A centrifugal pump fault diagnosis system and method integrating multi-source heterogeneous data

Abstract

The present invention discloses a centrifugal pump fault diagnosis system and method that integrates multi-source heterogeneous data, and relates to the technical field of centrifugal pump fault diagnosis. K environmental clusters are obtained for each fault type in each application scenario. In each application scenario, standard fault time and standard operating parameter sequences are collected, input feature vectors and time weight labels are constructed, and a time weight prediction model is trained. The actual application scenarios, actual environmental data and actual operating parameters of the centrifugal pump to be monitored are collected in real time. Based on the actual application scenarios and actual environmental data, the corresponding environmental clusters are calculated as target clusters. Based on the standard fault time and standard operating parameter sequences of the target clusters, the time weight prediction model, the actual environmental data and the actual operating parameters, the predicted fault times of various fault types are obtained, thereby improving the accuracy of centrifugal pump fault prediction and diagnosis.

Description

Technical Field

[0001] The present invention relates to the technical field of centrifugal pump fault diagnosis, and in particular to a centrifugal pump fault diagnosis system and method that integrates multi-source heterogeneous data. Background Art

[0002] Centrifugal pumps are widely used fluid conveying equipment in industrial production, playing a vital role in numerous fields, including water supply, HVAC, chemical engineering, and power generation. However, over long-term operation, centrifugal pumps can fail due to various reasons, such as bearing wear, impeller corrosion, shaft seal leakage, motor failure, and blockage and scaling. These failures not only affect the normal operation of the centrifugal pump but can also cause equipment damage, energy waste, and even safety accidents. Therefore, fault prediction and lifespan estimation for centrifugal pumps are crucial.

[0003] Currently, centrifugal pump fault prediction primarily relies on manual experience or data-driven models based on a single operating parameter (such as vibration). Manual experience methods are overly reliant on manual effort and lack systematicity and accuracy. While data-driven models utilize monitoring data, they ignore environmental influences and physical mechanisms, resulting in inconsistent predictive performance and making it difficult to fundamentally address the problem.

[0004] Chinese patent application publication number CN117846979A discloses a method and apparatus for determining centrifugal pump faults. The method comprises: obtaining real-time operating data of the centrifugal pump; extracting time-frequency domain features from the real-time operating data to obtain at least one feature matrix; inputting the feature matrix into a pre-trained centrifugal pump fault diagnosis model and / or a centrifugal pump operating status clustering model for processing to obtain at least one predictive diagnosis result; the centrifugal pump fault diagnosis model is trained based on the fusion result of multi-source historical information; and determining the centrifugal pump fault type based on the at least one predictive diagnosis result. However, this method fails to account for the impact of environmental factors in the application scenario.

[0005] To this end, the present invention proposes a centrifugal pump fault diagnosis system and method that integrates multi-source heterogeneous data. Summary of the Invention

[0006] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a centrifugal pump fault diagnosis system and method that integrates multi-source heterogeneous data, thereby improving the accuracy of centrifugal pump fault prediction and diagnosis.

[0007] To achieve the above objectives, a centrifugal pump fault diagnosis method integrating multi-source heterogeneous data is proposed, which includes the following steps:

[0008] Step 1: Collect a set of application scenarios. In each application scenario, use N test centrifugal pumps to perform an operation experiment. N is the number of preset test centrifugal pumps.

[0009] Step 2: Collect the historical operating parameter sequence, historical environmental data, historical fault type, and historical fault time of the centrifugal pump itself for each experimental test in each application scenario;

[0010] Step 3: For each fault type in each application scenario, cluster the historical environmental data to obtain K environmental clusters; K is the number of selected environmental clusters;

[0011] Step 4: Calculate the environmental data corresponding to the center point of each environmental cluster in each application scenario; and in each application scenario, select K new test centrifugal pumps to conduct new operation experiments, and collect standard failure time and standard operation parameter sequences;

[0012] Step 5: For each environmental cluster, construct an input feature vector based on historical environmental data, historical operating parameter sequences, and standard operating parameter sequences, and construct a time weight label based on historical failure time and standard failure time;

[0013] Step 6: Take the input feature vector as input and the time weight label as output to train the time weight prediction model;

[0014] Step 7: Collect the actual application scenarios, actual environmental data, and actual operating parameters of the centrifugal pump to be monitored in real time. Based on the actual application scenarios and actual environmental data, calculate the corresponding environmental cluster as the target cluster; based on the standard failure time and standard operating parameter sequence of the target cluster, the time weight prediction model, the actual environmental data, and the actual operating parameters, obtain the predicted failure time of various fault types;

[0015] The execution mode of the running experiment is:

[0016] In each application scenario, different environmental parameters are pre-set, and each test centrifugal pump is run until a failure of any type occurs in the test centrifugal pump. The operation process of each test centrifugal pump is regarded as one operation experiment;

[0017] The collection of historical operating parameter sequences, historical environmental data, historical fault types, and historical fault times for each running experiment in each application scenario includes the following steps:

[0018] Step 11: Collect the average values ​​of various environmental parameters during the entire running process of each experiment, and normalize each average value. The normalized average values ​​constitute the historical environmental data;

[0019] Step 12: During each operation experiment, collect data at each unit time and test the various operating parameters of the centrifugal pump corresponding to various fault types;

[0020] Step 13: For each running experiment, arrange each running parameter in time series order to obtain the historical running parameter sequence of the running experiment;

[0021] Step 14: Collect the fault type that occurs at the end of each experiment as the historical fault type, and the time when the fault occurs as the historical fault time;

[0022] For each fault type in each application scenario, the historical environmental data is clustered to obtain K environmental clusters as follows:

[0023] Step 21: Group the running experiments of each application scenario by fault type to obtain M experimental groups. The running experiments in each experimental group belong to the same fault type. M is the number of all fault types.

[0024] Step 22: Treat the vector consisting of each set of historical environmental data of the running experiments in each experimental group as a C-dimensional discrete point; where C is the number of environmental parameters in the historical environmental data, and the coordinate of each dimension of the C-dimensional discrete point corresponds to an environmental parameter in the historical environmental data;

[0025] Step 23: Randomly select K C-dimensional discrete points as initial cluster centers, and the remaining C-dimensional discrete points as non-initial cluster centers;

[0026] Step 24: For each non-initial cluster center, calculate the Euclidean distance to each initial cluster center, and divide each C-dimensional discrete point into the cluster cluster where the nearest initial cluster center is located;

[0027] Step 25: Calculate the average value of the environmental parameters corresponding to each dimension of the C-dimensional discrete points in each cluster, form a new C-dimensional discrete point with the average value of the environmental parameters corresponding to each dimension, and use the new C-dimensional discrete point as the new cluster center of the cluster;

[0028] Step 26: Recalculate the Euclidean distance from each C-dimensional discrete point to each new cluster center, and re-divide each C-dimensional discrete point into the cluster where the nearest new cluster center is located;

[0029] Step 27: Repeat steps 25-26 until the C-dimensional discrete points in all divided clusters no longer change, and mark each cluster with a number k, k = 1, 2, 3, ..., K; obtain all C-dimensional discrete points contained in each cluster, and form all the C-dimensional discrete points into an environmental cluster;

[0030] The environmental data corresponding to the center point of each environmental cluster in each application scenario is calculated, and in each application scenario, K new test centrifugal pumps are selected for operation experiments to collect standard failure times and standard operation parameter sequences in the following manner:

[0031] Obtain the C-dimensional discrete point corresponding to the center point of each environmental cluster as the cluster center point;

[0032] Obtain the value of each environmental parameter in the C-dimensional discrete point corresponding to the cluster center point;

[0033] The way to select K new test centrifugal pumps for new running experiments is:

[0034] For each environment cluster in each application scenario:

[0035] Select a new test centrifugal pump and set its environmental parameters to those corresponding to the cluster center point;

[0036] Under the environmental data conditions, the new test centrifugal pump is operated until a corresponding fault type fault occurs in the new test centrifugal pump, and the operation process of the new test centrifugal pump is regarded as a new operation experiment;

[0037] The failure time of each new running experiment is collected as the standard failure time, and the values ​​of various operating parameters of the newly tested centrifugal pump at each unit time are collected in chronological order to form a standard operating parameter sequence;

[0038] For each environmental cluster, the input feature vector is constructed based on the historical environmental data, the historical operating parameter sequence and the standard operating parameter sequence as follows:

[0039] For each run in each environment cluster:

[0040] Calculate the ratio of each environmental parameter in the historical environmental data of the running experiment to the environmental parameters corresponding to the center point of the environmental cluster to form an environmental ratio vector;

[0041] Obtaining each operating parameter in the historical operating parameter sequence and the standard operating parameter sequence at each unit moment to form a historical parameter vector and a standard parameter vector respectively, and calculating the ratio of each operating parameter in the historical parameter vector and the standard parameter vector to form an operating ratio vector;

[0042] The input feature vector is composed of the environment ratio vector and the operation ratio vector;

[0043] The method of constructing a time weight label based on historical failure time and standard failure time is:

[0044] For each environment cluster:

[0045] For each unit time of each experiment run, calculate the time from the unit time to the occurrence of the experiment run failure as the failure time difference;

[0046] For each unit time of the newly run experiment, calculate the time from the unit time to the occurrence of the new experiment failure as the standard failure time difference;

[0047] The ratio of the fault time difference to the standard fault time difference is used as the time weight label of the unit moment;

[0048] The method of training the time weight prediction model is:

[0049] Each set of input feature vectors is used as input to a time weight prediction model, and the time weight prediction model uses the predicted value of the fault time weight at each unit time as output; the time weight prediction model uses the time weight label corresponding to each unit time as a prediction target, uses the difference between the predicted value of the fault time weight and the time weight label as the prediction error, and uses minimizing the sum of squares of the prediction errors at all unit times as a training target; the time weight prediction model is trained until the sum of squares of the prediction errors reaches convergence, and training is terminated; the time weight prediction model is any one of the regression models;

[0050] The method for obtaining the predicted failure time of various failure types is as follows:

[0051] Calculate the ratio of each environmental parameter in the actual environmental data to the environmental parameters corresponding to the target cluster center point to form an actual environmental ratio vector;

[0052] Calculate the ratio of each operating parameter in the actual operating parameters to each operating parameter at the corresponding unit time in the standard operating parameter sequence of the target cluster to form an actual operating ratio vector;

[0053] The actual environment ratio vector and the actual operation ratio vector constitute an actual input vector, and the actual input vector is input into the time weight prediction model to obtain a predicted value of the predicted failure time weight;

[0054] The predicted value of the failure time weight is multiplied by the product of the unit moment and the standard failure time difference to obtain the predicted time of occurrence of the failure type.

[0055] A centrifugal pump fault diagnosis system integrating multi-source heterogeneous data is proposed, which includes a sample collection module, a clustering module, a model training module, and a model application module. The modules are electrically connected to each other.

[0056] The sample collection module collects a set of application scenarios. In each application scenario, N test centrifugal pumps are used to perform an operation experiment. The historical operation parameter sequence, historical environmental data, historical fault type, and historical fault time of each operation experiment in each application scenario are collected. The historical operation parameter sequence and historical environmental data are sent to the clustering module, and the historical environmental data, historical fault type, and historical fault time are sent to the model training module.

[0057] The clustering module clusters the historical environmental data for each fault type in each application scenario to obtain K environmental clusters, where K is the number of selected environmental clusters, and sends the environmental clusters to the model training module and the model application module;

[0058] The model training module calculates the environmental data corresponding to the center point of each environmental cluster in each application scenario. In each application scenario, K new test centrifugal pumps are selected for new operation experiments, and standard failure time and standard operation parameter sequences are collected. For each environmental cluster, an input feature vector is constructed based on historical environmental data, historical operation parameter sequences, and standard operation parameter sequences. A time weight label is constructed based on historical failure time and standard failure time. The time weight prediction model is trained using the input feature vector as input and the time weight label as output. The standard failure time, standard operation parameter sequence, and time weight prediction model are then sent to the model application module.

[0059] The model application module collects the actual application scenarios, actual environmental data and actual operating parameters of the centrifugal pump to be monitored in real time, and calculates the corresponding environmental cluster cluster as the target cluster cluster based on the actual application scenarios and actual environmental data; based on the standard failure time and standard operating parameter sequence of the target cluster cluster, the time weight prediction model, actual environmental data, and actual operating parameters, the predicted failure time of various fault types is obtained.

[0060] An electronic device is proposed, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0061] The processor executes the above-mentioned centrifugal pump fault diagnosis method integrating multi-source heterogeneous data by calling the computer program stored in the memory.

[0062] A computer-readable storage medium is provided, on which a rewritable computer program is stored.

[0063] When the computer program is executed on a computer device, the computer device is caused to execute the above-mentioned centrifugal pump fault diagnosis method integrating multi-source heterogeneous data.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] The present invention collects a set of application scenarios, and uses N test centrifugal pumps to perform an operation experiment in each application scenario; N is the preset number of test centrifugal pumps, and the historical operation parameter sequence, historical environmental data, historical fault type and historical fault time of each operation experiment in each application scenario are collected. For each fault type in each application scenario, the historical environmental data are clustered to obtain K environmental clusters; K is the number of selected environmental clusters, and the environmental data corresponding to the center point position of each environmental cluster in each application scenario are calculated; and in each application scenario, K new test centrifugal pumps are selected to perform a new operation experiment, and the standard fault time and standard operation parameter sequence are collected. For each environmental cluster, an input feature vector is constructed based on the historical environmental data, the historical operation parameter sequence and the standard operation parameter sequence. The time weight label is constructed based on the failure time and the standard failure time. The input feature vector is used as input and the time weight label is used as output to train the time weight prediction model. The actual application scenarios, actual environmental data and actual operating parameters of the centrifugal pump to be monitored are collected in real time. Based on the actual application scenarios and actual environmental data, the corresponding environmental clustering cluster is calculated as the target clustering cluster; based on the standard failure time and standard operating parameter sequence of the target clustering cluster, the time weight prediction model, the actual environmental data and the actual operating parameters, the predicted failure time of various fault types is obtained; the present invention comprehensively considers the environmental parameters and the operating parameters of the centrifugal pump by integrating the environmental impact and the operating health status, uses the environmental clustering analysis to analyze the influence under different working conditions, and integrates the operating parameter sequence at the same time to comprehensively evaluate the internal and external causes of the failure, thereby improving the accuracy of the centrifugal pump fault prediction diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a flow chart of a centrifugal pump fault diagnosis method integrating multi-source heterogeneous data in Example 1 of the present invention;

[0067] Figure 2 This is a module connection relationship diagram of a centrifugal pump fault diagnosis system that integrates multi-source heterogeneous data in Example 2 of the present invention;

[0068] Figure 3 This is a schematic diagram of the structure of an electronic device in Example 3 of the present invention;

[0069] Figure 4 This is a schematic diagram of the computer-readable storage medium structure in Example 4 of the present invention. DETAILED DESCRIPTION

[0070] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0071] Example 1

[0072] like Figure 1 As shown, a centrifugal pump fault diagnosis method integrating multi-source heterogeneous data includes the following steps:

[0073] Step 1: Collect a set of application scenarios. In each application scenario, use N test centrifugal pumps to perform an operation experiment. N is the number of preset test centrifugal pumps.

[0074] Step 2: Collect the historical operating parameter sequence, historical environmental data, historical fault type, and historical fault time of the centrifugal pump itself for each experimental test in each application scenario;

[0075] Step 3: For each fault type in each application scenario, cluster the historical environmental data to obtain K environmental clusters; K is the number of selected environmental clusters;

[0076] Step 4: Calculate the environmental data corresponding to the center point of each environmental cluster in each application scenario; and in each application scenario, select K new test centrifugal pumps to conduct new operation experiments, and collect standard failure time and standard operation parameter sequences;

[0077] Step 5: For each environmental cluster, construct an input feature vector based on historical environmental data, historical operating parameter sequences, and standard operating parameter sequences, and construct a time weight label based on historical failure time and standard failure time;

[0078] Step 6: Take the input feature vector as input and the time weight label as output to train the time weight prediction model;

[0079] Step 7: Collect the actual application scenarios, actual environmental data, and actual operating parameters of the centrifugal pump to be monitored in real time. Based on the actual application scenarios and actual environmental data, calculate the corresponding environmental cluster as the target cluster; based on the standard failure time and standard operating parameter sequence of the target cluster, the time weight prediction model, the actual environmental data, and the actual operating parameters, obtain the predicted failure time of various fault types;

[0080] The application scenario set includes several major application scenarios for centrifugal pumps, including but not limited to water supply systems, HVAC systems, fire protection systems, chemical processes, mining, sewage treatment, and power plants. It is understandable that the functions and environmental conditions of centrifugal pumps vary in different application scenarios, and different working environments cause different wear and tear on different parts of centrifugal pumps. Therefore, the possible failures that may occur in different application scenarios are different.

[0081] The execution mode of the running experiment is:

[0082] In each application scenario, different environmental parameters are pre-set, and each test centrifugal pump is run until a fault of any type occurs in the test centrifugal pump. The operation process of each test centrifugal pump is regarded as one operation experiment; the environmental parameters include but are not limited to temperature, humidity, corrosiveness, medium composition, pH value, viscosity, solid content concentration, etc.; the fault types include but are not limited to bearing failure, impeller failure, shaft seal failure, motor failure, blockage and scaling, etc.;

[0083] The collection of historical operating parameter sequences, historical environmental data, historical fault types, and historical fault times of the centrifugal pump itself for each experimental test run in each application scenario includes the following steps:

[0084] Step 11: Collect the average values ​​of various environmental parameters during the entire running process of each experiment, and normalize each average value. The normalized average values ​​constitute the historical environmental data;

[0085] Step 12: During each operation experiment, collect data at each unit time and test the various operating parameters of the centrifugal pump corresponding to various fault types;

[0086] It is understandable that for different fault types, the operating parameters of the centrifugal pump that need to be collected are different. Specifically, the corresponding relationship between the operating parameters corresponding to the various fault types can be as follows:

[0087] Bearing failure (wear, overheating, etc.): vibration data (axial, radial), temperature data (bearing temperature), speed data;

[0088] Impeller failure (wear, corrosion, blockage, etc.): vibration data, pressure data (inlet / outlet pressure), flow data, torque data;

[0089] Shaft seal failure (leakage, wear, etc.): pressure data, temperature data (shaft seal temperature), vibration data;

[0090] Motor fault (winding open circuit, overload, etc.): current data, voltage data, power data, temperature rise data;

[0091] Faults such as blockage and scaling: pressure data, temperature rise data, vibration data, flow data;

[0092] Step 13: For each running experiment, arrange each operating parameter in time series order to obtain the historical operating parameter sequence of the centrifugal pump itself tested in the running experiment;

[0093] Step 14: Collect the fault type that occurs at the end of each experiment as the historical fault type, and the time when the fault occurs as the historical fault time;

[0094] Furthermore, for each fault type in each application scenario, the historical environmental data is clustered to obtain K environmental clusters as follows:

[0095] Step 21: Group the running experiments of each application scenario by fault type to obtain M experimental groups. The running experiments in each experimental group belong to the same fault type. M is the number of all fault types.

[0096] Step 22: Treat the vector consisting of each set of historical environmental data of the running experiments in each experimental group as a C-dimensional discrete point; where C is the number of environmental parameters in the historical environmental data, and the coordinate of each dimension of the C-dimensional discrete point corresponds to an environmental parameter in the historical environmental data;

[0097] For example, a vector of historical environmental data corresponding to an operation experiment in a water supply system may be (0.5, 0.625, 0.25, 0.6, 0.692), where each value in the vector corresponds to the normalized results of the medium temperature, pressure, pH value, conductivity, and ambient temperature.

[0098] Step 23: Randomly select K C-dimensional discrete points as initial cluster centers, and the remaining C-dimensional discrete points as non-initial cluster centers;

[0099] Step 24: For each non-initial cluster center, calculate the Euclidean distance to each initial cluster center, and divide each C-dimensional discrete point into the cluster cluster where the nearest initial cluster center is located;

[0100] Step 25: Calculate the average value of the environmental parameters corresponding to each dimension of the C-dimensional discrete points in each cluster, form a new C-dimensional discrete point with the average value of the environmental parameters corresponding to each dimension, and use the new C-dimensional discrete point as the new cluster center of the cluster;

[0101] Step 26: Recalculate the Euclidean distance from each C-dimensional discrete point to each new cluster center, and re-divide each C-dimensional discrete point into the cluster where the nearest new cluster center is located;

[0102] Step 27: Repeat steps 25-26 until the C-dimensional discrete points in all divided clusters no longer change, and mark each cluster with a number k, k = 1, 2, 3, ..., K; obtain all C-dimensional discrete points contained in each cluster, and form all the C-dimensional discrete points into an environmental cluster;

[0103] Furthermore, the environmental data corresponding to the center point position of each environmental cluster in each application scenario is calculated, and in each application scenario, K new test centrifugal pumps are selected for operation experiments, and the standard failure time and standard operation parameter sequence are collected in the following manner:

[0104] Obtain the C-dimensional discrete point corresponding to the center point of each environmental cluster as the cluster center point;

[0105] Obtain the value of each environmental parameter in the C-dimensional discrete point corresponding to the cluster center point;

[0106] The way to select K new test centrifugal pumps for new running experiments is:

[0107] For each environment cluster in each application scenario:

[0108] Select a new test centrifugal pump and set its environmental parameters to those corresponding to the cluster center point;

[0109] Under the environmental data conditions, the new test centrifugal pump is operated until a corresponding fault type fault occurs in the new test centrifugal pump, and the operation process of the new test centrifugal pump is regarded as a new operation experiment;

[0110] The failure time of each new running experiment is collected as the standard failure time, and the values ​​of various operating parameters of the newly tested centrifugal pump at each unit time are collected in chronological order to form a standard operating parameter sequence;

[0111] It can be understood that the standard failure time and standard operating parameter sequence can be regarded as a standard comparative experiment performed in the corresponding environment cluster, and the remaining operating experiments can use this operating experiment as a reference;

[0112] Furthermore, for each environmental cluster, the input feature vector is constructed based on the historical environmental data, the historical operating parameter sequence and the standard operating parameter sequence as follows:

[0113] For each run in each environment cluster:

[0114] Calculate the ratio of each environmental parameter in the historical environmental data of the running experiment to the environmental parameters corresponding to the center point of the environmental cluster to form an environmental ratio vector. It can be understood that the environmental ratio vector measures the environmental difference between each running experiment and the standard running experiment, thereby achieving the purpose of evaluating the impact of environmental factors on failure time;

[0115] Obtain each operating parameter in the historical operating parameter sequence and the standard operating parameter sequence at each unit time to form a historical parameter vector and a standard parameter vector, respectively. Calculate the ratio of each operating parameter in the historical parameter vector and the standard parameter vector to form an operating ratio vector. It is understood that the operating ratio vector measures the difference in operating health status between the test centrifugal pump and the new test centrifugal pump at each unit time. Obviously, the greater the difference, the greater the impact on the failure time.

[0116] The input feature vector is composed of an environmental ratio vector and an operating ratio vector. It can be understood that the input feature vector includes both the impact of environmental parameters on the failure time prediction and the impact of the real-time health status of the centrifugal pump on the failure time prediction.

[0117] Furthermore, the method of constructing a time weight label based on the historical failure time and the standard failure time is:

[0118] For each environment cluster:

[0119] For each unit time of each experiment run, calculate the time from the unit time to the occurrence of the experiment run failure as the failure time difference;

[0120] For each unit time of the newly run experiment, calculate the time from the unit time to the occurrence of the new experiment failure as the standard failure time difference;

[0121] The ratio of the fault time difference to the standard fault time difference is used as the time weight label of the unit moment;

[0122] It can be understood that the time weight label measures the difference in failure time results caused by differences in environmental parameters and differences in the health status of the centrifugal pump;

[0123] Furthermore, the method of training the time weight prediction model with the input feature vector as input and the time weight label as output is:

[0124] Each set of input feature vectors is used as input to a time weight prediction model, and the time weight prediction model uses the predicted value of the fault time weight at each unit moment as output; the time weight prediction model uses the time weight label corresponding to each unit moment as a prediction target, the difference between the predicted value of the fault time weight and the time weight label as a prediction error, and minimizing the sum of squares of the prediction errors at all unit moments as a training goal; the time weight prediction model is trained until the sum of squares of the prediction errors reaches convergence, and the training is stopped; the time weight prediction model is any one of a regression model, including but not limited to an SVR model or a polynomial regression model;

[0125] Furthermore, the actual application scenario is the actual application scenario of the centrifugal pump to be monitored, the actual environmental data is a vector composed of various environmental parameters of the environment in which the centrifugal pump to be monitored is located, and the actual operating parameters include operating parameters corresponding to various fault types of the centrifugal pump to be monitored;

[0126] Furthermore, the method of calculating the corresponding environment cluster as the target cluster based on the actual application scenario and actual environment data is as follows:

[0127] For each fault type:

[0128] Calculate the Euclidean distance between the C-dimensional discrete points corresponding to the actual environmental data and the centers of each environmental cluster, and take the environmental cluster with the closest Euclidean distance as the target cluster;

[0129] The method for obtaining the predicted failure time of various failure types based on the standard failure time and standard operating parameter sequence of the target cluster, the time weight prediction model, the actual environment data, and the actual operating parameters is as follows:

[0130] Calculate the ratio of each environmental parameter in the actual environmental data to the environmental parameters corresponding to the target cluster center point to form an actual environmental ratio vector;

[0131] Calculate the ratio of each operating parameter in the actual operating parameters to each operating parameter at the corresponding unit time in the standard operating parameter sequence of the target cluster to form an actual operating ratio vector;

[0132] The actual environment ratio vector and the actual operation ratio vector constitute an actual input vector, and the actual input vector is input into the time weight prediction model to obtain a predicted value of the predicted failure time weight;

[0133] The predicted value of the failure time weight is multiplied by the product of the unit moment and the standard failure time difference to obtain the predicted time of occurrence of the failure type.

[0134] Example 2

[0135] like Figure 2 As shown, a centrifugal pump fault diagnosis system integrating multi-source heterogeneous data includes a sample collection module, a clustering module, a model training module, and a model application module; wherein each module is electrically connected;

[0136] The sample collection module collects a set of application scenarios. In each application scenario, N test centrifugal pumps are used to perform an operation experiment. The historical operation parameter sequence, historical environmental data, historical fault type, and historical fault time of the centrifugal pump itself are collected for each operation experiment in each application scenario. The historical operation parameter sequence and historical environmental data are sent to the clustering module, and the historical environmental data, historical fault type, and historical fault time are sent to the model training module.

[0137] The clustering module clusters the historical environmental data for each fault type in each application scenario to obtain K environmental clusters, where K is the number of selected environmental clusters, and sends the environmental clusters to the model training module and the model application module;

[0138] The model training module calculates the environmental data corresponding to the center point of each environmental cluster in each application scenario. In each application scenario, K new test centrifugal pumps are selected for new operation experiments, and standard failure time and standard operation parameter sequences are collected. For each environmental cluster, an input feature vector is constructed based on historical environmental data, historical operation parameter sequences, and standard operation parameter sequences. A time weight label is constructed based on historical failure time and standard failure time. The time weight prediction model is trained using the input feature vector as input and the time weight label as output. The standard failure time, standard operation parameter sequence, and time weight prediction model are then sent to the model application module.

[0139] The model application module collects the actual application scenarios, actual environmental data and actual operating parameters of the centrifugal pump to be monitored in real time, and calculates the corresponding environmental cluster cluster as the target cluster cluster based on the actual application scenarios and actual environmental data; based on the standard failure time and standard operating parameter sequence of the target cluster cluster, the time weight prediction model, actual environmental data, and actual operating parameters, the predicted failure time of various fault types is obtained.

[0140] Example 3

[0141] Figure 3 This is a schematic diagram of the electronic device structure provided by an embodiment of the present application. Figure 3As shown, the present application also provides an electronic device 100. The electronic device 100 may include one or more processors and one or more memories. The memories may store computer-readable code, which, when executed by the one or more processors, may execute the centrifugal pump fault diagnosis method for fusing multi-source heterogeneous data as described above.

[0142] The method or device according to the embodiment of the present application can also be used by Figure 3 The electronic device architecture shown in FIG. Figure 3 As shown, the electronic device 100 may include a bus 101, one or more CPUs 102, a ROM 103, a RAM 104, a communication port 105 connected to a network, an input / output component 106, a hard disk 107, etc. The storage device in the electronic device 100, such as the ROM 103 or the hard disk 107, may store a centrifugal pump fault diagnosis method for integrating multi-source heterogeneous data provided in the present application.

[0143] Furthermore, the electronic device 100 may further include a user interface 108. Figure 3 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 3 One or more components of an electronic device are shown.

[0144] Example 4

[0145] Figure 4 This is a schematic diagram of the computer-readable storage medium structure provided by an embodiment of the present application. Figure 4 As shown, the computer-readable storage medium 200 disclosed in the present application is shown. Computer-readable instructions are stored on the computer-readable storage medium 200. When the computer-readable instructions are executed by the processor, a centrifugal pump fault diagnosis method for integrating multi-source heterogeneous data according to an embodiment of the present application described with reference to the above figures can be executed. The computer-readable storage medium 200 includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0146] In addition, according to embodiments of the present application, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, the present application provides a non-transitory machine-readable storage medium storing machine-readable instructions capable of being executed by a processor to execute instructions corresponding to the steps of the method provided in the present application. When the computer program is executed by a central processing unit (CPU), the above-mentioned functions defined in the method of the present application are performed.

[0147] The methods, apparatuses, and devices of the present application may be implemented in many ways. For example, the methods, apparatuses, and devices of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present application are not limited to the order specifically described above unless otherwise specified. In addition, in some embodiments, the present application may also be implemented as programs recorded in a recording medium, which include machine-readable instructions for implementing the methods according to the present application. Therefore, the present application also covers recording media that store programs for executing the methods according to the present application.

[0148] In addition, the parts of the above technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0149] The above-described specific embodiments further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above description is merely a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

[0150] The above preset parameters or preset thresholds are all set by those skilled in the art according to actual conditions or obtained through large amounts of data simulation.

[0151] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A centrifugal pump fault diagnosis method integrating multi-source heterogeneous data, characterized in that: The following steps are involved: Step 1: Collect a set of application scenarios. In each application scenario, use N test centrifugal pumps to perform an operation experiment. N is the number of preset test centrifugal pumps. Step 2: Collect the historical operating parameter sequence, historical environmental data, historical fault type, and historical fault time of the centrifugal pump itself for each experimental test in each application scenario; Step 3: For each fault type in each application scenario, cluster the historical environmental data to obtain K environmental clusters; K is the number of selected environmental clusters; Step 4: Calculate the environmental data corresponding to the center point of each environmental cluster in each application scenario; and in each application scenario, select K new test centrifugal pumps to conduct new operation experiments, and collect standard failure time and standard operation parameter sequences; Step 5: For each environmental cluster, construct an input feature vector based on historical environmental data, historical operating parameter sequences, and standard operating parameter sequences, and construct a time weight label based on historical failure time and standard failure time; Step 6: Take the input feature vector as input and the time weight label as output to train the time weight prediction model; Step 7: Collect the actual application scenarios, actual environmental data, and actual operating parameters of the centrifugal pump to be monitored in real time, and calculate the corresponding environmental clusters as target clusters based on the actual application scenarios and actual environmental data; Based on the standard failure time and standard operating parameter sequence of the target cluster, the time weight prediction model, the actual environment data, and the actual operating parameters, the predicted failure time of various fault types is obtained.

2. A centrifugal pump fault diagnosis method integrating multi-source heterogeneous data according to claim 1, characterized in that: The collection of historical operating parameter sequences, historical environmental data, historical fault types, and historical fault times for each running experiment in each application scenario includes the following steps: Step 11: Collect the average values ​​of various environmental parameters during the entire running process of each experiment, and normalize each average value. The normalized average values ​​constitute the historical environmental data; Step 12: During each operation experiment, collect data at each unit time and test the various operating parameters of the centrifugal pump corresponding to various fault types; Step 13: For each running experiment, arrange each running parameter in time series order to obtain the historical running parameter sequence of the running experiment; Step 14: Collect the fault type that occurs at the end of each experiment run as the historical fault type, and the time when the fault occurs as the historical fault time.

3. A centrifugal pump fault diagnosis method integrating multi-source heterogeneous data according to claim 2, characterized in that: For each fault type in each application scenario, the historical environmental data is clustered to obtain K environmental clusters as follows: Step 21: Group the running experiments of each application scenario by fault type to obtain M experimental groups. The running experiments in each experimental group belong to the same fault type. M is the number of all fault types. Step 22: Treat the vector consisting of each set of historical environmental data of the running experiments in each experimental group as a C-dimensional discrete point; where C is the number of environmental parameters in the historical environmental data, and the coordinate of each dimension of the C-dimensional discrete point corresponds to an environmental parameter in the historical environmental data; Step 23: Randomly select K C-dimensional discrete points as initial cluster centers, and the remaining C-dimensional discrete points as non-initial cluster centers; Step 24: For each non-initial cluster center, calculate the Euclidean distance to each initial cluster center, and divide each C-dimensional discrete point into the cluster cluster where the nearest initial cluster center is located; Step 25: Calculate the average value of the environmental parameters corresponding to each dimension of the C-dimensional discrete points in each cluster, form a new C-dimensional discrete point with the average value of the environmental parameters corresponding to each dimension, and use the new C-dimensional discrete point as the new cluster center of the cluster; Step 26: Recalculate the Euclidean distance from each C-dimensional discrete point to each new cluster center, and re-divide each C-dimensional discrete point into the cluster where the nearest new cluster center is located; Step 27: Repeat steps 25-26 until the C-dimensional discrete points in all divided clusters no longer change, and mark the number of each cluster as k, k = 1, 2, 3, ..., K; obtain all C-dimensional discrete points contained in each cluster, and form all the C-dimensional discrete points into an environmental cluster.

4. A centrifugal pump fault diagnosis method integrating multi-source heterogeneous data according to claim 3, characterized in that: The environmental data corresponding to the center point of each environmental cluster in each application scenario is calculated, and in each application scenario, K new test centrifugal pumps are selected for operation experiments to collect standard failure times and standard operation parameter sequences in the following manner: Obtain the C-dimensional discrete point corresponding to the center point of each environmental cluster as the cluster center point; Obtain the value of each environmental parameter in the C-dimensional discrete point corresponding to the cluster center point; The way to select K new test centrifugal pumps for new running experiments is: For each environment cluster in each application scenario: Select a new test centrifugal pump and set its environmental parameters to those corresponding to the cluster center point; Under the environmental data conditions, the new test centrifugal pump is operated until a corresponding fault type fault occurs in the new test centrifugal pump, and the operation process of the new test centrifugal pump is regarded as a new operation experiment; The failure time of each new running experiment is collected as the standard failure time, and the values ​​of various operating parameters of the newly tested centrifugal pump at each unit time are collected in chronological order to form a standard operating parameter sequence.

5. A centrifugal pump fault diagnosis method integrating multi-source heterogeneous data according to claim 4, characterized in that: For each environmental cluster, the input feature vector is constructed based on the historical environmental data, the historical operating parameter sequence and the standard operating parameter sequence as follows: For each run in each environment cluster: Calculate the ratio of each environmental parameter in the historical environmental data of the running experiment to the environmental parameters corresponding to the center point of the environmental cluster to form an environmental ratio vector; Obtaining each operating parameter in the historical operating parameter sequence and the standard operating parameter sequence at each unit moment to form a historical parameter vector and a standard parameter vector respectively, and calculating the ratio of each operating parameter in the historical parameter vector and the standard parameter vector to form an operating ratio vector; The input feature vector is composed of the environment ratio vector and the operation ratio vector.

6. A centrifugal pump fault diagnosis method integrating multi-source heterogeneous data according to claim 5, characterized in that: The method of constructing a time weight label based on historical failure time and standard failure time is: For each environment cluster: For each unit time of each experiment run, calculate the time from the unit time to the occurrence of the experiment run failure as the failure time difference; For each unit time of the newly run experiment, calculate the time from the unit time to the occurrence of the new experiment failure as the standard failure time difference; The ratio of the fault time difference to the standard fault time difference is used as the time weight label of the unit moment.

7. A centrifugal pump fault diagnosis method integrating multi-source heterogeneous data according to claim 6, characterized in that: The method for obtaining the predicted failure time of various failure types is as follows: Calculate the ratio of each environmental parameter in the actual environmental data to the environmental parameters corresponding to the target cluster center point to form an actual environmental ratio vector; Calculate the ratio of each operating parameter in the actual operating parameters to each operating parameter at the corresponding unit time in the standard operating parameter sequence of the target cluster to form an actual operating ratio vector; The actual environment ratio vector and the actual operation ratio vector constitute an actual input vector, and the actual input vector is input into the time weight prediction model to obtain a predicted value of the predicted failure time weight; The predicted value of the failure time weight is multiplied by the product of the unit moment and the standard failure time difference to obtain the predicted time of occurrence of the failure type.

8. A centrifugal pump fault diagnosis system integrating multi-source heterogeneous data, which is used to implement the centrifugal pump fault diagnosis method integrating multi-source heterogeneous data according to any one of claims 1 to 7, characterized in that: It includes a sample collection module, a clustering module, a model training module, and a model application module; wherein each module is electrically connected; The sample collection module collects a set of application scenarios. In each application scenario, N test centrifugal pumps are used to perform an operation experiment. The historical operation parameter sequence, historical environmental data, historical fault type, and historical fault time of each operation experiment in each application scenario are collected. The historical operation parameter sequence and historical environmental data are sent to the clustering module, and the historical environmental data, historical fault type, and historical fault time are sent to the model training module. The clustering module clusters the historical environmental data for each fault type in each application scenario to obtain K environmental clusters, where K is the number of selected environmental clusters, and sends the environmental clusters to the model training module and the model application module. The model training module calculates the environmental data corresponding to the center point of each environmental cluster in each application scenario. In each application scenario, K new test centrifugal pumps are selected for new operation experiments, and standard failure time and standard operation parameter sequences are collected. For each environmental cluster, an input feature vector is constructed based on historical environmental data, historical operation parameter sequences, and standard operation parameter sequences. A time weight label is constructed based on historical failure time and standard failure time. The time weight prediction model is trained using the input feature vector as input and the time weight label as output. The standard failure time, standard operation parameter sequence, and time weight prediction model are then sent to the model application module. The model application module collects the actual application scenarios, actual environmental data and actual operating parameters of the centrifugal pump to be monitored in real time, and calculates the corresponding environmental cluster cluster as the target cluster cluster based on the actual application scenarios and actual environmental data; based on the standard failure time and standard operating parameter sequence of the target cluster cluster, the time weight prediction model, actual environmental data, and actual operating parameters, the predicted failure time of various fault types is obtained.

9. An electronic device, characterized in that: include: processor and memory, wherein The memory stores a computer program that can be called by the processor; The processor executes the centrifugal pump fault diagnosis method for fusing multi-source heterogeneous data as described in any one of claims 1 to 7 in the background by calling the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that A rewritable computer program is stored thereon; When the computer program is run on a computer device, the computer device executes the centrifugal pump fault diagnosis method for fusing multi-source heterogeneous data according to any one of claims 1 to 7 in the background.

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