Source: http://www.google.de/patents/US6772036
Timestamp: 2013-05-25 18:00:41
Document Index: 517801043

Matched Legal Cases: ['art 2', 'art 3', 'art 3', 'art 2', 'art 2', 'art 2', 'art 4', 'art 4']

Patent US6772036 - Control system using process model - Google PatenteSuche Bilder Maps Play YouTube News Gmail Drive Mehr » Erweiterte Patentsuche | Webprotokoll | Anmelden Erweiterte Patentsuche PatenteA control system is provided for controlling a process for making paper or paper pulp. The process has a process product output at an end of the process. The controls include a process variable sensor input configured to receive a process variable related to the process. The controller is configured...http://www.google.de/patents/US6772036?utm_source=gb-gplus-sharePatent US6772036 - Control system using process model Ver�ffentlichungsnummerUS6772036 B2PublikationstypErteilung Anmeldenummer09/943,333 Ver�ffentlichungsdatum3. Aug. 2004Eingetragen30. Aug. 2001 Priorit�tsdatum30. Aug. 2001Auch ver�ffentlicht unterUS20030045962 ErfinderEvren EryurekKadir KavakliogluUrspr�nglich Bevollm�chtigterFisher-Rosemount Systems, Inc.Rosemount Inc. US-Klassifikation700/127162/49162/262162/252700/128162/238Internationale KlassifikationG05B17/02G05B13/02D21H23/78D21G9/00G05B13/04 UnternehmensklassifikationD21G9/0027D21H23/78G05B13/042G05B17/02G05B13/029 Europ�ische KlassifikationD21G 9/00B4D21H 23/78G05B 13/02C5G05B 13/04BG05B 17/02ReferenzenPatentzitate (108)Nichtpatentzitate (129) Referenziert von (15)Externe LinksUSPTO USPTO-Zuordnung EspacenetControl system using process modelUS 6772036 B2 Zusammenfassung A control system is provided for controlling a process for making paper or paper pulp. The process has a process product output at an end of the process. The controls include a process variable sensor input configured to receive a process variable related to the process. The controller is configured to provide a control signal to control the process. A process model has a model product output which is a model representation of the process product output. The model process output is a function of the sensed process variable and a product output setpoint representative of a desired process product output. The control signal is a function of the product output setpoint and the modeled product output.
What is claimed is: 1. A control system for controlling a process for making paper or paper pulp having a process product output at an end of the process, comprising:
a process variable sensor input configured to receive a plurality of process variables related to the process for making paper or pulp paper sensed by a process variable sensor which provides a process variable output, the plurality of process variables measured at a plurality of locations along this process; a control element output configured to provide a control signal to a control element which controls the process in response to the control signal; a process model having a model product quality output comprising an inferred measurement of product quality output, the model product quality output comprising a model representation of the process product output in response to the sensed process variables, the process model based upon current process setpoints and current process control algorithms which are directly included in the process model; a product output quality setpoint representative of a desired process product output quality; a controller configured to compare the product output setpoint to the modeled product output and responsively provide the control signal to the control element; and wherein the process variable sensor input couples to a two-wire process control loop. 2. The control system of claim 1 wherein the model comprises a plurality of rules which model the process.
3. The control system of claim 1 wherein the model comprises a neural network which models the process.
4. The control system of claim 1 wherein the model comprises fuzzy logic which models the process.
5. The control system of claim 1 wherein the process variable sensor is in a first two-wire control loop, the control element is in a second two-wire control loop which is down stream from the first two-wire control loop, and wherein the controller corrects for process variations in the first two-wire control loop by adjusting the control element to achieve a desired product quality in the process product output.
6. The control system of claim 1 wherein the controller provides feed forward control as a function of the model product output.
7. The control system of claim 1 wherein the model is implemented in a microprocessor based system.
8. The control system of claim 1 wherein the model is implemented in a process device in physical proximity to the process variable sensor.
9. The control system of claim 1 wherein the process model is coupled to the process variable sensor over a control loop which operates in accordance with Fieldbus.
10. The control system of claim 1 wherein the model is a function of an observed process history.
11. A method for controlling a process for making paper or paper pulp, the process of the type having a process product output at the end of the process, comprising:
obtaining a plurality of process variables related to the process from a two-wire process control loop, the plurality of process variables measured at a plurality of locations along the process; modeling the process product output quality, the modeled process product output quality comprising an inferred measurement of product quality as a function of at least the obtained process variables, the process model based upon current process setpoints and current process control algorithms which are directly included in the process model; obtaining a setpoint related to a desired process product output quality; and generating a control signal as a function of the modeled process product output quality and the setpoint.
12. The method of claim 11 wherein the modeling is based upon a plurality of rules.
13. The method of claim 11 wherein the modeling is based upon a neural network.
14. The method of claim 11 wherein the modeling is based upon fuzzy logic.
15. The method of claim 11 wherein the control signal is generated to correct for potential future deviations in the process product output.
16. The method of claim 11 wherein the modeling is a function of an observed process history.
17. The method of claim 11 including adjusting the process setpoints and control algorithms based upon the modeled process product output quality.
18. The control system of claim 1 wherein the controller coanges the process setpoints and control algorithms based upon the model product output quality.
BACKGROUND OF THE INVENTION The present invention relates to industrial process control. More specifically, the present invention relates to controlling processes which are used to manufacture paper or paper pulp.
In a control system, including controls systems which are used to control paper and paper pulp manufacturing, the state of a process is monitored by measuring �process variables.� A process variable refers to a variable which is related to the condition of the process such as a flow rate, a level, a temperature, a pressure, etc. Based upon the measured process variable, a controller operates a control element in order to bring the process variable within a target range. For example, in order to achieve a desired flow rate (process variable) a controller adjusts a valve (control element) accordingly.
One type of process control observes the quality of the product output to adjust the various process variables within the process. The quality measurement is a process variable which is measured at the output of the process. It can be difficult to control paper and pulp manufacturing processes using this technique because there is both a physical distance between the process output and a significant lag time due to the time it takes for a product to move through the process. The physical separation and the separation in time can make controlling the process to achieve a desired quality difficult.
SUMMARY OF THE INVENTION A control system is provided for controlling a process for making paper or paper pulp. The process has a process product output at an end of the process. The controls include a process variable sensor input configured to receive a process variable related to the process. The controller is configured to provide a control signal to control the process. A process model has a model product output which is a model representation of the process product output. The model process output is a function of the sensed process variable and a product output setpoint representative of a desired process product output. The control signal is a function of the product output setpoint and the modeled product output.
BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a simplified diagram showing a portion of a paper manufacturing process.
FIG. 2 is a simplified block diagram of a control system in accordance with one embodiment of the invention.
FIG. 3 is a simplified block diagram showing a microprocessor used to implement the control system of FIG. 2.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Typical process control techniques in paper or paper pulp manufacturing rely on the feedback of the product quality to adjust the physical setpoints of the process variables within the manufacturing process. Such measurements are made at the output of the process. Although there are other process variable sensors located at various points along the process, they are typically only used for controlling an immediate process variable, such as a level, to thereby regulate the setpoints for a physical parameter as determined by a process control loop.
With this configuration, it is difficult to control product quality which is typically measured at the output of the process. The process output may be hundreds of feet away from the rest of the process and corrections can only be made after the fact. Any product output which is out of specification is discarded. Further, due to the dead time associated with the disparate locations between sensors and actuators, the control system cannot tolerate high gain in the control loop. With the present invention, a model is used to model the output of the process based upon one or more process variables measured at one or more locations along the process. The output of the model can be indicative of a quality measurement which related to the quality of the output product which will be obtained if the current process setpoints or control algorithms are not changed. Thus, the model is used to provide an inferred measurement related to a modeled product output. Based upon this inferred measurement related to the �quality� of the process output, the up stream process setpoints or control algorithms can be adjusted accordingly. In a more advanced embodiment, the information from the model can be used to coordinate distributed control algorithms which communicate on local process control loops in which control elements are controlled based upon local surrogate measurements.
The present invention can reduce the dead time between the control element and the surrogate or inferred measurements such that increased loop gain can be used in the control algorithms to improve the performance of the control loop without destabilizing the process. Further, a deviation in the product quality can be detected prior to actually producing the product and the control algorithms or setpoints can be adjusted to correct and compensate for the variation before the product exits the process. Integration of feed forward control, integrated sensors and inferred measurements with this feedback control technique provide improved control of the process and the quality of the output.
FIG. 1 is a simplified diagram showing one aspect of a paper sheet fabrication process. In this simplified embodiment, a vacuum pump 12 is coupled to a reservoir 14 through an adjusting valve 16. A receiving chamber 18 couples to the reservoir 14 through a fast acting valve 20 and is configured to receive a supply of paper sheets 22 through a manufacturing process. A drain valve 24 is configured to drain the reservoir 18. A pressure sensor 30, flow sensor 32 and temperature sensor 34 are configured to measure the pressure, flow rate and temperature associated with chamber 18. Information from sensors 30, 32 and 34 are provided to control system 40 which operates in accordance with the present invention.
In operation, control system 40 has at least one process variable input configured to receive a process variable from a process variable sensor such as sensors 30, 32 or 34. Sensors 30, 32 and 34 can be process variable transmitters coupled to a process control loop such as a two wire process control loop. As discussed below, control system 40 includes a process model and controls the quality of the paper produce manufactured by the process based upon an output from the model and a setpoint. This control is achieved by controlling a control element such as drain 24, fast acting valve 20 or adjusting valve 16. In the process illustrated in FIG. 1, paper sheets are received in receiving chamber 18 and are dried through the operation of drain 24 and vacuum supplied by vacuum pump 12. Excess liquid is collected in reservoir 14. The particular setpoints of the process used to dry the paper 22 can detrimentally effect the quality of the paper produced by the process.
Pursuant to one embodiment of the invention, FIG. 2 is a simplified block diagram of control system 40 used, for example, in the process illustrated in FIG. 1. The control system can couple to process devices (sensors or control elements) through one or more process control loops 41. Example control loops include two-wire loops such as 4-20 mA loops, loops in accordance with the Fieldbus standards, HART standards and others. Control system 40 includes a controller 60 which receives a setpoint 62 and provides a control signal output to a control element, such as one of the control elements illustrated in FIG. 1. In accordance with the present invention, control system 40 includes a process model 70 which receives a process variable sensor input 72 and provides a modeled product output 74 to controller 60. As discussed above, controller 60 controls one or more control elements in the process based upon the modeled product output 74 and the setpoint 62. The controller 60 can operate using any control algorithm including neural networks, regressive learning techniques, fuzzy logic, rules, any type of proportional integral and/or derivative control, etc. The present invention is not limited to the particular algorithm or technique implemented in a controller which is used to process the modeled product output. Controller 60 can receive other setpoints 76 and one or more process variables 78 and the control signal output 64 can be a function of these additional inputs. Model 70 can provide the modeled product output 74 as a function of other process signals 80. Examples of other process signals include other process variables, process setpoints, control signals, information related to the type or operation of a particular process sensor or control element, date or time information or any other information related to the operation of the process. The model can be preconfigured or can be generated using learning techniques such as that provided through the use of a neural network.
FIG. 3 is a simplified block diagram of control system 40 showing one example physical configuration of the control system. In the example of FIG. 3, control system 40 utilizes a microprocessor 100. Microprocessor 100 is configured to receive setpoint 62 which can be stored in memory, such as memory 102. Memory 102 can also contain permanent and/or temporary values such as used for programming or for storing program instructions or temporary storage. The process variable input can be through any type of physical layer such as through standard communication protocols. Example protocols include two-wire based protocols such as HART or Fieldbus. However, the control system can receive the process variable information, or other data, using any appropriate technique. Similarly, the control signal output can be implemented in any type of hardware including hardware which operates in accordance with process control industry standards. Example standards include two-wire based standards such as HART or Fieldbus. However, the control signal output 64 can be in accordance with any appropriate technique.
Although the present invention has been described with reference to preferred embodiments, workers skilled in the art will recognize that changes may be made in form and detail without departing from the spirit and scope of the invention. The present invention can be implemented in process devices located in the field, or in process devices or controls located at a remote location such as a control room or distant facility. Any appropriate hardware and/or software can implement the present invention including personal computers, microprocessors at various locations or configurations, etc. The invention can be implemented in any appropriate process device 40 including the illustrated control system or other devices which have access to process variables such as transmitters or controllers. The device can be at a remote location or in close physical proximity to a process variable sensor.
Patentzitate Zitiertes PatentEingetragen Ver�ffentlichungsdatum Antragsteller TitelUS309643428. Nov. 19612. Juli 1963Daniel Orifice Fitting CompanyMultiple integration flow computerUS340426419. Juli 19651. Okt. 1968American Meter Company, IncorporatedTelemetering system for determining rate of flowUS346816426. Aug. 196623. Sept. 1969Westinghouse Electric Corp.Open thermocouple detection apparatusUS35903709. Apr. 196929. Juni 1971Leeds & Northrup Co.Method and apparatus for detecting the open-circuit condition of a thermocouple by sending a pulse through the thermocouple and a reactive element in seriesUS361859211. Aug. 19699. Nov. 1971John Stewart Simpson StewartDiagnostic patient monitorUS368819025. Sept. 197029. Aug. 1972Rosemount Inc.Differential capacitance circuitry for differential pressure measuring instrumentsUS36918428. Sept. 197019. Sept. 1972Beckman Instruments Inc.Differential pressure transducerUS370128018. M�rz 197031. Okt. 1972Daniel Ind. Inc.Method and apparatus for determining the supercompressibility factor of natural gasUS384963722. Mai 197319. Nov. 1974Combustion Eng Inc,UsReactor megawatt demand setterUS38558581. Aug. 197324. Dez. 1974Cushing, Vincent J.Self synchronous noise rejection circuit for fluid velocity meterUS395275914. Aug. 197427. Apr. 1976M & J Valve CompanyLiquid line break control system and methodUS397318427. Jan. 19753. Aug. 1976Leeds & Northrup CompanyThermocouple circuit detector for simultaneous analog trend recording and analog to digital conversionUS40589758. Dez. 197522. Nov. 1977General Electric CompanyGas turbine temperature sensor validation apparatus and methodUS409941313. Juni 197711. Juli 1978Yokogawa Electric Works, Ltd.Thermal noise thermometerUS410219926. Aug. 197625. Juli 1978Megasystems, Inc.RTD measurement systemUS41227198. Juli 197731. Okt. 1978Environmental Systems CorporationSystem for accurate measurement of temperatureUS424916414. Mai 19793. Febr. 1981Tivy; Vincent V.Flow meterUS425049019. Jan. 197910. Febr. 1981Rosemount Inc.Two wire transmitter for converting a varying signal from a remote reactance sensor to a DC current signalUS427901331. Okt. 197914. Juli 1981The Valeron CorporationMachine process controllerUS433751626. Juni 198029. Juni 1982United Technologies CorporationSensor fault detection by activity monitoringUS43998245. Okt. 198123. Aug. 1983Air-Shields, Inc.Apparatus for detecting probe dislodgementUS44173128. Juni 198122. Nov. 1983Worcester Controls CorporationElectronic controller for valve actuatorsUS451746830. Apr. 198414. Mai 1985Westinghouse Electric Corp.Diagnostic system and methodUS452886927. Okt. 198016. Juli 1985Toyota Jidosha Kogyo Kabushiki KaishaAutomatic transmission for vehiclesUS453023430. Juni 198323. Juli 1985Mobil Oil CorporationMethod and system for measuring properties of fluidsUS454046826. Sept. 198310. Sept. 1985Board Of Trustees Of The University Of MaineMethod for determining the degree of completion and pulp yieldUS457168920. Okt. 198218. Febr. 1986The United States Of America As Represented By The Secretary Of The Air ForceMultiple thermocouple testing deviceUS463521429. Juni 19846. Jan. 1987Fujitsu LimitedFailure diagnostic processing systemUS464278231. Juli 198410. Febr. 1987Westinghouse Electric Corp.Rule based diagnostic system with dynamic alteration capabilityUS464447931. Juli 198417. Febr. 1987Westinghouse Electric Corp.Diagnostic apparatusUS46495151. Juli 198610. M�rz 1987Westinghouse Electric Corp.Methods and apparatus for system fault diagnosis and controlUS466847323. Aug. 198526. Mai 1987The Babcock & Wilcox CompanyControl system for ethylene polymerization reactorUS470779613. Aug. 198617. Nov. 1987Calabro; John A.Reliability and maintainability indicatorUS472080629. M�rz 198519. Jan. 1988Barmag AgMethod and apparaus for centrally collecting measured valuesUS473636722. Dez. 19865. Apr. 1988Chrysler Motors CorporationSmart control and sensor devices single wire bus multiplex systemUS473676326. Febr. 198712. Apr. 1988Air Force, United States Of America, The, As Represented By The Secretary Of TheAutomatic device for the detection and shutoff of unwanted liquid flow in pipesUS47583085. M�rz 198519. Juli 1988Carr; Wayne F.System for monitoring contaminants with a detector in a paper pulp streamUS47775853. Febr. 198611. Okt. 1988Hitachi, Ltd.Analogical inference method and apparatus for a control systemUS480715111. Apr. 198621. Febr. 1989Purdue Research FoundationElectrical technique for correcting bridge type mass air flow rate sensor errors resulting from ambient temperature variationsUS481899422. Okt. 19874. Apr. 1989Rosemount Inc.Transmitter with internal serial busUS483156422. Okt. 198716. Mai 1989Suga Test Instruments Co., Ltd.Apparatus for estimating and displaying remainder of lifetime of xenon lampsUS48412868. Febr. 198820. Juni 1989Honeywell Inc.Apparatus and method for detection of an open thermocouple in a process control networkUS48536938. Mai 19871. Aug. 1989Eaton-Williams; Raymond H.Air condition monitor unit for monitoring at least one variable of the ambient airUS487365521. Aug. 198710. Okt. 1989Board Of Regents, The University Of Texas SystemSensor conditioning method and apparatusUS490716730. Sept. 19876. M�rz 1990E. I. Du Pont De Nemours And CompanyProcess control system with action loggingUS492441823. Aug. 19888. Mai 1990Dickey-John CorporationUniversal monitorUS49341962. Juni 198919. Juni 1990Micro Motion, Inc.Coriolis mass flow rate meter having a substantially increased noise immunityUS493975324. Febr. 19893. Juli 1990Rosemount Inc.Time synchronization of control networksUS496412519. Aug. 198816. Okt. 1990Hughes Aircraft CompanyMethod and apparatus for diagnosing faultsUS498899026. Dez. 198929. Jan. 1991Rosemount Inc.Dual master implied token communication systemUS499296530. Nov. 198812. Febr. 1991Eftag-Entstaubungs- Und Fordertechnik AgCircuit arrangement for the evaluation of a signal produced by a semiconductor gas sensorUS500514224. Juli 19892. Apr. 1991Westinghouse Electric Corp.Smart sensor system for diagnostic monitoringUS50197607. Dez. 198928. Mai 1991Electric Power Research InstituteThermal life indicatorUS50438626. Apr. 198927. Aug. 1991Hitachi, Ltd.Method and apparatus of automatically setting PID constantsUS50538159. Apr. 19901. Okt. 1991Eastman Kodak CompanyReproduction apparatus having real time statistical process controlUS506709910. Apr. 198919. Nov. 1991Allied-Signal Inc.Methods and apparatus for monitoring system performanceUS508159821. Febr. 198914. Jan. 1992Westinghouse Electric Corp.Method for associating text in automatic diagnostic system to produce recommended actions automaticallyUS50899798. Febr. 198918. Febr. 1992Basic Measuring InstrumentsApparatus for digital calibration of detachable transducersUS508998415. Mai 198918. Febr. 1992Allen-Bradley Company, Inc.Adaptive alarm controller changes multiple inputs to industrial controller in order for state word to conform with stored state wordUS509819730. Jan. 198924. M�rz 1992The United States Of America As Represented By The United States Department Of EnergyOptical Johnson noise thermometryUS50994363. Nov. 198824. M�rz 1992Allied-Signal Inc.Methods and apparatus for performing system fault diagnosisUS51034093. Jan. 19907. Apr. 1992Hitachi, Ltd.Field measuring instrument and its abnormality managing methodUS51115318. Jan. 19905. Mai 1992Automation Technology, Inc.Process control using neural networkUS51214673. Aug. 19909. Juni 1992E.I. Du Pont De Nemours & Co., Inc.Neural network/expert system process control system and methodUS512279429. Okt. 199016. Juni 1992Rosemount Inc.Dual master implied token communication systemUS512297612. M�rz 199016. Juni 1992Westinghouse Electric Corp.Method and apparatus for remotely controlling sensor processing algorithms to expert sensor diagnosesUS513093614. Sept. 199014. Juli 1992Arinc Research CorporationMethod and apparatus for diagnostic testing including a neural network for determining testing sufficiencyUS513457427. Febr. 199028. Juli 1992The Foxboro CompanyPerformance control apparatus and method in a processing plantUS513737025. M�rz 199111. Aug. 1992Delta M CorporationThermoresistive sensor systemUS51426123. Aug. 199025. Aug. 1992E. I. Du Pont De Nemours & Co. (Inc.)Computer neural network supervisory process control system and methodUS51434524. Febr. 19911. Sept. 1992Rockwell International CorporationSystem for interfacing a single sensor unit with multiple data processing modulesUS514837819. Nov. 199015. Sept. 1992Omron CorporationSensor controller systemUS515028930. Juli 199022. Sept. 1992The Foxboro CompanyMethod and apparatus for process controlUS51670093. Aug. 199024. Nov. 1992E. I. Du Pont De Nemours & Co. (Inc.)On-line process control neural network using data pointersUS517567815. Aug. 199029. Dez. 1992Elsag International B.V.Method and procedure for neural control of dynamic processesUS51931437. Nov. 19899. M�rz 1993Honeywell Inc.Problem state monitoringUS51971143. Aug. 199023. M�rz 1993E. I. Du Pont De Nemours & Co., Inc.Computer neural network regulatory process control system and methodUS51973289. Jan. 199230. M�rz 1993Fisher Controls International, Inc.Diagnostic apparatus and method for fluid control valvesUS52127653. Aug. 199018. Mai 1993E. I. Du Pont De Nemours & Co., Inc.On-line training neural network system for process controlUS521458230. Jan. 199125. Mai 1993Edge Diagnostic SystemsInteractive diagnostic system for an automotive vehicle, and methodUS522420322. Juli 199229. Juni 1993E. I. Du Pont De Nemours & Co., Inc.On-line process control neural network using data pointersUS522878030. Okt. 199220. Juli 1993Martin Marietta Energy Systems, Inc.Dual-mode self-validating resistance/Johnson noise thermometer systemUS523552710. Nov. 199210. Aug. 1993Toyota Jidosha Kabushiki KaishaMethod for diagnosing abnormality of sensorUS526503126. Nov. 199023. Nov. 1993Praxair Technology, Inc.Diagnostic gas monitoring process utilizing an expert systemUS526522223. Nov. 199023. Nov. 1993Hitachi, Ltd.Symbolization apparatus and process control system and control support system using the same apparatusUS526931111. Mai 199214. Dez. 1993Abbott LaboratoriesMethod for compensating errors in a pressure transducerUS52745726. M�rz 199028. Dez. 1993Schlumberger Technology CorporationMethod and apparatus for knowledge-based signal monitoring and analysisUS528213121. Jan. 199225. Jan. 1994Brown And Root Industrial Services, Inc.Control system for controlling a pulp washing system using a neural network controllerUS52822613. Aug. 199025. Jan. 1994E. I. Du Pont De Nemours And Co., Inc.Neural network process measurement and controlUS529358517. Sept. 19928. M�rz 1994Kabushiki Kaisha ToshibaIndustrial expert systemUS530318120. Okt. 199212. Apr. 1994Harris CorporationProgrammable chip enable logic functionUS530523020. Nov. 199019. Apr. 1994Hitachi, Ltd.Process control system and power plant process control systemUS531142110. Dez. 199010. Mai 1994Hitachi, Ltd.Process control method and system for performing control of a controlled system by use of a neural networkUS53175201. Juli 199131. Mai 1994Moore Industries International Inc.Computerized remote resistance measurement system with fault detectionUS53273573. Dez. 19915. Juli 1994Praxair Technology, Inc.Method of decarburizing molten metal in the refining of steel using neural networksUS533324013. Apr. 199026. Juli 1994Hitachi, Ltd.Neural network state diagnostic system for equipmentUS534784323. Sept. 199220. Sept. 1994Korr Medical Technologies Inc.Differential pressure flowmeter with enhanced signal processing for respiratory flow measurementUS534954123. Jan. 199220. Sept. 1994Electric Power Research Institute, Inc.Method and apparatus utilizing neural networks to predict a specified signal value within a multi-element systemUS535744923. Dez. 199218. Okt. 1994Texas Instruments IncorporatedCombining estimates using fuzzy setsUS53616282. Aug. 19938. Nov. 1994Ford Motor CompanySystem and method for processing test measurements collected from an internal combustion engine for diagnostic purposesUS53654238. Jan. 199215. Nov. 1994Rockwell International CorporationControl system for distributed sensors and actuatorsUS56722473. M�rz 199530. Sept. 1997Union Camp Patent Holding, Inc.Control scheme for rapid pulp delignification and bleachingUS58256647. Juni 199520. Okt. 1998Rosemount Inc.Field-mounted control unitUS601714328. M�rz 199625. Jan. 2000Rosemount Inc.Device in a process system for detecting eventsUS61799642. Sept. 199730. Jan. 2001Voith Sulzer Papiermaschinen GmbhMethod and control device for paper web profile control with plurality of sensorsUS2003003304029. Mai 200213. Febr. 2003Billings JohnProcess control system and methodUSRE2938331. Jan. 19776. Sept. 1977Process Systems, Inc.Digital fluid flow rate measurement or control systemCA999950A124. Apr. 197316. Nov. 1976Measurex CorporationBleach plant control methodNichtpatentzitateReferenz1"A Decade of Progress in High Temperature Johnson Noise Thermometry," by T.V. Blalock et al., American Institute of Physics, 1982 pp. 1219-1223.2"A Fault-Tolerant Interface for Self-Validating Sensors", by M.P. Henry, Colloquium, pp. 3/1-3/2 (Nov. 1990).3"A Knowledge-Based Approach for Detection and Diagnosis of Out-Of-Control Events in Manufacturing Processes," by P. Love et al., IEEE, 1989, pp. 736-741.4"A Microcompuer-Based Instrument for Applications in Platinum Resistance Thermomety," by H. Rosemary Taylor and Hector A. Navarro, Journal of Physics E. Scientific Instrument, vol. 16, No. 11, pp. 1100-1104 (1983).5"A New Method of Johnson Noise Thermometry", by C.J. Borkowski et al., Rev. Sci. Instrum., vol. 45, No. 2, (Feb. 1974) pp. 151-162.6"A Self-Validating Thermocouple," Janice C-Y et al., IEEE Transactions on Control Systems Technology, vol. 5, No. 2, pp. 239-253 (Mar. 1997).7"Advanced Engine Diagnostics Using Universal Process Modeling", by P. O'Sullivan, Presented at the 1996 SAE Conference on Future Transportation Technology, pp. 1-9.8"An Integrated Architecture For Signal Validation in Power Plants," by B.R. Upadhyaya et al., Third IEEE International Symposium on Intelligent Control, Aug. 24-26, 1988, pp. 1-6.9"Application of Johnson Noise Thermometry to Space Nuclear Reactors," by M.J. Roberts et al., Presented at the 6th Symposium on Space Nuclear Power Systems, Jan. 9-12, 1989.10"Application of Neural Computing Paradigms for Signal Validation," by B.R. Upadhyaya et al., Department of Nuclear Engineering, pp. 1-18.11"Application of Neural Networks for Sensor Validation and Plant Monitoring," by B. Upadhyaya et al., Nuclear Technology, vol. 97, No. 2, Feb. 1992 pp. 170-176.12"Approval Standard Intrinsically Safe Apparatus and Associated Apparatus For Use In Class I, II, and III, Division 1 Hazardous (Classified) Locations", Factory Mutual Research, Cl. No. 3610, Oct. 1988, pp. 1-70.13"Approval Standards For Explosionproof Electrical Equipment General Requirements", Factory Mutual Research, Cl. No. 3615, Mar. 1989, pp. 1-34.14"Automated Generation of Nonlinear System Characterization for Sensor Failure Detection," by B.R. Upadhyaya et al., ISA, 1989 pp. 269-274.15"Automation On-line" by, Phillips et al., Plant Services, Jul. 1997, pp. 41-45.16"Caviation in Pumps, Pipes and Valves," Process Engineering, by Dr. Ronald Young, pp. 47-49 (Jan. 1990).17"Check of Semiconductor Thermal Resistance Elements by the Method of Noise Thermometry", by A. B. Kisilevskii et al., Measurement Techniques, vol. 25, No. 3, Mar. 1982, New York, USA, pp. 244-246.18"Climb to New Heights by Controlling your PLCs Over the Internet" by, Phillips et al., Intech, Aug. 1998, pp. 50-51.19"CompProcessor For Piezoresistive Sensors" MCA Technologies Inc. (MCA7707), pp. 1-8.20"Detecting Blockage in Process Connections of Differential Pressure Transmitters", by E. Taya et al., SICE, 1995, pp. 1605-1608.21"Detection of Hot Spots in Thin Metal Films Using an Ultra Sensitive Dual Channel Noise Measurement System," by G.H. Massiha et al., Energy and Information Technologies in the Southeast, vol. 3 of 3, Apr. 1989, pp. 1310-1314.22"Developing Predictive Models for Cavitation Erosion," Codes and Standards in A Global Environment, PVP-vol. 259, pp. 189-192 (1993).23"Development and Application of Neural Network Algorithms For Process Diagnostics," by B.R. Upadhyaya et al., Proceedings of the 29th Conference on Decision and Control, 1990, pp. 3277-3282.24"Development of a Long-Life, High-Reliability Remotely Operated Johnson Noise Thermometer," by R.L. Shepard et al., ISA, 1991, pp. 77-84.25"Development of a Resistance Thermometer For Use Up to 1600� C", by M.J. de Groot et al., CAL LAB, Jul./Aug. 1996, pp. 38-41.26"emWare's Releases EMIT 3.0, Allowing Manufacturers to Internet and Network Enable Devices Royalty Free," 3 pages, PR Newswire (Nov. 4, 1998).27"Ethernet emerges as viable, inexpensive fieldbus", Paul G. Schreier, Personal Engineering, Dec. 1997, p. 23-29.28"Ethernet Rules Closed-loop System" by, Eidson et al., Intech, Jun. 1998, pp. 39-42.29"Experience in Using Estelle for the Specification and Verification of a Fieldbus Protocol: FIP," by Barretto et al., Computer Networking, pp. 295-304 (1990).30"Field-based Architecture is Based on Open Systems, Improves Plant Performance", by P. Cleaveland, I&CS, Aug. 1996, pp. 73-74.31"Fieldbus Standard for Use in Industrial Control Systems Part 2: Physical Layer Specification and Service Definition", ISA-S50.02-1992, pp. 1-93.32"Fieldbus Standard for Use in Industrial Control Systems Part 3: Data Link Service Definition", ISA-S50.02-1997, Part 3, Aug. 1997, pp. 1-159.33"Fieldbus Support For Process Analysis" by, Blevins et al., Fisher-Rosemount Systems, Inc., 1995, pp. 121-128.34"Fieldbus Technical Overview Understanding FOUNDATION(TM) fieldbus technology", Fisher-Rosemount, 1998, pp. 1-23.35"Fuzzy Logic and Artificial Neural Networks for Nuclear Power Plant Applications," by R.C. Berkan et al., Proceedings of the American Power Conference.36"Fuzzy Logic and Neural Network Applications to Fault Diagnosis", by P. Frank et al., International Journal of Approximate Reasoning, (1997), pp. 68-88.37"Hypertext Transfer Protocol-HTTP/1.0" by, Berners-Lee et al., MIT/LCS, May 1996, pp. 1-54.38"In Situ Claibration of Nuclear Plant Platinum Resistance Thermometers Using Johnson Noise Methods," EPRI, Jun. 1983.39"Infranets, Intranets, and the Internet" by, Pradip Madan, Echelon Corp, Sensors, Mar. 1997, pp. 46-50.40"In-Situ Response Time Testing of Thermocouples", ISA, by H.M. Hashemian et al., Paper No. 89-0056, pp. 587-593, (1989).41"Integration of Multiple Signal Validation Modules for Sensor Monitoring," by B. Upadhyaya et al., Department of Nuclear Engineering, Jul. 8, 1990, pp. 1-6.42"Intelligent Behaviour for Self-Validating Sensors", by M.P. Henry, Advances In Measurement, pp. 1-7, (May 1990).43"Internal Statistical Quality Control for Quality Monitoring Instruments", by P. Girling et al., ISA, 15 pgs., 1999.44"Internet Protocol Darpa Internet Program Protocol Specification" by, Information Sciences Institute, University of Southern California, RFC 791, Sep. 1981, pp. 1-43.45"Internet Technology Adoption into Automation" by, Fondl et al., Automation Business, pp. 1-5.46"Introduction to Emit", emWare, Inc., 1997, pp. 1-22.47"Introduction to the Internet Protocols" by, Charles L. Hedrick, Computer Science Facilities Group, Rutgers University, Oct. 3, 1988, pp. 1-97.48"Is There A Future For Ethernet in Industrial Control?", Miclot et al., Plant Engineering, Oct. 1988, pp. 44-46, 48, 50.49"Johnson Noise Power Thermometer and its Application in Process Temperature Measurement," by T.V. Blalock et al., American Institute of Physics 1982, pp. 1249-1259.50"Johnson Noise Thermometer for High Radiation and High-Temperature Environments," by L. Oakes et al., Fifth Symposium on Space Nuclear Power Systems, Jan. 1988, pp. 2-23.51"Keynote Paper: Hardware Compilation-A New Technique for Rapid Prototyping of Digital Systems-Applied to Sensor Validation", by M.P. Henry, Control Eng. Practice, vol. 3, No. 7., pp. 907-924, (1995).52"Managing Devices with the Web" by, Howard et al., Byte, Sep. 1997, pp. 45-64.53"Measurement of the Temperature Fluctuation in a Resistor Generating 1/F Fluctuation," by S. Hashiguchi, Japanese Journal of Applied Physics, vol. 22, No. 5, Part 2, May 1983, pp. L284-L286.54"Modular Microkernel Links GUI And Browser For Embedded Web Devices" by, Tom Williams, pp. 1-2.55"Monitoring and Diagnosis of Cavitation in Pumps and Valves Using the Wigner Distribution," Hydroaccoustic Facilities, Instrumentation, and Experimental Techniques, NCA-vol. 10, pp. 31-36 (1991).56"Neural Networks for Sensor Validation and Plant Monitoring," by B. Upadhyaya, International Fast Reactor Safety Meeting, Aug. 12-16, 1990, pp. 2-10.57"Neural Networks for Sensor Validation and Plantwide Monitoring," by E. Eryurek, 1992.58"Noise Thermometry for Industrial and Metrological Applications at KFA Julich," by H. Brixy et al., 7th International Symposium on Temperature, 1992.59"On-Line Statistical Process Control for a Glass Tank Ingredient Scale," by R.A. Weisman, IFAC real Time Programming, 1985, pp. 29-38.60"PC Software Gets Its Edge From Windows, Components, and the Internet", Wayne Labs, I&CS, Mar. 1997, pp. 23-32.61"Programmable Hardware Architectures for Sensor Validation", by M.P. Henry et al., Control Eng. Practice, vol. 4, No. 10., pp. 1339-1354, (1996).62"Quantification of Heart Valve Cavitation Based on High Fidelity Pressure Measurements," Advances in Bioengineering 1994, by Laura A. Garrison et al., BED-vol. 28, pp. 297-298 (Nov. 6-11, 1994).63"Self-Diagnosing Intelligent Motors: A Key Enabler for Next Generation Manufacturing System," by Fred M. Discenzo et al., pp. 3/1-3/4 (1999).64"Sensor and Device Diagnostics for Predictive and Proactive Maintenance", by B. Boynton, A Paper Presented at the Electric Power Research Institute-Fossil Plant Maintenance Conference in Baltimore, Maryland, Jul. 29-Aug. 1, 1996, pp. 50-1-50-6.65"Sensor Validation for Power Plants Using Adaptive Backpropagation Neural Network," IEEE Transactions on Nuclear Science, vol. 37, No. 2, by E. Eryurek et al. Apr. 1990, pp. 1040-1047.66"Signal Processing, Data Handling and Communications: The Case for Measurement Validation", by M.P. Henry, Department of Engineering Science, Oxford University.67"Smart Field Devices Provide New Process Data, Increase System Flexibility," by Mark Boland, I&CS, Nov. 1994, pp. 45-51.68"Smart Sensor Network of the Future" by, Jay Warrior, Sensors, Mar. 1997, pp. 40-45.69"Smart Temperature Measurement in the '90s", by T. Kerlin et al., C&I, (1990).70"Software-Based Fault-Tolerant Control Design for Improved Power Plant Operation," IEEE/IFAC Joint Symposium on Computer-Aided Control System Design, Mar. 7-9, 1994 pp. 585-590.71"Statistical Process Control (Practice Guide Series Book)", Instrument Society of America, 1995, pp. 1-58 and 169-204.72"Survey, Applications, And Prospects of Johnson Noise Thermometry," by T. Blalock et al., Electrical Engineering Department, 1981 pp. 2-11.73"Taking Full Advantage of Smart Transmitter Technology Now," by G. Orrison, Control Engineering, vol. 42, No. 1, Jan. 1995.74"The Embedded Web Site" by, John R. Hines, IEEE Spectrum, Sep. 1996, p. 23.75"The Implications of Digital Communications on Sensor Validation", by M. Henry et al., Report No. QUEL 1912/92, (1992).76"The Performance of Control Charts for Monitoring Process Variation," by C. Lowry et al., Commun. Statis.-Simula., 1995, pp. 409-437.77"Thermocouple Continuity Checker," IBM Technical Disclosure Bulletin, vol. 20, No. 5, pp. 1954 (Oct. 1977).78"Time-Frequency Analysis of Transient Pressure Signals for a Mechanical Heart Valve Cavitation Study," ASAIO Journal, by Alex A. Yu et al., vol. 44, No. 5, pp. M475-M479, (Sep.-Oct. 1998).79"Transient Pressure Signals in Mechanical Heart Valve Caviation," by Z.J. Wu et al., pp. M555-M561 (undated).80"Transmission Control Protocol: Darpa Internet Program Protocol Specification" Information Sciences Institute, Sep. 1981, pp. 1-78.81"Tuned-Circuit Dual-Mode Johnson Noise Thermometers," by R.L. Shepard et al., Apr. 1992.82"Tuned-Circuit Johnson Noise Thermometry," by Michael Roberts et al., 7<th >Symposium on Space Nuclear Power Systems, Jan. 1990.83"Using Artificial Neural Networks to Identify Nuclear Power Plant States," by Israel E. Alguindigue et al., pp. 1-4.84"Wavelet Analysis of Vibration, Part 2: Wavelet Maps," by D.E. Newland, Journal of Vibration and Acoustics, vol. 116, Oct. 1994, pp. 417-425.85"Wavelet Analysis of Vibration, Part I: Theory<1>," by D.E. Newland, Journal of Vibration and Acoustics, vol. 116, Oct. 1994, pp. 409-416.86"Additional Information From Flowmeters via Signal Analysis," by J.E. Amadi-Echendu and E.H. Higham, pp. 187-193.87"Bus de campo para la inteconexi�n del proceso con sistemas digitales de control," Tecnolog�a, pp. 141-147 (1990).88"Computer Stimulation of H1 Field Bus Transmission," by Utsumi et al., Advances in Instrumentation and Control, vol. 46, Part 2, pp. 1815-1827 (1991).89"Dezentrale Installation mit Echtzeit-Feldbus," Netzwerke, Jg. Nr.3 v. 14.3, 4 pages (1990).90"Ein Emulationssystem zur Leitstungsanalyse von Feldbussystemen, Teil 1," by R. Hoyer, pp. 335-336 (1991).91"Ein Modulares, verteiltes Diagnose-Expertensystem f�r die Fehlerdiagnose in lokalen Netzen," by J�rgen M. Schr�der, pp. 557-565 (1990).92"Fault Diagnosis of Fieldbus Systems," by J�rgen Quade, pp. 577-581 (10/92).93"Feldbusnetz f�r Automatisierungssysteme mit intelligenten Funktionseinheiten," by W. Kriesel et al., pp. 486-489 (1987).94"Fieldbus Technical Overview Understanding FOUNDATION� fieldbus technology", Fisher-Rosemount, 1998, pp. 1-23.95"Hypertext Transfer Protocol�HTTP/1.0" by, Berners-Lee et al., MIT/LCS, May 1996, pp. 1-54.96"Improving Dynamic Performance of Temperature Sensors With Fuzzy Control Techniques," by Wang Lei et al., pp. 872-873 (1992).97"Mod�lisation et simulation d'un bus de terrain: FIP," by Song et al, pp. 5-9 (undated).98"Notification of Transmittal of the International Search Report or the Declaration" for PCT/US01/40782.99"Notification of Transmittal of the International Search Report or the Declaration" for PCT/US01/40791.100"Process Measurement and Analysis," by Liptak et al., Instrument Engineers' Handbook, Third Edition, pp. 528-530, (1995).101"PROFIBUS-Infrastrukturmaβnahmen," by Tilo Pfeifer et al., pp. 416-419 (8/91).102"Progress in Fieldbus Developments for Measuring and Control Application," by A. Schwaier, Sensor and Actutors, pp. 115-119 (1991).103"Sensor and Device Diagnostics for Predictive and Proactive Maintenance", by B. Boynton, A Paper Presented at the Electric Power Research Institute�Fossil Plant Maintenance Conference in Baltimore, Maryland, Jul. 29-Aug. 1, 1996, pp. 50-1�50-6.104"Simulation des Zeitverhaltens von Feldbussystemen," by O. Schnelle, pp. 440-442 (1991).105"Simulatore Integrato: Controllo su bus di campo," by Barabino et al., Automazione e Strumentazione, pp. 85-91 (Oct. 1993).106"The Performance of Control Charts for Monitoring Process Variation," by C. Lowry et al., Commun. Statis.�Simula., 1995, pp. 409-437.107"Time-Frequency Analysis of Transient Pressure Signals for a Mechanical Heart Valve Cavitation Study," ASAIO Journal, by Alex A. Yu et al., vol. 44, No. 5, pp. M475-M479, (Sep.�Oct. 1998).108"Tuned-Circuit Johnson Noise Thermometry," by Michael Roberts et al., 7th Symposium on Space Nuclear Power Systems, Jan. 1990.109"Wavelet Analysis of Vibration, Part I: Theory1," by D.E. Newland, Journal of Vibration and Acoustics, vol. 116, Oct. 1994, pp. 409-416.110"Ziele und Anwndungen von Feldbussystemen," by T. Pfeifer et al., pp. 549-557 (10/87).111A Standard Interface for Self-Validating Sensors, by M.P. Henry et al., Report No. QUEL 1884/91, (1991).112Fieldbus Standard For Use in Industrial Control Systems Part 4: Data Link Protocol Specification, ISA-S50.02-1997, Part 4, Aug. 1997, pp. 1-148.113IEEE Instrumentation and Measurement, "New approach to a main error estimation for primary transducer of electromagnetic flow meter," pp. 1093-1097.114IEEE Transactions on Magnetics, vol. 30, No. 2, Mar. 1994, "Magnetic Fluid Flow Meter for Gases," pp. 936-938.115IEEE Transactions on Magnetics, vol. 34, No. 5, Sep. 1998, "Optical Design of the Coils of an Electromagnetic Flow Meter," pp. 2563-2566.116Instrument Engineers' Handbook, Chapter IV entitled "Temperature Measurements," by T.J. Claggett, pp. 266-333 (1982).117International Search Report for International application No. PCT/US 02/14560, filed May 8, 2002, Search Report dated Sep. 3, 2002.118International Search Report for International application No. PCT/US 02/14934, filed May 8, 2002, Search Report dated Apr. 28, 2002.119Journal of Intelligent Manufacturing (1997) 8, 271-276 article entitled "On-line tool condition monitoring system with wavelet fuzzy neural network".120LFM/SIMA Internet Remote Diagnostics Research Project Summary Report, Stanford University, Jan. 23, 1997, pp. 1-6.121Microsoft Press Computer Dictionary, 3<rd >Edition, p. 124.122Microsoft Press Computer Dictionary, 3rd Edition, p. 124.123Parallel, Fault-Tolerant Control and Diagnostics System for Feedwater Regulation in PWRS, by E. Eryurek et al., Proceedings of the American Power Conference.124Proceedings Sensor Expo, Aneheim, California, Produced by Expocon Managemnet Associates, Inc., Apr. 1996, pp. 9-21.125Proceedings Sensor Expo, Boston, Massachuttes, Produced by Expocon Management Associates, Inc., May 1997, pp. 1-416.126Warrier, J., "The IEEE P1451.1 Object Model Network Independent Interfaces for Sensors and Actuators," pp. 1-14, Rosemount Inc. (1997).127Warrior, J., "The Collision Between the Web and Plant Floor Automation," 6<Th>. WWW Conference Workshop on Embedded Web Technology, Santa Clara, CA (Apr. 7, 1997).128Warrior, J., "The Collision Between the Web and Plant Floor Automation," 6Th. WWW Conference Workshop on Embedded Web Technology, Santa Clara, CA (Apr. 7, 1997).129Web Pages from www.triant.com (3 pgs.). Referenziert von Zitiert von PatentEingetragen Ver�ffentlichungsdatum Antragsteller TitelUS71071085. Juni 200212. Sept. 2006Elverson Hopewell LlcController and method of controlling an apparatus using predictive filtersUS755863430. Aug. 20067. Juli 2009Elverson Hopewell LlcController and method of controlling an apparatus using predictive filtersUS764389131. Jan. 20055. Jan. 2010Siemens Industry, Inc.Virtual field controllerUS76805494. Apr. 200616. M�rz 2010Fisher-Rosemount Systems, Inc.Diagnostics in industrial process control systemUS794559427. Sept. 200717. Mai 2011Sap AgUsing status models with inhibiting status values in a computer systemUS797048314. M�rz 200728. Juni 2011Applied Materials, Inc.Methods and apparatus for improving operation of an electronic device manufacturing systemUS803223416. Mai 20064. Okt. 2011Rosemount Inc.Diagnostics in process control and monitoring systemsUS803223727. Mai 20094. Okt. 2011Elverson Hopewell LlcCorrection signal capable of diminishing a future change to an output signalUS803676026. Sept. 200811. Okt. 2011Fisher-Rosemount Systems, Inc.Method and apparatus for intelligent control and monitoring in a process control systemUS812206328. Dez. 200621. Febr. 2012Sap AgUsing status models in a computer systemUS813204923. Febr. 20056. M�rz 2012Fuji Xerox Co., Ltd.Failure diagnosis method, failure diagnosis apparatus, conveyance device, image forming apparatus, program, and storage mediumUS820071510. Dez. 200912. Juni 2012Sap AgUsing status models with adaptable process steps in a computer systemUS821965028. Dez. 200610. Juli 2012Sap AgCommunicating with a status management component in a computer systemUS822894629. Juli 200924. Juli 2012General Electric CompanyMethod for fail-safe communicationUS83652001. Okt. 200929. Jan. 2013Sap AgUsing cancellation status models in a computer systemDrehenOriginalbildGoogle-Startseite - Sitemap - USPTO-Bulk-Downloads - Datenschutzerkl�rung - Nutzungsbedingungen - �ber Google Patente - Feedback gebenDaten bereitgestellt von IFI CLAIMS Patent Services.© 2012 Google