CN114484731B - Central air conditioner fault diagnosis method and device based on stacking fusion algorithm - Google Patents
Central air conditioner fault diagnosis method and device based on stacking fusion algorithm Download PDFInfo
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Abstract
The invention discloses a central air conditioner fault diagnosis method based on a stacking fusion algorithm, which comprises the following steps: establishing a digital twin model of a central air conditioning system; collecting state data of a central air conditioning system during normal operation and different faults, and obtaining a sample data set after data preprocessing and feature extraction; dividing a sample data set into a training data set and a test data set, and simultaneously constructing a double-layer stacking model; training each base learner by adopting a k-fold cross validation method to obtain a prediction result of each base learner as a secondary training data set; when training each base learner, selecting a plurality of groups of different machine learning algorithms to combine to generate a secondary training data set in a plurality of groups of combined modes; inputting a plurality of groups of secondary training data sets into a secondary learner to train to obtain a plurality of central air conditioner fault diagnosis models; and evaluating the prediction performance of the plurality of central air conditioner fault diagnosis models, and selecting the model with the best performance as the optimal central air conditioner fault diagnosis model to carry out fault diagnosis.
Description
Technical Field
The invention belongs to the technical field of central air conditioners, and particularly relates to a central air conditioner fault diagnosis method and device based on a stacking fusion algorithm.
Background
In the development process of urban mass, the number and the mass of large public buildings are obviously increased along with the expansion of the mass of the urban mass, wherein the two major trends of building electrification which is rapidly developed and building intellectualization which is gradually developed are widely focused in the society. Because of the large-scale public refrigeration demands, the central air conditioning system and the automatic control system thereof are increasingly large in scale, and the equipment types and the quantity are increasingly large, so that the complexity of the system is higher and higher. In the running process of the system, various faults are inevitably generated, if the faults cannot be removed in time, the running parameters of the system are seriously deviated from the required set values, discomfort is brought to indoor staff, the working efficiency and the working quality are affected, the energy consumption of the system is increased, and the service life of equipment is shortened. Moreover, once a fault occurs in the central air conditioning system, it often takes a long time to determine the point of the fault and complete subsequent maintenance work, which causes unnecessary energy waste. According to incomplete statistics, in the process of troubleshooting the central air conditioning system, the time for searching the cause of the fault generally accounts for more than 50% of the total troubleshooting time.
Because the central air conditioning system has the characteristics of nonlinearity, complexity, multiple system parameters, mutual coupling and the like, the fault diagnosis method for the central air conditioning system, which is extremely complete and universal, is difficult to realize. At present, the commonly adopted fault diagnosis method of the central air conditioner mainly starts from historical empirical data, and a neural network is utilized to train a fault diagnosis model. However, when a single learning algorithm is adopted to predict the fault diagnosis model, the complexity of the prediction model is improved due to excessive input data variables, so that the overfitting of the prediction output is caused, and the accuracy of model prediction is reduced.
Based on the technical problems, a new central air conditioner fault diagnosis method based on a stacking fusion algorithm needs to be designed.
Disclosure of Invention
The invention aims to provide a central air conditioner fault diagnosis method based on a stacking fusion algorithm, which can effectively reduce model prediction errors and improve model prediction accuracy compared with a single model by stacking fusion of multiple models.
In order to solve the technical problems, the invention provides a central air conditioning system fault diagnosis method based on a stacking fusion algorithm, which comprises the following steps:
S1, establishing a digital twin model of a central air conditioning system by adopting a mechanism modeling and data identification method;
s2, collecting state data of a central air conditioning system during normal operation and different faults through a plurality of sensors, preprocessing the state data, extracting features of preprocessed data variables by adopting a wavelet packet decomposition algorithm and a wavelet packet reconstruction algorithm, and selecting the extracted features according to a gray correlation algorithm to obtain a sample data set;
s3, dividing a sample data set into a training data set and a test data set, simultaneously building a double-layer stacking model, and determining that the number of the basic learners is m and the number of the secondary learners is 1;
s4, training each base learner by adopting a k-fold cross validation method, and obtaining a prediction result of each base learner as a secondary training data set; when training each base learner, selecting a plurality of groups of different machine learning algorithms to combine, and generating a secondary training data set under a plurality of groups of combined modes;
s5, inputting a plurality of groups of secondary training data sets into a secondary learner to train to obtain a plurality of central air conditioner fault diagnosis models;
and S6, evaluating the prediction performances of the plurality of central air conditioner fault diagnosis models through the test data set, selecting the model with the best prediction performances as the optimal central air conditioner fault diagnosis model, and carrying out fault diagnosis of the central air conditioner system through the optimal central air conditioner fault diagnosis model.
Further, in the step S1, a digital twin model of the central air conditioning system is established by adopting a mechanism modeling and data identification method, which specifically includes:
constructing a physical model, a logical model and a simulation model of the central air conditioning system; wherein,,
the construction of the physical model comprises the following steps: at least establishing physical models of a water chilling unit, a chilled water circulation system and a cooling water circulation system, wherein the water chilling unit provides chilled water with a certain temperature for the tail end and consists of a compressor, an evaporator, a condenser and a throttle valve; the chilled water circulation system is used for conveying chilled water to the cooling coil pipe to cool indoor return air, and consists of a chilled water pump, a chilled water pipe and an air treatment unit; the cooling water circulation system releases heat absorbed by a refrigerating fluid of the water chilling unit into the atmosphere and consists of a cooling water pump, a cooling water pipe and a cooling tower;
the construction of the logic model comprises the following steps: establishing a controllable closed-loop logic model according to the logic mechanism relation among all physical entities of the central air conditioning system, and mapping the physical model to the logic model;
the construction of the simulation model comprises the following steps: constructing a central air conditioning system simulation model based on the collected operation data, state data and physical attribute data of the central air conditioning system;
Carrying out virtual-real fusion on the physical model, the logic model and the simulation model, and constructing a system-level digital twin model of a physical entity of the central air conditioning system in a virtual space;
and accessing the multi-working-condition real-time operation data of the central air conditioning system into the system-level digital twin model, and carrying out self-adaptive identification correction on the simulation result of the system-level digital twin model by adopting a reverse identification method to obtain the digital twin model of the central air conditioning system after identification correction.
Further, the building of the water chiller model includes:
neglecting the suction and exhaust pressure loss of the compressor and neglecting the heat exchange between the compressor and the environment, and establishing a compressor model to be expressed as:
wherein m is r Is the refrigerant mass flow; v (V) th Gas delivery capacity is a mechanism of compression; v 1 The specific volume of suction gas for the compressor; ζ is the gas transmission coefficient; p (P) ths Theoretical power consumption of the isentropic compression process of the compressor; p (P) i The power consumption of the actual compression process of the compressor is the indicated power; p (P) el The electric power input for the actual compression process of the compressor, namely the power measured by a power meter; k is an isentropic compression index; p (P) e Is the evaporating pressure, i.e. the compressor suction pressure; p (P) k Is the condensing pressure, i.e., compressor discharge pressure; η (eta) i An indicated efficiency for the compressor; η (eta) el Is the electrical efficiency of the compressor; h is a 2 Enthalpy for the compressor outlet refrigerant; h is a 1 Enthalpy for the compressor inlet refrigerant;
the modeling of the condenser comprises:
neglecting heat exchange between the condenser and the outside and the flow of the refrigerant and the cooling water to be regarded as one-dimensional uniform flow, the process of obtaining the heat exchange in the condenser is expressed as follows:
Q c ＝m w,c c p,w (t wo,c -t wi,c )＝m r (h ri,c -h ro,c )；
Q 1,c ＝K 1,c F 1,c Δt 1,c ；
Q 2,c ＝K 2,c F 2,c Δt 2,c ；
Q 3,c ＝K 3,c F 3,c Δt 3,c ；
wherein Q is c The total heat exchange amount of the condenser; m is m w,c Is the flow of cooling water; c p,w The constant pressure specific heat of water; t is t wi,c The inlet temperature of cooling water; t is t wo,c Is the cooling water outlet temperature; t is t ri,c Is the refrigerant inlet temperature; t is t ro,c Is the refrigerant outlet temperature; m is m r Is the refrigerant flow; h is a ri,c Is the enthalpy of the condenser inlet refrigerant; h is a ro,c Is the outlet enthalpy value of the condenser; q (Q) 1,c 、Q 2,c 、Q 3,c The heat exchange amount of the condenser superheat zone, the two-phase zone and the supercooling zone is respectively; f (F) 1,c 、F 2,c 、F 3,c The heat exchange areas of the condenser superheat zone, the two-phase zone and the supercooling zone are respectively; Δt (delta t) 1,c 、Δt 2,c 、Δt 3,c The heat exchange temperature difference is respectively the heat exchange temperature difference of the condenser superheat zone, the two-phase zone and the supercooling zone; k (K) 1,c 、K 2,c 、K 3,c The heat transfer coefficients of the condenser superheat zone, the two-phase zone and the supercooling zone are respectively;
the modeling of the evaporator comprises the following steps:
neglecting heat exchange between the evaporator and the outside and the flow of the refrigerant and the chilled water to be regarded as one-dimensional uniform flow, the process of obtaining the heat exchange in the evaporator is expressed as follows:
Q e ＝m w,e c p,w (t wi,e -t wo,e )＝m r (1-x)(h ro,e -h ri,e )；
Q 1,e ＝K 1,e F 1,e Δt 1,e ；
Q 2,e ＝K 2,e F 2,e Δt 2,e ；
Wherein Q is e Heat exchange capacity for the evaporator; m is m w,e Is the flow of the chilled water; t is t wi,e Chilled water temperature for evaporator inlet; t is t wo,e Chilled water temperature for evaporator outlet; t is t w1,e Chilled water temperature for the inlet of the two-phase zone; h is a ri,e Is the evaporator inlet enthalpy; h is a ro,e Is the evaporator outlet enthalpy; x is the dryness of the refrigerant at the inlet of the evaporator; q (Q) 1,e Heat exchange amount for the overheat area of the evaporator;
Q 2,e heat exchange capacity is carried out for the two-phase region of the evaporator; Δt (delta t) 1,e The heat exchange temperature difference is the superheat region of the evaporator; Δt (delta t) 2,e The heat exchange temperature difference is the two-phase area of the evaporator; t is t ro,e The suction temperature of the compressor, namely the outlet refrigerant temperature of the evaporator; t is t r,e Is the evaporation temperature;
the modeling of the throttle valve comprises the following steps:
the thermal expansion valve is formed by the pressure P of a temperature sensing medium in a temperature sensing bulb b Providing a valve opening force from the steam pressure P c And a spring force providing a valve closing force, the spring force being minimal when the valve is in a closed state, ΔP min The method comprises the steps of carrying out a first treatment on the surface of the Valve displacement y and P b 、P c 、ΔP min The relationship between them is expressed as: y=k (P b -P c -ΔP min ) The method comprises the steps of carrying out a first treatment on the surface of the k is the reciprocal of the spring rate;
the water pump model establishment comprises the following steps:
the rotation speed ratio f of the water pump is defined as the rotation speed n of the water pump motor and the rated motor rotation speed n 0 The ratio is expressed as:
the relationship between the pump lift, pump efficiency and pump flow and speed ratio is expressed as:
wherein H is pu Is the lift of the water pump; m is m w Is the mass flow of the water pump; η (eta) pu The efficiency of the water pump is achieved; h is a 01 、h 02 、h 03 、h 11 、h 12 、h 13 Is the fitting coefficient.
Further, in the step S2, status data during normal operation and different faults of the central air conditioning system are collected through a plurality of sensors, and a sample data set is obtained after data preprocessing and feature extraction, which specifically includes:
thermocouples arranged on the walls of the inlet and outlet pipe of the compressor, the water inlet and outlet of the evaporator, the water inlet and outlet of the condenser and the refrigerant pipe wall of the inlet and outlet of the condenser are used for collecting inlet and outlet temperatures of the compressor, inlet and outlet temperatures of the evaporator, inlet and outlet temperatures of the refrigerant of the condenser and inlet and outlet temperatures of the refrigerant of the condenser during normal operation and simulated faults; collecting the inlet and outlet pressure of the compressor during normal operation and simulated faults through pressure sensors arranged at the inlet and outlet of the compressor; collecting cold water flow and cooling water flow during normal operation and simulated faults through flow sensors arranged on the cold water pump and the cooling water pump outlet horizontal pipe;
denoising, missing value filling, repeated invalid value deleting and normalization preprocessing are carried out on the acquired data;
taking a state characteristic parameter of central air conditioning equipment as an independent variable, taking a fault label characteristic of the central air conditioner as an independent variable, adopting a wavelet packet decomposition algorithm and a wavelet packet reconstruction algorithm to perform characteristic extraction on the preprocessed data variable, selecting the extracted characteristic according to a gray correlation algorithm, establishing a mapping relation between the state characteristic parameter of the central air conditioner and the fault label characteristic, and obtaining a sample data set;
Wherein the fault signature comprises at least: the flow rate of cooling water is increased or reduced, the flow rate of freezing water is increased or reduced, the water inlet temperature of the condenser is too high, and non-condensable gas and refrigerant are leaked in the refrigerant; the feature variables selected by the fault tag feature correspond at least comprise condenser water inlet temperature, condenser water outlet temperature, evaporator water inlet temperature and evaporator water outlet temperature.
Further, the feature extraction of the preprocessed data variable by using a wavelet packet decomposition algorithm and a wavelet packet reconstruction algorithm comprises: decomposing the preprocessed data variable into a plurality of wavelet values through a wavelet packet decomposition algorithm, reconstructing wavelet characteristics of the decomposed wavelet values through a wavelet packet reconstruction algorithm, and finally outputting a wavelet characteristic data set of the preprocessed data;
the selecting the extracted features according to the gray correlation algorithm comprises the following steps: calculating the association degree value corresponding to the extracted feature according to a gray association degree algorithm, sorting the association degree values corresponding to the extracted features, distinguishing the association degree between the state feature parameters of each central air conditioning equipment and the fault label features, and taking the feature parameters with larger association degree as a sample data set to carry out fault diagnosis.
Further, in the step S4, training each base learner by using a k-fold cross validation method, and obtaining a prediction result of each base learner as a secondary training data set; when training each base learner, selecting a plurality of groups of different machine learning algorithms to combine to generate a secondary training data set in a plurality of groups of combined modes, wherein the method specifically comprises the following steps:
determining the number of the basic learners as m, and randomly dividing the sample data set D into m data sets D with equal size 1 、D 2 、D 3 ……D j Definition D j And D -j ＝D-D j J=1, 2,3, … …, m is the j-th fold test dataset and training dataset of k-fold cross validation, respectively, in training dataset D -j Training the base learner to obtain a model
m for test dataset D j In (2) samples, model->
Outputting a test result; when the cross-validation process is finished, obtaining an output result of the base learner for the whole training data set;
converting the output result of the obtained basic learner into a probability result, keeping the results of m basic learners in the [0,1] interval, and splicing the probability output result and the training set label to form a new training set as a secondary training data set;
wherein, m basic learners are trained, and the selectable machine learning algorithm comprises: SVM, BP neural network, random forest, GBDT model, XGBoost model, light GBM model, linear regression model, support vector machine, weighted extreme learning machine; according to the prediction performance of different algorithms, fixing the value of m, selecting various combination working conditions of various different algorithms to generate the base learner or carrying out different values on m, and selecting various combination working conditions of different algorithms to generate the base learner.
Further, in the step S5, inputting a plurality of sets of secondary training data sets into a secondary learner to train to obtain a plurality of fault diagnosis models of the central air conditioner, including: sequentially inputting the secondary training data sets obtained by training the base learners under different combined working conditions into the secondary learners for training to obtain a plurality of central air conditioner fault diagnosis models; the machine learning algorithm used by the secondary learner is one of SVM, BP neural network, random forest, GBDT model, XGBoost model, light GBM model, linear regression model, support vector machine and weighted extreme learning machine.
Further, in the step S6, the predicting performance of the plurality of central air conditioner fault diagnosis models is evaluated through the test data set, a model with the best predicting performance is selected as the optimal central air conditioner fault diagnosis model, and the fault diagnosis of the central air conditioner system is performed through the model, including:
calculating average absolute error value MAE sum of modelsVariance value RMSE, fitness R 2 As the evaluation standard of the fault diagnosis model of the central air conditioner, the better the model performance is, the smaller the average absolute error value MAE and the root mean square error value RMSE are, and the fitting degree R is 2 The larger;
wherein y is i 、
Respectively an actual value, a predicted value and an average value of the sample; n is the size of the test dataset.
Further, the base learner in the double-layer stacking model is a weighted base learner, a weight is given to each base learner by introducing a weight formula based on a G-mean value, an output result is corrected according to the weight, and the corrected result is fused into a secondary training data set and is input into a secondary learner to obtain a final central air conditioner fault diagnosis model;
the weight calculation formula of each base learner is as follows:
α i the weight is output; GM (GM) i G-mean values for the input sample set for the ith basis learner;
the secondary learner in the stacking model is a secondary learner which introduces an attention mechanism, after n-dimensional input variables in a secondary training data set are input to the secondary learner at the time t, the weight of n-dimensional features is calculated, the obtained weight is normalized to obtain weight importance degree duty ratio of different features, and finally the obtained weight and weight duty ratio are weighted to obtain a final feature vector, so that the central air conditioner fault diagnosis model is optimized and output.
The second aspect of the present invention also provides a fault diagnosis device for a central air conditioning system based on a stacking fusion algorithm, where the fault diagnosis device for a central air conditioning system includes:
Digital twin model establishment module: establishing a digital twin model of the central air conditioning system by adopting a mechanism modeling and data identification method;
sample data acquisition module: acquiring state data of a central air conditioning system during normal operation and different faults through a plurality of sensors, and acquiring a sample data set after data preprocessing and feature extraction;
stacking model building module: dividing a sample data set into a training data set and a test data set, simultaneously constructing a double-layer stacking model, determining the number of the basic learners as m, and determining the number of the secondary learners as 1;
the base learner training module: training each base learner by adopting a k-fold cross validation method to obtain a prediction result of each base learner as a secondary training data set; when training each base learner, selecting a plurality of groups of different machine learning algorithms to combine, and generating a secondary training data set under a plurality of groups of combined modes;
the secondary learner training module: inputting a plurality of groups of secondary training data sets into a secondary learner to train to obtain a plurality of central air conditioner fault diagnosis models;
the fault diagnosis model evaluation module: and evaluating the prediction performance of the plurality of central air conditioner fault diagnosis models through the test data set, selecting the model with the best prediction performance as the optimal central air conditioner fault diagnosis model, and carrying out fault diagnosis of the central air conditioner system through the model.
The beneficial effects of the invention are as follows:
(1) According to the invention, a digital twin model of the central air conditioning system is established by adopting a mechanism modeling and data identification method, virtual simulation mapping is carried out on an actual central air conditioning system, actual measurement data is input for identification and correction, the accuracy of the model is improved, a foundation is provided for the subsequent establishment of a fault diagnosis model of the central air conditioning, the prediction of the fault diagnosis model based on the digital twin model is realized, and the fault diagnosis decision is made based on the model prediction;
(2) The method comprises the steps of denoising, missing value filling, repeated invalid value deleting and normalization preprocessing of the acquired data; taking a state characteristic parameter of central air conditioning equipment as an independent variable, taking a fault label characteristic of the central air conditioner as a dependent variable, adopting a wavelet packet decomposition algorithm and a wavelet packet reconstruction algorithm to perform characteristic extraction on the preprocessed data variable, selecting the extracted characteristic according to a gray correlation algorithm, establishing a mapping relation between the state characteristic parameter of the central air conditioner and the fault label characteristic, obtaining a sample data set, performing characteristic extraction and correlation analysis on the input characteristic parameter, screening out important characteristic parameters, and reducing the influence of irrelevant factors;
(3) According to the invention, by constructing a double-layer stacking model, the number of the basic learners is determined to be m, and the number of the secondary learners is determined to be 1; training each base learner by adopting a k-fold cross validation method to obtain a prediction result of each base learner as a secondary training data set; when training each base learner, selecting a plurality of groups of different machine learning algorithms to combine to generate a secondary training data set in a plurality of groups of combined modes; inputting a plurality of groups of secondary training data sets into a secondary learner to train to obtain a plurality of central air conditioner fault diagnosis models; the prediction performance of a plurality of central air conditioner fault diagnosis models is evaluated through a test data set, a model with the best prediction performance is selected as the optimal central air conditioner fault diagnosis model, and a plurality of models are fused through stacking, so that compared with a single model, the prediction error can be effectively reduced, and the prediction precision is improved;
(4) The invention sets the base learner in the double-layer stacking model as a weighted base learner, assigns a weight to each base learner by introducing a weight formula based on a G-mean value, corrects the output result according to the weight, and then fuses the corrected result into a secondary training data set to be input into a secondary learner to obtain a final central air conditioner fault diagnosis model, so that the weight is set for the base learner according to the classification effect of the base learner, the classification result is corrected and then fused, and the corrected secondary learner has positive effect on making a final decision for the whole integrated learner model, thereby optimizing the distribution characteristics of the output information of the base learner; the secondary learner is a secondary learner which introduces an attention mechanism, so that the utilization effect of the secondary learner on the characteristics of the base learner is enhanced, and the prediction accuracy of the fault diagnosis model is improved.
Additional features and advantages will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a fault diagnosis method of a central air conditioning system based on a stacking fusion algorithm;
FIG. 2 is a schematic diagram of a central air conditioning system according to the present invention;
FIG. 3 is a schematic diagram of heat exchange of a central air conditioning system according to the present invention;
FIG. 4 is a block diagram of the stacking algorithm of the present invention;
fig. 5 is a schematic structural diagram of a fault diagnosis device of a central air conditioning system based on a stacking fusion algorithm.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Fig. 1 is a flowchart of a fault diagnosis method of a central air conditioning system based on a stacking fusion algorithm.
As shown in fig. 1, embodiment 1 provides a method for diagnosing a fault of a central air conditioning system based on a stacking fusion algorithm, where the method for diagnosing a fault of a central air conditioning system includes:
s1, establishing a digital twin model of a central air conditioning system by adopting a mechanism modeling and data identification method;
s2, collecting state data of a central air conditioning system during normal operation and different faults through a plurality of sensors, preprocessing the state data, extracting features of preprocessed data variables by adopting a wavelet packet decomposition algorithm and a wavelet packet reconstruction algorithm, and selecting the extracted features according to a gray correlation algorithm to obtain a sample data set;
S3, dividing a sample data set into a training data set and a test data set, simultaneously constructing an improved double-layer stacking model, and determining that the number of the basic learners is m and the number of the secondary learners is 1;
s4, training each base learner by adopting a k-fold cross validation method, and obtaining a prediction result of each base learner as a secondary training data set; when training each base learner, selecting a plurality of groups of different machine learning algorithms to combine, and generating a secondary training data set under a plurality of groups of combined modes;
s5, inputting a plurality of groups of secondary training data sets into a secondary learner to train to obtain a plurality of central air conditioner fault diagnosis models;
and S6, evaluating the prediction performances of the plurality of central air conditioner fault diagnosis models through the test data set, selecting the model with the best prediction performances as the optimal central air conditioner fault diagnosis model, and carrying out fault diagnosis of a central air conditioner system through the model.
Fig. 2 is a schematic structural diagram of a central air conditioning system according to the present invention.
Fig. 3 is a schematic diagram of heat exchange of a central air conditioning system according to the present invention.
As shown in fig. 2 and 3, in the embodiment, in the step S1, a digital twin model of the central air conditioning system is established by adopting a mechanism modeling and data identification method, which specifically includes:
Constructing a physical model, a logical model and a simulation model of the central air conditioning system; wherein,,
the construction of the physical model comprises the following steps: at least establishing physical models of entities of a water chilling unit, a chilled water circulation system and a cooling water circulation system, wherein the water chilling unit provides chilled water with a certain temperature for the tail end and consists of a compressor, an evaporator, a condenser and a throttle valve; the chilled water circulation system is used for conveying chilled water to a cooling coil or a tail end fan coil in the air treatment machine to cool indoor return air, and consists of a chilled water pump, a chilled water pipe and an air treatment unit or a fan coil; the cooling water circulation system releases heat absorbed by a refrigerating fluid of the water chilling unit into air through a cooling tower, and consists of a cooling water pump, a cooling water pipe and the cooling tower;
the construction of the logic model comprises the following steps: establishing a controllable closed-loop logic model according to the logic mechanism relation among all physical entities of the central air conditioning system, and mapping the physical model to the logic model;
the construction of the simulation model comprises the following steps: constructing a central air conditioning system simulation model based on the collected operation data, state data and physical attribute data of the central air conditioning system;
Carrying out virtual-real fusion on the physical model, the logic model and the simulation model, and constructing a system-level digital twin model of a physical entity of the central air conditioning system in a virtual space;
and accessing the multi-working-condition real-time operation data of the central air conditioning system into the system-level digital twin model, and carrying out self-adaptive identification correction on the simulation result of the system-level digital twin model by adopting a reverse identification method to obtain the digital twin model of the central air conditioning system after identification correction.
In this embodiment, the building of the water chiller model includes:
neglecting the suction and exhaust pressure loss of the compressor and neglecting the heat exchange between the compressor and the environment, and establishing a compressor model to be expressed as:
wherein m is r Is the refrigerant mass flow; v (V) th Gas delivery capacity is a mechanism of compression; v 1 The specific volume of suction gas for the compressor; ζ is the gas transmission coefficient; p (P) ths Theoretical power consumption of the isentropic compression process of the compressor; p (P) i The power consumption of the actual compression process of the compressor is the indicated power; p (P) el The electric power input for the actual compression process of the compressor, namely the power measured by a power meter; k is an isentropic compression index; p (P) e Is the evaporating pressure, i.e. the compressor suction pressure; p (P) k Is the condensing pressure, i.e., compressor discharge pressure; η (eta) i An indicated efficiency for the compressor; η (eta) el Is the electrical efficiency of the compressor; h is a 2 Enthalpy for the compressor outlet refrigerant; h is a 1 Enthalpy for the compressor inlet refrigerant;
the condenser modeling includes:
neglecting heat exchange between the condenser and the outside and the flow of the refrigerant and the cooling water to be regarded as one-dimensional uniform flow, the process of obtaining the heat exchange in the condenser is expressed as follows:
Q c ＝m w,c c p,w (t wo,c -t wi,c )＝m r (h ri,c -h ro,c )；
Q 1,c ＝K 1,c F 1,c Δt 1,c ；
Q 2,c ＝K 2,c F 2,c Δt 2,c ；
Q 3,c ＝K 3,c F 3,c Δt 3,c ；
wherein Q is c The total heat exchange amount of the condenser; m is m w,c Is the flow of cooling water; c p,w The constant pressure specific heat of water; t is t wi,c The inlet temperature of cooling water; t is t wo,c Is the cooling water outlet temperature; t is t ri,c Is the refrigerant inlet temperature; t is t ro,c Is the refrigerant outlet temperature; m is m r Is the refrigerant flow; h is a ri,c Is the enthalpy of the condenser inlet refrigerant; h is a ro,c Is the outlet enthalpy value of the condenser; q (Q) 1,c 、Q 2,c 、Q 3,c The heat exchange amount of the condenser superheat zone, the two-phase zone and the supercooling zone is respectively; f (F) 1,c 、F 2,c 、F 3,c The heat exchange areas of the condenser superheat zone, the two-phase zone and the supercooling zone are respectively; Δt (delta t) 1,c 、Δt 2,c 、Δt 3,c The heat exchange temperature difference is respectively the heat exchange temperature difference of the condenser superheat zone, the two-phase zone and the supercooling zone; k (K) 1,c 、K 2,c 、K 3,c The heat transfer coefficients of the condenser superheat zone, the two-phase zone and the supercooling zone are respectively;
the evaporator modeling includes:
neglecting heat exchange between the evaporator and the outside and the flow of the refrigerant and the chilled water to be regarded as one-dimensional uniform flow, the process of obtaining the heat exchange in the evaporator is expressed as follows:
Q e ＝m w,e c p,w (t wi,e -t wo,e )＝m r (1-x)(h ro,e -h ri,e )；
Q 1,e ＝K 1,e F 1,e Δt 1,e ；
Q 2,e ＝K 2,e F 2,e Δt 2,e ；
Wherein Q is e Heat exchange capacity for the evaporator; m is m w,e Is the flow of the chilled water; t is t wi,e Chilled water temperature for evaporator inlet; t is t wo,e Chilled water temperature for evaporator outlet; t is t w1,e Chilled water temperature for the inlet of the two-phase zone; h is a ri,e Is the evaporator inlet enthalpy; h is a ro,e Is the evaporator outlet enthalpy; x is the dryness of the refrigerant at the inlet of the evaporator; q (Q) 1,e Heat exchange amount for the overheat area of the evaporator; q (Q) 2,e Heat exchange capacity is carried out for the two-phase region of the evaporator; Δt (delta t) 1,e The heat exchange temperature difference is the superheat region of the evaporator; Δt (delta t) 2,e The heat exchange temperature difference is the two-phase area of the evaporator; t is t ro,e The suction temperature of the compressor, namely the outlet refrigerant temperature of the evaporator; t is t r,e Is the evaporation temperature;
the throttle valve model establishment includes:
the thermal expansion valve is formed by the pressure P of a temperature sensing medium in a temperature sensing bulb b Providing a valve opening force from the steam pressure P c And the spring force provides a valve closing force,the spring force is minimal when the valve is in the closed state, ΔP min The method comprises the steps of carrying out a first treatment on the surface of the Valve displacement y and P b 、P c 、ΔP min The relationship between them is expressed as: y=k (P b -P c -ΔP min ) The method comprises the steps of carrying out a first treatment on the surface of the k is the reciprocal of the spring rate;
the water pump model establishment comprises the following steps:
the rotation speed ratio f of the water pump is defined as the rotation speed n of the water pump motor and the rated motor rotation speed n 0 The ratio is expressed as:
the relationship between the pump lift, pump efficiency and pump flow and speed ratio is expressed as:
wherein H is pu Is the lift of the water pump; m is m w Is the mass flow of the water pump; η (eta) pu The efficiency of the water pump is achieved; h is a 01 、h 02 、h 03 、h 11 、h 12 、h 13 Is the fitting coefficient.
In this embodiment, in step S2, status data during normal operation and different faults of the central air conditioning system are collected through a plurality of sensors, and a sample data set is obtained after data preprocessing and feature extraction, which specifically includes:
thermocouples arranged on the walls of the inlet and outlet pipe of the compressor, the water inlet and outlet of the evaporator, the water inlet and outlet of the condenser and the refrigerant pipe wall of the inlet and outlet of the condenser are used for collecting inlet and outlet temperatures of the compressor, inlet and outlet temperatures of the evaporator, inlet and outlet temperatures of the refrigerant of the condenser and inlet and outlet temperatures of the refrigerant of the condenser during normal operation and simulated faults; collecting the inlet and outlet pressure of the compressor during normal operation and simulated faults through pressure sensors arranged at the inlet and outlet of the compressor; collecting cold water flow and cooling water flow during normal operation and simulated faults through flow sensors arranged on the cold water pump and the cooling water pump outlet horizontal pipe;
denoising, missing value filling, repeated invalid value deleting and normalization preprocessing are carried out on the acquired data;
taking a state characteristic parameter of central air conditioning equipment as an independent variable, taking a fault label characteristic of the central air conditioner as an independent variable, adopting a wavelet packet decomposition algorithm and a wavelet packet reconstruction algorithm to perform characteristic extraction on the preprocessed data variable, selecting the extracted characteristic according to a gray correlation algorithm, establishing a mapping relation between the state characteristic parameter of the central air conditioner and the fault label characteristic, and obtaining a sample data set;
Wherein the fault signature comprises at least: the flow rate of cooling water is increased or reduced, the flow rate of freezing water is increased or reduced, the water inlet temperature of the condenser is too high, and non-condensable gas and refrigerant are leaked in the refrigerant; the feature variables selected by the fault tag feature correspond at least comprise condenser water inlet temperature, condenser water outlet temperature, evaporator water inlet temperature and evaporator water outlet temperature.
The fault simulation method for increasing or decreasing the cooling water flow comprises the following steps: when the water chilling unit stably operates under the rated working condition, a gate valve on the cooling water pipe and a stop valve on the bypass pipe are regulated; the fault simulation method for increasing or decreasing the cold water quantity comprises the following steps: when the water chilling unit stably operates under the rated working condition, a gate valve on the cold water pipe and a stop valve on the bypass pipe are regulated; the fault simulation method for the excessive water inlet temperature of the condenser comprises the following steps: turning off or reversing the cooling tower fan; the fault simulation method for the non-condensable gas in the refrigerant comprises the following steps: when the water chilling unit stably operates under the rated working condition, nitrogen is flushed from a fluorine adding port of the unit; the refrigerant fault simulation method comprises the following steps: the evaporator outlet temperature increases.
In this embodiment, the feature extraction of the preprocessed data variable by using a wavelet packet decomposition algorithm and a wavelet packet reconstruction algorithm includes: decomposing the preprocessed data variable into a plurality of wavelet values through a wavelet packet decomposition algorithm, reconstructing wavelet characteristics of the decomposed wavelet values through a wavelet packet reconstruction algorithm, and finally outputting a wavelet characteristic data set of the preprocessed data;
The selecting the extracted features according to the gray correlation algorithm comprises the following steps: calculating the association degree value corresponding to the extracted feature according to a gray association degree algorithm, sorting the association degree values corresponding to the extracted features, distinguishing the association degree between the state feature parameters of each central air conditioning equipment and the fault label features, and taking the feature parameters with larger association degree as a sample data set to carry out fault diagnosis.
Fig. 4 is a structural diagram of an improved stacking algorithm according to the present invention.
In the embodiment, as shown in fig. 4, in the step S4, each base learner is trained by adopting a k-fold cross validation method, and a prediction result of each base learner is obtained as a secondary training data set; when training each base learner, selecting a plurality of groups of different machine learning algorithms to combine to generate a secondary training data set in a plurality of groups of combined modes, wherein the method specifically comprises the following steps:
determining the number of the basic learners as m, and randomly dividing the sample data set D into m data sets D with equal size 1 、D 2 、D 3 ……D j Definition D j And D -j ＝D-D j J=1, 2,3, … …, m is the j-th fold test dataset and training dataset of k-fold cross validation, respectively, in training dataset D -j Training the base learner to obtain a model 
m for test dataset D j In (2) samples, model->
Outputting a test result; when the cross-validation process is finished, obtaining an output result of the base learner for the whole training data set;
converting the output result of the obtained basic learner into a probability result, keeping the results of m basic learners in the [0,1] interval, and splicing the probability output result and the training set label to form a new training set as a secondary training data set;
wherein, m basic learners are trained, and the selectable machine learning algorithm comprises: SVM, BP neural network, random forest, GBDT model, XGBoost model, light GBM model, linear regression model, support vector machine, weighted extreme learning machine; according to the prediction performance of different algorithms, fixing the value of m, selecting various combination working conditions of various different algorithms to generate the base learner or carrying out different values on m, and selecting various combination working conditions of different algorithms to generate the base learner.
In this embodiment, in step S5, a plurality of sets of secondary training data sets are input into a secondary learner to perform training to obtain a plurality of central air conditioner fault diagnosis models, including: sequentially inputting the secondary training data sets obtained by training the base learners under different combined working conditions into the secondary learners for training to obtain a plurality of central air conditioner fault diagnosis models; the machine learning algorithm used by the secondary learner is one of SVM, BP neural network, random forest, GBDT model, XGBoost model, light GBM model, linear regression model, support vector machine and weighted extreme learning machine.
In practical application, the learning effect of the base learner is analyzed by observing the learning curve of the base learner, whether the phenomenon of over fitting or under fitting occurs is judged, and the parameter value of the base learner is regulated, so that the model has a better prediction effect. And calling the selected classification learning algorithm in the second layer model, performing fusion learning on the prediction result of the first layer model, training to obtain a strong classifier, and setting the parameter setting value of the selected classification algorithm.
In this embodiment, in the step S6, the predicting performance of the plurality of central air conditioning fault diagnosis models is evaluated through the test data set, a model with the best predicting performance is selected as the optimal central air conditioning fault diagnosis model, and the fault diagnosis of the central air conditioning system is performed through the model, including:
calculating average absolute error value MAE and root mean square difference value RMSE and fitting degree R of model 2 As the evaluation standard of the fault diagnosis model of the central air conditioner, the better the model performance is, the smaller the average absolute error value MAE and the root mean square error value RMSE are, and the fitting degree R is 2 The larger;
wherein y is i 、
Respectively an actual value, a predicted value and an average value of the sample; n is the size of the test dataset.
In this embodiment, the base learner in the double-layer stacking model is a weighted base learner, a weight is given to each base learner by introducing a weight formula based on a G-mean value, an output result is corrected according to the weight, and the corrected result is fused into a secondary training data set to be input into a secondary learner to obtain a final central air conditioner fault diagnosis model;
The weight calculation formula of each base learner is as follows:
α i the weight is output; GM (GM) i G-mean values for the input sample set for the ith basis learner;
the secondary learner in the improved stacking model is a secondary learner which introduces an attention mechanism, after n-dimensional input variables in a secondary training data set are input to the secondary learner at the time t, the weight of n-dimensional features is calculated, the obtained weight is normalized to obtain weight importance degree duty ratios of different features, and finally the obtained weight and weight duty ratio are weighted to obtain a final feature vector, so that the central air conditioner fault diagnosis model is optimized and output.
Example 2
Fig. 5 is a schematic structural diagram of a fault diagnosis device for a central air conditioning system based on a stacking fusion algorithm according to the present invention.
As shown in fig. 5, in this embodiment, the second aspect of the present invention further provides a central air conditioning system fault diagnosis device based on a stacking fusion algorithm, where the central air conditioning system fault diagnosis device includes:
digital twin model establishment module: establishing a digital twin model of the central air conditioning system by adopting a mechanism modeling and data identification method;
sample data acquisition module: acquiring state data of a central air conditioning system during normal operation and different faults through a plurality of sensors, and acquiring a sample data set after data preprocessing and feature extraction;
stacking model building module: dividing a sample data set into a training data set and a test data set, simultaneously constructing a double-layer stacking model, determining the number of the basic learners as m, and determining the number of the secondary learners as 1;
the base learner training module: training each base learner by adopting a k-fold cross validation method to obtain a prediction result of each base learner as a secondary training data set; when training each base learner, selecting a plurality of groups of different machine learning algorithms to combine, and generating a secondary training data set under a plurality of groups of combined modes;
the secondary learner training module: inputting a plurality of groups of secondary training data sets into a secondary learner to train to obtain a plurality of central air conditioner fault diagnosis models;
the fault diagnosis model evaluation module: and evaluating the prediction performance of the plurality of central air conditioner fault diagnosis models through the test data set, selecting the model with the best prediction performance as the optimal central air conditioner fault diagnosis model, and carrying out fault diagnosis of the central air conditioner system through the model.
According to the invention, a digital twin model of the central air-conditioning system is established by adopting a mechanism modeling and data identification method, virtual simulation mapping is carried out on an actual central air-conditioning system, actual measurement data is input for identification and correction, the accuracy of the model is improved, a foundation is provided for the subsequent establishment of a fault diagnosis model of the central air-conditioning system, the prediction of the fault diagnosis model based on the digital twin model is realized, and the fault diagnosis decision is made based on the model prediction.
The method comprises the steps of denoising, missing value filling, repeated invalid value deleting and normalization preprocessing of the acquired data; the method comprises the steps of taking a state characteristic parameter of central air conditioning equipment as an independent variable, taking a fault label characteristic of the central air conditioner as a dependent variable, adopting a wavelet packet decomposition algorithm and a wavelet packet reconstruction algorithm to conduct characteristic extraction on a preprocessed data variable, selecting extracted characteristics according to a gray correlation algorithm, establishing a mapping relation between the state characteristic parameter of the central air conditioner and the fault label characteristic, obtaining a sample data set, conducting characteristic extraction and correlation analysis on an input characteristic parameter, screening out an important characteristic parameter, and reducing influence of irrelevant factors.
According to the invention, by constructing a double-layer stacking model, the number of the basic learners is determined to be m, and the number of the secondary learners is determined to be 1; training each base learner by adopting a k-fold cross validation method to obtain a prediction result of each base learner as a secondary training data set; when training each base learner, selecting a plurality of groups of different machine learning algorithms to combine to generate a secondary training data set in a plurality of groups of combined modes; inputting a plurality of groups of secondary training data sets into a secondary learner to train to obtain a plurality of central air conditioner fault diagnosis models; the prediction performance of the plurality of central air conditioner fault diagnosis models is evaluated through the test data set, the model with the best prediction performance is selected as the optimal central air conditioner fault diagnosis model, and a plurality of models are fused through stacking, so that compared with a single model, the prediction error can be effectively reduced, and the prediction precision is improved.
The invention sets the base learner in the double-layer stacking model as a weighted base learner, assigns a weight to each base learner by introducing a weight formula based on a G-mean value, corrects the output result according to the weight, and then fuses the corrected result into a secondary training data set to be input into a secondary learner to obtain a final central air conditioner fault diagnosis model, so that the weight is set for the base learner according to the classification effect of the base learner, the classification result is corrected and then fused, and the corrected secondary learner has positive effect on making a final decision for the whole integrated learner model, thereby optimizing the distribution characteristics of the output information of the base learner; the secondary learner is a secondary learner which introduces an attention mechanism, so that the utilization effect of the secondary learner on the characteristics of the base learner is enhanced, and the prediction accuracy of the fault diagnosis model is improved.
In the several embodiments provided in this application, it should be understood that the disclosed systems and methods may be implemented in other ways as well. The system embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present invention may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored on a computer readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
With the above-described preferred embodiments according to the present invention as an illustration, the above-described descriptions can be used by persons skilled in the relevant art to make various changes and modifications without departing from the scope of the technical idea of the present invention. The technical scope of the present invention is not limited to the description, but must be determined according to the scope of claims.
Claims (10)
1. The utility model provides a central air conditioning system fault diagnosis method based on stacking fusion algorithm which is characterized in that the central air conditioning system fault diagnosis method includes:
s1, establishing a digital twin model of a central air conditioning system by adopting a mechanism modeling and data identification method;
s2, collecting state data of a central air conditioning system during normal operation and different faults through a plurality of sensors, preprocessing the state data, extracting features of preprocessed data variables by adopting a wavelet packet decomposition algorithm and a wavelet packet reconstruction algorithm, and selecting the extracted features according to a gray correlation algorithm to obtain a sample data set;
s3, dividing a sample data set into a training data set and a test data set, simultaneously building a double-layer stacking model, and determining that the number of the basic learners is m and the number of the secondary learners is 1;
s4, training each base learner by adopting a k-fold cross validation method, and obtaining a prediction result of each base learner as a secondary training data set; when training each base learner, selecting a plurality of groups of different machine learning algorithms to combine to generate a secondary training data set in a plurality of groups of combined modes;
s5, inputting a plurality of groups of secondary training data sets into a secondary learner to train to obtain a plurality of central air conditioner fault diagnosis models;
And S6, evaluating the prediction performances of the plurality of central air conditioner fault diagnosis models through a test data set, selecting a model with the best prediction performances as an optimal central air conditioner fault diagnosis model, and carrying out fault diagnosis of a central air conditioner system through the optimal central air conditioner fault diagnosis model.
2. The method for diagnosing faults of a central air conditioning system based on a stacking fusion algorithm according to claim 1, wherein in the step S1, a digital twin model of the central air conditioning system is established by adopting a mechanism modeling and data identification method, and the method comprises the following steps:
constructing a physical model, a logical model and a simulation model of the central air conditioning system; wherein,,
the construction of the physical model comprises the following steps: at least establishing a water chilling unit, a chilled water circulation system and a cooling water circulation system; the water chilling unit comprises a compressor, an evaporator, a condenser and a throttle valve; the chilled water circulation system comprises a chilled water pump, a chilled water pipe and an air treatment unit; the cooling water circulation system comprises a cooling water pump, a cooling water pipe and a cooling tower;
the construction of the logic model comprises the following steps: establishing a controllable closed-loop logic model according to the logic mechanism relation among all physical entities of the central air conditioning system, and mapping the physical model to the logic model;
The construction of the simulation model comprises the following steps: constructing a central air conditioning system simulation model based on the collected operation data, state data and physical attribute data of the central air conditioning system;
carrying out virtual-real fusion on the physical model, the logic model and the simulation model, and constructing a system-level digital twin model of a physical entity of the central air conditioning system in a virtual space;
and accessing the multi-working-condition real-time operation data of the central air conditioning system into the system-level digital twin model, and carrying out self-adaptive identification correction on the simulation result of the system-level digital twin model by adopting a reverse identification method to obtain the digital twin model of the central air conditioning system after identification correction.
3. The method for diagnosing faults of a central air conditioning system based on a stacking fusion algorithm as claimed in claim 2, wherein the modeling of the water chilling unit comprises the following steps:
neglecting the suction and exhaust pressure loss of the compressor and neglecting the heat exchange between the compressor and the environment, and establishing a compressor model to be expressed as:
wherein m is r Is the refrigerant mass flow; v (V) th Gas delivery capacity is a mechanism of compression; v 1 The specific volume of suction gas for the compressor; ζ is the gas transmission coefficient; p (P) ths Theoretical power consumption of the isentropic compression process of the compressor; p (P) i The power consumption of the actual compression process of the compressor is the indicated power; p (P) el The electric power input for the actual compression process of the compressor, namely the power measured by a power meter; k is an isentropic compression index; p (P) e Is the evaporating pressure, i.e. the compressor suction pressure; p (P) k Is the condensing pressure, i.e., compressor discharge pressure; η (eta) i An indicated efficiency for the compressor; η (eta) el Is the electrical efficiency of the compressor; h is a 2 Enthalpy for the compressor outlet refrigerant; h is a 1 Enthalpy for the compressor inlet refrigerant;
the modeling of the condenser comprises:
neglecting heat exchange between the condenser and the outside and the flow of the refrigerant and the cooling water to be regarded as one-dimensional uniform flow, the process of obtaining the heat exchange in the condenser is expressed as follows:
Q c ＝m w,c c p,w (t wo,c -t wi,c )＝m r (h ri,c -h ro,c )；
Q 1,c ＝K 1,c F 1,c Δt 1,c ；
Q 2,c ＝K 2,c F 2,c Δt 2,c ；
Q 3,c ＝K 3,c F 3,c Δt 3,c ；
wherein Q is c The total heat exchange amount of the condenser; m is m w,c Is the flow of cooling water; c p,w The constant pressure specific heat of water; t is t wi,c The inlet temperature of cooling water; t is t wo,c Is the cooling water outlet temperature; t is t ri,c Is the refrigerant inlet temperature; t is t ro,c Is the refrigerant outlet temperature; m is m r Is the refrigerant flow; h is a ri,c Is the enthalpy of the condenser inlet refrigerant; h is a ro,c Is the outlet enthalpy value of the condenser; q (Q) 1,c 、Q 2,c 、Q 3,c The heat exchange amount of the condenser superheat zone, the two-phase zone and the supercooling zone is respectively; f (F) 1,c 、F 2,c 、F 3,c The heat exchange areas of the condenser superheat zone, the two-phase zone and the supercooling zone are respectively; Δt (delta t) 1,c 、Δt 2,c 、Δt 3,c The heat exchange temperature difference is respectively the heat exchange temperature difference of the condenser superheat zone, the two-phase zone and the supercooling zone; k (K) 1,c 、K 2,c 、K 3,c The heat transfer coefficients of the condenser superheat zone, the two-phase zone and the supercooling zone are respectively;
the modeling of the evaporator comprises the following steps:
neglecting heat exchange between the evaporator and the outside and the flow of the refrigerant and the chilled water to be regarded as one-dimensional uniform flow, the process of obtaining the heat exchange in the evaporator is expressed as follows:
Q e ＝m w,e c p,w (t wi,e -t wo,e )＝m r (1-x)(h ro,e -h ri,e )；
Q 1,e ＝K 1,e F 1,e Δt 1,e ；
Q 2,e ＝K 2,e F 2,e Δt 2,e ；
wherein Q is e Heat exchange capacity for the evaporator; m is m w,e Is the flow of the chilled water; t is t wi,e Chilled water temperature for evaporator inlet; t is t wo,e Chilled water temperature for evaporator outlet; t is t w1,e Chilled water temperature for the inlet of the two-phase zone; h is a ri,e Is the evaporator inlet enthalpy; h is a ro,e Is the evaporator outlet enthalpy; x is the dryness of the refrigerant at the inlet of the evaporator; q (Q) 1,e Heat exchange amount for the overheat area of the evaporator; q (Q) 2,e Heat exchange capacity is carried out for the two-phase region of the evaporator; Δt (delta t) 1,e The heat exchange temperature difference is the superheat region of the evaporator; Δt (delta t) 2,e The heat exchange temperature difference is the two-phase area of the evaporator; t is t ro,e The suction temperature of the compressor, namely the outlet refrigerant temperature of the evaporator; t is t r,e Is the evaporation temperature;
the modeling of the throttle valve comprises the following steps:
the thermal expansion valve is formed by the pressure P of a temperature sensing medium in a temperature sensing bulb b Providing a valve opening force from the steam pressure P c And a spring force providing a valve closing force, the spring force being minimal when the valve is in a closed state, ΔP min The method comprises the steps of carrying out a first treatment on the surface of the Valve displacement y and P b 、P c 、ΔP min The relationship between them is expressed as: y=k (P b -P c -ΔP min ) The method comprises the steps of carrying out a first treatment on the surface of the k is the reciprocal of the spring rate;
The modeling of the water pump comprises the following steps:
the rotation speed ratio f of the water pump is defined as the rotation speed n of the water pump motor and the rated motor rotation speed n 0 The ratio is expressed as:
the relationship between the pump lift, pump efficiency and pump flow and speed ratio is expressed as:
wherein H is pu Is the lift of the water pump; m is m w Is the mass flow of the water pump; η (eta) pu The efficiency of the water pump is achieved; h is a 01 、h 02 、h 03 、h 11 、h 12 、h 13 To fit toCoefficients.
4. The method for diagnosing faults of a central air conditioning system based on a stacking fusion algorithm according to claim 1, wherein in the step S2, status data during normal operation and different faults of the central air conditioning system are collected through a plurality of sensors, and a sample data set is obtained after data preprocessing and feature extraction, including:
thermocouples arranged on the walls of the inlet and outlet pipe of the compressor, the water inlet and outlet of the evaporator, the water inlet and outlet of the condenser and the refrigerant pipe wall of the inlet and outlet of the condenser are used for collecting inlet and outlet temperatures of the compressor, inlet and outlet temperatures of the evaporator, inlet and outlet temperatures of the refrigerant of the evaporator and inlet and outlet temperatures of the refrigerant of the condenser during normal operation and simulated faults; collecting the inlet and outlet pressure of the compressor during normal operation and simulated faults through pressure sensors arranged at the inlet and outlet of the compressor; collecting cold water flow and cooling water flow during normal operation and simulated faults through flow sensors arranged on the cold water pump and the cooling water pump outlet horizontal pipe;
Denoising, missing value filling, repeated invalid value deleting and normalization preprocessing are carried out on the acquired data;
taking a state characteristic parameter of central air conditioning equipment as an independent variable, taking a fault label characteristic of the central air conditioner as an independent variable, adopting a wavelet packet decomposition algorithm and a wavelet packet reconstruction algorithm to perform characteristic extraction on the preprocessed data variable, selecting the extracted characteristic according to a gray correlation algorithm, establishing a mapping relation between the state characteristic parameter of the central air conditioner and the fault label characteristic, and obtaining a sample data set; wherein,,
the fault signature features include at least: the flow rate of cooling water is increased or reduced, the flow rate of freezing water is increased or reduced, the water inlet temperature of the condenser is too high, and non-condensable gas and refrigerant are leaked in the refrigerant; the feature variables selected by the fault tag feature correspond at least to the condenser water inlet temperature, the condenser water outlet temperature, the evaporator water inlet temperature and the evaporator water outlet temperature.
5. The method for diagnosing faults in a central air conditioning system based on a stacking fusion algorithm as claimed in claim 4, wherein the feature extraction of the preprocessed data variables by using a wavelet packet decomposition algorithm and a wavelet packet reconstruction algorithm comprises the following steps: decomposing the preprocessed data variable into a plurality of wavelet values through a wavelet packet decomposition algorithm, reconstructing wavelet characteristics of the decomposed wavelet values through a wavelet packet reconstruction algorithm, and finally outputting a wavelet characteristic data set of the preprocessed data;
The selecting the extracted features according to the gray correlation algorithm comprises the following steps: calculating the association degree value corresponding to the extracted feature according to a gray association degree algorithm, sorting the association degree values corresponding to the extracted features, distinguishing the association degree between the state feature parameters of each central air conditioning equipment and the fault label features, and taking the feature parameters with larger association degree as a sample data set to carry out fault diagnosis.
6. The method for diagnosing faults of a central air conditioning system based on a stacking fusion algorithm according to claim 1, wherein in the step S4, each base learner is trained by adopting a k-fold cross validation method, and a prediction result of each base learner is obtained as a secondary training data set; when training each base learner, selecting a plurality of groups of different machine learning algorithms to combine, and generating a secondary training data set under a plurality of groups of combined modes, wherein the method comprises the following steps:
determining the number of the basic learners as m, and randomly dividing the sample data set D into m data sets D with equal size 1 、D 2 、D 3 ……D j Definition D j And D -j ＝D-D j J=1, 2,3, … …, m is the j-th fold test dataset and training dataset of k-fold cross validation, respectively, in training dataset D -j Training the base learner to obtain a model
k=1, 2,3, … …, m for test dataset D j In (2) samples, model->
Outputting a test result; when the cross-validation process is finished, obtaining an output result of the base learner for the whole training data set;
converting the output result of the obtained basic learner into a probability result, keeping the results of m basic learners in the [0,1] interval, and splicing the probability output result and the training set label to form a new training set as a secondary training data set;
wherein, m basic learners are trained, and the selectable machine learning algorithm comprises: SVM, BP neural network, random forest, GBDT model, XGBoost model, light GBM model, linear regression model, support vector machine, weighted extreme learning machine; according to the prediction performance of different algorithms, fixing the value of m, selecting various combination working conditions of various different algorithms to generate the base learner or carrying out different values on m, and selecting various combination working conditions of different algorithms to generate the base learner.
7. The method for diagnosing faults of a central air conditioning system according to claim 1, wherein in the step S5, a plurality of sets of secondary training data sets are input to a secondary learner for training to obtain a plurality of fault diagnosis models of the central air conditioning system, and the method comprises the steps of: sequentially inputting the secondary training data sets obtained by training the base learners under different combined working conditions into the secondary learners for training to obtain a plurality of central air conditioner fault diagnosis models; the machine learning algorithm used by the secondary learner is one of SVM, BP neural network, random forest, GBDT model, XGBoost model, light GBM model, linear regression model, support vector machine and weighted extreme learning machine.
8. The method for diagnosing faults of a central air conditioning system according to claim 1, wherein in step S6, the prediction performance of a plurality of central air conditioning fault diagnosis models is evaluated by a test data set, a model with the best prediction performance is selected as an optimal central air conditioning fault diagnosis model, and fault diagnosis of the central air conditioning system is performed by the model, and the method comprises the steps of:
calculating average absolute error value MAE and root mean square difference value RMSE and fitting degree R of model 2 The expression is:
as the evaluation standard of the fault diagnosis model of the central air conditioner, the better the model performance is, the smaller the average absolute error value MAE and the root mean square difference value RMSE are, and the fitting degree R is 2 The larger;
wherein y is i 、
Respectively an actual value, a predicted value and an average value of the sample; n is the size of the test dataset.
9. The method for diagnosing faults of a central air conditioning system based on a stacking fusion algorithm according to claim 1, wherein a base learner in the double-layer stacking model is a weighted base learner, a weight is given to each base learner by introducing a weight formula based on a G-mean value, an output result is corrected according to the weight, and the corrected result is fused into a secondary training data set;
The weight calculation formula of each base learner is as follows:
wherein alpha is i The weight is output; GM (GM) i G-mean values for the input sample set for the ith basis learner;
the secondary learner in the stacking model is a secondary learner which introduces an attention mechanism, after n-dimensional input variables in a secondary training data set are input to the secondary learner at the time t, the weight of n-dimensional features is calculated, the obtained weight is normalized to obtain weight importance degree duty ratio of different features, and finally the obtained weight and weight duty ratio are weighted to obtain a final feature vector, so that the central air conditioner fault diagnosis model is optimized and output.
10. A central air conditioning system fault diagnosis device based on a stacking fusion algorithm, which is characterized in that the central air conditioning system fault diagnosis device comprises:
the digital twin model building module is used for building a digital twin model of the central air conditioning system by adopting a mechanism modeling and data identification method;
the sample data acquisition module acquires state data of the central air conditioning system during normal operation and different faults through a plurality of sensors, and acquires a sample data set after data preprocessing and feature extraction;
The stacking model building module divides a sample data set into a training data set and a test data set, builds a double-layer stacking model at the same time, determines the number of the basic learners as m, and determines the number of the secondary learners as 1;
the basic learner training module is used for training each basic learner by adopting a k-fold cross validation method to obtain the prediction result of each basic learner as a secondary training data set; when training each base learner, selecting a plurality of groups of different machine learning algorithms to combine, and generating a secondary training data set under a plurality of groups of combined modes;
the secondary learner training module inputs a plurality of groups of secondary training data sets into the secondary learner to train to obtain a plurality of central air conditioner fault diagnosis models;
the fault diagnosis model evaluation module evaluates the prediction performances of the plurality of central air conditioner fault diagnosis models through the test data set, selects the model with the best prediction performance as the optimal central air conditioner fault diagnosis model, and performs fault diagnosis of the central air conditioner system through the model.
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| CN115127192B (en) * | 2022-05-20 | 2024-01-23 | 中南大学 | Semi-supervised water chilling unit fault diagnosis method and system based on graph neural network |
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| CN117232097B (en) * | 2023-11-09 | 2024-02-20 | 上海轻环能源科技有限公司 | Central air conditioner refrigerating station optimal control method and system based on self-learning fusion model |
| CN117704880B (en) * | 2023-12-13 | 2024-05-07 | 中建-大成建筑有限责任公司 | Energy-saving temperature monitoring method and system for evaporator |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000346496A (en) * | 1999-06-09 | 2000-12-15 | Hitachi Ltd | Air conditioner for railway vehicle |
| CN102538143A (en) * | 2012-02-06 | 2012-07-04 | 广东美的电器股份有限公司 | Intelligent phonic search engine air-conditioning system and control method thereof |
| CN102563814A (en) * | 2012-02-08 | 2012-07-11 | 广东志高空调有限公司 | Cloud air conditioning system based on cloud computation technology |
| JP2015182821A (en) * | 2014-03-20 | 2015-10-22 | 三菱重工業株式会社 | Method for determining index of article temperature, temperature sensor, air conditioner, delivery vehicle, and stockroom |
| CN204880544U (en) * | 2014-08-01 | 2015-12-16 | 三菱电机株式会社 | Remote operation device of air conditioner |
| CN106547858A (en) * | 2016-10-21 | 2017-03-29 | 珠海格力电器股份有限公司 | A kind of air conditioning unit big data analysis method and air conditioning unit |
| CN207570077U (en) * | 2017-12-05 | 2018-07-03 | 南京贝龙通信科技有限公司 | Remote elevator air-conditioner control system based on GSM/GPRS communications |
| CN109766952A (en) * | 2019-01-21 | 2019-05-17 | 福州大学 | Photovoltaic array fault detection method based on Partial Least Squares and extreme learning machine |
| AU2020101535A4 (en) * | 2020-07-28 | 2020-09-03 | Guangdong Ocean University | Fault Diagnosis Method for Ship Power System Based on Integrated Learning |
| 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 |
| CN113203589A (en) * | 2021-04-29 | 2021-08-03 | 华中科技大学 | Distributed fault diagnosis method and system for multi-split air conditioning system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11054342B2 (en) * | 2017-02-20 | 2021-07-06 | Lifewhere, Llc | System for abnormal condition detection using nearest neighbor |
-
2021
- 2021-12-27 CN CN202111614025.7A patent/CN114484731B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000346496A (en) * | 1999-06-09 | 2000-12-15 | Hitachi Ltd | Air conditioner for railway vehicle |
| CN102538143A (en) * | 2012-02-06 | 2012-07-04 | 广东美的电器股份有限公司 | Intelligent phonic search engine air-conditioning system and control method thereof |
| CN102563814A (en) * | 2012-02-08 | 2012-07-11 | 广东志高空调有限公司 | Cloud air conditioning system based on cloud computation technology |
| JP2015182821A (en) * | 2014-03-20 | 2015-10-22 | 三菱重工業株式会社 | Method for determining index of article temperature, temperature sensor, air conditioner, delivery vehicle, and stockroom |
| CN204880544U (en) * | 2014-08-01 | 2015-12-16 | 三菱电机株式会社 | Remote operation device of air conditioner |
| CN106547858A (en) * | 2016-10-21 | 2017-03-29 | 珠海格力电器股份有限公司 | A kind of air conditioning unit big data analysis method and air conditioning unit |
| CN207570077U (en) * | 2017-12-05 | 2018-07-03 | 南京贝龙通信科技有限公司 | Remote elevator air-conditioner control system based on GSM/GPRS communications |
| CN109766952A (en) * | 2019-01-21 | 2019-05-17 | 福州大学 | Photovoltaic array fault detection method based on Partial Least Squares and extreme learning machine |
| AU2020101535A4 (en) * | 2020-07-28 | 2020-09-03 | Guangdong Ocean University | Fault Diagnosis Method for Ship Power System Based on Integrated Learning |
| 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 |
| CN113203589A (en) * | 2021-04-29 | 2021-08-03 | 华中科技大学 | Distributed fault diagnosis method and system for multi-split air conditioning system |
Non-Patent Citations (1)
| Title |
|---|
| 暖通空调系统故障诊断技术的应用分析;王党琴;大众标准化;第120-121页 * |
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