliminary research using Proportional-Integral-Derivative (PID) control in SAGD operations has proved beneficial towards steam conformance, PID control is responsive only to deviation from set-points and lacks constraint-handling capabilities. This results in suboptimal actuation signals that are sometimes unattainable. This paper summarizes research on a Model-Predictive-Controller (MPC) with proactive adjustments of steam injection rate. The steam injection rate was determined based on recursive parameter updates of a suitable time varying dynamic model describing the implicit relationship between the subcool temperature difference and the input heat rate, to achieve optimal steam conformance. Furthermore, the steam injection rate was constrained such that the pressure with which the steam impinged on the formation, called well bottom hole pressure (BHP), was below the formation fracture pressure of 4500 kPa at all times. The real time control study was made possible by establishing a bidirectional communication link between the Computer Modelling Group (CMG) STARS™, and MATLAB/Simulink software. The three-dimensional heterogeneous reservoir model, developed in STARS™ acted as a virtual plant and the MPC, developed in MATLAB/Simulink, acted as an onsite controller. Results show 35.7% improvement in oil recovery and a more efficient cumulative steam-to-oil ratio (cSOR) profile in comparison to the base case of steam injection at a constant BHP of 4000 kPa.

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Multi response Optimization of Induction Hardening process -a New Approach

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IFAC Proceedings Volumes 2014年第1期47卷 862-869页

作者： Mohan K Misra Bishakh Bhattacharya Onkar Singh A Chatterjee Ph D scholar GB Technical University Lucknow India Dept. of Mechanical Engineering Indian Institute of Technology Kanpur India Dept. of Mechanical Engineering HBTI Kanpur India

In this present paper, an investigation has been performed to find out the combination of input process parameters for induction hardening of AISI 1045 steel component based on desirability function. To enhance the mechanical properties of steel components using induction hardening process, quality responses like effective case depth (ECD) and hardness values are analysed for different combinations of medium frequency power, feed rate, quench pressure and temperature in a selected range. The experimental trials are conducted, based on the design matrix obtained from the rotatable central composite design (CCD), with the help of an induction hardening station equipped with a 150 kW power converter. A significant regression model is also developed to predict these quality responses using response surface methodology (RSM). To establish desirability index in a multi-response process like induction hardening, a new approach has been devised. This approach i.e. selection of both, heating and quenching parameters proved significant, which is considered as one of the major contribution of presented research work. A non destructive evaluation technique, based on dynamic response, is also developed which will further combine with optimization strategy to develop an innovative quality assurance system for industrial induction hardening process.

关键词： Induction hardening Multi response Optimization Desirability process parameter estimation Doppler Vibrometry Experimental Modal Analysis Analysis of variance

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Model Predictive Control of Cavity Pressure in an Injection Moulding process

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IFAC Proceedings Volumes 2014年第3期47卷 4358-4363页

作者： M. Reiter S. Stemmler Ch. Hopmann A. Ressmann D. Abel Institute of Automatic Control RWTH Aachen University Aachen Germany Institute of Plastics Processing (IKV) at RWTH Aachen University

Cavity pressure control is a means of improving repeatability and product quality in injection moulding processes. As the system behaviour is greatly dependent on the mould - which is interchangeable and typically designed and manufactured independently of the machine's control system - challenges arise in designing a controller that yields high performance and robustness to be suitable for universal use. A cavity pressure controller intended to be used for a wide variety of moulds will likely need some form of reparametrisation. In order to gain user acceptance, the process of manual or automatic parametrisation of the controller to a new mould needs to be simple enough to be performed and understood by staff that are not necessarily control experts. Addressing this issue, the authors suggest an approach using a Model Predictive Controller that is based on a physically motivated grey-box model. The model is simple enough to be intuitively checked for plausibility but sophisticated enough to reproduce the dominant behaviour of the system. For automated parametrisation, a strategy based on two experiments is suggested. The experiments are tailored to be suitable for incorporation into the regular production process. The concept is presented and first experimental results are shown.

关键词： Model-based control Predictive control Pressure control Injection moulding Physical Models Closed-loop identification process parameter estimation Production systems Self-adjusting sytems Self-optimizing systems

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