A real-time prediction method and device for turbocharger failure risk

Abstract

The present application relates to a real-time prediction method and device for turbocharger failure risk, and relates to the field of engine energy-saving control technology. The method includes the following steps: monitoring the working condition of the target turbocharger to obtain real-time working condition information; identifying the surge boundary, overspeed boundary and blockage boundary in the compressor characteristic diagram corresponding to the target turbocharger; obtaining a surge risk probability value or an overspeed risk probability value or a blockage risk probability value based on the real-time working condition information and the surge boundary, overspeed boundary and blockage boundary in the compressor characteristic diagram; obtaining a turbocharger failure risk probability value based on the surge risk probability value or the overspeed risk probability value or the blockage risk probability value. The present application compares the working condition of the turbocharger with the corresponding compressor characteristic diagram, and then performs risk assessment in combination with the surge boundary, overspeed boundary and blockage boundary, so as to ensure the normal use of the user and effectively improve the working efficiency of the prediction and the reliability of the results.

Description

Technical Field

[0001] The present application relates to the field of engine energy-saving control technology, and specifically to a real-time prediction method and device for turbocharger failure risk. Background Art

[0002] A turbocharger is a device commonly used in automobile engines. Its main function is to increase the pressure of the intake air flow to improve power performance and fuel economy. However, if the turbocharger fails, it will not only reduce the performance of the car, but may also cause serious failures or accidents. By analyzing the failure risk, the design and manufacturing process can be optimized to improve the reliability and safety of the turbocharger, thereby ensuring the operating quality of the car and the safety of the passengers. However, the turbocharger may fail during long-term use, resulting in reduced engine performance or malfunction. Regular inspection and maintenance of turbochargers requires a lot of manpower and material resources. By analyzing the failure risk, the key factors affecting the life and performance of the turbocharger can be identified, and maintenance strategies can be formulated in a targeted manner to reduce ineffective maintenance work, reduce maintenance costs, and extend the service life of the turbocharger.

[0003] At present, in the automotive industry, the main methods for analyzing turbocharger failures include the following aspects: Visual inspection: Technicians can perform a visual inspection of the turbocharger to look for possible signs of damage or wear. For example, whether there are oil leaks, cracks, deformations and other problems. Disassembly inspection: If it is suspected that the turbocharger has an internal fault, it needs to be disassembled for inspection. By observing the condition of components such as turbine blades, bearings, and seals, it can be determined whether there is damage or wear. Turbine balancing test: The balance of the turbocharger is crucial to its normal operation. Through the turbine balancing test, the balancing performance of the turbocharger at different speeds can be tested to determine whether it meets the requirements.

[0004] The existing working method requires staff to perform manual analysis and prediction, which has low work efficiency and high professional skills requirements. Therefore, in order to meet the needs, a real-time prediction technology for turbocharger failure risk is provided. Summary of the invention

[0005] The present application provides a real-time prediction method and device for turbocharger failure risk, which compares the operating conditions of the turbocharger with the corresponding compressor characteristic diagram, and then conducts risk assessment in combination with surge boundaries, overspeed boundaries, and blockage boundaries, thereby ensuring normal use by users and effectively improving the prediction work efficiency and the reliability of the results.

[0006] To achieve the above objectives, this application provides the following solutions.

[0007] In a first aspect, the present application provides a method for real-time prediction of turbocharger failure risk, the method comprising the following steps:

[0008] Monitor the operating condition of the target turbocharger to obtain real-time operating condition information;

[0009] Identifying a surge boundary, an overspeed boundary, and a jam boundary in a compressor characteristic diagram corresponding to the target turbocharger;

[0010] Obtaining a surge risk probability value, an overspeed risk probability value, or a blockage risk probability value based on the real-time operating condition information and the surge boundary, the overspeed boundary, and the blockage boundary in the compressor characteristic diagram;

[0011] Based on the surge risk probability value or the overspeed risk probability value or the blockage risk probability value, a supercharger failure risk probability value is obtained.

[0012] Further, obtaining a surge risk probability value, an overspeed risk probability value, or a blockage risk probability value based on the real-time operating condition information and the surge boundary, the overspeed boundary, and the blockage boundary in the compressor characteristic diagram comprises the following steps:

[0013] Comparing the real-time operating condition information with the surge boundary, the overspeed boundary, and the blockage boundary in the compressor characteristic diagram;

[0014] If the distance value between the operating point of the real-time operating condition information in the compressor characteristic diagram and the surge boundary, the overspeed boundary or the blockage boundary in the compressor characteristic diagram is lower than a first preset distance value, the corresponding surge risk probability value or the overspeed risk probability value or the blockage risk probability value is calculated.

[0015] Furthermore, the method is configured with a surge risk probability value calculation formula:

[0016] in,

[0017] SM is the surge risk probability value, Q2 is the compressor flow at the operating point of the real-time operating condition information in the compressor characteristic diagram, and Q1 is the compressor flow on the surge boundary at the same pressure ratio at the operating point of the real-time operating condition information in the compressor characteristic diagram.

[0018] Furthermore, the method is configured with a speeding risk probability value calculation formula:

[0019] in,

[0020] OS is the surge risk probability value, S1 is the turbocharger speed limit value, and S2 is the turbocharger speed value corresponding to the real-time operating condition information.

[0021] Furthermore, the method is configured with a calculation formula for the probability value of a blockage risk:

[0022] in,

[0023] BR is the blocking risk probability value, Q2 is the compressor flow at the operating point of the real-time operating information in the compressor characteristic diagram, and Q3 is the compressor flow on the blocking boundary at the same pressure ratio as the operating point of the real-time operating information in the compressor characteristic diagram.

[0024] Furthermore, the method is configured with a supercharger failure risk probability value calculation formula:

[0025] PF=SM+OS+BR; among them,

[0026] PF is the supercharger failure risk probability value, SM is the surge risk probability value, OS is the surge risk probability value, and BR is the blockage risk probability value;

[0027] If there is no surge risk probability value, overspeed risk probability value, or jam risk probability value, the corresponding value is 0.

[0028] In a second aspect, the present application provides a method for real-time prediction of turbocharger failure risk, the device comprising:

[0029] An operating condition identification module, which is used to monitor the operating condition of the target turbocharger and obtain real-time operating condition information;

[0030] a characteristic diagram recognition module, which is used to recognize a surge boundary, an overspeed boundary, and a jam boundary in a compressor characteristic diagram corresponding to the target turbocharger;

[0031] a probability calculation module, which is used to obtain a surge risk probability value, an overspeed risk probability value, or a blockage risk probability value based on the real-time operating condition information and the surge boundary, the overspeed boundary, and the blockage boundary in the compressor characteristic diagram;

[0032] A risk prediction module is used to obtain a supercharger failure risk probability value based on the surge risk probability value or the overspeed risk probability value or the blockage risk probability value.

[0033] Furthermore, the probability calculation module is further used to compare the real-time operating condition information with the surge boundary, the overspeed boundary and the blockage boundary in the compressor characteristic diagram;

[0034] The probability calculation module is also used to calculate the corresponding surge risk probability value, the overspeed risk probability value or the blockage risk probability value if the distance value between the operating point of the real-time operating condition information in the compressor characteristic diagram and the surge boundary, the overspeed boundary or the blockage boundary in the compressor characteristic diagram is lower than a first preset distance value.

[0035] Furthermore, the device is equipped with a surge risk probability value calculation formula, an overspeed risk probability value calculation formula, and a jam risk probability value calculation formula;

[0036] The surge risk probability value calculation formula is:

[0037] The speeding risk probability value calculation formula is:

[0038] The calculation formula of the blocking risk probability value is: in,

[0039] SM is the surge risk probability value, Q2 is the compressor flow rate at the operating point of the real-time operating condition information in the compressor characteristic diagram, and Q1 is the compressor flow rate on the surge boundary at the same pressure ratio of the operating point of the real-time operating condition information in the compressor characteristic diagram;

[0040] OS is the surge risk probability value, S1 is the turbocharger speed limit value, and S2 is the turbocharger speed value corresponding to the real-time operating condition information;

[0041] BR is the blocking risk probability value, and Q3 is the compressor flow rate on the blocking boundary at the same pressure ratio of the operating point in the compressor characteristic diagram of the real-time operating condition information.

[0042] Furthermore, the device is equipped with a supercharger failure risk probability value calculation formula:

[0043] PF=SM+OS+BR; among them,

[0044] PF is the supercharger failure risk probability value, SM is the surge risk probability value, OS is the surge risk probability value, and BR is the blockage risk probability value;

[0045] If there is no surge risk probability value, overspeed risk probability value, or jam risk probability value, the corresponding value is 0.

[0046] The beneficial effects of the technical solution provided by this application include:

[0047] This application compares the operating conditions of the turbocharger with the corresponding compressor characteristic diagram, and then conducts risk assessment in combination with the surge boundary, overspeed boundary and blockage boundary, so as to ensure the normal use of the user and effectively improve the prediction work efficiency and the reliability of the results. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 A flowchart of the steps of a real-time prediction method for turbocharger failure risk provided in an embodiment of the present application;

[0050] Figure 2 A compressor characteristic diagram of the real-time prediction method for turbocharger failure risk provided in an embodiment of the present application;

[0051] Figure 3 A schematic diagram of the execution basis of the real-time prediction method of turbocharger failure risk provided in an embodiment of the present application;

[0052] Figure 4 This is a structural block diagram of a real-time prediction device for turbocharger failure risk provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0054] The embodiments of the present application are further described in detail below in conjunction with the accompanying drawings.

[0055] The embodiments of the present application provide a real-time prediction method and device for the risk of turbocharger failure, which compares the operating conditions of the turbocharger with the corresponding compressor characteristic diagram, and then performs risk assessment in combination with surge boundaries, overspeed boundaries, and blockage boundaries, thereby ensuring normal use by users and effectively improving the prediction work efficiency and the reliability of the results.

[0056] In order to achieve the above technical effects, the overall idea of ​​this application is as follows:

[0057] A real-time prediction method for turbocharger failure risk, the method comprising the following steps:

[0058] S1. Monitor the operating condition of the target turbocharger to obtain real-time operating condition information;

[0059] S2, identifying a surge boundary, an overspeed boundary, and a jam boundary in a compressor characteristic diagram corresponding to the target turbocharger;

[0060] S3. Obtaining a surge risk probability value, an overspeed risk probability value, or a blockage risk probability value based on the real-time operating condition information and a surge boundary, an overspeed boundary, and a blockage boundary in a compressor characteristic diagram;

[0061] S4. Obtain a supercharger failure risk probability value based on the surge risk probability value, the overspeed risk probability value, or the blockage risk probability value.

[0062] The embodiments of the present application are further described in detail below in conjunction with the accompanying drawings.

[0063] First, see Figures 1 to 3 As shown, an embodiment of the present application provides a real-time prediction method for turbocharger failure risk, the method comprising the following steps:

[0064] S1. Monitor the operating condition of the target turbocharger to obtain real-time operating condition information;

[0065] S2, identifying a surge boundary, an overspeed boundary, and a jam boundary in a compressor characteristic diagram corresponding to the target turbocharger;

[0066] S3. Obtaining a surge risk probability value, an overspeed risk probability value, or a blockage risk probability value based on the real-time operating condition information and a surge boundary, an overspeed boundary, and a blockage boundary in a compressor characteristic diagram;

[0067] S4. Obtain a supercharger failure risk probability value based on the surge risk probability value, the overspeed risk probability value, or the blockage risk probability value.

[0068] A turbocharger is a device commonly used in automobile engines. Its main function is to increase the pressure of the intake air flow to improve power performance and fuel economy. However, if the turbocharger fails, it will not only reduce the performance of the car, but may also cause serious failures or accidents. By analyzing the failure risk, the design and manufacturing process can be optimized to improve the reliability and safety of the turbocharger, thereby ensuring the operating quality of the car and the safety of the passengers. However, the turbocharger may fail during long-term use, resulting in reduced engine performance or malfunction. Regular inspection and maintenance of turbochargers requires a lot of manpower and material resources. By analyzing the failure risk, the key factors affecting the life and performance of the turbocharger can be identified, and maintenance strategies can be formulated in a targeted manner to reduce ineffective maintenance work, reduce maintenance costs, and extend the service life of the turbocharger.

[0069] At present, in the automotive industry, the main methods for analyzing turbocharger failures include the following aspects: Visual inspection: Technicians can perform a visual inspection of the turbocharger to look for possible signs of damage or wear. For example, whether there are oil leaks, cracks, deformations and other problems. Disassembly inspection: If it is suspected that the turbocharger has an internal fault, it needs to be disassembled for inspection. By observing the condition of components such as turbine blades, bearings, and seals, it can be determined whether there is damage or wear. Turbine balancing test: The balance of the turbocharger is crucial to its normal operation. Through the turbine balancing test, the balancing performance of the turbocharger at different speeds can be tested to determine whether it meets the requirements.

[0070] The defects of existing technical means are: visual inspection requires professional technicians to conduct on-site inspection; disassembly inspection requires the vehicle to enter the station for technicians to remove the supercharger from the engine for inspection, which causes the vehicle to be temporarily unable to operate, causing economic losses to customers; dynamic balance testing uses professional testing equipment based on supercharger disassembly. None of the above inspections can predict and analyze the status of the supercharger of the vehicle in transit and its failure risk online.

[0071] The technical solution of the embodiment of the present application monitors and records the working status of the turbocharger in real time based on the ambient temperature, ambient pressure, engine intake resistance, intercooler after-pressure, intercooler after-temperature, engine speed, output torque, intake flow, turbocharger speed, etc. in the CAN message information provided by the current engine ECU;

[0072] Analyze the operating status of the supercharger through the above parameters and determine whether there is an abnormality in the supercharger, and predict the risk of failure of the supercharger if an abnormality occurs;

[0073] When the risk of failure occurs, a supercharger failure risk warning is promptly sent to the driver's mobile phone APP through the cloud platform, reminding the driver to adjust the engine working status and stop in time to confirm the health status of the supercharger.

[0074] In an embodiment of the present application, the operating condition of the turbocharger is compared with the corresponding compressor characteristic diagram, and then a risk assessment is performed in combination with the surge boundary, overspeed boundary and blockage boundary, so as to ensure normal use by the user and effectively improve the prediction work efficiency and the reliability of the results.

[0075] Further, obtaining a surge risk probability value, an overspeed risk probability value, or a blockage risk probability value based on the real-time operating condition information and the surge boundary, the overspeed boundary, and the blockage boundary in the compressor characteristic diagram comprises the following steps:

[0076] Comparing the real-time operating condition information with the surge boundary, the overspeed boundary, and the blockage boundary in the compressor characteristic diagram;

[0077] If the distance value between the operating point of the real-time operating condition information in the compressor characteristic diagram and the surge boundary, the overspeed boundary or the blockage boundary in the compressor characteristic diagram is lower than a first preset distance value, the corresponding surge risk probability value or the overspeed risk probability value or the blockage risk probability value is calculated.

[0078] Furthermore, the method is configured with a surge risk probability value calculation formula:

[0079] in,

[0080] SM is the surge risk probability value, Q2 is the compressor flow at the operating point of the real-time operating condition information in the compressor characteristic diagram, and Q1 is the compressor flow on the surge boundary at the same pressure ratio at the operating point of the real-time operating condition information in the compressor characteristic diagram.

[0081] Furthermore, the method is configured with a speeding risk probability value calculation formula:

[0082] in,

[0083] OS is the surge risk probability value, S1 is the turbocharger speed limit value, and S2 is the turbocharger speed value corresponding to the real-time operating condition information.

[0084] Furthermore, the method is configured with a calculation formula for the probability value of a blockage risk:

[0085] in,

[0086] BR is the blocking risk probability value, Q2 is the compressor flow at the operating point of the real-time operating information in the compressor characteristic diagram, and Q3 is the compressor flow on the blocking boundary at the same pressure ratio as the operating point of the real-time operating information in the compressor characteristic diagram.

[0087] Furthermore, the method is configured with a supercharger failure risk probability value calculation formula:

[0088] PF=SM+OS+BR; among them,

[0089] PF is the supercharger failure risk probability value, SM is the surge risk probability value, OS is the surge risk probability value, and BR is the blockage risk probability value;

[0090] If there is no surge risk probability value, overspeed risk probability value, or jam risk probability value, the corresponding value is 0.

[0091] The technical solution of the embodiment of the present application adopts multi-dimensional supercharger failure risk factors, including external environmental conditions (such as ambient temperature, pressure, humidity, altitude and dust, etc.), engine operating conditions (such as engine speed, load, oil temperature, oil pressure, etc.), driver driving behavior (such as throttle, gear, etc.), maintenance conditions (such as vehicle and engine maintenance times, oil change frequency, oil usage time, vehicle mileage, etc.), so as to comprehensively evaluate the failure risk of the supercharger.

[0092] The supercharger failure risk assessment method driven by big data uses sensor technology and big data analysis methods to monitor the key parameters of the supercharger, obtain operating data in real time and establish a trend analysis model. Through the collection, storage and analysis of data, the risk of supercharger failure is predicted and evaluated, and corresponding maintenance measures are taken in time to reduce the failure risk. Surge margin, speed limit and blockage limit are the three main aspects of supercharger failure risk.

[0093] Among them, the failure risk function is calculated using the following method:

[0094] in,

[0095] f(x) represents the probability of supercharger failure risk;

[0096] x represents the value of the failure event;

[0097] λ is the scale parameter, representing the failure time of the supercharger;

[0098] k represents the shape parameter, which determines the distribution shape of the supercharger failure probability

[0099] It should be noted that the selection of scale parameters and shape parameters is based on the calculation results of the simulation analysis tool, and the least squares method is used to fit the data so that the model is most consistent with the actual collected data. Based on the collected structural parameters and performance index data of the supercharger and the index requirements for the supercharger, a numerical model of the engine is established using professional simulation software (such as ANSYS, GT-Power, Converge, etc.). The model includes key components such as the engine's geometric structure, turbocharger, intake and exhaust system, and adds corresponding boundary conditions and constraints. The internal flow and thermodynamic characteristics of the supercharger are analyzed in detail, and the simulation calculation results are implanted into the big data analysis platform. By comparing the actual vehicle data with the calculation results, a relationship model between the operating parameters of the supercharger and the failure risk parameters is established, thereby improving the efficiency and recognition rate of big data analysis of supercharger failure risks, thereby reducing failure risks.

[0100] As shown in the attached figure of the specification Figure 2 As shown in Figure 1, the supercharger failure event is determined by the left, right, and top thick lines on the compressor characteristic diagram, respectively. Figure 2 In the figure, the thick line on the left is the surge boundary, the thick line on the top is the overspeed boundary, and the thick line on the right is the congestion boundary.

[0101] When the engine operating condition point is close to the surge boundary represented by the thick line on the left, the surge margin value, that is, the surge risk probability value, is calculated. The surge margin calculation method is as follows:

[0102] in,

[0103] SM is the surge risk probability value, Q2 is the compressor flow at the operating point of the real-time operating condition information in the compressor characteristic diagram, and Q1 is the compressor flow on the surge boundary at the same pressure ratio at the operating point of the real-time operating condition information in the compressor characteristic diagram.

[0104] When the engine operating condition point is close to the overspeed boundary represented by the top thick line, the overspeed risk probability value is calculated. The overspeed risk probability value calculation formula is as follows:

[0105] in,

[0106] os is the surge risk probability value, S1 is the turbocharger speed limit value, and S2 is the turbocharger speed value corresponding to the real-time operating condition information.

[0107] When the engine operating condition point is close to the congestion boundary represented by the thick line on the right, the congestion risk probability value is calculated. The calculation formula of the congestion risk probability value is:

[0108] in,

[0109] BR is the blocking risk probability value, Q2 is the compressor flow at the operating point of the real-time operating information in the compressor characteristic diagram, and Q3 is the compressor flow on the blocking boundary at the same pressure ratio as the operating point of the real-time operating information in the compressor characteristic diagram.

[0110] Big data-based supercharger health monitoring and fault diagnosis can reduce the risk of supercharger failure by monitoring supercharger status parameters in real time and analyzing them through fault diagnosis algorithms, thereby detecting and locating potential faults early and providing corresponding maintenance suggestions.

[0111] The failure risk of turbochargers is assessed using the fault tree analysis (FTA) method, which considers the failure of turbochargers as a combination of surge risk, overspeed risk, and blockage risk. A fault tree is established, with turbocharger failure as the root node, surge risk, overspeed risk, and blockage risk as leaf nodes, and logical operators (such as AND, OR, NOT, etc.) are used to connect the nodes to represent the logical relationship between the faults.

[0112] It should be noted that the construction of the fault tree and related operation procedures are as follows:

[0113] The first step is to determine the failure target. Possible failure targets include: increased noise and vibration, increased pressure fluctuations, decreased supercharger efficiency, decreased vehicle acceleration performance, poor fuel economy, blade bending or breakage, decreased intake pressure, reduced flow, bearing wear, damage or failure, unstable rotation, increased thermal load, etc.

[0114] The second step is to identify the basic events that cause failure: including surge, overspeed, blockage, etc.

[0115] The third step is to draw a fault tree diagram.

[0116] Step 4: Assign failure concepts and related parameters: assign failure probability and other related parameters, such as failure rate and repair time, to each basic event;

[0117] It is obtained through the engine's historical fault data and maintenance status (such as vehicle and engine maintenance times, oil change frequency, oil usage time, vehicle mileage, etc.).

[0118] The fifth step is to conduct fault tree analysis: based on the logical relationship and the probability of basic events, calculate the probability of failure target occurrence.

[0119] Step 6: Conduct sensitivity analysis and optimization of the fault tree: Conduct sensitivity analysis on the fault tree, identify the most important basic events and paths, and understand the impact on the target probability of failure. Based on the analysis results, formulate optimization measures and risk control policy responses.

[0120] Specifically, the method is configured with a supercharger failure risk probability value calculation formula:

[0121] PF=SM+OS+BR; among them,

[0122] PF is the supercharger failure risk probability value, SM is the surge risk probability value, OS is the surge risk probability value, and BR is the blockage risk probability value;

[0123] If there is no surge risk probability value, overspeed risk probability value, or jam risk probability value, the corresponding value is 0.

[0124] Calculate the probability or risk of supercharger failure: By simplifying and solving the fault tree, the probability or risk of supercharger failure can be obtained;

[0125] For example, assuming that the probability of surge risk is 0.08, the probability of overspeed risk is 0.05, and the probability of blockage risk is 0.01, then the probability of supercharger failure = 0.08 + 0.05 + 0.01 = 0.14, that is, the failure risk of the supercharger is 14%.

[0126] As shown in the attached figure of the specification Figure 3 As shown, the technical solution of the embodiment of the present application can be implemented based on a real-time prediction system for turbocharger failure risk, which real-time prediction system at least includes a controller, a vehicle-mounted T-BOX, a cloud platform and a mobile phone APP;

[0127] The controller, instrument system and on-board T-BOX are all installed on the vehicle, the cloud platform is a data network platform, and the mobile APP needs to be installed on the driver's mobile phone;

[0128] The controller collects information in real time, such as external environmental condition parameters (such as ambient temperature, pressure, humidity, altitude, and dust), engine operating parameters (such as engine speed, load, oil temperature, oil pressure, etc.), and driver driving behavior parameters (such as throttle, gear position, etc.);

[0129] The controller sends the collected signals to the vehicle-mounted T-BOX, which then sends the signals to the cloud platform. The cloud platform evaluates the status of the turbocharger based on the collected signals through the big data platform and predicts the failure risk based on the evaluation results.

[0130] The failure risk prediction result is sent to the mobile phone APP according to the set time period, reminding the driver to adjust the engine working status and stop in time to confirm the health status of the supercharger.

[0131] Second, see Figure 4As shown, based on the same inventive concept as the method embodiment, the embodiment of the present application provides a real-time prediction device for turbocharger failure risk, the device comprising:

[0132] An operating condition identification module, which is used to monitor the operating condition of the target turbocharger and obtain real-time operating condition information;

[0133] a characteristic diagram recognition module, which is used to recognize a surge boundary, an overspeed boundary, and a jam boundary in a compressor characteristic diagram corresponding to the target turbocharger;

[0134] a probability calculation module, which is used to obtain a surge risk probability value, an overspeed risk probability value, or a blockage risk probability value based on the real-time operating condition information and the surge boundary, the overspeed boundary, and the blockage boundary in the compressor characteristic diagram;

[0135] A risk prediction module is used to obtain a supercharger failure risk probability value based on the surge risk probability value or the overspeed risk probability value or the blockage risk probability value.

[0136] In an embodiment of the present application, the operating condition of the turbocharger is compared with the corresponding compressor characteristic diagram, and then a risk assessment is performed in combination with the surge boundary, overspeed boundary and blockage boundary, so as to ensure normal use by the user and effectively improve the prediction work efficiency and the reliability of the results.

[0137] Furthermore, the probability calculation module is further used to compare the real-time operating condition information with the surge boundary, the overspeed boundary and the blockage boundary in the compressor characteristic diagram;

[0138] The probability calculation module is also used to calculate the corresponding surge risk probability value, the overspeed risk probability value or the blockage risk probability value if the distance value between the operating point of the real-time operating condition information in the compressor characteristic diagram and the surge boundary, the overspeed boundary or the blockage boundary in the compressor characteristic diagram is lower than a first preset distance value.

[0139] Furthermore, the device is equipped with a surge risk probability value calculation formula, an overspeed risk probability value calculation formula, and a jam risk probability value calculation formula;

[0140] The surge risk probability value calculation formula is:

[0141] The speeding risk probability value calculation formula is:

[0142] The calculation formula of the blocking risk probability value is: in,

[0143] SM is the surge risk probability value, Q2 is the compressor flow rate at the operating point of the real-time operating condition information in the compressor characteristic diagram, and Q1 is the compressor flow rate on the surge boundary at the same pressure ratio of the operating point of the real-time operating condition information in the compressor characteristic diagram;

[0144] OS is the surge risk probability value, S1 is the turbocharger speed limit value, and S2 is the turbocharger speed value corresponding to the real-time operating condition information;

[0145] BR is the blocking risk probability value, and Q3 is the compressor flow rate on the blocking boundary at the same pressure ratio of the operating point in the compressor characteristic diagram of the real-time operating condition information.

[0146] Furthermore, the device is equipped with a supercharger failure risk probability value calculation formula:

[0147] PF=SM+OS+BR; among them,

[0148] PF is the supercharger failure risk probability value, SM is the surge risk probability value, OS is the surge risk probability value, and BR is the blockage risk probability value;

[0149] If there is no surge risk probability value, overspeed risk probability value, or jam risk probability value, the corresponding value is 0.

[0150] It should be noted that the real-time prediction method for turbocharger failure risk provided in the embodiment of the present application, and its corresponding technical problems, technical means and technical effects are similar to the principles of the real-time prediction method for turbocharger failure risk in terms of principle.

[0151] It should be noted that, in this application, relational terms such as "compared to" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0152] The above is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

Claims

1. A real-time prediction method for turbocharger failure risk, characterized in that: The method comprises the following steps: Monitor the operating condition of the target turbocharger to obtain real-time operating condition information; Identifying a surge boundary, an overspeed boundary, and a jam boundary in a compressor characteristic diagram corresponding to the target turbocharger; Obtaining a surge risk probability value, an overspeed risk probability value, or a blockage risk probability value based on the real-time operating condition information and the surge boundary, the overspeed boundary, and the blockage boundary in the compressor characteristic diagram; Based on the surge risk probability value or the overspeed risk probability value or the blockage risk probability value, obtaining a supercharger failure risk probability value; Obtaining a surge risk probability value, an overspeed risk probability value, or a blockage risk probability value based on the real-time operating condition information and the surge boundary, the overspeed boundary, and the blockage boundary in the compressor characteristic diagram comprises the following steps: Comparing the real-time operating condition information with the surge boundary, the overspeed boundary, and the blockage boundary in the compressor characteristic diagram; If the distance between the operating point of the real-time operating condition information in the compressor characteristic diagram and the surge boundary, the overspeed boundary or the jam boundary in the compressor characteristic diagram is lower than a first preset distance value, the corresponding surge risk probability value, the overspeed risk probability value or the jam risk probability value is calculated; The method is configured with a surge risk probability value calculation formula, an overspeed risk probability value calculation formula and a jam risk probability value calculation formula; The surge risk probability value calculation formula is: The speeding risk probability value calculation formula is: The calculation formula of the blocking risk probability value is: in, SM is the surge risk probability value, Q2 is the compressor flow rate at the operating point of the real-time operating condition information in the compressor characteristic diagram, and Q1 is the compressor flow rate on the surge boundary at the same pressure ratio of the operating point of the real-time operating condition information in the compressor characteristic diagram; OS is the surge risk probability value, S1 is the turbocharger speed limit value, and S2 is the turbocharger speed value corresponding to the real-time operating condition information; BR is the blocking risk probability value, and Q3 is the compressor flow rate on the blocking boundary at the same pressure ratio of the operating point in the compressor characteristic diagram of the real-time operating condition information.

2. The real-time prediction method for turbocharger failure risk according to claim 1, characterized in that: The method is configured with a supercharger failure risk probability value calculation formula: PF=SM+OS+BR; among them, PF is the supercharger failure risk probability value, SM is the surge risk probability value, OS is the surge risk probability value, and BR is the blockage risk probability value; If there is no surge risk probability value, overspeed risk probability value, or jam risk probability value, the corresponding value is 0.

3. A real-time prediction device for turbocharger failure risk, characterized in that: The device comprises: An operating condition identification module, which is used to monitor the operating condition of the target turbocharger and obtain real-time operating condition information; a characteristic diagram recognition module, which is used to recognize a surge boundary, an overspeed boundary, and a jam boundary in a compressor characteristic diagram corresponding to the target turbocharger; a probability calculation module, which is used to obtain a surge risk probability value, an overspeed risk probability value, or a blockage risk probability value based on the real-time operating condition information and the surge boundary, the overspeed boundary, and the blockage boundary in the compressor characteristic diagram; a risk prediction module, configured to obtain a supercharger failure risk probability value based on the surge risk probability value, the overspeed risk probability value, or the blockage risk probability value; The probability calculation module is also used to compare the real-time operating condition information with the surge boundary, the overspeed boundary and the jam boundary in the compressor characteristic diagram; The probability calculation module is further configured to calculate and obtain the corresponding surge risk probability value, the overspeed risk probability value, or the blockage risk probability value if the distance value between the operating point of the real-time operating condition information in the compressor characteristic diagram and the surge boundary, the overspeed boundary, or the blockage boundary in the compressor characteristic diagram is lower than a first preset distance value; The device is equipped with a surge risk probability value calculation formula, an overspeed risk probability value calculation formula and a jam risk probability value calculation formula; The surge risk probability value calculation formula is: The speeding risk probability value calculation formula is: The calculation formula of the blocking risk probability value is: in, SM is the surge risk probability value, Q2 is the compressor flow rate at the operating point of the real-time operating condition information in the compressor characteristic diagram, and Q1 is the compressor flow rate on the surge boundary at the same pressure ratio of the operating point of the real-time operating condition information in the compressor characteristic diagram; OS is the surge risk probability value, S1 is the turbocharger speed limit value, and S2 is the turbocharger speed value corresponding to the real-time operating condition information; BR is the blocking risk probability value, and Q3 is the compressor flow rate on the blocking boundary at the same pressure ratio of the operating point in the compressor characteristic diagram of the real-time operating condition information.

4. The real-time prediction device for turbocharger failure risk according to claim 3, characterized in that: The device is equipped with a supercharger failure risk probability value calculation formula: PF=SM+OS+BR; among them, PF is the supercharger failure risk probability value, SM is the surge risk probability value, OS is the surge risk probability value, and BR is the blockage risk probability value; If there is no surge risk probability value, overspeed risk probability value, or jam risk probability value, the corresponding value is 0.

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