Heat supply secondary pipe network leakage fault diagnosis method based on dual temperature and dual pressure

Abstract

A heat supply secondary pipe network leakage fault diagnosis method based on dual temperature and dual pressure belongs to the technical field of intelligent heat supply, and comprises the following steps: constructing a heat supply secondary pipe network hydraulic and thermal simulation model according to actual heat supply secondary pipe network parameters; coupling the hydraulic simulation model with the thermal simulation model to construct an actual heat supply system simulation model; according to the heat supply system simulation model, constructing hydraulic and thermal data sets of the pipe network when the heat supply pipe network has different fault working conditions; according to the data set, training a BP neural network leakage fault diagnosis model based on dual-pressure data and training an LSTM neural network leakage fault diagnosis model based on dual-temperature time sequence data; and calling the trained leakage fault diagnosis model to carry out fault diagnosis on the leakage of the heat supply secondary pipe network. According to the method, the pipe section with leakage can be rapidly and accurately positioned, maintenance is conducted in time, and the operation safety of a heat supply system is guaranteed.

Description

Technical Field

[0001] The present invention relates to a pipeline leakage diagnosis method, and in particular to a heating secondary pipe network leakage fault diagnosis method based on dual temperature and dual pressure, belonging to the technical field of smart heating. Background Art

[0002] The rapid development of smart heating technology has enriched the number of sensors in the secondary heating network. Some secondary heating networks with a high degree of informatization are equipped with sufficient dual-temperature and dual-pressure sensors at the heat inlet of heat users, which provides the basic conditions for diagnosing network faults using data-driven methods. Related research still has certain defects:

[0003] (1) Research on fault diagnosis of heating pipeline networks mainly focuses on macroscopic heating systems or primary pipeline network failures, but lacks research on secondary pipeline networks.

[0004] (2) Some studies did not conduct hydraulic and thermal modeling of the pipeline network to be diagnosed, but only performed anomaly identification on individual sensor data. They lacked an overall consideration of the changes in dual temperatures and dual pressures at multiple nodes under pipeline network fault conditions.

[0005] In view of the above, it is still impossible to accurately diagnose pipeline leakage in order to facilitate timely maintenance and ensure the safe operation of the heating system. Summary of the invention

[0006] In order to overcome the defects of the prior art, the present invention further provides a dual-temperature and dual-pressure heating secondary pipe network leakage fault diagnosis method. This application constructs a hydraulic and thermal operating condition model and a coupled simulation model of a heating secondary pipe network including a heat source, a pipe section, a heat user and a node model based on actual heating secondary pipe network parameters; based on the coupled simulation model, the normal operating condition, fault operating condition, etc. of the secondary heating network in different heating stages are simulated, and a multi-operating condition data set is constructed. Based on the data set, a pressure-based BP neural network model and an LSTM neural network model based on temperature time series data are trained respectively. Finally, a fault diagnosis model capable of identifying the leakage location of the pipe network through the dual-temperature and dual-pressure data of the pipe network is obtained, thereby improving the accuracy of the model.

[0007] A dual-temperature and dual-pressure heating secondary pipe network leakage fault diagnosis method comprises the following steps:

[0008] S1. Construct hydraulic and thermal simulation models of the secondary heating pipe network according to the actual parameters of the secondary heating pipe network;

[0009] S2, coupling the hydraulic simulation model with the thermal simulation model to construct a simulation model of the actual heating system;

[0010] S3. Constructing hydraulic and thermal data sets of the heating network when different fault conditions occur in the heating network according to the heating system simulation model;

[0011] S4, training a BP neural network leakage fault diagnosis model based on dual-pressure data and a LSTM neural network leakage fault diagnosis model based on dual-temperature time series data according to the data set;

[0012] S5. Obtain a section of time series data on water replenishment to detect whether there is a leak in the pipe network. When the water replenishment volume of the heating pipe network does not exceed the threshold, there is no leak. When the water replenishment volume of the heating pipe network exceeds the threshold continuously, a pipe network leakage alarm signal is sent. At this time, the data of the dual-temperature and dual-pressure sensors of the heating system are collected and input into the trained dual-temperature and dual-pressure data leakage fault diagnosis model to obtain the faulty pipe section where the leak occurs, and complete the leakage fault diagnosis of the secondary heating pipe network.

[0013] Compared with the prior art, the present invention has the following beneficial effects:

[0014] 1. This application is based on the data-driven concept, making full use of the rich dual-temperature and dual-pressure data of the secondary heating pipe network to build a pipe network leakage diagnosis model, which can quickly and accurately locate the pipe section where the leakage occurs and carry out timely maintenance. It ensures the safety and economy of the operation of the heating system and creates good social and economic benefits.

[0015] 2. The hydraulic and thermal coupling simulation model proposed in this application can fully and accurately reflect the dynamic thermal and hydraulic characteristics of the secondary heating pipe network. For different secondary heating pipe networks, it can carry out targeted working condition simulation and fault diagnosis model construction, which improves the accuracy of the model.

[0016] 3. The present application performs leakage diagnosis based on dual-pressure and dual-temperature time series data respectively. The two complement each other to improve the accuracy and applicability of diagnosis. Among them, the leakage diagnosis model based on dual-pressure data responds quickly, and the diagnosis accuracy is limited by the accuracy of the dual-pressure sensor. The leakage diagnosis model based on temperature time series data requires a certain response time and has a higher diagnosis accuracy.

[0017] The present application will be further described below in conjunction with the accompanying drawings and implementation methods: BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a method for diagnosing leakage of a heating secondary pipe network based on dual temperature and dual pressure according to the present application;

[0019] Figure 2 A flow chart of constructing a heating system simulation model in step S2;

[0020] Figure 3 is the directed connectivity graph of the heating system pipe network;

[0021] Figure 4 Schematic diagram of simulated pipeline leakage when building a multi-case data set;

[0022] Figure 5 It is the BP neural network diagram in the leakage fault diagnosis model;

[0023] Figure 6 Schematic diagram of the LSTM unit structure in the leakage fault diagnosis model;

[0024] Figure 7 It is a schematic diagram of a heating network plane in the embodiment;

[0025] Figure 8 It is a dynamic temperature change curve diagram of the heat user in the embodiment;

[0026] Fig. 9 It is a diagram of the training process of the BP neural network and LSTM neural network fault diagnosis model in the embodiment;

[0027] Fig.10 It is a confusion matrix diagram of the BP fault diagnosis model on the test set in the embodiment;

[0028] Fig.11 It is a confusion matrix diagram of the LSTM fault diagnosis model on the test set in the embodiment;

[0029] Fig.12 The figure is a flow chart of the application of the fault diagnosis model in the embodiment. DETAILED DESCRIPTION

[0030] The embodiments of the technical solution of the present application will be described in detail below in conjunction with the accompanying drawings. Unless otherwise specified, the technical terms or scientific terms used in the present application are generally understood by those skilled in the art.

[0031] This embodiment constructs a coupled simulation model of hydraulic and thermal conditions of a secondary heating network, including heat source, pipe section, heat user and node model, based on actual secondary heating network parameters. Based on the coupled simulation model, the normal working conditions and fault conditions of the secondary heating network in different heating stages are simulated to construct a multi-condition data set. Based on the data set, a pressure-based BP neural network model and a temperature time series data-based LSTM neural network model are trained respectively, and finally a fault diagnosis model is obtained that can identify the leakage location of the network through the dual temperature and dual pressure data of the network.

[0032] Specifically, a dual-temperature and dual-pressure heating secondary pipe network leakage fault diagnosis method is provided, which comprises the following steps:

[0033] S1. Construct hydraulic and thermal simulation models of the secondary heating pipe network according to the actual parameters of the secondary heating pipe network;

[0034] To build a hydraulic simulation model, the following assumptions need to be made when performing hydraulic calculations:

[0035] ① The flow velocity, pressure and other parameters in any cross section of the pipeline are evenly distributed, that is, the flow of the fluid in the pipe is simplified to a one-dimensional process.

[0036] ②. The fluid in the tube is incompressible.

[0037] Based on the node flow balance equation, loop pressure balance equation, pipeline resistance loss equation, and pump head characteristic equation, a steady-state hydraulic simulation model equation group is constructed. The steady-state hydraulic model is solved according to the basic loop method to obtain the flow, pressure, pipe section pressure drop, and flow of each node.

[0038]

[0039] Where: B represents the number of branches of the heating network;

[0040] N represents the number of heating network nodes;

[0041] A represents the incidence matrix, N-1×B dimensions;

[0042] B f represents the basic loop matrix, B-N+1×B dimension;

[0043] G represents the pipeline flow column vector, B dimension, kg / s;

[0044] Q represents the column vector of the node's net inflow, that is, the flow exchanged between the node and the outside world of the system, inflow is positive, outflow is negative, (N-1) dimension, kg / s;

[0045] ΔH represents the pipeline pressure drop column vector, B dimension, Pa;

[0046] Z represents the pipeline potential energy difference column vector, B dimension, Pa;

[0047] S represents the pipeline impedance coefficient matrix, B×B dimension, Pa·s 2 / kg 2 ;

[0048] ΔH pump represents the pump head column vector, B dimension, Pa;

[0049] C0, C1, and C2 represent the diagonal matrices of pump characteristic parameters, respectively, with B×B dimensions.

[0050] Build a thermal simulation model: When the heating system is adjusted, the response time of the non-steady-state hydraulic process is much shorter than the response time of the thermal process. Therefore, the hydraulic changes of the pipe network can be regarded as a series of quasi-steady-state processes. The approximate instantaneous flow and pressure information of the pipe network can be obtained through the steady-state hydraulic simulation model, and the dynamic thermal conditions of the pipe network can be analyzed on this basis. The node method needs to make the following assumptions when performing thermal calculations:

[0051] ① The temperature distribution in any cross section of the tube is uniform, which means that there is no radial heat transfer inside the fluid.

[0052] ②. Ignore the axial heat conduction effect of the fluid.

[0053] ③. For the supply and return pipe sections, the thermal resistance between the fluid in the pipe, the pipe wall, the soil and the atmosphere is evenly distributed in the flow direction of the pipe.

[0054] 1) Build a pipe network model

[0055] According to the law of conservation of energy, the temperature change of the fluid flowing in the pipeline can be expressed by equation (2). Based on equation (2), backward difference is taken for the time term, and first-order upwind difference is taken for the space term to obtain equation (3);

[0056]

[0057] Where: ρ represents the density of the fluid in the tube, kg / m 3 ;

[0058] T represents the temperature of the fluid in the tube, °C;

[0059] T env Indicates outdoor ambient temperature, ℃;

[0060] u represents the flow velocity in the pipe, m / s;

[0061] c p Indicates the specific heat capacity of the fluid in the tube, J / (kg·℃);

[0062] t, Δt represents time, unit time length, s;

[0063] x, Δx represents the pipe length, unit pipe length, m;

[0064] A c represents the cross-sectional area of ​​the pipe, m 2 ;

[0065] d represents the inner diameter of the pipe, m;

[0066] λ represents the thermal conductivity of the fluid in the tube, W / (m·℃);

[0067] R represents the equivalent heat transfer resistance between the fluid per unit length of the pipe and the pipe wall-soil-atmosphere environment, m·℃ / W;

[0068] u (n) It represents the velocity of the flow in the pipe at time n, m / s;

[0069] Indicates the outdoor ambient temperature at time n, ℃;

[0070] represents the temperature of the ith discrete node at time n after the network is discretized, ℃;

[0071] 2) Build a hot user model

[0072] The heating building is simplified as a mass point with lumped parameters, and the indoor temperature of each building is assumed to be an average temperature. The building exchanges heat with the radiator and the outdoors. The heat capacity of the equivalent heating building is concentrated on the mass point, and equation (4) is listed. The backward difference method is used for the time term to obtain the indoor temperature of the building, as shown in equation (5). The heat dissipation of the heat dissipation equipment is shown in equation (6), where the inlet and outlet water temperatures of the heat dissipation equipment are equivalent to the supply and return water temperatures of the secondary pipe network. Combining equations (4) to (6), the indoor temperature change and supply and return water temperatures of the heat users can be calculated.

[0073]

[0074] Where: C b It represents the equivalent total heat capacity of the building, J / ℃;

[0075] T n Indicates the average indoor temperature of the building, °C;

[0076] represents the average indoor temperature of the building at time n, ℃;

[0077] Q rad , G rad Indicates the heat dissipation and flow rate of the radiator, W;

[0078] They represent the heat dissipation and flow rate of the radiator at time n, W;

[0079] K b It represents the comprehensive heat transfer coefficient of the building envelope, W / ℃;

[0080] K rad Indicates the equivalent heat transfer coefficient of the radiator, W / ℃;

[0081] η rad It represents the flow time of the fluid in the radiator, which can be taken as a unit time step, η rad =1;

[0082] T su , T re Indicates the inlet and outlet water temperatures of the heat dissipation equipment, ℃;

[0083] Respectively represent heat dissipation devices n-η rad Inlet water temperature at time n, outlet water temperature at time n (equivalent to the supply and return water temperature of the node), °C;

[0084] 3) Construct heat source model

[0085] The heat source is the starting point for the dynamic calculation of the heating network, and its thermal characteristics are the boundary conditions of the dynamic simulation model. They can be obtained in the following way: given the heat source at each moment, i.e., the outlet water temperature on the secondary pipe network side of the heat exchange station, The outlet water temperature is controlled to reach the set value through the heat exchange station automatic control system;

[0086] 4) Build pipeline node model

[0087] According to the energy conservation equation, the node temperature calculation formula (7) is obtained:

[0088]

[0089] Where: T i represents the temperature of node i, °C;

[0090] T c,j represents the terminal temperature of pipe j flowing into node i, °C;

[0091] T s，k represents the starting temperature of pipe k flowing out of node i, °C;

[0092] Φ start,i represents the set of all pipes flowing out of node i;

[0093] Φ end，i represents the set of all pipes that flow into node i.

[0094] S2, coupling the hydraulic simulation model with the thermal simulation model to construct a simulation model of the actual heating system;

[0095] Reference Figure 2-Figure 4 , based on hydraulic simulation and thermal model theory, an integrated method is proposed. Figure 2 The simulation process of the actual heating system modeling is demonstrated. The hydraulic model is coupled with the thermal model. The thermal models of the heat source, pipe section, and heat user are connected in a certain order. The temperature of each node is calculated using the node model, and then a complete simulation model of the actual heating system is constructed, that is, a hydraulic-thermal coupling simulation model. Specifically:

[0096] S21. According to the design information of the heating pipe network, the node information and pipe segment information of the heating pipe network and the connection relationship are input into the computer, and according to the graph theory, a directed connection graph of the heating pipe network is generated. Figure 3As shown; Based on graph theory, the heating system is abstracted into a directed connection graph consisting of two types of elements, pipes and nodes, and the spatial network topology of the secondary heating network is established. The heating network is a closed-loop flow system. Each branch represents each section of the heating pipeline. The node is the connection position of the heating pipeline. Each node is connected to at least two pipelines, and there is at most one branch between two nodes. The nodes of the secondary heating network are represented by a set N consisting of {n1, n2, n3, ...}, and there are N heating network nodes in total; the branch set is B = {n1-n2, n2-n3, n3-n4, ...}, and there are B heating network branches in total;

[0097] S22, setting the time step, space step and simulation period of the discrete solution, and setting the initial value of the iteration according to the measured data or the design value;

[0098] S23, using a breadth-first traversal algorithm to determine the calculation order of the pipeline;

[0099] S24, based on the hydraulic simulation model of step S1, iteratively calculate and obtain the hydraulic parameters of the heating network at time t, and provide the flow parameters of each node and pipe section for thermal calculation;

[0100] S25. Based on the thermal simulation model of step S2, the system is divided into three parts: heat source, heat user, and heating network. First, the heat source model is called to determine the water supply temperature of the heat source at the current moment. According to the calculation sequence of the network, the network model is called to perform dynamic thermal calculations on each water supply pipe section in turn starting from the heat source to obtain the outlet temperature of the pipe, and the node temperature is calculated according to formula (7). Then, the heat user model is called to obtain the temperature and flow of the return water node. According to the calculation sequence of the network, the network model is called to calculate the outlet temperature of each return water pipe section.

[0101] S3. Constructing hydraulic and thermal data sets of the heating network when different fault conditions occur in the heating network according to the heating system simulation model;

[0102] The simulation model constructed based on the above method can accurately reflect the operating status of the heating system. At this time, the hydraulic and thermal characteristics data set of the heating network when different fault conditions occur in the heating network is constructed through the hydraulic and thermal coupling simulation model.

[0103] Further use machine learning methods to train the leakage diagnosis model based on the data set:

[0104] First, when constructing the leakage condition data set, it is necessary to add a new node nleak to the normal pipe network topology structure to represent the specific location where the leakage occurs. Figure 4 As shown, the original pipeline n2-n4 assumed to be leaking is split into two new pipelines n2-nleak and nleak-n4, and the other pipelines remain unchanged.

[0105] The main parameters of the heating pipeline are pipe length l, pipe diameter d, impedance S and heat transfer coefficient per unit pipe length K. p , before and after the leaking pipeline is split into two new pipe sections, its pipe diameter d and the unit pipe length heat transfer coefficient K p The pipe length l and the impedance S remain unchanged, while the pipe length l and the impedance S are redistributed to two new pipe sections according to the location of the leakage point, and then:

[0106] l2=l0-l1 (8)

[0107] l1=μl0 (9)

[0108] S1=μS0 (10)

[0109] S2=S0-S1 (11)

[0110] Where l0 represents the length of the original pipe section, m;

[0111] l1, l2 represent the length of each pipe segment after splitting, m;

[0112] μ represents the ratio of the leakage point to the total length of the original pipe section, which is dimensionless;

[0113] S0 represents the impedance of the original pipe section, Pa·s 2 / kg 2 ;

[0114] S1 and S2 represent the impedance of each pipe segment after splitting, Pa·s 2 / kg 2 .

[0115] In view of the above, the process of constructing the hydraulic and thermal data sets of the heating network when different fault conditions occur in the heating network is as follows: first, the pipe section is selected according to the actual situation of the heating network, and multiple different leakage positions μ = {μ1, μ2, μ3, μ4, ...} are set for each pipe section. Multiple leakage amounts are set for each leakage position for simulation calculation to obtain node pressure data and temperature time series data, where each data is a vector containing multiple pressure or temperature measurement point values. The faulty pipe section is encoded with a unique hot label to complete the construction of the fault diagnosis data set.

[0116] S4, training a BP neural network leakage fault diagnosis model based on dual-pressure data and a LSTM neural network leakage fault diagnosis model based on dual-temperature time series data according to the data set;

[0117] In this step, the pipeline leakage diagnosis model training uses machine learning methods to mine the potential fault features contained in multi-node dual-temperature and dual-pressure, and trains and obtains pipeline leakage diagnosis models based on pressure data and temperature time series data respectively;

[0118] Among them, the pipeline network fault diagnosis model based on the BP neural network of dual pressure data is trained. The structure of the BP neural network is as follows: Figure 5 As shown in the figure, the BP neural network leakage fault diagnosis model is a multi-layer neural network structure, where the number of neurons in the input layer is x n is equal to the number of thermal users equipped with dual pressure sensors for thermal inlet, and the number of neurons in the output layer y n The model takes the dual pressure data of heat users as the input value of the neural network and the possibility of failure of each pipe section to be diagnosed as the output value.

[0119] Among them, the LSTM neural network leakage diagnosis model based on dual-temperature time series data is trained. The LSTM unit structure is as follows Figure 6 As shown, the LSTM neural network leakage fault diagnosis model is a multi-layer neural network structure, where the number of neurons in the input layer is x n is equal to the number of thermal users equipped with dual temperature sensors at the thermal inlet, and the number of neurons in the output layer y n The model takes the dual-temperature time series data of heat users as input variables and the probability of failure of each pipe section to be diagnosed as the output value.

[0120] S5. Obtain a section of time series data on water replenishment to detect whether there is a leak in the pipe network. When the water replenishment volume of the heating pipe network does not exceed the threshold, there is no leak. When the water replenishment volume of the heating pipe network exceeds the threshold continuously, a pipe network leakage alarm signal is sent. At this time, the data of the dual-temperature and dual-pressure sensors of the heating system are collected and input into the trained dual-temperature and dual-pressure data leakage fault diagnosis model to obtain the faulty pipe section where the leak occurs, and complete the leakage fault diagnosis of the secondary heating pipe network.

[0121] In this step, for the actual secondary heating network, after the network leakage diagnosis model is constructed according to the above method, the model can be used to perform fault diagnosis based on dual-temperature and dual-pressure data. The network is leaked by monitoring the water replenishment volume of the heating system. The leakage threshold can be set according to 2% of the circulating water volume of the secondary heating network. When the water replenishment volume of the heating network exceeds the threshold continuously, a network leakage alarm signal is sent. At this time, the dual-temperature and dual-pressure sensor data of the heating system are collected and input into the corresponding trained fault diagnosis model to obtain the faulty pipe section with leakage. The overall diagnosis process is as follows: Figure 1 shown.

[0122] The present application will be further described below with examples:

[0123] Taking an actual secondary heating network in Beijing as an example, the specific implementation method of this method is as follows:

[0124] The specific implementation process can be divided into four steps: constructing hydraulic and thermal simulation models of the secondary heating pipeline network and a coupling-based simulation model of the actual heating system, constructing a multi-condition data set for the pipeline network, constructing a pipeline network leakage diagnosis model, and applying a fault diagnosis model.

[0125] Step 1: Construct hydraulic, thermal and coupling simulation models for the secondary heating network

[0126] A typical secondary heating system in Beijing is selected as the research object, and its design parameters are shown in Table 1;

[0127] Table 1 Heating network design parameters

[0128]

[0129] like Figure 7 As shown in the figure, the district heating network consists of 23 water supply pipes, 23 return pipes and 48 nodes. Among them, the maximum pipe diameter is DN400 and the minimum pipe diameter is DN100. The heat source is equivalent to a pipe section in the heating network, numbered n1'-n1, such as Figure 4 As shown, the system is equipped with a water replenishment constant pressure device at node n1' to maintain a constant pressure of 0.2MPa under design conditions.

[0130] Based on the hydraulic and thermal simulation model of the secondary heating pipe network, a hydraulic and thermal coupling simulation model is constructed to obtain the actual heating system simulation model, which accurately reflects the dynamic thermal and hydraulic characteristics of the heating pipe network.

[0131] Figure 8 This is a dynamic temperature simulation of the heating system for 7 days from January 1 to January 7 in the middle of the heating period, taking heat user n11 as an example. It can be seen that the heat exchange station adjusts the water supply temperature as the ambient temperature changes, and the water supply temperature of heat user n11 is close to that of the heating station. The indoor temperature fluctuates around 20℃, the fluctuation range is smaller than the ambient temperature, and the change trend lags slightly behind the ambient temperature.

[0132] Step 2: Construction of multi-condition data set for pipeline network

[0133] A total of 23 pipelines n1-n24 and their adjacent branch pipelines were selected, as shown in Table 2. Nine different leakage positions μ = {0.1, 0.2, 0.3, ..., 0.9} were set for each pipeline segment, and 10 leakage amounts were simulated and calculated for each leakage position.

[0134] The time step Δt=10 minutes and the space step Δx=10 m are set, and a large amount of steady-state pressure data and temperature time series data are obtained according to the simulation results, where each piece of data is a vector containing the values ​​of multiple pressure or temperature measurement points.

[0135] Table 2 Leakage setting of simulation data set

[0136]

[0137] Step 3: Pipeline network leakage diagnosis model construction

[0138] (1) BP neural network leakage diagnosis model based on dual pressure data

[0139] Build and train an LSTM pipeline fault diagnosis model based on dual-temperature time series data, which has a multi-layer neural network structure. The dual-temperature time series of hot users after leakage is used as the input value of the neural network, and the number of neurons in the input layer is 12. The number of pipeline sections to be diagnosed, that is, the number of neurons in the output layer is 23. The number of hidden layers is 2, the number of neurons in the first layer is 128, and the Sigmoid activation function is applied to it; the number of neurons in the second layer is 64, and the ReLU activation function is applied to it. Select cross entropy loss as the loss function, select Adam method as the optimization function, and finally use the Softmax function to convert the final output into the probability of pipeline section failure.

[0140] The pressure data set is randomly divided into a training set, a validation set, and a test set, which account for 60%, 20%, and 20% of the data set respectively. When the pressure accuracy is 0.001MPa, the training process is as follows Fig. 9 The model with the highest accuracy on the validation set is used as the final model obtained through training. At this time, the accuracy of the model in the test set is 86.23%, and the confusion matrix of its diagnosis results is as follows: Fig.10 shown.

[0141] (2) LSTM neural network leakage diagnosis model based on dual-temperature time series data

[0142] Build and train an LSTM pipeline fault diagnosis model based on dual-temperature time series data. The model has a multi-layer neural network structure. The dual-temperature time series of hot users after leakage is used as the input value of the neural network, and the number of neurons in the input layer is 12. The number of pipe sections to be diagnosed, that is, the number of neurons in the output layer is 23. The number of hidden layers is 2, and the number of neurons is 256. The Softmax function is applied to the output value of the model to convert it into the probability of fault occurrence. The loss function and optimization function are the same as those of the BP neural network.

[0143] The temperature time series dataset is randomly divided into training set, validation set and test set, accounting for 60%, 20% and 20% of the dataset respectively. Finally, the LSTM model is trained for 5000 generations. The training process is as follows Fig. 9 as shown in the figure on the right. Fig.11The performance of the optimal model on the test set is shown. As can be seen from the figure, the model can accurately diagnose the faulty pipe section under most fault conditions, while the diagnostic accuracy has decreased for individual pipe sections such as n6 and n10, but it still maintains a certain degree of effectiveness. Overall, the diagnostic accuracy rate reaches 90.62%, which meets the actual engineering requirements.

[0144] The method of this embodiment proposes a fault diagnosis model for heating secondary pipe network based on pressure data with a diagnostic accuracy of 86.23%, but the reduction of pressure sensor measurement accuracy will cause the accuracy of the fault diagnosis model based on pressure data to decrease or become invalid. The proposed fault diagnosis model based on dual temperature has a diagnostic accuracy of 90.62%, which meets the actual engineering requirements.

[0145] Step 4: Fault diagnosis model application

[0146] After the model training is completed, the model can be used to diagnose faults in the actual secondary heating network. The specific application process is as follows:

[0147] Fig.12 Determine whether the heating system is leaking based on the water replenishment volume monitored by the system. If a leak occurs, perform the following steps:

[0148] 1) Based on the dual temperature and dual pressure sensors of the secondary pipe network, data is collected for fault diagnosis.

[0149] 2) In combination with the actual engineering situation of the secondary pipe network, the data collection frequency is determined based on the performance of the sensor and data collection centralized system, the number of historical failures, the complaint rate and other data.

[0150] 3) Complete data cleaning and feature engineering at the data layer of the smart heating system.

[0151] 4) Input the processed dual-temperature and dual-pressure data into the fault diagnosis model to perform fault diagnosis and obtain the faulty pipe section.

[0152] The fault diagnosis models based on dual-temperature and dual-pressure data can complement each other and provide a complete and feasible method to improve the accuracy and reliability of fault diagnosis in the secondary heating network.

[0153] This embodiment makes full use of the dual-temperature and dual-pressure data of the secondary heating pipe network to construct a pipe network leakage diagnosis model, which improves the accuracy of the model, can quickly and accurately locate the pipe section where the leakage occurs, and carry out maintenance in a timely manner to ensure the safety and economy of the heating system operation. For different secondary heating pipe networks, it is possible to carry out targeted working condition simulation and fault diagnosis model construction. During diagnosis, leakage diagnosis is performed based on dual-pressure and dual-temperature time series data respectively. The two complement each other to improve the accuracy and applicability of diagnosis. Among them, the leakage diagnosis model based on dual-pressure data responds quickly, and the diagnosis accuracy is limited by the accuracy of the dual-pressure sensor; the leakage diagnosis model based on temperature time series data requires a certain response time, and the diagnosis accuracy is higher.

[0154] The present application has been disclosed as above with preferred implementation cases, but it is not intended to limit the present application. Any technician familiar with the profession can make slight changes or modifications to the above-disclosed structures and technical contents without departing from the scope of the technical solution of the present application, and the equivalent implementation cases with equivalent changes are still within the scope of the technical solution of the present application.

Claims

1. A dual-temperature and dual-pressure heating secondary pipe network leakage fault diagnosis method, characterized in that: The following steps are involved: S1. Construct hydraulic and thermal simulation models of the secondary heating pipe network according to the actual parameters of the secondary heating pipe network; S2, coupling the hydraulic simulation model with the thermal simulation model to construct a simulation model of the actual heating system; S3. Constructing hydraulic and thermal data sets of the heating network when different fault conditions occur in the heating network according to the heating system simulation model; S4, training a BP neural network leakage fault diagnosis model based on dual-pressure data and a LSTM neural network leakage fault diagnosis model based on dual-temperature time series data according to the data set; S5. Obtain a section of time series data on water replenishment to detect whether there is a leak in the pipe network. When the water replenishment volume of the heating pipe network does not exceed the threshold, there is no leak. When the water replenishment volume of the heating pipe network exceeds the threshold continuously, a pipe network leakage alarm signal is sent. At this time, the data of the dual-temperature and dual-pressure sensors of the heating system are collected and input into the trained dual-temperature and dual-pressure data leakage fault diagnosis model to obtain the faulty pipe section where the leak occurs, and complete the leakage fault diagnosis of the secondary heating pipe network.

2. According to claim 1, a dual-temperature and dual-pressure heating secondary pipe network leakage fault diagnosis method is characterized in that: The construction process of the hydraulic simulation model in step S1 is: Assume that the velocity and pressure parameters of any cross section of the pipe network are evenly distributed. Assume that the fluid in the pipes of the pipe network is an incompressible fluid; Then, the equation group of the steady-state hydraulic simulation model constructed is: According to the basic loop method, the steady-state hydraulic model is solved to obtain the flow rate, pressure, pipe section pressure drop, and flow rate of each node; Where: B represents the number of branches of the heating network; N represents the number of heating network nodes; A represents the incidence matrix, N-1×B dimensions; B f represents the basic loop matrix, B-N+1×B dimension; G represents the pipeline flow column vector, B dimension, kg / s; Q represents the column vector of the node's net inflow, that is, the flow exchanged between the node and the outside world of the system, inflow is positive, outflow is negative, (N-1) dimension, kg / s; ΔH represents the pipeline pressure drop column vector, B dimension, Pa; Z represents the pipeline potential energy difference column vector, B dimension, Pa; S represents the pipeline impedance coefficient matrix, B×B dimension, Pa·s 2 / kg 2 ; ΔH pump represents the pump head column vector, B dimension, Pa; C0, C1, and C2 represent the diagonal matrices of pump characteristic parameters, respectively, with B×B dimensions.

3. According to claim 2, a dual-temperature and dual-pressure heating secondary pipe network leakage fault diagnosis method is characterized in that: The construction process of the thermal simulation model in step S1 is: Assumptions: The temperature distribution in any cross section of the pipe network is uniform; Ignore the axial heat conduction effect of the fluid; For the supply and return pipe sections, the thermal resistance between the fluid in the pipe, the pipe wall, the soil and the atmosphere is evenly distributed in the pipe flow direction; 1) Build a pipe network model According to the law of conservation of energy, the temperature change of the fluid flowing in the pipeline can be expressed by equation (2). Based on equation (2), backward difference is taken for the time term, and first-order upwind difference is taken for the space term to obtain equation (3); Where: ρ represents the density of the fluid in the tube, kg / m 3 ; T represents the temperature of the fluid in the tube, °C; T env Indicates outdoor ambient temperature, ℃; u represents the flow velocity in the pipe, m / s; c p Indicates the specific heat capacity of the fluid in the tube, J / (kg·℃); t, Δt represents time, unit time length, s; x, Δx represents the pipe length, unit pipe length, m; A c represents the cross-sectional area of ​​the pipe, d represents the inner diameter of the pipe, m; λ represents the thermal conductivity of the fluid in the tube, W / (m·℃); R represents the equivalent heat transfer resistance between the fluid per unit length of the pipe and the pipe wall-soil-atmosphere environment, m·℃ / W; u (n) It represents the velocity of the flow in the pipe at time n, m / s; Indicates the outdoor ambient temperature at time n, ℃; represents the temperature of the ith discrete node at time n after the network is discretized, ℃; 2) Build a hot user model The heating building is simplified as a mass point with lumped parameters, and the indoor temperature of each building is assumed to be an average temperature. The building exchanges heat with the radiator and the outdoors. The heat capacity of the equivalent heating building is concentrated on the mass point, and equation (4) is listed. The backward difference method is used for the time term to obtain the indoor temperature of the building, as shown in equation (5). The heat dissipation of the heat dissipation equipment is shown in equation (6), where the inlet and outlet water temperatures of the heat dissipation equipment are equivalent to the supply and return water temperatures of the secondary pipe network. Combining equations (4)-(6), the indoor temperature change and supply and return water temperatures of the heat users can be calculated. Where: C b It represents the equivalent total heat capacity of the building, J / ℃; T n Indicates the average indoor temperature of the building, °C; represents the average indoor temperature of the building at time n, ℃; Q rad , G rad Indicates the heat dissipation and flow rate of the radiator, W; They represent the heat dissipation and flow rate of the radiator at time n, W; K b It represents the comprehensive heat transfer coefficient of the building envelope, W / ℃; K rad Indicates the equivalent heat transfer coefficient of the radiator, W / ℃; η rad It represents the flow time of the fluid in the radiator, which can be taken as a unit time step, η rad =1; T su ,T re Indicates the inlet and outlet water temperatures of the heat dissipation equipment, ℃; Respectively represent heat dissipation devices n-η rad Inlet water temperature at time n, outlet water temperature at time n (equivalent to the supply and return water temperature of the node), °C; 3) Construct heat source model The heat source is the starting point for the dynamic calculation of the heating network, and its thermal characteristics are the boundary conditions of the dynamic simulation model. They can be obtained in the following way: given the heat source at each moment, i.e., the outlet water temperature on the secondary pipe network side of the heat exchange station, The outlet water temperature is controlled to reach the set value through the heat exchange station automatic control system; 4) Build pipeline node model According to the energy conservation equation, the node temperature calculation formula (7) is obtained: Where: T i represents the temperature of node i, °C; T c，j represents the terminal temperature of pipe j flowing into node i, °C; T s，k represents the starting temperature of pipe k flowing out of node i, °C; Φ start，i represents the set of all pipes flowing out of node i; Φ end，i represents the set of all pipes that flow into node i.

4. According to claim 3, a dual-temperature and dual-pressure heating secondary pipe network leakage fault diagnosis method is characterized in that: The process of constructing the heating system simulation model in step S2 is: S21, according to the design information of the heating pipe network, input the node information and pipe segment information and connection relationship of the heating pipe network into the computer, and generate a directed connected graph of the heating pipe network according to graph theory; S22, setting the time step, space step and simulation period of the discrete solution, and setting the initial value of the iteration according to the measured data or the design value; S23, using a breadth-first traversal algorithm to determine the calculation order of the pipeline; S24, based on the hydraulic simulation model of step S1, iteratively calculate and obtain the hydraulic parameters of the heating network at time t, and provide the flow parameters of each node and pipe section for thermal calculation; S25, based on the thermal simulation model of step S2, the system is divided into three parts: heat source, heat user, and heating pipe network; firstly, the heat source model is called to determine the water supply temperature of the heat source at the current moment; According to the pipeline network calculation sequence, the pipeline network model is called to perform dynamic thermal calculations on each water supply pipe segment starting from the heat source to obtain the outlet temperature of the pipeline, and the node temperature is calculated according to formula (7); then the heat user model is called to obtain the temperature and flow of the return water node; according to the pipeline network calculation sequence, the pipeline network model is called to calculate the outlet temperature of each return water pipe segment.

5. According to claim 3, a dual-temperature and dual-pressure heating secondary pipe network leakage fault diagnosis method is characterized in that: The process of constructing the hydraulic and thermal data sets of the heating network when different fault conditions occur in the heating network in step S3 is as follows: Firstly, pipe sections are selected according to the actual situation of the heating network. Multiple leakage positions μ={μ1,μ2,μ3,μ4,…} are set for each pipe section. Multiple leakage amounts are set for each leakage position for simulation calculation to obtain node pressure data and temperature time series data. Each data is a vector containing multiple pressure or temperature measurement point values. The faulty pipe section is encoded with a unique hot label to complete the construction of the fault diagnosis data set.

6. A dual-temperature and dual-pressure heating secondary pipe network leakage fault diagnosis method according to claim 1 or 5, characterized in that: The BP neural network leakage fault diagnosis model in step S4 is a multi-layer neural network structure, in which the number of neurons in the input layer is x. n is equal to the number of thermal users equipped with dual pressure sensors for thermal inlet, and the number of neurons in the output layer y n The model takes the dual pressure data of heat users as the input value of the neural network and the possibility of failure of each pipe section to be diagnosed as the output value.

7. A dual-temperature and dual-pressure heating secondary pipe network leakage fault diagnosis method according to claim 1 or 5, characterized in that: The LSTM neural network leakage fault diagnosis model in step S4 is a multi-layer neural network structure, in which the number of neurons in the input layer is x n is equal to the number of thermal users equipped with dual temperature sensors at the thermal inlet, and the number of neurons in the output layer y n The model takes the dual-temperature time series data of heat users as input variables and the probability of failure of each pipe section to be diagnosed as the output value.

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