WO2001073515A1 - Control systems for extrusion or drawing plants - Google Patents
Control systems for extrusion or drawing plants Download PDFInfo
- Publication number
- WO2001073515A1 WO2001073515A1 PCT/GB2001/001165 GB0101165W WO0173515A1 WO 2001073515 A1 WO2001073515 A1 WO 2001073515A1 GB 0101165 W GB0101165 W GB 0101165W WO 0173515 A1 WO0173515 A1 WO 0173515A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- plant
- mathematical
- extrusion
- model
- fault
- Prior art date
Links
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C48/00—Extrusion moulding, i.e. expressing the moulding material through a die or nozzle which imparts the desired form; Apparatus therefor
- B29C48/25—Component parts, details or accessories; Auxiliary operations
- B29C48/92—Measuring, controlling or regulating
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C2948/00—Indexing scheme relating to extrusion moulding
- B29C2948/92—Measuring, controlling or regulating
- B29C2948/92009—Measured parameter
- B29C2948/92019—Pressure
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C2948/00—Indexing scheme relating to extrusion moulding
- B29C2948/92—Measuring, controlling or regulating
- B29C2948/92009—Measured parameter
- B29C2948/92066—Time, e.g. start, termination, duration or interruption
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C2948/00—Indexing scheme relating to extrusion moulding
- B29C2948/92—Measuring, controlling or regulating
- B29C2948/92009—Measured parameter
- B29C2948/92085—Velocity
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C2948/00—Indexing scheme relating to extrusion moulding
- B29C2948/92—Measuring, controlling or regulating
- B29C2948/92009—Measured parameter
- B29C2948/92114—Dimensions
- B29C2948/92123—Diameter or circumference
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C2948/00—Indexing scheme relating to extrusion moulding
- B29C2948/92—Measuring, controlling or regulating
- B29C2948/92009—Measured parameter
- B29C2948/92209—Temperature
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C2948/00—Indexing scheme relating to extrusion moulding
- B29C2948/92—Measuring, controlling or regulating
- B29C2948/92819—Location or phase of control
- B29C2948/92857—Extrusion unit
- B29C2948/92876—Feeding, melting, plasticising or pumping zones, e.g. the melt itself
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C48/00—Extrusion moulding, i.e. expressing the moulding material through a die or nozzle which imparts the desired form; Apparatus therefor
- B29C48/03—Extrusion moulding, i.e. expressing the moulding material through a die or nozzle which imparts the desired form; Apparatus therefor characterised by the shape of the extruded material at extrusion
- B29C48/06—Rod-shaped
Definitions
- the present invention relates to a control system for controlling extrusion or drawing plants.
- Extrusion plants for coating electrical conductors with electrically insulating material employ a number of sensors to monitor the diameter, the capacitance and wall thickness of the extruded material.
- a control system responds to the sensors to feed-back control signals to the plant in a sense to keep the diameter capacitance and wall thickness constant.
- the problem with this system is that there is a delay between the time that the extrusion is actually occurring and the time any irregularity is sensed and as a result the system will tend to oscillate between over and under compensation until a steady state situation is again reached.
- a method of constructing a control system for a real extrusion or drawing plant comprising the steps of: a) creating a mathematical model of the plan:, by exciting the plant actuators and establishing the mathematical relationship between the plant inputs and outputs. b) creating a mathematical disturbance model to represent the differences between the real plant inputs and the model outputs. c) using the mathematical model and the disturbance model to create a mathematical model based controller; and d) using the mathematical model based controller to control the real extrusion or drawing plant.
- a method of constructing a fault deletion system for an extrusion or drawing plant comprising the steps of: a) inducing a fault into the plant and detecting the effect on the plant output. b) creating a mathematical relationship between the fault and the output. c) monitoring the plant to detect in the plant output a characteristic indicative of the mathematical relationship.
- Figure 1 is front elevation of an extrusion plant
- Figure 2 is a block diagram of a mathematical modelling system
- Figure 3 is a flow chart illustrating the construction of a plant model
- Figure 4 is a block diagram of a system for constructing a model controller
- Figure 5 illustrates graphically the prediction of outputs in response to inputs
- Figure 6 is block diagram of a model optimiser
- Figure 7 illustrates graphically the inputs and outputs in a closed loop control system
- Figure 8 illustrates graphically the inputs and outputs in a closed loop system both with and without disturbance modelling
- Figure 9 is a block diagram illustrating the claim of possible faults in the plant model
- Figure 10 is a block diagram of a system for processing faults
- Figure 11 is a front elevation of a drawing plant
- Figures 12 and 13 are graphs illustrating the characteristics of the drawing plant of Figure 11.
- the plant shown in Figure 1 comprises a plant for producing a high grade data communication single wire coated conductor.
- the raw conductor core 2 travels progressively through a tensioning and preheating unit 4, and a diameter measuring device 6 before entering an extruder 8.
- the coated cable emerging from the extruder 8 then passes through another diameter measuring device 10 following which it travels through an elongate cooling bath 12.
- the coated cable passes around a driven capstan unit 14 and emerges to pass through a third diameter measuring device 16.
- a tension sensor 20 and temperature sensor 22 within the unit 4 respectively feed output signals representing core tension and core temperature to a fast logging system 24.
- a pressure sensor 26 and a speed sensor 28 within the extruder respectively feed signals representing the melt pressure and the extruder speed to the fast logging unit 24.
- a capacitance sensor 30 and a capstan speed sensor 32 within the capstan unit 14 respectively feed signals representing the coated core capacitance and the capstan speed to the fast logging system.
- the three temperature sensors 6, 10 and 16 feed signals respectively representing pre-heat temperature, not diameter temperature and cold diameter temperature to the fast logging system 24.
- Figure 2 is a block diagram of a system for obtaining a mathematical model of the plant of Figure 1.
- the plant includes various process actuators for controlling the speed of the core 2, the temperatures generated and the speed of the extruder.
- Test signals can be sent to these actuators shown as a single block 40 by a modeller 42 through a hardware interface 44.
- the process sensors which are described in connection with Figure 1 are shown as a single block 46 and these send signals back to the modeller 42.
- the modeller is a software routine which can inject a variety of test signals into the process via the actuators and log the outputs of the process obtained from the sensors.
- One or more test signals maybe injected simultaneously and one or more outputs logged.
- the Modeller can derive linear or non-linear models of the process and the disturbances acting upon it. These models may also be of the parametric or non-parametric types. Some of the test signals are step, impulse, band-limited white noise, pseudo random binary signals, chirp signals and multisines.
- the modeller can also perform relay feedback tests to find the optimal parameters of PUD (Proportional, Integral and Derivative value) controllers and variants of it.
- the parameters of interest are Diameter, Capacitance, Extruder speed, Capstan speed, Melt pressure, Tension, Extruder Zones Temperature, Eccentricity, Elongation, Wire Temperature, Core Diameter.
- the structure and order of the process model and disturbances can be chosen by software based on a based on a priori results of preliminary tests.
- the modeller can be configured by software to calculate the parameters of the process model and disturbance model at every sampling interval to permit the implementation of an adaptive controller or to calculate the parameters of the process model and disturbance model just once to permit the implementation of a self-tuning controller.
- the modeller 42 is used to create a mathematical model for this plant in the manner shown in the flow chart of Figure 3. In order to reduce the complexity of the modeller it was assumed that the relationship between inputs and outputs were linear and that noise was purely random.
- test signals 50 were injected into the physical plant 52.
- the test signals and the outputs 54 of the plant were use to create the proposed mathematical model 58.
- the injected signals 50A were again fed to the physical plant 52A and also to the proposed model 58.
- the plant outputs 56A and the model outputs 60 were compared by a validation mathematics and if valid the relevant component of the proposed mathematical model 58 were included in the final model 58. If not valid the identification experiments were repeated until a suitable model was obtained.
- the model based controller 62 is a software program that incorporates a variety of simple and advanced control algorithms.
- the simple algorithms are of the Proportional (P), Proportional plus Integral (PI) and Proportional plus Integral plus Derivative (PED) types.
- the parameters of these simple models are calculated from relay feedback tests performed by the Modeller.
- the advanced control algorithms are based on model predictive control, the parameters of which are also obtained from the Modeller also minimum variance control and Linear Quadralic Gaussian control (LQG), algorithms are provided based on the Dynamic Matrix Control (DMC) technique and Generalised Predictive Control technique (GPC).
- DMC Dynamic Matrix Control
- GPC Generalised Predictive Control technique
- the future outputs y(t) for a determined horizon N are predicted at each instant t using the process model.
- the set of future control signals is calculated by optimising a pre-determined criterion in order to keep the process as close as possible to the reference trajectory w(t+k) (which can be the set point itself or a close approximation to it).
- This criterion usually takes the form of a quadratic function of the errors between the predicted output signal and the predicted reference trajectory.
- the control effort is included in the objective function in most cases.
- the control signal u(t ⁇ t) is sent to the process while the next control signals calculated are rejected, because at the next sampling instant y(t + 1) is already known and the future outputs are recalculated using this new information and all the sequences are brought up to date.
- t + 1) is calculated using the receding horizon concept.
- Model Predictive Control The methodology of Model Predictive Control is depicted in Figure 6.
- a model is used to predict the future plant outputs, based on past and current values and on the proposed optimal future control actions. These actions are calculated by an optimiser 64 taking into account the cost function as well as the constraints.
- a Dynamic Matrix Control example is illustrated as follows:
- the disturbance model is given by the Modeller as:
- the objective in this example is for reference tracking and disturbance rejection.
- y(t+k ⁇ t) is the predicted value of y at time t+k given y up to time t
- n(t+k 11) is the predicted value of the disturbance n at time t+k given n
- the disturbance are considered to be constant in Dynamic Matrix control, that
- n(t + k ⁇ t) y m (t)-y(t ⁇ t)
- J[t+k) is the free response of the system, that is, the part of response that does not depend on the future control actions and is given by:
- a simulation of the closed loop control system without disturbance and a square wave reference is shown in FIG 7. Note that weights in the cost function can be chosen to increase or decrease the speed of response.
- the controller explicitly considers the measurable disturbances it is able to reject them, since the controller starts acting when the disturbance appears, not when its effects appears in the output. On the other hand, if the controller does not take into account the measurable disturbances, it reacts later, when the effect on the output is considerable.
- the mathematical process model as hereinbefore described describes the process behaviour under normal operating conditions.
- the values of the parameters of the process model are difficult to compute exactly.
- the uncertainty in the process parameters, disturbances and measurement noise not representing faults can influence measurements and thereby make it more difficult to detect faults.
- Figure 9 is a block diagram showing an actuator 70 receiving an input u, actuator faults and an unknown input.
- the components 72 within the actuation 70 receive component faults and unknown faults.
- the outputs of the components 72 are fed to the sensors 76 which in turn receive the sensor faults and unknown faults.
- Unknown faults include parameter uncertainty, disturbances and measurement noise in order to distinguish them from real faults.
- the fault detection algorithm generates a signal which enables a statement to be made about the appearance of a fault.
- This signal called the residual
- This signal is generated by an observer or filter which computes an estimate of the measured signal y(t) as depicted in FIG 10.
- the difference between the measured signal y(t) and the estimated signal y(t) yields the residual.
- the residual should be zero in the fault free case and non-zero in the case of a fault. Ideally, a comparison of the residual with zero should yield a decision about the occurrence of a fault. But the unknown inputs mentioned previously produce a residual which in nonzero even in the fault free case. Therefore a threshold other than zero is employed in order to prevent false alarm. This threshold is user selectable by software.
- the observer or filter is designed in such a way that faults are de-coupled from the unknown inputs so that the residual is hardly ever affected by them. This method is called robust fault detection in the literature since the residual is then robust against unknown inputs and only sensitive to faults. This concept of de-coupling is also used for isolating different faults from each other.
- the filter or observer is designed so that it is sensitive to one fault but insensitive to other faults.
- a filter is designed for each fault which gives a bank of filters or observers. Logical evaluation of their residuals leads to a clear decision as which fault has occurred.
- the hardware and its interfaces may be realised in many ways, one of which is a Personal Computer with a plug-in D/A (Digital to Analogue) card.
- the D/A card has its own processor and on board memory.
- user interface is a software program which allows the user to set parameters like set-points, tolerance limits on variables and to configure the Modeller and Controller for particular choices of modelling and control techniques.
- the User Interface also provide graphical displays of important process variables like diameter, capacitance, and others together with tolerance limits set by the user.
- the program can also perform statistical process control analyses to process variables.
- the User Interface also provide the user with a powerful spectrum estimation tool which is based on advanced parametric and non-parametric spectrum estimation techniques.
- the extrusion plant can extrude solid plastics in which case a plant model having multiple inputs and a single output can be used.
- the establishment of a mathematical model of the plant also allows the establishment of a mathematical characteristic of a fault introduced into the real plant so that the subsequent detection of the characteristic in the plant output will allow corrective action to be taken before the fault can cause a failure of this plant.
- the drawing plant shown in Figures 11 is a plant for drawing optical fibres and a control system for controlling the operation of the plant in accordance with the present invention.
- a glass perform 102 is fed into a furnace 124 and drawn through a die a fibre is drawn from the base 106 of the perform 106 by capstan 114 located vertically below the furnace 124.
- the drawn fibre 130 passes a tension measurement device 122 for measuring the tension in the fibre, a diameter measuring device 108/120 for measuring the diameter of the fibre and a pair of surface coating stations 110 and 112 for providing the fibre with an outer coating before reaching the capstan 114.
- a capstan.speed controller 116 controls the speed of the capstan 114.
- a feed control and feed rate detection system 126 controls and measures the feed rate of the perform 102.
- a heating control and temperature measurement system 124 controls and monitors the temperature of the furnace 104.
- a control system 118 controls the operating parameters of the plant in a manner which will be described in more detail hereinafter.
- the control system 118 receives signals from the tension measurement device 122, the diameter measuring device 108/120 as well as from the temperature measuring system 124 and the feed rate detection system 126 and responds these to feed control signals to the perform feed control system 126 to the heating control system 124 and to the capstan speed controller 116 in a manner to ensures that the optical fibre 130 drawn lies within a predetermined range of tolerances.
- the design of the control system 118 is based on a H-infinity design.
- H-infinity design is the name given to a family of design methods. For example Mixed Sensitivity H- infinity design, Signal-Based H-infinity design and Robust Loop-Shaping H-infinity design.
- the Robust Loop-Shaping method is applied to control the drawing plant with the objective of rriinimising the effects of disturbances.
- the systematic Robust-Loop shaping procedure has its origin in the PhD thesis of Hyde (1991). The procedure was extended to a second-degree-of-freedom in the controller by Limebeer et al (1993).
- the plant Given a plant model function G(s) and a disturbance model function G d (s) obtained by system identification or otherwise, the plant is shaped with pre- and post compensators W ⁇ and W2 to obtain the shaped plant function G s (s).
- the perturbed plant can then be written as
- the stability property is robust if and only if the nominal feed back system is stable and
- the graph shown in Figure 12 illustrates the frequency response of the shaped plant Gs(s) in dotted lines and that of the robustified plant Gs(s) K(s). It is seen that the slope of Gs(s) K(s) around the crossover frequency is much gentler than that of Gs(s). This translates into better margins.
- the graph of Figure 13 illustrates the time domain response to a unit step disturbance. The disturbance is driven down to zero in about 2 seconds and it stays below 1 for all time. This shows that for a properly scaled plant and disturbance model the distance stays bellows the level we are prepare to tolerate at all times.
Abstract
Description
Claims
Priority Applications (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US10/239,115 US20030158610A1 (en) | 2000-03-28 | 2001-03-19 | Control systems for extrusion or drawing plants |
EP01911949A EP1269275A1 (en) | 2000-03-28 | 2001-03-19 | Control systems for extrusion or drawing plants |
AU2001240868A AU2001240868A1 (en) | 2000-03-28 | 2001-03-19 | Control systems for extrusion or drawing plants |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
GB0007372A GB2360855B (en) | 2000-03-28 | 2000-03-28 | Control systems for extrusion plants |
GB0007372.6 | 2000-03-28 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2001073515A1 true WO2001073515A1 (en) | 2001-10-04 |
Family
ID=9888483
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/GB2001/001165 WO2001073515A1 (en) | 2000-03-28 | 2001-03-19 | Control systems for extrusion or drawing plants |
Country Status (4)
Country | Link |
---|---|
EP (1) | EP1269275A1 (en) |
AU (1) | AU2001240868A1 (en) |
GB (1) | GB2360855B (en) |
WO (1) | WO2001073515A1 (en) |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9134713B2 (en) * | 2012-05-11 | 2015-09-15 | Siemens Corporation | System and method for fault prognostics enhanced MPC framework |
Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPS5723107A (en) * | 1980-07-18 | 1982-02-06 | Toshiba Corp | Process controller |
US4539633A (en) * | 1982-06-16 | 1985-09-03 | Tokyo Shibaura Denki Kabushiki Kaisha | Digital PID process control apparatus |
US4882526A (en) * | 1986-08-12 | 1989-11-21 | Kabushiki Kaisha Toshiba | Adaptive process control system |
DE4009200A1 (en) * | 1990-03-22 | 1991-09-26 | Diehl Gmbh & Co | Recursive parameter control system - uses adaptive parameter adjustment of transfer function using integral regulator for error signal corresp. to instantaneous deviation |
JPH0784608A (en) * | 1993-09-14 | 1995-03-31 | Toshiba Corp | Control device |
US5408406A (en) * | 1993-10-07 | 1995-04-18 | Honeywell Inc. | Neural net based disturbance predictor for model predictive control |
US5520037A (en) * | 1991-12-13 | 1996-05-28 | Siemens Aktiengesellschaft | Roll stand adjusting method |
US5587899A (en) * | 1994-06-10 | 1996-12-24 | Fisher-Rosemount Systems, Inc. | Method and apparatus for determining the ultimate gain and ultimate period of a controlled process |
US5673368A (en) * | 1993-11-11 | 1997-09-30 | Siemens Aktiengesellschaft | Method and device for conducting a process in a controlled system with at least one precomputed process parameter determined using a mathematical model having variable model parameters adjusted based on a network response of a neural network |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
FR2682208B1 (en) * | 1991-10-07 | 1994-01-07 | Sollac | METHOD AND DEVICE FOR MONITORING SENSORS AND LOCATING FAULTS IN AN INDUSTRIAL PROCESS. |
-
2000
- 2000-03-28 GB GB0007372A patent/GB2360855B/en not_active Expired - Fee Related
-
2001
- 2001-03-19 AU AU2001240868A patent/AU2001240868A1/en not_active Abandoned
- 2001-03-19 EP EP01911949A patent/EP1269275A1/en not_active Withdrawn
- 2001-03-19 WO PCT/GB2001/001165 patent/WO2001073515A1/en not_active Application Discontinuation
Patent Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPS5723107A (en) * | 1980-07-18 | 1982-02-06 | Toshiba Corp | Process controller |
US4539633A (en) * | 1982-06-16 | 1985-09-03 | Tokyo Shibaura Denki Kabushiki Kaisha | Digital PID process control apparatus |
US4882526A (en) * | 1986-08-12 | 1989-11-21 | Kabushiki Kaisha Toshiba | Adaptive process control system |
DE4009200A1 (en) * | 1990-03-22 | 1991-09-26 | Diehl Gmbh & Co | Recursive parameter control system - uses adaptive parameter adjustment of transfer function using integral regulator for error signal corresp. to instantaneous deviation |
US5520037A (en) * | 1991-12-13 | 1996-05-28 | Siemens Aktiengesellschaft | Roll stand adjusting method |
JPH0784608A (en) * | 1993-09-14 | 1995-03-31 | Toshiba Corp | Control device |
US5408406A (en) * | 1993-10-07 | 1995-04-18 | Honeywell Inc. | Neural net based disturbance predictor for model predictive control |
US5673368A (en) * | 1993-11-11 | 1997-09-30 | Siemens Aktiengesellschaft | Method and device for conducting a process in a controlled system with at least one precomputed process parameter determined using a mathematical model having variable model parameters adjusted based on a network response of a neural network |
US5587899A (en) * | 1994-06-10 | 1996-12-24 | Fisher-Rosemount Systems, Inc. | Method and apparatus for determining the ultimate gain and ultimate period of a controlled process |
Non-Patent Citations (3)
Title |
---|
PATENT ABSTRACTS OF JAPAN vol. 006, no. 084 (P - 117) 22 May 1982 (1982-05-22) * |
PATENT ABSTRACTS OF JAPAN vol. 1995, no. 06 31 July 1995 (1995-07-31) * |
PIECHOTTKA U ET AL: "VERWENDUNG VON STORGROSSENMODELLEN ZUR VERBESSERUNG DES REGELVERHALTENS, TEIL 1 APPLICATION OF DISTURBANCE MODELS FOR THE IMPROVEMENT OF FEEDBACK CONTROL, PART 1", AUTOMATISIERUNGSTECHNIK - AT,DE,OLDENBOURG VERLAG. MUNCHEN, vol. 42, no. 11, 1 November 1994 (1994-11-01), pages 483 - 487, XP000483089, ISSN: 0178-2312 * |
Also Published As
Publication number | Publication date |
---|---|
GB0007372D0 (en) | 2000-05-17 |
GB2360855A (en) | 2001-10-03 |
GB2360855B (en) | 2004-08-18 |
EP1269275A1 (en) | 2003-01-02 |
AU2001240868A1 (en) | 2001-10-08 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US4954975A (en) | Weigh feeding system with self-tuning stochastic control and weight and actuator measurements | |
CN102272565B (en) | Process temperature transmitter with improved temperature calculation | |
US5987398A (en) | Method and apparatus for statistical process control of machines and processes having non-constant mean of a response variable | |
EP1247268B2 (en) | Low power two-wire self validating temperature transmitter | |
CN105242534B (en) | Based on telemetry parameter and it is associated with the satellitosis monitoring method to satellite controlling behavior | |
KR101996375B1 (en) | Control system of water treatment system for detecting for predicting anomalies and for easily expanding functions | |
JPH11510898A (en) | Method and apparatus for detecting and identifying defect sensors in a process | |
US6915173B2 (en) | Advance failure prediction | |
US20020019722A1 (en) | On-line calibration process | |
JP2022519228A (en) | Systems and methods for detecting and measuring signal anomalies generated by components used in industrial processes | |
KR100249914B1 (en) | Method and apparatus for rolling metal band | |
GB2405496A (en) | A method for evaluating the aging of components | |
CN111767183B (en) | Equipment abnormality detection method and device, electronic equipment and storage medium | |
KR102173653B1 (en) | System state prediction | |
JP2698215B2 (en) | Method for manufacturing extruded product having flat or annular cross section and apparatus for manufacturing extruded product | |
KR19990082532A (en) | Anomaly Detection Method and Anomaly Detection System | |
US20030158610A1 (en) | Control systems for extrusion or drawing plants | |
KR102222125B1 (en) | Apparatus and method for managing yield based on machine learning | |
WO2001073515A1 (en) | Control systems for extrusion or drawing plants | |
KR20190041836A (en) | Apparatus and method for evaluating conditions of equipment | |
Abeykoon | Soft sensing of melt temperature in polymer extrusion | |
CN115839617A (en) | Sintering temperature control method and device | |
Tarifa et al. | Fault diagnosis for a MSF using neural networks | |
McAfee et al. | A Soft Sensor for viscosity control of polymer extrusion | |
WO2021250959A1 (en) | Controller, system, method, and program |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AK | Designated states |
Kind code of ref document: A1 Designated state(s): AE AG AL AM AT AU AZ BA BB BG BR BY BZ CA CH CN CO CR CU CZ DE DK DM DZ EE ES FI GB GD GE GH GM HR HU ID IL IN IS JP KE KG KP KR KZ LC LK LR LS LT LU LV MA MD MG MK MN MW MX MZ NO NZ PL PT RO RU SD SE SG SI SK SL TJ TM TR TT TZ UA UG US UZ VN YU ZA ZW |
|
AL | Designated countries for regional patents |
Kind code of ref document: A1 Designated state(s): GH GM KE LS MW MZ SD SL SZ TZ UG ZW AM AZ BY KG KZ MD RU TJ TM AT BE CH CY DE DK ES FI FR GB GR IE IT LU MC NL PT SE TR BF BJ CF CG CI CM GA GN GW ML MR NE SN TD TG |
|
121 | Ep: the epo has been informed by wipo that ep was designated in this application | ||
DFPE | Request for preliminary examination filed prior to expiration of 19th month from priority date (pct application filed before 20040101) | ||
WWE | Wipo information: entry into national phase |
Ref document number: 2001911949 Country of ref document: EP |
|
WWE | Wipo information: entry into national phase |
Ref document number: 10239115 Country of ref document: US |
|
WWP | Wipo information: published in national office |
Ref document number: 2001911949 Country of ref document: EP |
|
WWW | Wipo information: withdrawn in national office |
Ref document number: 2001911949 Country of ref document: EP |
|
NENP | Non-entry into the national phase |
Ref country code: JP |