CN107063663A - A kind of air-conditioner water system intelligent diagnostics regulating valve - Google Patents
A kind of air-conditioner water system intelligent diagnostics regulating valve Download PDFInfo
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- CN107063663A CN107063663A CN201710260489.XA CN201710260489A CN107063663A CN 107063663 A CN107063663 A CN 107063663A CN 201710260489 A CN201710260489 A CN 201710260489A CN 107063663 A CN107063663 A CN 107063663A
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Abstract
The problem of present invention aim to address can not in time recognize and handle in time during the failure of existing regulating valve, a kind of air-conditioner water system intelligent diagnostics regulating valve is disclosed, it is characterised in that:Including the regulating valve for the water system for being installed on air-conditioning system.The signal that the regulating valve is exported according to controller, adjusts its valve position.The controller transmits output signal to host computer.Flow passes to host computer by signal measuring apparatus after the valve position feedback signal and regulating valve of regulating valve.The host computer is stored with Fault Model and fault diagnosis model.Using valve position feedback signal as the input of Fault Model, judge whether the water system of air-conditioning system occurs regulating valve failure.If so, using flow after controller output signal, valve position feedback signal and regulating valve as fault diagnosis model input, to detect fault type.
Description
Technical field
Type of the present invention is related to regulating valve fault diagnosis and detection technique field, and system is controlled in particular to building automatic
System.
Background technology
Lasting highest attention with China to building energy utilization rate, finds effective method and improves building energy
Utilization rate, reduction building energy consumption it is extremely urgent.In the Practical Project operation of building, even if experienced more perfect building
Operation debugging, can also be gradually deviated from optimal operational effect in actual motion.
Air-conditioner water system is responsible for building the regulation of heating ventilation air-conditioning system cooling and heating load, and the energy consumption to heating ventilation air-conditioning system is played
Key effect.And in air-conditioner water system, valve is then very important control unit, it is used for changing the path of pipe-line system
Section and media flow direction, with blocking, non-return, regulation, peace congruous function.
When air-conditioner water system regulating valve breaks down, the execution of system control logic is influenced whether, end water system is caused
The serious water conservancy of system is unbalance, while can not ensure the defencive function to Cooling and Heat Source unit, the normal fortune of unit can be influenceed when serious
When turning and service life, unit operation security and indoor comfort are not only influenceed, and increase cost of equipment and whole system
The energy consumption of system, causes energy waste, so as to cause unnecessary economic loss.Therefore, air-conditioner water system valve is run in real time
State is monitored, and prevents the generation of failure using reliable fault detection and diagnosis strategy, high all the time to air-conditioner water system
Effect operation has huge realistic meaning.
The traditional approach of fault detection and diagnosis (FDD) relies on the professional standing and experience of attendant, take time and effort and
Higher to attendant's competency profiling, reliability and sustainability can not meet the requirement of modern society.
The content of the invention
The problem of present invention aim to address can not in time recognize and handle in time during the failure of existing regulating valve
To realize that the technical scheme that the object of the invention is used is a kind of such, air-conditioner water system intelligent diagnostics regulation
Valve, it is characterised in that:Including the regulating valve for the water system for being installed on air-conditioning system；The letter that the regulating valve is exported according to controller
Number, adjust its valve position；The controller transmits output signal to host computer；Flowed after the valve position feedback signal and regulating valve of regulating valve
Amount passes to host computer by signal measuring apparatus；
The host computer is stored with SVM Fault Models and SVM fault diagnosis models；
The Fault Model and fault diagnosis model to set up process as follows：
1) air-conditioner water system model is set up
Simulation model is set up according to the pipe network system of the water system of actual air-conditioning system in building；
2) actual condition parameter collection, characterisitic parameter are extracted
According to step 1) described in simulation model, test setting nominal situation and several regulating valve fault condition, repeatedly
Extract under various operating modes, the characterisitic parameter of characterization failure；The characterisitic parameter of described characterization failure include controller output signal,
Flow after the valve position feedback signal and regulating valve of regulating valve；
3) the Fault Model training based on SVM
3-1) selecting step 2) regulating valve under whole regulating valve fault conditions for being obtained valve position feedback signal, by it
It is used as a fault condition data set；
Selecting step 2) regulating valve under the regulating valve nominal situation that is obtained valve position feedback signal, as normal
Floor data collection；
The input that fault condition data set and nominal situation data set are trained as SVM fault diagnosis model；
Data are carried out equidistantly to be split as two parts, a part of training set as svm classifier model, so as to be adjusted
Valve Fault Model is saved, another part is then as model measurement collection.
3-2) data in training set and test set are carried out after visualization processing, the normalization letter carried using MATLAB
Several normalizeds that data are carried out with deviation standardization；
3-3) after the normalized of data is completed, the parameter for proceeding by SVMs failure modes model is excellent
Select process.The purpose of preferred process is the nuclear parameter g and penalty factor c for obtaining optimal SVM；
3-4) according to step 3-3) optimized parameter that obtains carries out the training of model, and training process passes through LIBSVM tool boxes
Complete, finally give Fault Model；
4) the fault diagnosis model training based on SVM
4-1) by step 2) obtained carry out the data of every kind of operating mode be equidistantly split as two parts, a part of conduct
The training set of svm classifier model, so as to obtain regulating valve Fault Model, another part is then as model measurement collection.
4-2) data in training set and test set are carried out after visualization processing, the normalization letter carried using MATLAB
Several normalizeds that data are carried out with deviation standardization；
4-3) after the normalized of data is completed, the parameter for proceeding by SVMs failure modes model is excellent
Select process.The purpose of preferred process is the nuclear parameter g and penalty factor c for obtaining optimal SVM, to obtain failure the most accurate
Detect disaggregated model；
4-4) according to step 4-3) optimized parameter that obtains carries out the training of model, and training process passes through LIBSVM tool boxes
Complete, finally give the fault diagnosis model of each type；
Failure diagnostic process is as follows：
A valve position feedback signal) is judged whether the water system of air-conditioning system is sent out as the input of SVM Fault Models
Raw regulating valve failure；If so, into step B), using flow after controller output signal, valve position feedback signal and regulating valve as
The input of fault diagnosis model, to detect fault type；
B) characterisitic parameter of the type failure is predicted with the fault diagnosis SVM models of a certain type, then calculated
Go out the residual error of test data and predicted value, and then gauge index weighted moving average (EWMA) value.
C) the EWMA control figures under the type fault condition according to step B, by test value and the residual error of predicted value
EWMA values compare：
If test value can determine whether such non-fault condition of current working, return to step B beyond control limit threshold value), change
The fault diagnosis SVM models of an other type；
If test value limits threshold value without departing from control, can determine whether current working is such fault condition.
What deserves to be explained is, 6 kinds of most common failures of regulating valve in air-conditioner water system have：It is hysteresis quality, controlling dead error, out of control
Band, viscosity, leakage, blocking.Wherein, the first four kinds nonlinear fault features for valve actuator, latter two is valve body valve
Seat failure.
This patent considers that when regulating valve has failure corresponding change can also occur for each characterisitic parameter, in order to improve
The operability and applicability of fault detect flow, the 3 typical valves most easily measured using air-conditioner water system regulating valve
Energy parameter is used as the characterisitic parameter for indicating failsafe valve：Flow after controller output signal, valve position feedback signal, regulating valve (or
Pressure difference before and after person's regulating valve).FDD based on SVM is set up according to the analogue data under nominal situation and under all kinds of fault conditions
Disaggregated model, the disaggregated model that training data is obtained is classified to test data, and event is carried out to valve member according to classification results
Barrier detection, judges whether valve breaks down.
When classification and Detection Model Diagnosis valve characteristic parameter drift-out nominal situation, after judging that valve breaks down, further according to
Fault characteristic supplemental characteristic under all kinds of fault conditions sets up the fault diagnosis regression model based on SVM, and model predication value
As benchmark, the measured value of estimated performance parameter and the residual error of a reference value are gone forward side by side so as to be distributed failure judgement type by residual error t
Row diagnosis.So far, a kind of air-conditioner water system intelligent diagnostics regulating valve can just be arrived.
Brief description of the drawings
Fig. 1 is building air conditioning water system model of the invention.
In Fig. 1, dotted line frame inner assembly designates position of the regulating valve in air-conditioner water system described in type patent of the present invention.Member
Part 8 is water pump.Element 9-21 is ducting module and end resistance devices module.Element 26-29 is ball valve (regulating valve), element 53
Diaphragm valve is bypassed for pressure difference, element 50,51 is butterfly valve.Element 30,32 is signal measuring apparatus.Element 33,35,36,49 is valve
Controller, element 31,48 is signal controller.Element 25 is level pressure pressure source.
Fig. 2 Failure Diagnostic Code figures.
Fig. 3 is fault diagnosis technology route map.
Embodiment
With reference to embodiment, the invention will be further described, but should not be construed above-mentioned subject area of the invention only
It is limited to following embodiments.Without departing from the idea case in the present invention described above, according to ordinary skill knowledge and used
With means, various replacements and change are made, all should be included within the scope of the present invention.
A kind of air-conditioner water system intelligent diagnostics regulating valve, it is characterised in that:Water system including being installed on air-conditioning system
Regulating valve.The signal that the regulating valve is exported according to controller, adjusts its valve position.The controller is believed to host computer transmission output
Number.Flow passes to host computer by signal measuring apparatus after the valve position feedback signal and regulating valve of regulating valve.
The host computer is stored with Fault Model and fault diagnosis model.
Using valve position feedback signal as the input of Fault Model, judge whether the water system of air-conditioning system is adjusted
Valve failure.If so, regarding flow after controller output signal, valve position feedback signal and regulating valve as the defeated of fault diagnosis model
Enter, to detect fault type：
The Fault Model and fault diagnosis model to set up process as follows：
1) air-conditioner water system model is set up
Simulation model, the foundation of model are set up according to the pipe network system of actual constant-primary-flow air-conditioner water system in building
Need to ignore some parts little on result influence and parameter.(Fig. 2 is the building air conditioning water system model of the present embodiment)
2) actual condition parameter collection, characterisitic parameter are extracted
According to step 1) described in simulation model, test setting nominal situation, delayed fault condition, viscous fault condition,
Tape jam operating mode out of control, controlling dead error fault condition, leakage fault condition, under blocking fault condition can characterization failure characteristic ginseng
Number:Flow (or pressure difference before and after regulating valve) after controller output signal, the valve position feedback signal of regulating valve and regulating valve.And root
Faulty tag is added to initial data according to fault condition type, such as 0 nominal situation, 1 representing fault operating mode is represented.
Show that data are handled by Flowmaster simulations, it is met under MATLAB LIBSVM tool boxes to training
The file format requirements of data and inspection data.The data that every kind of operating mode is included, are added according to fault type to initial data
Faulty tag, e.g., 0 represents nominal situation, 1 representing fault operating mode.
3) the Fault Model training based on SVM
3-1) selecting step 2) the valve position feedback signal that obtains regulating valve under several regulating valve fault condition merges
It is used as a fault condition data set.
Selecting step 2) the valve position feedback signal of regulating valve under several regulating valve nominal situation is obtained as normal
Floor data collection.
Data are carried out equidistantly to be split as two parts, a part of training set as svm classifier model, according to training set
The training of svm classifier model is carried out with the faulty tag of division, so as to obtain regulating valve Fault Model, another part is then made
For model measurement collection.
The input that fault condition data set and nominal situation data set are trained as SVM Fault Model.
3-2) data in training set and test set are carried out after visualization processing, the normalization letter carried using MATLAB
Several normalizeds that data are carried out with deviation standardization, the method is the linear transformation to initial data, maps end value
To between [0-1].
3-3) after the normalized of data is completed, the parameter for proceeding by SVMs failure modes model is excellent
Select process.The purpose of preferred process is the nuclear parameter g and penalty factor c for obtaining optimal SVM.What deserves to be explained is, for core
Parameter g and penalty factor c selection, this patent carry out optimization of parameters using cross-validation method (K-CV).For given interval interior
Nuclear parameter g and penalty factor c, training sample is divided into three groups, i.e. K=3 first for each group of collocation, then will be each
Subset data makees one-time authentication collection respectively, while remaining two subset data can so obtain 3 models as training set,
The average of so final classification accuracy rate of their checking collection is exactly accuracy rate when grader takes the model parameter.Then
The collocation for choosing next group of nuclear parameter g and penalty factor c proceeds cross validation, until complete in given interval all takes
With type.
3-4) according to step 3-3) optimized parameter that obtains carries out the training of model, and training process passes through LIBSVM tool boxes
Complete.Afterwards, failure detection result is obtained.
4) the fault diagnosis model training based on SVM
4-1) by step 2) obtained carry out the data of every kind of operating mode be equidistantly split as two parts, a part of conduct
The training set of svm classifier model, according to training set and valve position feedback signal, so that regulating valve fault diagnosis model is obtained, it is another
Part is then as model measurement collection.
4-2) data in training set and test set are carried out after visualization processing, the normalization letter carried using MATLAB
Several normalizeds that data are carried out with deviation standardization, the method is the linear transformation to initial data, maps end value
To between [0-1].
4-3) after the normalized of data is completed, the parameter for proceeding by SVMs failure modes model is excellent
Select process.The purpose of preferred process is the nuclear parameter g and penalty factor c for obtaining optimal SVM, to obtain failure the most accurate
Detect disaggregated model.As previously mentioned, for nuclear parameter g and penalty factor c selection, entered herein using cross-validation method (K-CV)
Row optimization of parameters.It is equal first by training sample for each group of collocation for the nuclear parameter g and penalty factor c in given interval
It is divided into three groups, i.e. K=3, each subset data is then made into one-time authentication collection respectively, while remaining two subset data conduct
Training set, can so obtain 3 models, then the average of the final classification accuracy rate of their checking collection is exactly that grader takes
The accuracy rate during model parameter.Then the collocation for choosing next group of nuclear parameter g and penalty factor c proceeds cross validation,
Until completing all collocation types in given interval.
4-4) according to step 4-3) optimized parameter that obtains carries out the training of model, and training process passes through LIBSVM tool boxes
Complete.Finally give fault diagnosis model.
Below by taking the delayed failure of regulating valve as an example, the air-conditioner water system regulating valve fault diagnosis model based on SVM is introduced：Press
According to step 2) the performance parameter data of model training collection and test set under fault condition are taken, will be according to step 4) training completes
The predicted value of diagnostic model draws test set as benchmark, the test set data and the residual error of a reference value of estimated performance parameter
Root-mean-square error and coefficient correlation between data and a reference value, are used as the basis of fault diagnosis.
Bring the characteristic parameter data under nominal situation and other 5 kinds of failures into the fault diagnosis as new test set again
Model, completes the diagnostic process of this failure.
, can be according to delayed fault diagnosis model according to the root-mean-square error and the numerical value of coefficient correlation obtained under each operating mode
Whether the deviation of predicted value and actual measured value currently occurs delayed failure to diagnose.Now, fault diagnosis result is just obtained.
The diagnosis of other fault conditions is carried out with above-mentioned steps.Diagnostic techniques route map is shown in Fig. 3.
Claims (1)
1. a kind of air-conditioner water system intelligent diagnostics regulating valve, it is characterised in that:Including the tune for the water system for being installed on air-conditioning system
Save valve；The signal that the regulating valve is exported according to controller, adjusts its valve position；The controller is believed to host computer transmission output
Number；Flow passes to host computer by signal measuring apparatus after the valve position feedback signal and regulating valve of regulating valve；
The host computer is stored with SVM Fault Models and SVM fault diagnosis models；
The Fault Model and fault diagnosis model to set up process as follows：
1) the air-conditioner water system model is set up
Simulation model is set up according to the pipe network system of the water system of actual air-conditioning system in building；
2) actual condition parameter collection, characterisitic parameter are extracted
According to step 1) described in simulation model, test setting nominal situation and several regulating valve fault condition are repeatedly extracted
Under various operating modes, the characterisitic parameter of characterization failure；The characterisitic parameter of described characterization failure includes controller output signal, regulation
Flow after the valve position feedback signal and regulating valve of valve；
3) the Fault Model training based on SVM
3-1) selecting step 2) regulating valve under whole regulating valve fault conditions for being obtained valve position feedback signal, as
One fault condition data set；
Selecting step 2) regulating valve under the regulating valve nominal situation that is obtained valve position feedback signal, as nominal situation
Data set；
The input that fault condition data set and nominal situation data set are trained as SVM fault diagnosis model；
Data are carried out equidistantly to be split as two parts, a part of training set as svm classifier model, so as to obtain regulating valve
Fault Model, another part is then as model measurement collection.
3-2) data in training set and test set are carried out after visualization processing, the normalized function pair carried using MATLAB
Data carry out the normalized of deviation standardization；
3-3) after the normalized of data is completed, the preferred mistake of parameter of SVMs failure modes model is proceeded by
Journey.The purpose of preferred process is the nuclear parameter g and penalty factor c for obtaining optimal SVM；
3-4) according to step 3-3) optimized parameter that obtains carries out the training of model, and training process is complete by LIBSVM tool boxes
Into finally giving Fault Model；
4) the fault diagnosis model training based on SVM
4-1) by step 2) obtained carry out the data of every kind of operating mode be equidistantly split as two parts, and a part is used as SVM
The training set of disaggregated model, so as to obtain regulating valve Fault Model, another part is then as model measurement collection.
4-2) data in training set and test set are carried out after visualization processing, the normalized function pair carried using MATLAB
Data carry out the normalized of deviation standardization；
4-3) after the normalized of data is completed, the preferred mistake of parameter of SVMs failure modes model is proceeded by
Journey.The purpose of preferred process is the nuclear parameter g and penalty factor c for obtaining optimal SVM, to obtain fault detect the most accurate
Disaggregated model；
4-4) according to step 4-3) optimized parameter that obtains carries out the training of model, and training process is complete by LIBSVM tool boxes
Into finally giving the fault diagnosis model of each type；
Failure diagnostic process is as follows：
A valve position feedback signal) is judged whether the water system of air-conditioning system is adjusted as the input of SVM Fault Models
Save valve failure；If so, into step B), it regard flow after controller output signal, valve position feedback signal and regulating valve as failure
The input of diagnostic model, to detect fault type；
B) characterisitic parameter of the type failure is predicted with the fault diagnosis SVM models of a certain type, survey is then calculated
Try the residual error of data and predicted value, and then gauge index weighted moving average (EWMA) value.
C) the EWMA control figures under the type fault condition according to step B, by the residual error of test value and predicted value
EWMA values compare：
If test value can determine whether such non-fault condition of current working, return to step B beyond control limit threshold value), change in addition
The fault diagnosis SVM models of one type；
If test value limits threshold value without departing from control, can determine whether current working is such fault condition.
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| CN112257779A (en) * | 2020-10-22 | 2021-01-22 | 重庆中源绿蓝环境科技有限公司 | Method for acquiring self-learning working condition parameters of central air conditioner |
| CN117251738A (en) * | 2023-11-17 | 2023-12-19 | 四川中测仪器科技有限公司 | Data-based adjusting valve group vibration threshold setting method |
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Application publication date: 20170818 |