CN101048714B - System, device, and methods for updating system-monitoring models - Google Patents

System, device, and methods for updating system-monitoring models Download PDF

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Publication number
CN101048714B
CN101048714B CN2005800371119A CN200580037111A CN101048714B CN 101048714 B CN101048714 B CN 101048714B CN 2005800371119 A CN2005800371119 A CN 2005800371119A CN 200580037111 A CN200580037111 A CN 200580037111A CN 101048714 B CN101048714 B CN 101048714B
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estimation
model
monitored
module
upgrade
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CN101048714A (en
Inventor
C·元
C·诺伊鲍尔
Z·卡塔尔特佩
W·麦科尔克
H·-G·布鲁默尔
M·房
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Siemens Energy Inc
Siemens Corporate Research Inc
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Siemens Power Generations Inc
Siemens Corporate Research Inc
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Abstract

A system for updating a plurality of monitoring models is provided. The system includes a model association module that, for each of a plurality of monitored systems determines, an association between a particular monitored system and at least one of a plurality of estimation models. Each estimation model is based upon one of a plurality of distinct sets of estimation properties, and each set uniquely corresponds to a particular estimation model. The system also includes an updating module that updates at least one of the estimation properties and propagates the updated estimation properties to each estimation model that corresponds to a distinct set containing at least one estimation property that is updated. The system further includes a model modification module that modifies each estimation model that corresponds to a distinct set containing at least one estimation property that is updated.

Description

The system, equipment and the method that are used for the update system monitoring model
The cross reference of related application
The application requires in the right of priority that the application number that on August 27th, 2004 submitted to is 60/605,346, name is called the U.S. Provisional Patent Application of " MODEL ASSOCIATION IN FLEET MONITORING SYSTEM FOR LARGEPOWER PLANTS (model interaction in the computers group monitoring of big generating plant) ".
Technical field
The present invention relates to the field monitored based on sensor, more specifically, relate to and utilize a plurality of sensors monitoring multiple component systems.
Background technology
Can comprise the various tasks of common execution so that realize a plurality of elements complicated integrated of expection output or target such as the multiple component system of generating the factory.Because this complicacy, this system is monitored so that stops or the performance level itself that reduces inefficacy or be lower than expectation may be exactly the task of complexity.
A kind of monitoring technique in this environment uses estimation model and sensor.According to this technology, sensor generates signal, draws sensor vector in output that described input generated by this signal based on the detection limit of the physics of system or other input with by system responses.Sensor vector is used to " training " estimation model on statistics at first.Described model the input of system is provided and the correspondence output that generates by system between mathematics or statistical relationship.During system monitoring subsequently, from the raw data of sensor be imported in the model and with compare by using the estimated value that described model obtains.Large deviation between the actual value of sensing data and the estimated value that generates by model can be indicated and the system failure occurred.
Sensor-based monitoring can be used in various the setting.Generating plant, manufacturing process, complicated medicine equipment and comprise many other systems of a large amount of associated components of synthetic operation or process and device usually can by sensor-based effective monitoring be monitored and control.In fact, sensor-based monitoring can advantageously be applied to and need monitor in fact any environment of the specific parameter of various systems along with the past of time under the condition that changes.
The generating plant provides a useful example of the system that can use sensor-based monitoring effectively.Generating comprises synthetic operation complicated integrated with a plurality of electrification components of producing electric energy.These assemblies can comprise gas turbine, waste heat recovery steam generator, steam turbine and generator, and it is combined fuel-bound can be converted to electric energy via mechanical energy.Should be monitored important operation variable with the performance of one or more (such as gas turbine) in evaluation whole generating factory or its assembly by strictness and comprise pressure and temperature in each zone of system and vibration and other important parameter of display system equipment state.
Regardless of the environment that uses sensor-based monitoring, the accuracy of employed model is whether accurately to monitor key factor.The accuracy of model depends on that often whether model is suitably upgraded structural change or other variation that utilizes the system that this model monitors with reflection.In addition, can develop the new model that enhanced system is monitored.Can use more than one model with respect to the system that is monitored.
When based on the more than one system of a plurality of Model Monitoring, if make the task of update system monitoring model become complicated more. the attribute that is applied to the basic model of two or more systems is updated, on one or more computing equipments of the various Model Calculation of carrying out each special system, load each model according to the common needs of each model of the attribute modification of being upgraded so. therefore, upgrade estimation model properties and revise estimation model and must be performed individually according to each system usually in response to described more newly arriving.
For diagnosis engineering teacher or technician, carrying out these tasks individually at this estimation model when estimation model is applied to different system is tasks effort, consuming time.Suppose that the slip-stick artist and the technician of diagnostic center can bear continuously or monitor continually hundreds of systems in many cases, this especially like this.Therefore, when this estimation model is used to monitor a large amount of system, need upgrade the method for estimating attribute and revising the system monitoring model in response to described renewal more effectively a kind of and efficiently.
Summary of the invention
The invention provides a kind of model interaction and a kind of related mechanism that is used to upgrade the system monitoring model in a large amount of systems.Can realize the present invention electronically so that realize being used to upgrade the saving of needed time of the model that is applied to various systems and resource.
According to a kind of system that is used to upgrade a plurality of monitoring models of the present invention, described system comprises: model association module, be used to each system in a plurality of monitored systems to determine the association between one of them at least in monitored system and a plurality of estimation model, wherein each estimation model is based on one of them of a plurality of different estimation community sets, each estimates that community set comprises the sensor tabulation, sensor threshold value, cycle of training and estimation model algorithm at least one of them, and wherein each estimates that community set is uniquely corresponding to an estimation model; Update module, be used to upgrade described estimation attribute at least one of them and the estimation attribute that at least one upgraded sent to corresponding to comprising at least one that be updated estimate each estimation model of the different sets of attribute; And model modification module, be used to revise each estimation model corresponding to the different sets that comprises at least one the estimation attribute that was updated, wherein said model association module is configured to, if the structure of at least one monitored system changes, then upgrade the association between one of them at least of one or more estimation models and described a plurality of monitored systems.
One embodiment of the present of invention are the electronics implementation model correlating methods that are used to upgrade the estimation model that is used to system monitoring.Described method can comprise to be determined special monitored system and is used for association between in each a plurality of estimation models of a plurality of monitored systems at least one.Each estimation model can be based on one of a plurality of different estimation community sets, and each set can be uniquely corresponding to particular estimation model.
Described method can comprise that in addition upgrading at least one estimates attribute and the estimation attribute that at least one upgraded is sent to each estimation model corresponding to the different sets that comprises at least one the estimation attribute that was updated.In addition, described method can comprise modification each estimation model corresponding to the different sets that comprises at least one the estimation attribute that was updated.
An alternative embodiment of the invention is the system that is used to upgrade a plurality of monitoring models.Described system can comprise model association module, this model association module is that in a plurality of monitored systems each is determined the association between special monitored system and in a plurality of estimation model at least one, wherein each estimation model is based on one of a plurality of different estimation community sets, and wherein each set uniquely corresponding to particular estimation model.
In addition, described system can comprise update module, and this update module is upgraded at least one and estimated attribute and the estimation attribute that at least one upgraded is sent to each estimation model corresponding to the different sets that comprises at least one the estimation attribute that was updated.Described system can also comprise model modification module, and it revises each estimation model corresponding to the different sets that comprises at least one the estimation attribute that was updated.
An alternative embodiment of the invention is a kind of computer-readable recording medium that comprises computer instruction. and computer instruction can comprise that each that be used in a plurality of monitored systems determines the instruction of the association between special monitored system and in a plurality of estimation model at least one. and computer instruction can also comprise that upgrading at least one estimates attribute.
Computer instruction can also comprise such instruction, and the estimation attribute that this instruction is used for that at least one was upgraded is sent to each estimation model corresponding to the different sets that comprises at least one the estimation attribute that was updated.In addition, computer instruction can comprise such instruction, and this instruction is used to revise each estimation model corresponding to the different sets that comprises at least one the estimation attribute that was updated.
Description of drawings
Presently preferred embodiment shown in the drawings.Yet it should be understood that the present invention is not limited to the shown accurate step and puts and means.
Fig. 1 is the synoptic diagram of exemplary environments according to an embodiment of the invention, is used for the system of update system monitoring model in this environment.
Fig. 2 is shown in Fig. 1, be used for the synoptic diagram of an embodiment of the system of update system monitoring model.
Fig. 3 is the synoptic diagram of model association scheme according to another embodiment of the invention.
What Fig. 4 was according to another embodiment of the invention an institute's group of planes (fleet) form of constructing and revising schematically shows.
Fig. 5 is a synoptic diagram according to still another embodiment of the invention, that be used for the system of update system monitoring model.
Fig. 6 is the process flow diagram of the illustrative steps of method according to another embodiment of the invention, that be used to upgrade a plurality of system monitoring models.
Fig. 7 is the process flow diagram of the illustrative steps of method according to still another embodiment of the invention, that be used to upgrade a plurality of system monitoring models.
Embodiment
Fig. 1 is the exemplary environments 100 that can use system 102 according to an embodiment of the invention, as to be used to upgrade a plurality of monitoring models.Described environment 100 comprises a plurality of monitored system 104a, 104b 104c.Although show three so monitored systems clearly, it should be understood that environment 100 can comprise so monitored system more or still less in the embodiment that substitutes.More specifically, monitored system 104a, 104b, 104c can comprise electricity generation system, processing factory, multicompartment Medical Devices or other this system, it is characterized in that the synthetic operation of a plurality of or complex assemblies in the process that generates one or more measurable outputs in response to one or more measurable inputs.
Among monitored system 104a, 104b, the 104c each is all monitored by a plurality of sensor 106a, 106b, 106c respectively to illustrative.Each sensor 106a, 106b, 106c can comprise transducer, and this transducer response is in producing electric signal with the corresponding various physical phenomenons of the input and output of each system.For example, in the environment of generating, if each among monitored system 104a, 104b, the 104c all comprises the generating plant, then the output of sensor measurement not only comprises electric energy, and comprises it being other output of secondary product of inevitably generating electricity.For example other output can comprise temperature, pressure and the vibration of main electrification component (such as gas turbine, boiler, steam turbine and generator).In equivalent environment, for example the input of generating plant can comprise combustion gas, air and/or steam.
By a plurality of sensor 106a, 106b, the response signal that among the 106c each produced provides data or sensor vector, these data or sensor vector can be used to monitoring and detect monitored system 104a, 104b, the fault of 104c. the signal that can processes sensor produces is to produce quantifiable data. and for example, the signal that sensor produces can be digitized and be transformed to produce sensor vector by digital signal processor. comprise that other known technology of analog signal processing can be alternatively or additionally be used to produce corresponding to monitored system 104a, 104b, the quantifiable data of the operation of 104c.
Here, the sensor vector that obtains of the signal that is produced by sensor is that being illustrated property ground is described in the context that reasoning detects.Reasoning detects and makes being constructed to of estimation model essential, and this estimation model is carrying out modeling to the operation of monitored system 104a, 104b, 104c on the mathematics or on statistics.This estimation model provides the correlativity between each measured input and output of monitored system 104a, 104b, 104c.As one of ordinary skill will be understood, estimation model produces estimated value, and this estimated value can be compared with actual value to determine the tolerance interval of one or more residual errors and definite residual error.If the determined residual error of run duration drops on outside its tolerance interval in monitored system, represent fault so.
The model that can be used to this inference system comprise such as the standard regression model of least square and such as the different modification of nuclear regression model than new model with based on the model of neural network.From it is evident that this description, system 102 according to the present invention does not benefit from the characteristic limitations of the particular module of monitoring monitored system 104a, 104b, 104c.Regardless of employed particular module, the structure of this model is all finished during the training stage usually, in this training stage, raw data is used to " training " particular module so that produce the sensor estimator, as one of ordinary skill will be understood.During monitor stages subsequently, the sensing data that newly produces is input in the model of being trained like this or a plurality of model to detect the fault of the corresponding monitored system among a plurality of monitored system 104a, 104b, the 104c.
Be connected to system's 102 being illustrated property a plurality of sensors-system interface 108a, 108b108c, these a plurality of sensor-system interfaces are connected to monitoring a plurality of monitored system 104a, a plurality of sensor 106a of one of 104b, 104c, the special subset of 106b, 106c again separately.Illustrative ground, the signal that sensor produces offers among a plurality of monitored system 104a, 104b, the 104c corresponding one by each sensor among a plurality of sensor 106a, 106b, the 106c.It is the function of having described of quantifiable data that sensor- system interface 108a, 108b, 108c carry out conversion of signals.Therefore, sensor- system interface 108a, 108b, 108c can comprise one or more multiplexers, are used for the signal that multiplexed a plurality of sensor produces.According to another embodiment, sensor- system interface 108a, 108b 108c can comprise the digital signal processor that is used to handle the digitized signal that the signal that produced by sensor obtains.In the embodiment that substitutes, these signal processing functions are to carry out by being included in system's 102 interior elements own.In addition, alternatively, a plurality of sensor 106a, 106b, 106c can directly be connected to system 102.According among these various embodiment any one, the signal after the processing is used to construct aforesaid estimation model.
As schematically show, monitored system 104a, 104b, 104c are by system's 102 remote monitoring.Therefore, this system can be positioned at the position away from monitored system, for example, and the diagnostic center (not shown) place in the various systems that monitor away from it.Utilize remote monitoring, sensor vector (comprising the service data about monitored system 104a, 104b, 104c here) is sent to system 102 continuously, perhaps alternatively be sent to system 102 in the mode of sending in batch, wherein each batch comprises that such data, these data have been included in the performance of the monitored system of time durations since the last time of data sends in batch.Although system 102 is shown as the various monitored system 104a of remote monitoring, 104b, 104c, it should be understood that described system 102 alternatively can be included in a plurality of systems that independent monitored system place uses here.For example, the local system that uses can connect via data communication network, so that can work in coordination with local monitor.In addition, can be with the local a plurality of assemblies that use individual system to monitor monitored system of above-mentioned same way as.
Now additionally with reference to figure 2, schematically showing the specific embodiments of the system 102 that is used to upgrade a plurality of monitoring models. system comprises to 102 illustratives the model association module 202 that communicates each other, update module 204 and model modification module 206. are according to an embodiment, one or more modules in the described module realize with one or more dedicated, hardwired circuit that are used to carry out following each function. alternatively, one or more modules in the described module can realize to be arranged to the machine readable code of moving on general or specialized equipment. in another embodiment, one or more modules of described module realize with the combination of hard-wired circuit and machine readable code.
In the operation, among a plurality of monitored system 104a-c each, model association module 202 is determined monitored systems and the association between a plurality of estimation models of structure as mentioned above.Therefore, each special system among monitored system 104a, 104b, the 104c is associated with at least one this estimation model.Yet, one or more can being associated with more than one estimation model among monitored system 104a, 104b, the 104c by model association module.For example, a monitored system 104a can only be associated with recurrence pattern type.Another monitored system 104b can be with regression model, be associated based on the model of auto-associating neural network and/or nuclear regression model.Another monitored system 104c can only be associated with two this models.
In Fig. 3, schematically show the more general example of the model association scheme of carrying out by model association module 202 300.Described model association scheme 300 makes J monitored system S 1, S 2..., S JWith K estimation model M 1, M 2..., M KBe associated.As in this example describe first S of system 1Only by a model M 1Come modeling and therefore only with a model M 1Be associated.Second S of system 2With three different model M 1, M 2And M 3Be associated, although wherein be different models, these three models all are applicable to second S of system separately 2J the S of system jWith second model M in K the model 2And K model be associated, and each model all provides the S of system JDifferent modelings aspect.
It is evident that easily that from this example the model association scheme of being carried out by model association module 202 300 is enough general in to comprise various other possible combinations.Certainly, particular associative combination mainly is by the properties specify of monitored system and employed different models.
Further illustrate as the synoptic diagram of Fig. 3, each particular estimation model is based on a plurality of different estimation community set { x 1, x 2..., x L} T, { y 1, y 2..., y K} TAnd { z 1, z 2..., z L} TOne of.For example described estimation attribute can comprise sensor tabulation, sensor threshold value, cycle of training, estimation model algorithm and/or various algorithm parameter.These estimate that all or some combinations of the special estimation attribute in the attribute go for each in the different models.Therefore, though one or more can being suitable in the described estimation attribute to more than one model, but each estimates that community set is uniquely corresponding to particular estimation model.
Update module 204 is imported in response to the user and is upgraded one or more in the described estimation attribute.When estimating that attribute is updated, the estimation attribute that update module 204 then will be upgraded is sent to each estimation model corresponding to the different sets that comprises the estimation attribute that now has been updated.The estimation attribute that upgraded is replaced the preceding version of renewal in the set.
In case the estimation attribute of one or more renewals is sent to one or more estimation models by update module 204, this estimation model is corresponding at least one the unique estimation community set that comprises in the present estimation attribute that upgraded, and the estimation attribute after the renewal is sent to those estimation models that need be corrected or upgrade.Modification is carried out by model modification module 206, and it revises each estimation model corresponding to the different sets that comprises at least one the estimation attribute that was updated.As more specifically described as the following, in response to can " being trained again " to produce the sensor vector that new sensor produces subsequently to being comprised in the estimation model that was modified corresponding to the renewal of the one or more estimation attributes in the community set of estimation model.
In particular example, may think and be used to monitored system 104a, 104b, one or more estimation models that carry out modeling among the 104c are finished the work deficiently with respect to one of monitored system. and for example this may be because monitored system 104a, 104b, the variation of the foundation structure of one of 104c. on the contrary, the variation of system architecture or other environment may cause model to become being more suitable for inapplicable monitored system 104a before this model of monitoring, 104b, one of 104c. in addition, can develop new model for monitored system 104a, 104b, one or more uses among the 104c.
Therefore, according to another embodiment of the invention, described model association module 202 is arranged in response to the deletion of the interpolation of the variation of system architecture and other environment and new model or old model and upgrades association between monitored system and the associated estimation model.
Now additionally with reference to figure 4, in any given example, the association between M different system and N the estimation model can provide with fleet table 400 concisely.Described fleet table 400 may be implemented as M * N matrix, and wherein M is the integer corresponding to the number of monitored system greater than 1, and N is the integer that equals the number of the estimation model that is associated with different monitored systems.Association between i system and j the model is the i that is assigned with numerical value 1 by matrix, j unit's expression usually; When not having association, the i of matrix, j element is 0.Change 1 and 0 and easily revise fleet table 400 by upgrading association in response to environmental change (changing and/or the interpolation or the deletion of estimation model) such as system architecture along with model association module 202.
With reference now to Fig. 5,, the alternate embodiment that is used to upgrade the system 500 of a plurality of monitoring models comprises training module 508 in addition.Training module 508 is system's particular version of each monitored each estimation model of systematic training.Training is to utilize the sensing data that is produced by the sensor that communicates to connect special system to carry out, and wherein trains special system's particular version of estimation model for this special system.Communicate by letter to training module 508 illustratives with model association module 502, update module 504 and model modification module 506.
Described function before model association module 502, update module 504 and model modification module 506 are carried out separately.Therefore, In yet another embodiment, training module 508 can be arranged to other module in each system's particular version of each estimation model of being modified owing to the performed operation of other module with training again of each synthetic operation.
As Fig. 6 illustrative steps schematically showed, an alternative embodiment of the invention is to be used to upgrade the model association method 600 that the electronics of estimation model is realized.Each estimation model can comprise one of various models that are used for the detection of reasoning as has been described.Method 600 illustrative ground in step 602 comprises the association of determining between special monitored system and in a plurality of estimation model at least one.Each estimation model is based on one of a plurality of different estimation community sets, and each set is uniquely corresponding to particular estimation model.
Method 600 comprises in step 604 that additionally upgrading at least one estimates attribute.In step 606, method 600 comprises that further the estimation attribute that will upgrade is sent to each estimation model corresponding to the different sets that comprises at least one the estimation attribute that was updated.Method 600 comprises modification each estimation model corresponding to the different sets that comprises at least one the estimation attribute that was updated in step 608.Described method illustrative ground finishes in step 610.
According to another embodiment, method 600 can comprise at least one association of upgrading between monitored system and the associated estimation model.Additional step can be alternatively any point during being used for upgrading the process of a plurality of system monitoring models according to the step of having described carry out.
With reference now to the process flow diagram of Fig. 7,, shows the model association method 700 that realizes according to another embodiment electronics.Method 700 illustrative ground in step 702 comprises the association of determining between special monitored system and in a plurality of estimation model at least one.Each associated estimation is trained in step 704.Each model is trained for each system individually, and this model utilization comes modeling by the sensor vector that the sensor that communicates to connect special system provides.Each model definition of being trained like this is corresponding to system's particular version of the estimation model of system, and wherein this model is trained for this system.
Method 700 comprises also that in step 706 upgrading at least one estimates attribute.Method 700 comprises also that in step 708 the estimation attribute that will upgrade is sent to each estimation model corresponding to the different sets that comprises at least one the estimation attribute that was updated.Method 700 comprises modification each estimation model corresponding to the different sets that comprises at least one the estimation attribute that was updated in step 710.In step 712, method 700 comprises each system's particular version of each estimation model that training again has been modified.As training pattern, train described model based on the sensing data that produces by the sensor that communicates to connect special system again, wherein train special system's particular version of estimation model again for this special system.Described method illustrative ground finishes in step 712.
As described in the whole text, the present invention can realize with the combination of hardware, software or hardware and software.The present invention also can realize with centralized system in a computer system, and perhaps the distributed way that is distributed in the computer system of a plurality of interconnection with different elements realizes.The computer system or the miscellaneous equipment that are suitable for carrying out any kind of said method all are fit to.The typical combination of hardware and software can be the general-purpose computing system with computer program, and this computer program is controlled computer system when being loaded and be performed, so that it carries out method described herein.
The present invention can be embedded in the computer program, and this computer program comprises all features that can realize method described herein, and this computer program can be carried out these methods in being loaded into computer system the time.Computer program in current context means any expression of any language, code or the sign format of instruction set, described instruction set intention makes the system with information processing capability directly or in one of following operation or after both carry out special function, and described operation comprises: a) be converted to another kind of language, code or symbol; B) reproduce with the different materials form.
The present invention can realize with other form under the situation that does not break away from spirit of the present invention or base attribute.Therefore, should be with reference to the following claim of indication scope of the present invention, rather than with reference to above-mentioned instructions.

Claims (5)

1. system that is used to upgrade a plurality of monitoring models, described system comprises:
Model association module, be used to each system in a plurality of monitored systems to determine the association between one of them at least in monitored system and a plurality of estimation model, wherein each estimation model is based on one of them of a plurality of different estimation community sets, each estimate community set comprise sensor tabulation, sensor threshold value, cycle of training and estimation model algorithm at least one of them, and wherein each estimates that community set is uniquely corresponding to an estimation model;
Update module, be used to upgrade described estimation attribute at least one of them and the estimation attribute that at least one upgraded sent to corresponding to comprising at least one that be updated estimate each estimation model of the different sets of attribute; And
Model modification module is used to revise each estimation model corresponding to the different sets that comprises at least one the estimation attribute that was updated,
Wherein said model association module is configured to, if the structure of at least one monitored system changes, then upgrades the association between one of them at least of one or more estimation models and described a plurality of monitored systems.
2. the system that is used to upgrade a plurality of monitoring models as claimed in claim 1, also comprise training module, this training module is arranged to based on by sensing data that the sensor that communicates to connect system the produced system's particular version for each monitored each estimation model of systematic training.
3. the system that is used to upgrade a plurality of monitoring models as claimed in claim 2, wherein said training module also is arranged to each system's particular version of each estimation model that training again revised.
4. the system that is used to upgrade a plurality of monitoring models as claimed in claim 1, wherein said each estimation community set also comprises algorithm parameter.
5. the system that is used to upgrade a plurality of monitoring models as claimed in claim 1, the wherein said system that is used to upgrade a plurality of monitoring models is used in the position away from described a plurality of monitored systems.
CN2005800371119A 2004-08-27 2005-08-25 System, device, and methods for updating system-monitoring models Expired - Fee Related CN101048714B (en)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1202240A (en) * 1995-11-15 1998-12-16 安蒂·阿米·依玛里·兰格 Method for adaptive kalman filtering in dynamic systems
US6085183A (en) * 1995-03-09 2000-07-04 Siemens Aktiengesellschaft Intelligent computerized control system
CN1262835A (en) * 1998-02-02 2000-08-09 摩托罗拉公司 Method and apparatus for location based intercept
US6208953B1 (en) * 1997-07-31 2001-03-27 Sulzer Innotec Ag Method for monitoring plants with mechanical components

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6085183A (en) * 1995-03-09 2000-07-04 Siemens Aktiengesellschaft Intelligent computerized control system
CN1202240A (en) * 1995-11-15 1998-12-16 安蒂·阿米·依玛里·兰格 Method for adaptive kalman filtering in dynamic systems
US6208953B1 (en) * 1997-07-31 2001-03-27 Sulzer Innotec Ag Method for monitoring plants with mechanical components
CN1262835A (en) * 1998-02-02 2000-08-09 摩托罗拉公司 Method and apparatus for location based intercept

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