The Experts below are selected from a list of 267 Experts worldwide ranked by ideXlab platform
Peter Avitabile - One of the best experts on this subject based on the ideXlab platform.
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Using digital image correlation and three dimensional point tracking in conjunction with real time Operating Data expansion techniques to predict full-field dynamic strain
2014Co-Authors: Peter Avitabile, Javad Baqersad, Christopher NiezreckiAbstract:Large structures pose unique difficulties in the acquisition of measured dynamic Data with conventional techniques that are further complicated when the structure also has rotating members such as wind turbine blades and helicopter blades. Optical techniques (digital image correlation and dynamic point tracking) are used to measure line of sight Data without the need to contact the structure, eliminating cumbersome cabling issues. The Data acquired from these optical approaches are used in conjunction with a unique real time Operating Data expansion process to obtain full-field dynamic displacement and dynamic strain. The measurement approaches are described in this paper along with the expansion procedures. The Data is collected for a single blade from a wind turbine and also for a three bladed assembled wind turbine configuration. Measured strains are compared to results from a limited set of optical measurements used to perform the expansion to obtain full-field strain results including locations that are not available from the line of sight measurements acquired. The success of the approach clearly shows that there are some very extraordinary possibilities that exist to provide very desperately needed full field displacement and strain information that can be used to help identify the structural health of structures.Large structures pose unique difficulties in the acquisition of measured dynamic Data with conventional techniques that are further complicated when the structure also has rotating members such as wind turbine blades and helicopter blades. Optical techniques (digital image correlation and dynamic point tracking) are used to measure line of sight Data without the need to contact the structure, eliminating cumbersome cabling issues. The Data acquired from these optical approaches are used in conjunction with a unique real time Operating Data expansion process to obtain full-field dynamic displacement and dynamic strain. The measurement approaches are described in this paper along with the expansion procedures. The Data is collected for a single blade from a wind turbine and also for a three bladed assembled wind turbine configuration. Measured strains are compared to results from a limited set of optical measurements used to perform the expansion to obtain full-field strain results including locations that ...
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using high speed stereophotogrammetry techniques to extract shape information from wind turbine rotor Operating Data
2012Co-Authors: Troy Lundstrom, Javad Baqersad, Christopher Niezrecki, Peter AvitabileAbstract:Stereophotogrammetry techniques used in concert with 3D point tracking (dynamic photogrammetry) software are advantageous for the collection of Operating Data on large wind turbines (or helicopter rotors) over conventional accelerometer-Data acquisition systems (DAQ) for several reasons. First, this is a non-contacting technique that doesn’t require the use of mounted accelerometers and electrically noisy slip rings. Second, the optical targets (measurement points) that are mounted to the blade surfaces can remain in place for long periods of time and be used for subsequent measurements without extended/overly complicated setup time. Third, deflection Data can be collected on many more points on a turbine/rotor surface beyond what is capable of a conventional multi-channel Data acquisition system and accelerometer setup. Operating Data has previously been collected on a 1.17 m Southwest Windpower Air BreezeTM wind turbine [1] using Stereophotogrammetry and this Data has been used to extract Operating deflection shapes from the structure. The purpose of this work is to improve upon the experimental methods used on the 1.17 turbine by Warren [2] and apply these improved methods to a larger, 2.56 m diameter turbine/rotor analog, and collect Operating Data on the structure. This Data was collected outdoors and shape information was extracted from this Operating Data and compared to that taken with a standard, impact test.
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Expansion of transient Operating Data
Mechanical Systems and Signal Processing, 2012Co-Authors: Christopher Chipman, Peter AvitabileAbstract:Abstract Real time Operating Data is very important to understand actual system response. Unfortunately, the amount of physical Data points typically collected is very small and often interpretation of the Data is difficult. Expansion techniques have been developed using traditional experimental modal Data to augment this limited set of Data. This expansion process allows for a much improved description of the real time Operating response. This paper presents the results from several different structures to show the robustness of the technique. Comparisons are made to a more complete set of measured Data to validate the approach. Both analytical simulations and actual experimental Data are used to illustrate the usefulness of the technique.
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Using High-Speed Stereophotogrammetry Techniques to Extract Shape Information from Wind Turbine/Rotor Operating Data
Topics in Modal Analysis II Volume 6, 2012Co-Authors: Troy Lundstrom, Javad Baqersad, Christopher Niezrecki, Peter AvitabileAbstract:Stereophotogrammetry techniques used in concert with 3D point tracking (dynamic photogrammetry) software are advantageous for the collection of Operating Data on large wind turbines (or helicopter rotors) over conventional accelerometer-Data acquisition systems (DAQ) for several reasons. First, this is a non-contacting technique that doesn’t require the use of mounted accelerometers and electrically noisy slip rings. Second, the optical targets (measurement points) that are mounted to the blade surfaces can remain in place for long periods of time and be used for subsequent measurements without extended/overly complicated setup time. Third, deflection Data can be collected on many more points on a turbine/rotor surface beyond what is capable of a conventional multi-channel Data acquisition system and accelerometer setup. Operating Data has previously been collected on a 1.17 m Southwest Windpower Air BreezeTM wind turbine [1] using Stereophotogrammetry and this Data has been used to extract Operating deflection shapes from the structure. The purpose of this work is to improve upon the experimental methods used on the 1.17 turbine by Warren [2] and apply these improved methods to a larger, 2.56 m diameter turbine/rotor analog, and collect Operating Data on the structure. This Data was collected outdoors and shape information was extracted from this Operating Data and compared to that taken with a standard, impact test.
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Assessment of modal and Operating Data for computer application
2002Co-Authors: Peter Avitabile, Hiromichi Tsuji, Stephen Graf, James NevilleAbstract:Components of massive storage devices are exposed to severe vibration environments during stress screening tests. As a result, some response levels on large cards are of concern. To understand the dynamic behavior of these boards, both modal Data and Operating Data was collected for several configurations. Modal testing was performed using both single and multiple input excitations. Operating Data was acquired at several Operating levels through the use of the pneumatically excited stress screen vibration table. Extracted modal Data and Operating Data were evaluated and compared. At low force Operating levels, good agreement exists between the modal and Operating Data. As the levels are increased, the agreement for several modes remains basically unchanged whereas other modes show dramatic change. Discussion of the results and possible reasons for discrepancy are discussed.
Biao Huang - One of the best experts on this subject based on the ideXlab platform.
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Data quality assessment of routine Operating Data for process identification
Computers & Chemical Engineering, 2013Co-Authors: Yuri A W Shardt, Biao HuangAbstract:Abstract In many chemical engineering plants, process identification is often performed de novo each time that it is needed. However, it is quite possible that sufficiently excited Data regions, including routine Operating regions, have already been collected and are available for identifying particular model structures. Therefore, there is a need to develop techniques for extracting these regions from the other uninformative regions. One potential approach to solving this problem is to consider the condition number of the Fisher information matrix for the desired model structure. The sensitivity of this approach to changes in sampling time, model structure, controller type, and number of Data points is also examined. It is shown, through theoretical and simulation analysis that the proposed method determines Data quality based on the situation. Practically, the proposed method can be used to determine the upper bound for the process model order that may be identified from the given Data.
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technical communique closed loop identification condition for armax models using routine Operating Data
Automatica, 2011Co-Authors: Yuri A W Shardt, Biao HuangAbstract:In industrial process control engineering, using routine Operating Data obtained from closed-loop operation of a process for model identification would be extremely valuable in applications such as control performance monitoring, root cause diagnosis, and controller retuning. However, the conditions for closed-loop identifiability using routine Operating Data are still largely unknown or untried. In this paper, criteria for closed-loop identification of an autoregressive, moving average process with exogenous input (ARMAX) regulated with an arbitrary, rational, polynomial controller are derived. The theoretical criteria that are developed for the closed-loop identification of an ARMAX process are compared with Monte Carlo simulations and previous theoretical results. It is shown that the newly-proposed theoretical results are in agreement with the simulation results.
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Closed-Loop Identification using Routine Operating Data: the Effect of Time Delay
IFAC Proceedings Volumes, 2011Co-Authors: Yuri A W Shardt, Biao HuangAbstract:Abstract In industry, in order to store the reams of Data that are collected from all the different flow, level, and temperature sensors, the fast-sampled Data are very often downsampled before being stored in a Data historian. This downsampled or even compressed Data are, then, used by process engineers to recover the appropriate process parameters. However, little has been written about the effects of the sampling on the quality of the model obtained. Therefore, in this paper, the effects of sampling time are investigated from both a theoretical and practical perspective using results that come out of the theory of closed-loop system identification with routine Operating Data. It is shown that, if the ratio between the time delay and sampling time is sufficiently large, then it is possible to recover the true system parameters. The most common industrial processes that fulfill this constraint are temperature control loops. On the other hand, for processes, such as flows, pressures, or levels, with almost no time delay, then the sampling time must be extremely small in order to identify the process parameters. These results suggest that the sampling time has an important bearing on the quality of the model estimated from routine Operating Data. Using an experimental set-up with a heated tank, the effect of time delay on the identification of the true continuous time parameters was considered for different sampling times. It was shown that increasing the sampling time above a given threshold resulted in identifying an incorrect model.
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Closed-loop identification with routine Operating Data: Effect of time delay and sampling time ☆
Journal of Process Control, 2011Co-Authors: Yuri A W Shardt, Biao HuangAbstract:Abstract In industry, in order to store the reams of Data that are collected from all the different flow, level, and temperature sensors, the fast-sampled Data is very often downsampled before being stored in a Data historian. This downsampled or even compressed Data is, then, often used by process engineers to recover the appropriate process parameters. However, little has been written about the effects of the sampling on the quality of the model obtained. Therefore, in this paper, the effects of sampling time are investigated from both a theoretical and practical perspective using results that come out of the theory of closed-loop system identification with routine Operating Data. It is shown that if the discrete time delay in a process is sufficiently large or the sampling time is sufficiently small, then it is possible to recover the true system parameters. The most common industrial processes that fulfill this constraint are temperature control loops. These results suggest that the sampling time has an important bearing on the quality of the model estimated from routine Operating Data. Using both Monte Carlo simulations and an experimental set-up with a heated tank, the effect of discrete time delay on the identification of the true continuous time parameters was considered for different sampling times. It was shown that increasing the sampling time above a given threshold resulted in identifying an incorrect model. As well, the models obtained using a PID controller were less sensitive to sampling time than those obtained using a PI controller. However, the PID controller values were more sensitive to the effects of aliasing at larger sampling times.
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Conditions for Identifiability Using Routine Operating Data for a First-Order ARX Process Regulated by a Lead-Lag Controller
IFAC Proceedings Volumes, 2010Co-Authors: Yuri A W Shardt, Biao HuangAbstract:Abstract The conditions for closed-loop identifiability using routine Operating Data are largely unknown. In this paper, the closed-loop identifiability conditions for a first-order autoregressive process with exogenous input (ARX) that is regulated using a 3-parameter lead-lag controller and that has no external excitation will be examined using an analytical approach. Despite the convoluted nature of the intermediate results, the final conditions for absolute identification of the stable region can be concisely stated. These results suggest that the class of internal model controllers (IMCs) can, despite their aggressive behaviour, successfully identify an ARX model without any external excitation. As well, Monte Carlo simulations performed using MATLAB confirmed the analytical results that were obtained. Future work in this area can focus on extending the results to other model structures, to other types of controllers, and to higher order processes.
Yuri A W Shardt - One of the best experts on this subject based on the ideXlab platform.
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Data quality assessment of routine Operating Data for process identification
Computers & Chemical Engineering, 2013Co-Authors: Yuri A W Shardt, Biao HuangAbstract:Abstract In many chemical engineering plants, process identification is often performed de novo each time that it is needed. However, it is quite possible that sufficiently excited Data regions, including routine Operating regions, have already been collected and are available for identifying particular model structures. Therefore, there is a need to develop techniques for extracting these regions from the other uninformative regions. One potential approach to solving this problem is to consider the condition number of the Fisher information matrix for the desired model structure. The sensitivity of this approach to changes in sampling time, model structure, controller type, and number of Data points is also examined. It is shown, through theoretical and simulation analysis that the proposed method determines Data quality based on the situation. Practically, the proposed method can be used to determine the upper bound for the process model order that may be identified from the given Data.
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technical communique closed loop identification condition for armax models using routine Operating Data
Automatica, 2011Co-Authors: Yuri A W Shardt, Biao HuangAbstract:In industrial process control engineering, using routine Operating Data obtained from closed-loop operation of a process for model identification would be extremely valuable in applications such as control performance monitoring, root cause diagnosis, and controller retuning. However, the conditions for closed-loop identifiability using routine Operating Data are still largely unknown or untried. In this paper, criteria for closed-loop identification of an autoregressive, moving average process with exogenous input (ARMAX) regulated with an arbitrary, rational, polynomial controller are derived. The theoretical criteria that are developed for the closed-loop identification of an ARMAX process are compared with Monte Carlo simulations and previous theoretical results. It is shown that the newly-proposed theoretical results are in agreement with the simulation results.
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Closed-Loop Identification using Routine Operating Data: the Effect of Time Delay
IFAC Proceedings Volumes, 2011Co-Authors: Yuri A W Shardt, Biao HuangAbstract:Abstract In industry, in order to store the reams of Data that are collected from all the different flow, level, and temperature sensors, the fast-sampled Data are very often downsampled before being stored in a Data historian. This downsampled or even compressed Data are, then, used by process engineers to recover the appropriate process parameters. However, little has been written about the effects of the sampling on the quality of the model obtained. Therefore, in this paper, the effects of sampling time are investigated from both a theoretical and practical perspective using results that come out of the theory of closed-loop system identification with routine Operating Data. It is shown that, if the ratio between the time delay and sampling time is sufficiently large, then it is possible to recover the true system parameters. The most common industrial processes that fulfill this constraint are temperature control loops. On the other hand, for processes, such as flows, pressures, or levels, with almost no time delay, then the sampling time must be extremely small in order to identify the process parameters. These results suggest that the sampling time has an important bearing on the quality of the model estimated from routine Operating Data. Using an experimental set-up with a heated tank, the effect of time delay on the identification of the true continuous time parameters was considered for different sampling times. It was shown that increasing the sampling time above a given threshold resulted in identifying an incorrect model.
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Closed-loop identification with routine Operating Data: Effect of time delay and sampling time ☆
Journal of Process Control, 2011Co-Authors: Yuri A W Shardt, Biao HuangAbstract:Abstract In industry, in order to store the reams of Data that are collected from all the different flow, level, and temperature sensors, the fast-sampled Data is very often downsampled before being stored in a Data historian. This downsampled or even compressed Data is, then, often used by process engineers to recover the appropriate process parameters. However, little has been written about the effects of the sampling on the quality of the model obtained. Therefore, in this paper, the effects of sampling time are investigated from both a theoretical and practical perspective using results that come out of the theory of closed-loop system identification with routine Operating Data. It is shown that if the discrete time delay in a process is sufficiently large or the sampling time is sufficiently small, then it is possible to recover the true system parameters. The most common industrial processes that fulfill this constraint are temperature control loops. These results suggest that the sampling time has an important bearing on the quality of the model estimated from routine Operating Data. Using both Monte Carlo simulations and an experimental set-up with a heated tank, the effect of discrete time delay on the identification of the true continuous time parameters was considered for different sampling times. It was shown that increasing the sampling time above a given threshold resulted in identifying an incorrect model. As well, the models obtained using a PID controller were less sensitive to sampling time than those obtained using a PI controller. However, the PID controller values were more sensitive to the effects of aliasing at larger sampling times.
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Conditions for Identifiability Using Routine Operating Data for a First-Order ARX Process Regulated by a Lead-Lag Controller
IFAC Proceedings Volumes, 2010Co-Authors: Yuri A W Shardt, Biao HuangAbstract:Abstract The conditions for closed-loop identifiability using routine Operating Data are largely unknown. In this paper, the closed-loop identifiability conditions for a first-order autoregressive process with exogenous input (ARX) that is regulated using a 3-parameter lead-lag controller and that has no external excitation will be examined using an analytical approach. Despite the convoluted nature of the intermediate results, the final conditions for absolute identification of the stable region can be concisely stated. These results suggest that the class of internal model controllers (IMCs) can, despite their aggressive behaviour, successfully identify an ARX model without any external excitation. As well, Monte Carlo simulations performed using MATLAB confirmed the analytical results that were obtained. Future work in this area can focus on extending the results to other model structures, to other types of controllers, and to higher order processes.
Shinya Ochiai - One of the best experts on this subject based on the ideXlab platform.
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Calculating process control parameters from steady state Operating Data
ISA Transactions, 1997Co-Authors: Shinya OchiaiAbstract:Abstract The tutorial paper will show that chemical process control parameters can be obtained from steady state Operating Data in combination with simple algebraic equations. The parameters include steady state process gain, feedforward control factor and approximate process time constant. Here, we treat ‘self-regulating’ processes first. Integrating processes and unstable processes are treated separately, unlike the standard method of setting up linear differential equations followed by Laplace transformations. With the standard method, a steady state portion of dynamic response, if there is any, appears as a part of equations of a complex variable. Many control engineers in the chemical industries who might have studied the method, do not use it because of mathematical difficulties. Instead, they may resort to plant tests that are often time consuming and costly. The parameters obtained by this paper's method will aid improvement of control systems. We will address the relation of this method with that of commercial software, which implement advanced process controls based on plant tests.
H.b. Karayaka - One of the best experts on this subject based on the ideXlab platform.
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synchronous generator model identification and parameter estimation from Operating Data
IEEE Transactions on Energy Conversion, 2003Co-Authors: H.b. Karayaka, Ali Keyhani, G T Heyd, L Agrawal, D SeliAbstract:A novel technique to estimate and model parameters of a 460-MVA large steam turbine generator from Operating Data is presented. First, Data from small excitation disturbances are used to estimate linear model armature circuit and field winding parameters of the machine. Subsequently, for each set of steady state Operating Data, saturable inductances L/sub ds/ and L/sub qs/ are identified and modeled using nonlinear mapping functions-based estimators. Using the estimates of the armature circuit parameters, for each set of disturbance Data collected at different Operating conditions, the rotor body parameters of the generator are estimated using an output error method (OEM). The developed nonlinear models are validated with measurements not used in the estimation procedure.
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Identification of armature, field, and saturated parameters of a large steam turbine-generator from Operating Data
IEEE Transactions on Energy Conversion, 2000Co-Authors: H.b. Karayaka, Ali Keyhani, B.l. Agrawal, D.a. Selin, G.t. HeydtAbstract:This paper presents a step by step identification procedure of armature, field and saturated parameters of a large steam turbine-generator from real time Operating Data. First, Data from a small excitation disturbance is utilized to estimate armature circuit parameters of the machine. Subsequently, for each set of steady state Operating Data, saturable mutual inductances L/sub ads/ and L/sub aqs/ are estimated. The recursive maximum likelihood estimation technique is employed for identification in these first two stages. An artificial neural network (ANN) based estimator is used to model these saturated inductances based on the generator Operating conditions. Finally, using the estimates of the armature circuit parameters, the field winding and some damper winding parameters are estimated using an output error method (OEM) of estimation. The developed models are validated with measurements not used in the training of ANN and with large disturbance responses.
