Diaphragm urea pump fault diagnosis method, device, electronic equipment and medium

Abstract

The present invention relates to a method, device, electronic device, and medium for diaphragm urea pump fault diagnosis, and belongs to the technical field of engine exhaust aftertreatment. The method comprises: inputting acquired operating data of the diaphragm urea pump into a trained prediction model to obtain a urea pressure prediction value output by the prediction model; the prediction model is trained based on historical operating data and the urea injection pressure corresponding to the historical operating data; and diaphragm urea pump fault diagnosis is performed based on the urea pressure prediction value and the measured urea pressure value. The diaphragm urea pump fault diagnosis method provided by the present invention accurately predicts the urea pressure prediction value using the prediction model, and accurately determines the fault by comparing the model prediction value with the measured value.

Description

Technical Field

[0001] The present invention relates to the technical field of engine exhaust aftertreatment, and in particular to a fault diagnosis method, device, electronic equipment and medium for a diaphragm type urea pump. Background Art

[0002] Currently, the market is plagued by insufficient urea injection due to clogged urea injection system filters. This causes a drop in urea pump pressure. However, due to the significant variation in urea injection pressure across different operating conditions and vehicles, traditional fault diagnosis methods, limited by the characteristics of diaphragm pumps, can result in false alarms or omissions. Existing urea pump pressure faults are diagnosed through table lookup. This involves testing to obtain a table that correlates urea injection volume and urea pressure. During vehicle operation, the table is then used to determine the theoretical urea pressure at the current injection volume, thereby determining whether the urea injection system is functioning properly.

[0003] Due to the characteristics of diaphragm pumps, urea injection pressure is not constant even at a steady injection rate. When the injection rate is constant, the operating pressure exhibits a sinusoidal distribution. The urea pressure value obtained by table lookup is poorly consistent with the transient urea pressure. Furthermore, a simple table lookup fails to account for the effects of exhaust back pressure, exhaust temperature, ambient temperature, and previous operating conditions on urea injection pressure.

[0004] At the same injection rate, the envelope pressure has a wide distribution, making traditional low urea pressure fault diagnosis methods (for example, if the measured pressure at a certain injection rate falls below a limit, it is considered a fault) difficult to ensure robustness. Urea pressure is affected by multiple factors, and it may not increase with injection rate, or the urea pressure may vary at the same injection rate, making traditional diagnostic methods more difficult. Summary of the Invention

[0005] In view of this, it is necessary to provide a fault diagnosis method, device, electronic equipment and medium for a diaphragm type urea pump, so as to solve the problem of how to improve the fault diagnosis accuracy of the diaphragm type urea pump.

[0006] In order to solve the above problems, the present invention provides a fault diagnosis method for a diaphragm type urea pump, comprising:

[0007] Inputting the acquired operating data of the diaphragm urea pump into a trained prediction model to obtain a urea pressure prediction value output by the prediction model; the prediction model is trained based on historical operating data and the urea injection pressure corresponding to the historical operating data;

[0008] Based on the predicted urea pressure value and the measured urea pressure value, a fault diagnosis is performed on the diaphragm urea pump.

[0009] In a possible implementation, the performing fault diagnosis on the diaphragm urea pump based on the predicted urea pressure value and the measured urea pressure value includes:

[0010] fusing the predicted urea pressure value and the measured urea pressure value based on an extended Kalman filter method to obtain a fused value;

[0011] Based on the ratio of the measured urea pressure value to the fusion value, a fault diagnosis is performed on the diaphragm urea pump.

[0012] In a possible implementation, the performing fault diagnosis on the diaphragm urea pump based on the ratio of the measured urea pressure value to the fusion value includes:

[0013] When the ratio is less than a first preset value, the fault is diagnosed as filter blockage;

[0014] When the ratio is greater than a second preset value, the fault is diagnosed as nozzle blockage;

[0015] The first preset value is smaller than the second preset value.

[0016] In a possible implementation, before performing fault diagnosis on the diaphragm urea pump based on the predicted urea pressure value and the measured urea pressure value, the method further includes:

[0017] The NOx concentration measured by the NOx sensor determines the conversion efficiency of the SCR system;

[0018] When the conversion efficiency is higher than a third preset value, the actual measured value of the urea pressure is obtained.

[0019] In one possible implementation, the prediction model is trained in the following manner:

[0020] The historical operating data is used as a model input, and the urea injection pressure is used as an output, and a Transformer model is trained to obtain the prediction model.

[0021] In a possible implementation, the expression of the fusion value is as follows:

[0022]

[0023]

[0024] in, represents the fusion value, Represents the corrected urea pressure prediction value, represents the Kalman gain, Indicates the measured value of urea pressure, represents the weight parameter of the linear link, Indicates the predicted value of urea pressure, is the correction parameter of the linear link, Indicates time.

[0025] In a possible implementation, the operating data includes:

[0026] Ambient temperature, exhaust flow, urea injection amount and SCR inlet temperature.

[0027] The present invention also provides a fault diagnosis device for a diaphragm type urea pump, comprising:

[0028] A prediction module, configured to input the acquired operating data of the diaphragm urea pump into a trained prediction model to obtain a urea pressure prediction value output by the prediction model; the prediction model is trained based on historical operating data and the urea injection pressure corresponding to the historical operating data;

[0029] The diagnostic module is used to perform fault diagnosis on the diaphragm urea pump based on the predicted urea pressure value and the measured urea pressure value.

[0030] On the other hand, the present invention also provides an electronic device, comprising a memory and a processor, wherein:

[0031] The memory is used to store programs;

[0032] The processor is coupled to the memory and is configured to execute the program stored in the memory to implement the fault diagnosis method for the diaphragm type urea pump described in any of the above implementations.

[0033] On the other hand, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the fault diagnosis method for the diaphragm type urea pump described in any of the above implementations is implemented.

[0034] The beneficial effects of the present invention are as follows: the fault diagnosis method, device, electronic device and medium of the diaphragm urea pump provided by the present invention obtain a prediction model based on historical operating data and corresponding urea injection pressure training, thereby accurately predicting the working pressure of the urea pump through the operating data of the diaphragm urea pump and the prediction model to obtain a urea pressure prediction value, and combining the urea pressure prediction value and the urea pressure measured value to diaphragm urea pump fault diagnosis, for example, it can be judged whether the diaphragm urea pump has a fault through the difference between the urea pressure prediction value and the urea pressure measured value, the urea pressure prediction value is accurately predicted by the prediction model, and the fault is accurately judged by comparing the model prediction value with the measured value. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0036] Figure 1 This is a flow chart of one embodiment of a method for diaphragm type urea pump fault diagnosis provided by the present invention;

[0037] Figure 2 This is a second method flow chart of an embodiment of a fault diagnosis method for a diaphragm type urea pump provided by the present invention;

[0038] Figure 3 A schematic structural diagram of an embodiment of a fault diagnosis system for a diaphragm type urea pump provided by the present invention;

[0039] Figure 4 A schematic structural diagram of an embodiment of a fault diagnosis device for a diaphragm type urea pump provided by the present invention;

[0040] Figure 5 This is a schematic structural diagram of an embodiment of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0042] In the description of the embodiments of the present invention, unless otherwise specified, "plurality" means two or more. "And / or" describes the association relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone.

[0043] The terms "first," "second," and so on, used in the embodiments of the present invention are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, technical features designated as "first" or "second" may explicitly or implicitly include at least one such feature.

[0044] Figure 1 This is a flow chart of one embodiment of a method for diaphragm type urea pump fault diagnosis provided by the present invention, as shown in FIG. Figure 1As shown in the figure, the fault diagnosis method of the diaphragm urea pump includes:

[0045] S101: Inputting the acquired operating data of the diaphragm urea pump into a trained prediction model to obtain a urea pressure prediction value output by the prediction model; the prediction model is trained based on historical operating data and the urea injection pressure corresponding to the historical operating data;

[0046] S102: Perform fault diagnosis on the diaphragm urea pump based on the predicted urea pressure value and the measured urea pressure value.

[0047] Compared with the prior art, the fault diagnosis method for a diaphragm urea pump provided in an embodiment of the present invention obtains a prediction model based on historical operating data and corresponding urea injection pressure training, thereby accurately predicting the working pressure of the urea pump through the operating data of the diaphragm urea pump and the prediction model to obtain a urea pressure prediction value, and combining the urea pressure prediction value and the urea pressure measured value to perform fault diagnosis on the diaphragm urea pump. For example, whether the diaphragm urea pump has a fault can be judged by the difference between the urea pressure prediction value and the urea pressure measured value. The urea pressure prediction value is accurately predicted by the prediction model, and the fault is accurately judged by comparing the model prediction value with the measured value.

[0048] In some embodiments of the present invention, the operating data includes:

[0049] Ambient temperature, exhaust flow, urea injection amount and SCR inlet temperature.

[0050] In some embodiments of the present invention, the prediction model is trained in the following manner:

[0051] The historical operating data is used as a model input, and the urea injection pressure is used as an output, and a Transformer model is trained to obtain the prediction model.

[0052] Operating data includes ambient temperature, exhaust flow, urea injection amount, and SCR inlet temperature. Historical operating data from vehicles operating normally and without any problems, including ambient temperature, exhaust flow, urea injection amount, SCR inlet temperature, and the corresponding urea injection pressure, can be used to train the machine learning model.

[0053] The machine learning model used in the embodiment of the present invention is a Transformer model, which includes an encoding layer, a multi-head attention layer, an FNN layer and a SoftMax layer.

[0054] The vehicle information is input into the encoding layer for position encoding, and then encoded and decoded by the multi-head attention module and processed by the FNN layer and SoftMax layer to obtain the urea pressure value predicted by the model. By considering the impact of vehicle operating conditions on the working pressure of the injection system, the time series data takes into account the historical operating status, making the prediction of the urea pump working pressure more accurate and reasonable.

[0055] The fault diagnosis method for a diaphragm urea pump provided in an embodiment of the present invention adopts a Transformer-based machine learning model to predict the working pressure of the urea pump through operating data such as urea injection amount, ambient temperature, SCR temperature and exhaust flow. A data fusion algorithm is used to fuse the fault-free measured working pressure with the pressure predicted by the machine learning model, and the working pressure differences caused by factors such as installation layout and manufacturing deviation are introduced. This can achieve model prediction values ​​that vary from vehicle to vehicle and working conditions, and diagnose faults more reasonably and accurately.

[0056] In some embodiments of the present invention, the performing fault diagnosis on the diaphragm urea pump based on the predicted urea pressure value and the measured urea pressure value includes:

[0057] fusing the predicted urea pressure value and the measured urea pressure value based on an extended Kalman filter method to obtain a fused value;

[0058] Based on the ratio of the measured urea pressure value to the fusion value, a fault diagnosis is performed on the diaphragm urea pump.

[0059] In some embodiments of the present invention, the expression of the fusion value is as follows:

[0060]

[0061]

[0062] in, represents the fusion value, Represents the corrected urea pressure prediction value, represents the Kalman gain, Indicates the measured value of urea pressure, represents the weight parameter of the linear link, Indicates the predicted value of urea pressure, is the correction parameter of the linear link, Indicates time.

[0063] The predicted urea pressure value and the measured urea pressure value are fused by the Extended Kalman Filter (EKF) method to obtain a fused value.

[0064] Optionally, the NOx emissions of the current SCR system are added as an enabling condition during fusion, because normal system emissions mean that the injection system is working well. At this time, the sensor measured pressure can represent the normal allowable level of the vehicle. The fusion of measurement values ​​and models can take into account the differences in vehicles caused by manufacturing deviations, installation layout and other factors, and further improve the prediction of the urea pump working pressure specific to each vehicle.

[0065] The specific fusion process is as follows:

[0066]

[0067]

[0068]

[0069]

[0070]

[0071] in, represents the prediction result of the Transformer model (i.e., the predicted value of urea pressure), is the predicted value after Kalman filter fusion correction (i.e. fusion value), is the predicted value before Kalman filter fusion correction, is the Kalman gain, is the actual value of the urea pressure sensor at time t (i.e. the measured value of urea pressure), is the weight parameter of the linear link, is the correction parameter of the linear link, is the actual process noise, is the noise of the prediction process, is the prior covariance matrix, is the posterior covariance matrix at time t, is the posterior covariance matrix of the previous moment before time t.

[0072] The fault diagnosis method for a diaphragm urea pump provided in an embodiment of the present invention adopts a fusion algorithm to fuse the measured urea pressure under normal working conditions with the predicted pressure of a machine learning model, and integrates the working pressure changes caused by factors such as installation layout and manufacturing deviation into the pressure value predicted based on the working condition.

[0073] In some embodiments of the present invention, the fault diagnosis of the diaphragm urea pump based on the ratio of the measured urea pressure value to the fusion value includes:

[0074] When the ratio is less than a first preset value, the fault is diagnosed as filter blockage;

[0075] When the ratio is greater than a second preset value, the fault is diagnosed as nozzle blockage;

[0076] The first preset value is smaller than the second preset value.

[0077] The ratio of the measured urea pressure value to the fusion value is calculated. When the ratio is less than a first preset value (eg, 0.5), the fault is diagnosed as filter blockage, and a filter maintenance reminder is activated.

[0078] When the ratio is greater than a second preset value (eg, 1.5), the fault is diagnosed as nozzle clogging, and a nozzle cleaning maintenance reminder is activated.

[0079] Furthermore, when the engine is operating in regeneration mode, the model's predicted operating pressure will deviate significantly from the actual operating pressure, so fault diagnosis in regeneration mode should be avoided. Furthermore, when the system is operating well and the SCR conversion efficiency is normal, there is no need to determine whether the injection system has a fault.

[0080] The fault diagnosis method for a diaphragm urea pump provided in an embodiment of the present invention determines whether the urea operating pressure is normal by comparing the fused predicted urea pressure value with the measured value. This is more accurate than the traditional judgment method of setting a fixed threshold.

[0081] In some embodiments of the present invention, before performing fault diagnosis on the diaphragm urea pump based on the predicted urea pressure value and the measured urea pressure value, the method further includes:

[0082] The NOx concentration measured by the NOx sensor determines the conversion efficiency of the SCR system;

[0083] When the conversion efficiency is higher than a third preset value, the actual measured value of the urea pressure is obtained.

[0084] Based on the NOx concentration measured by the NOx sensor, the conversion efficiency of the current SCR system can be calculated.

[0085] When the SCR system's conversion efficiency is higher than the third preset value, the entire SCR system is considered healthy, indicating normal urea injection. The urea injection pressure measured by the pressure sensor is then considered the pressure for normal urea pump operation. This allows for Kalman fusion of the pressure predictions from deep learning and the actual pressure sensor measurements.

[0086] When the conversion efficiency of the SCR system is lower than the third preset value, it is considered that there may be a fault in some aspects of the SCR system. At this time, the actual measured value of the urea injection pressure is no longer integrated with the machine learning model to avoid the influence of the fault data on the prediction result, making the prediction result more accurate. Finally, the predicted pressure is compared with the actual measured pressure of the sensor to diagnose whether there is a fault in the system.

[0087] The fault diagnosis method for a diaphragm urea pump provided in an embodiment of the present invention sets a system working state judgment through fault discrimination. When the SCR conversion efficiency is normal and the vehicle is operating in a regeneration or other working condition, fault diagnosis is not enabled, which can improve the reliability of diagnosis.

[0088] Figure 2 This is a second method flow chart of an embodiment of the fault diagnosis method of the diaphragm type urea pump provided by the present invention, as shown in FIG. Figure 2 As shown, the fault diagnosis method of the diaphragm type urea pump provided by the present invention includes:

[0089] S201, obtaining historical vehicle operation data and corresponding urea injection system operating pressure and fault form;

[0090] S202, extracting features from the operating data and matching the corresponding working pressure with the fault form;

[0091] S203, performing machine learning calculations based on operating data characteristics and work pressure;

[0092] S204, combining the current operating working pressure with the operating data characteristics and the working pressure machine learning calculation results to predict the working pressure of the current system under normal conditions;

[0093] S205: Make a fault warning judgment based on the predicted working pressure and the measured working pressure.

[0094] Figure 3 This is a structural diagram of an embodiment of a fault diagnosis system for a diaphragm type urea pump provided by the present invention, as shown in FIG. Figure 3 As shown, the fault diagnosis system of the diaphragm type urea pump provided by the present invention includes a machine learning module, a fusion module and a fault reminder module.

[0095] The fault diagnosis system and method of the diaphragm type urea pump provided by the present invention are described in detail below in conjunction with specific application scenarios.

[0096] The diaphragm urea pump fault diagnosis method provided by this invention first collects operating data from the urea injection system under different operating and fault modes, correlates the collected data with the urea operating pressure and system status, and then trains a Transformer-based machine learning model to obtain model parameters. The trained machine learning model and vehicle operating data are used to predict the current urea injection system operating pressure. Finally, the measured urea pump pressure is compared with the predicted value, and a fault warning is issued.

[0097] The specific method is as follows:

[0098] Operational data selection. Model training uses historically trouble-free vehicles, as well as vehicles with clogged urea pump filters and nozzles. The model also acquires operational data collected by the vehicle's remote terminal (Tbox), including ambient temperature, exhaust flow, urea injection volume, SCR inlet temperature, urea injection pressure, front / rear NOx sensor status, front / rear NOx sensor concentration, and engine combustion mode. After the model is integrated into the ECU, the prediction process directly captures this vehicle operating information within the ECU.

[0099] Machine learning module: The machine learning model is trained using the ambient temperature, exhaust flow, urea injection amount, SCR inlet temperature, and urea injection pressure from historical operating data of vehicles operating normally and without faults. The machine learning model used here is a Transformer-based urea pressure model. The Transformer model includes an encoding layer, a multi-head attention layer, an FNN layer, and a SoftMax layer.

[0100] The vehicle information is input into the encoding layer for position encoding, and then encoded and decoded by the multi-head attention module and processed by the FNN layer and SoftMax layer to obtain the urea pressure value predicted by the model. The impact of the vehicle operating conditions on the working pressure of the injection system is taken into account here, and the time series data takes into account the historical operating status, which makes the prediction of the urea pump working pressure more accurate and reasonable.

[0101] Fusion module: By extending the Kalman filter, the machine learning pressure model value and the measured pressure model value are fused. The NOx emissions of the current SCR system are added as an enabling condition. Because normal system emissions mean that the injection system is working well, the sensor-measured pressure can represent the normal allowable level of the vehicle. The fusion of measurement values ​​and models can take into account the differences in vehicles caused by manufacturing deviations, installation layout and other factors, which further improves the prediction of the urea pump working pressure for each specific vehicle.

[0102]

[0103]

[0104]

[0105]

[0106]

[0107] in, Represents the prediction result of the Transformer model, is the predicted value after Kalman filter fusion correction, is the predicted value before Kalman filter fusion correction, is the Kalman gain, is the actual value of the urea pressure sensor at time t, is the weight parameter of the linear link, is the correction parameter of the linear link, is the actual process noise, is the noise of the prediction process, is the prior covariance matrix, is the posterior covariance matrix at time t, is the posterior covariance matrix of the previous moment before time t.

[0108] Fault Alert Module: Calculates the ratio of the measured urea pump pressure to the fused urea model pressure. If the ratio is less than 0.5, a filter maintenance reminder is activated; if the ratio is greater than 1.5, a nozzle cleaning maintenance reminder is activated. When the engine is operating in regeneration mode, the model-predicted operating pressure will deviate significantly from the actual pressure, so fault diagnosis in regeneration mode should be avoided. Furthermore, when the system is operating properly and SCR conversion efficiency is normal, there is no need to determine whether the injection system has a fault.

[0109] A fault diagnosis method for a diaphragm urea pump provided in an embodiment of the present invention obtains urea pressure and historical operating data for different vehicle operating states, including urea pump fault states, extracts features from the historical data, performs deep machine learning on the historical data, and predicts the system pressure that a normal system should exhibit in the current operating state based on the deep learning results and current operating information. Simultaneously, the current SCR system conversion efficiency is calculated based on the NOx concentration measured by the NOx sensor. When the SCR efficiency is higher than a set value, it is considered that the entire SCR system is in good condition, indicating normal urea injection. In this case, the urea injection pressure measured by the pressure sensor is considered to be the pressure for normal operation of the urea pump. Kalman fusion is performed on the deep learning predicted pressure and the actual measured result of the urea pressure sensor. When the SCR conversion efficiency is lower than a set threshold, it is considered that some aspect of the system may have a fault. In this case, the actual measured urea injection pressure is no longer fused with the machine learning model, thereby preventing the fault data from affecting the prediction result and making the prediction result more accurate. Finally, the predicted pressure is compared with the actual measured pressure of the sensor to diagnose whether the system has a fault.

[0110] By using a machine learning model to predict the working pressure of the urea pump based on the vehicle's operating conditions, the data fusion algorithm will fuse the fault-free measured working pressure with the pressure predicted by the machine learning model, and introduce the working pressure differences caused by factors such as installation layout and manufacturing deviations. This can achieve model prediction values ​​that vary from vehicle to vehicle and working conditions, and diagnose faults more reasonably and accurately.

[0111] The AI ​​urea pressure model is used to predict the working pressure of the urea pump, and accurate judgment of filter blockage and nozzle blockage is achieved by comparing the model prediction with actual measurement.

[0112] In order to better implement the fault diagnosis method of the diaphragm urea pump in the embodiment of the present invention, based on the fault diagnosis method of the diaphragm urea pump, the embodiment of the present invention further provides a fault diagnosis device of the diaphragm urea pump. Figure 4 This is a structural diagram of an embodiment of a fault diagnosis device for a diaphragm type urea pump provided by the present invention, as shown in FIG. Figure 4 As shown, the fault diagnosis device 400 of the diaphragm type urea pump includes:

[0113] The prediction module 410 is configured to input the acquired operating data of the diaphragm urea pump into a trained prediction model to obtain a urea pressure prediction value output by the prediction model; the prediction model is trained based on historical operating data and the urea injection pressure corresponding to the historical operating data;

[0114] The diagnosis module 420 is configured to perform fault diagnosis on the diaphragm urea pump based on the predicted urea pressure value and the measured urea pressure value.

[0115] The fault diagnosis device 400 for a diaphragm urea pump provided in the above embodiment can implement the technical solution described in the above embodiment of the fault diagnosis method for a diaphragm urea pump. The specific implementation principles of the above modules or units can be found in the corresponding contents in the embodiment of the fault diagnosis method for a diaphragm urea pump, which will not be repeated here.

[0116] Optionally, the diagnosis module 420 is specifically configured to:

[0117] fusing the predicted urea pressure value and the measured urea pressure value based on an extended Kalman filter method to obtain a fused value;

[0118] Based on the ratio of the measured urea pressure value to the fusion value, a fault diagnosis is performed on the diaphragm urea pump.

[0119] Optionally, the diagnosis module 420 is further configured to:

[0120] When the ratio is less than a first preset value, the fault is diagnosed as filter blockage;

[0121] When the ratio is greater than a second preset value, the fault is diagnosed as nozzle blockage;

[0122] The first preset value is smaller than the second preset value.

[0123] Optionally, the method further includes: a determination module configured to:

[0124] The NOx concentration measured by the NOx sensor determines the conversion efficiency of the SCR system;

[0125] When the conversion efficiency is higher than a third preset value, the actual measured value of the urea pressure is obtained.

[0126] Optionally, the prediction model is trained in the following manner:

[0127] The historical operating data is used as a model input, and the urea injection pressure is used as an output, and a Transformer model is trained to obtain the prediction model.

[0128] Optionally, the expression of the fusion value is as follows:

[0129]

[0130]

[0131] in, represents the fusion value, Represents the corrected urea pressure prediction value, represents the Kalman gain, Indicates the measured value of urea pressure, represents the weight parameter of the linear link, Indicates the predicted value of urea pressure, is the correction parameter of the linear link, Indicates time.

[0132] Optionally, the operating data includes:

[0133] Ambient temperature, exhaust flow, urea injection amount and SCR inlet temperature.

[0134] like Figure 5 As shown, the present invention also provides an electronic device 500. The electronic device 500 includes a processor 501, a memory 502 and a display 503. Figure 5 Only some of the components of the electronic device 500 are shown, but it should be understood that implementation of all of the shown components is not required, and more or fewer components may be implemented instead.

[0135] In some embodiments, the processor 501 may be a central processing unit (CPU), a microprocessor, or other data processing chip, configured to execute program codes or process data stored in the memory 502 , such as the fault diagnosis method for a diaphragm urea pump of the present invention.

[0136] In some embodiments, the processor 501 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processor 501 may be local or remote. In some embodiments, the processor 501 may be implemented in a cloud platform. In some embodiments, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, multiple clouds, or any combination thereof.

[0137] In some embodiments, the memory 502 may be an internal storage unit of the electronic device 500, such as a hard disk or memory of the electronic device 500. In other embodiments, the memory 502 may also be an external storage device of the electronic device 500, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 500.

[0138] Furthermore, the memory 502 may include both an internal storage unit of the electronic device 500 and an external storage device. The memory 502 is used to store application software installed in the electronic device 500 and various data.

[0139] In some embodiments, display 503 can be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an organic light-emitting diode (OLED) touchscreen. Display 503 is used to display information on electronic device 500 and to display a visual user interface. Components 501-503 of electronic device 500 communicate with each other via a system bus.

[0140] In one embodiment, when the processor 501 executes the fault diagnosis program of the diaphragm urea pump in the memory 502, the following steps may be implemented:

[0141] Inputting the acquired operating data of the diaphragm urea pump into a trained prediction model to obtain a urea pressure prediction value output by the prediction model; the prediction model is trained based on historical operating data and the urea injection pressure corresponding to the historical operating data;

[0142] Based on the predicted urea pressure value and the measured urea pressure value, a fault diagnosis is performed on the diaphragm urea pump.

[0143] It should be understood that, when executing the fault diagnosis program of the diaphragm urea pump in the memory 502 , the processor 501 may implement other functions in addition to the above functions. For details, please refer to the description of the corresponding method embodiment above.

[0144] Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 500 mentioned. The electronic device 500 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, or the like. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with iOS, Android, Microsoft, or other operating systems. The above-mentioned portable electronic devices may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 500 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0145] Accordingly, embodiments of the present invention further provide a computer-readable storage medium for storing a computer-readable program or instruction. When executed by a processor, the program or instruction implements the steps or functions of the diaphragm urea pump fault diagnosis methods provided in the aforementioned method embodiments. Those skilled in the art will appreciate that all or part of the process flow of the aforementioned method embodiments can be implemented by instructing related hardware (such as a processor, controller, etc.) through a computer program, which can be stored in a computer-readable storage medium. The computer-readable storage medium may be a magnetic disk, an optical disk, a read-only memory, or a random access memory, etc.

[0146] The above is a detailed introduction to the fault diagnosis method, device, electronic device and medium of the diaphragm urea pump provided by the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A fault diagnosis method for a diaphragm urea pump, characterized in that: include: Inputting the acquired operating data of the diaphragm type urea pump into the trained prediction model to obtain a urea pressure prediction value output by the prediction model; The prediction model is obtained by training based on historical operating data and the urea injection pressure corresponding to the historical operating data; performing a fault diagnosis on the diaphragm urea pump based on the predicted urea pressure value and the measured urea pressure value; The fault diagnosis of the diaphragm urea pump based on the predicted urea pressure value and the measured urea pressure value includes: fusing the predicted urea pressure value and the measured urea pressure value based on an extended Kalman filter method to obtain a fused value; performing a fault diagnosis on the diaphragm urea pump based on a ratio of the measured urea pressure value to the fusion value; The operating data includes: Ambient temperature, exhaust flow, urea injection amount and SCR inlet temperature.

2. The fault diagnosis method of the diaphragm type urea pump according to claim 1, characterized in that: The fault diagnosis of the diaphragm urea pump based on the ratio of the measured urea pressure value to the fusion value includes: When the ratio is less than a first preset value, the fault is diagnosed as filter blockage; When the ratio is greater than a second preset value, the fault is diagnosed as nozzle blockage; The first preset value is smaller than the second preset value.

3. The fault diagnosis method of the diaphragm type urea pump according to claim 1, characterized in that: Before performing fault diagnosis on the diaphragm urea pump based on the predicted urea pressure value and the measured urea pressure value, the method further includes: The NOx concentration measured by the NOx sensor determines the conversion efficiency of the SCR system; When the conversion efficiency is higher than a third preset value, the actual measured value of the urea pressure is obtained.

4. The fault diagnosis method of a diaphragm type urea pump according to claim 1, characterized in that: The prediction model is trained in the following way: The historical operating data is used as a model input, and the urea injection pressure is used as an output, and a Transformer model is trained to obtain the prediction model.

5. The fault diagnosis method of a diaphragm type urea pump according to claim 1, characterized in that: The expression of the fusion value is as follows: in, represents the fusion value, Represents the corrected urea pressure prediction value, represents the Kalman gain, Indicates the measured value of urea pressure, represents the weight parameter of the linear link, Indicates the predicted value of urea pressure, is the correction parameter of the linear link, Indicates time.

6. A fault diagnosis device for a diaphragm type urea pump, characterized in that: include: A prediction module, configured to input the acquired operating data of the diaphragm urea pump into a trained prediction model to obtain a urea pressure prediction value output by the prediction model; The prediction model is obtained by training based on historical operating data and the urea injection pressure corresponding to the historical operating data; a diagnostic module, configured to perform fault diagnosis on the diaphragm urea pump based on the predicted urea pressure value and the measured urea pressure value; The fault diagnosis of the diaphragm urea pump based on the predicted urea pressure value and the measured urea pressure value includes: fusing the predicted urea pressure value and the measured urea pressure value based on an extended Kalman filter method to obtain a fused value; performing a fault diagnosis on the diaphragm urea pump based on a ratio of the measured urea pressure value to the fusion value; The operating data includes: Ambient temperature, exhaust flow, urea injection amount and SCR inlet temperature.

7. An electronic device, characterized in that: comprising a memory and a processor, wherein, The memory is used to store programs; The processor is coupled to the memory and is configured to execute the program stored in the memory to implement the fault diagnosis method for the diaphragm type urea pump according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the fault diagnosis method for a diaphragm type urea pump according to any one of claims 1 to 5 is implemented.

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