Diesel engine abnormal wear diagnosis system

Abstract

A diesel engine abnormal wear diagnosis system belongs to the technical field of diesel engine wear detection, and comprises a data acquisition and processing module, a model calculation module, a fault diagnosis module and a man-machine interaction module, the data acquisition and processing module is used for acquiring diesel engine lubricating oil temperature and metal abrasive particle concentration data in lubricating oil; the model calculation module is used for running a diagnosis model and fault prediction of the running state of the diesel engine; and the fault diagnosis module is provided with a fault rule base and is used for outputting a final diagnosis result according to the fault prediction result of the model calculation module and the fault rule base by fusing the real-time working condition of the diesel engine and the simulation model data of the diesel engine. The method achieves the purposes of recognizing the abnormal trend of the diesel engine in advance and avoiding sudden engine faults.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of diesel engine wear detection, and particularly relates to a diesel engine abnormal wear diagnosis system. BACKGROUND

[0002] The existing diesel engine is a compression ignition internal combustion engine, which works based on a four-stroke cycle: in the intake stroke, the piston goes down to form a negative pressure, and filtered air enters the cylinder; in the compression stroke, the piston goes up to compress the air to a high-temperature and high-pressure state (compression ratio is 16-22) of 500-700 DEG C; in the power stroke, the high-pressure atomized diesel oil is injected into the oil nozzle, and the high-temperature and high-pressure gas generated after self-ignition pushes the piston to go down, and the mechanical energy is output through the connecting rod and the crankshaft; in the exhaust stroke, the piston goes up to exhaust the exhaust gas through the exhaust valve, and the cycle is completed; in the whole working process, the engine relies on the cooperation of multiple systems such as crank connecting rod mechanism, valve train, fuel supply system, lubrication system, cooling system, etc.; among them, the lubrication system delivers oil to the key friction pair surfaces such as crankshaft bearing, connecting rod bearing, camshaft, piston ring through the oil pump to form an oil film to reduce friction and wear, and the cooling system removes the heat generated by combustion and friction through the cooling liquid circulation to maintain the engine in the appropriate working temperature range, usually 80-95 DEG C.

[0003] The diesel engine is applied to different fields such as transportation, agriculture, industry, etc. due to its characteristics of large torque, low fuel consumption and high reliability. The transportation generally has long distance demand, and the agricultural engine generally needs long time operation, so the diesel engine will have a series of problems under long time working condition, such as wear of key parts, e.g. wear of crankshaft and bearing, wear of piston and cylinder liner, wear of camshaft and tappet; performance degradation of lubrication system, e.g. oil pollution, oil filter blockage; failure of auxiliary system, e.g. poor heat dissipation of cooling system, cylinder seizure, cylinder sticking.

[0004] Currently, the detection of diesel engine wear is mainly divided into offline detection and online detection. The specific offline detection methods include physical and chemical performance detection of engine oil, wear particle analysis, and disassembly detection method. The online detection methods include vibration detection, temperature and pressure detection, and acoustic detection method. However, the existing wear detection technology has some shortcomings. The offline detection has hysteresis and limitations, long detection cycle, and cannot detect the current wear state of the engine in real time. The disassembly detection method needs to disassemble the engine, which is time-consuming and labor-intensive, has high maintenance cost, and may cause secondary damage to other parts during disassembly. The accuracy and adaptability of the online detection method are insufficient. The vibration detection method is easily affected by engine working conditions (such as load and speed change) and external environmental vibration interference, making it difficult to extract characteristic frequencies and reducing detection accuracy. It is difficult to distinguish between slight wear and normal vibration differences. Temperature and pressure detection is an indirect detection method that cannot directly locate the wear position and wear degree, and is prone to false alarms or missed alarms. Acoustic detection is seriously affected by environmental noise and has large differences between different models. The existing detection methods are mostly aimed at a single wear indicator or a single component, and lack comprehensive evaluation capability for the overall wear state of the engine, making it difficult to achieve synchronous monitoring and fault warning of multiple friction pairs. SUMMARY

[0005] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present application provides a diesel engine abnormal wear diagnosis system, which takes the metal particle concentration in the diesel engine lubricating oil as the detection object, compares the fault through the set fault rule library, calculates the final diagnosis result by fusing the fault prediction result of the fusion model calculation module, the fault rule library, the real-time working condition of the diesel engine, and the diesel engine simulation model data, and realizes the purpose of identifying the abnormal trend of the diesel engine in advance and avoiding sudden engine failure.

[0006] In order to achieve the above-mentioned purpose, the main technical solutions adopted by the present application include: A diesel engine abnormal wear diagnosis system, comprising a data acquisition and processing module, a model calculation module, a fault diagnosis module, and a man-machine interaction module. The data acquisition and processing module is used to collect the temperature of the diesel engine lubricating oil and the metal particle concentration data in the lubricating oil, and to send the preprocessed data to the model calculation module. The model calculation module has a diesel engine simulation model and a diesel engine running state diagnosis model. The model calculation module is used to run the diesel engine running state diagnosis model and fault prediction. The diesel engine simulation model has three-dimensional geometric data of key components of the diesel engine. The diesel engine running state diagnosis model is established by detecting the dynamic change of the metal particle content in the lubricating oil sample, and establishing the corresponding relationship between the engine wear state and the metal particle concentration. The metal particles are Fe, Cu, and Al particles. The model includes the following models: Metal Fe and metal Cu, Al particle concentration model: ; ; wherein C Fe (t) is the concentration of Fe element at time t; C Fe0 0 is the initial concentration of Fe element; k Fe is the wear rate coefficient of Fe element; C (Cu / Al) (t) is the concentration of Cu and Al elements at time t; C (Cu / Al)0 0 is the initial concentration of Cu and Al elements; k (Cu / Al) is the wear rate coefficient of Cu and Al elements; wear feature vector model: ; wear particle concentration model: ; wherein C i (t) is the concentration of metal abrasive element i at time t, C i 0 is the initial concentration of metal abrasive element i, V0 is the volume of lubricating oil, α i is the material wear coefficient, N(τ) is the speed of diesel engine, P(τ) is the load of diesel engine, β i is the sedimentation coefficient, C i (τ) is the concentration-dependent nonlinear sedimentation; The fault diagnosis module has a fault rule base, the fault diagnosis module fuses diesel engine real-time working condition and diesel engine simulation model data according to the fault prediction result of the model calculation module, the fault rule base, and outputs the final diagnosis result, which is sent to the man-machine interaction module, and the man-machine interaction module displays the fault prediction result of the model calculation module and the diagnosis result of the fault diagnosis module.

[0007] Further, the data acquisition and processing module pre-processes the collected data, including denoising, filtering and correcting the collected original signals, and time alignment processing of the collected data.

[0008] Further, the data acquisition and processing module pre-processes the collected data, including extracting real-time concentration values as key features for abrasive particle concentration data, and performing sliding average processing on continuous multiple measurement results.

[0009] Further, the diesel engine simulation model of the model calculation module is based on the inherent design parameters of the diesel engine to be tested, a diesel engine model is established in the AVL-EXCITE platform, and the inherent design parameters of the diesel engine are input to the database module of the AVL-EXCITE platform; and the three-dimensional geometric data of the key components are imported through the geometric modeling interface of the AVL-EXCITE.

[0010] Further, the diesel engine key components include pistons, connecting rods, crankshafts and cylinders.

[0011] Further, the fault prediction of the diesel engine operating state by the model calculation module includes calculating the current metal particle concentration theoretical prediction value based on the real-time collected metal particle concentration data, comparing the metal particle concentration measured value with the theoretical prediction value, triggering a warning for the metal particles exceeding the threshold concentration, and triggering and grading the warning according to the degree of the metal particle concentration exceeding the threshold.

[0012] Further, the fault rule base includes the fault component name, fault mode and fault reason corresponding to the metal element exceeding the threshold.

[0013] Further, when the metal particle concentration exceeds the threshold, the fault diagnosis module calls the fault rule base, matches the current wear characteristic vector with the fault mode in the fault rule base, and generates a fault candidate set.

[0014] Further, the fault diagnosis module sets the confidence of the fault prediction result of the model calculation module, the matching result of the fault rule base and the simulation verification result of the diesel engine simulation model, respectively, and obtains a comprehensive confidence by weighted averaging the confidence of the fault prediction result of the model calculation module, the matching result of the fault rule base and the simulation verification result of the diesel engine simulation model, and outputs the final diagnosis result and the corresponding fault mode according to the information of the comprehensive confidence.

[0015] Further, the system displays the comprehensive confidence in grades according to the set comprehensive confidence threshold range.

[0016] The beneficial effects of the present application are that the diesel engine abnormal wear diagnosis system of the present application has a built-in diesel engine operating state diagnosis model, the system compares the measured value of the metal particle concentration in the lubricating oil with the predicted value calculated by the model, triggers a warning for the metal particles exceeding the threshold concentration, matches the fault type according to the fault rule base built in the fault diagnosis module, and realizes fault monitoring. In addition, the system of the present application adopts a three-layer judgment mechanism combined with feature threshold judgment, fault matching rule judgment and simulation verification judgment, improves the accuracy of the diesel engine abnormal wear diagnosis result of the system, and reduces the false alarm rate. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 Flowchart of the diesel engine abnormal wear diagnosis system of the present application. DETAILED DESCRIPTION

[0018] In order to better explain the present application, so as to be understood, the present application is described in detail below by specific embodiments, combined with the accompanying drawings.

[0019] As Figure 1 shown, the present application provides a diesel engine abnormal wear diagnosis system, comprising a data acquisition and processing module, a model calculation module, a fault diagnosis module, and a man-machine interaction module, wherein the man-machine interaction module applies QtCreator platform to design a user interface for displaying the fault prediction results of the model calculation module and the diagnosis results of the fault diagnosis module, and the system of the present application can further comprise a recording module connected with the data acquisition and processing module, the model calculation module, the fault diagnosis module, and the man-machine interaction module, for recording the data in the system.

[0020] The data acquisition and processing module is used for acquiring the lubricating oil temperature of the diesel engine and the metal abrasive particle concentration data in the lubricating oil, and sending the preprocessed data to the model calculation module. Specifically, the data acquisition and processing module is connected with the abrasive particle sensor and the temperature sensor installed on the main passage of the lubricating oil of the diesel engine, to acquire the lubricating oil temperature of the diesel engine and the metal abrasive particle concentration data in the lubricating oil; the metal abrasive particle concentration data in the lubricating oil is recorded as the main monitoring data, and the size of the metal abrasive particle in the lubricating oil of the diesel engine is recorded as the subsidiary reference data.

[0021] Preferably, after the diesel engine runs normally for a period of time, the oil sample is collected from the oil drain port of the oil circulation system when the diesel engine reaches a stable working temperature (about 75℃-95℃), and the oil sample is detected by the abrasive particle sensor. The detection accuracy of the abrasive particle sensor can be the detection and identification ability (equivalent spherical shape) of ferromagnetic abrasive particles (Fe): 0.050mm; the identification ability of non-ferromagnetic abrasive particles (Cu, Al): 0.150mm, the thread aperture of the oil inlet and outlet holes Φ8±0.5mm; quantitative differentiation of 10μm; detection rate not less than 80%.

[0022] Specifically, the preprocessing of the data collected by the data acquisition and processing module includes denoising, filtering, and correction of the collected original signals, time alignment processing of the collected data, data synchronization, and subsequent data processing. The data acquisition and processing module extracts real-time concentration values as key features for abrasive particle concentration data, performs sliding average processing on continuous multiple measurement results, forms more stable and reliable comprehensive concentration values and trend data, and provides high-quality input for model calculation.

[0023] The model calculation module has a diesel engine simulation model and a diesel engine operating state diagnosis model, and the model calculation module is used for running the diesel engine operating state diagnosis model and fault prediction. The diesel engine simulation model has three-dimensional geometric data of key components of the diesel engine. Specifically, the diesel engine simulation model of the model calculation module is based on the inherent design parameters of the diesel engine to be tested, and a diesel engine model is established in the AVL-EXCITE platform. The inherent design parameters of the diesel engine are input to the database module of the AVL-EXCITE platform. The three-dimensional geometric data of the key components are imported through the geometric modeling interface of AVL-EXCITE. Specifically, the core friction components of the diesel engine include pistons, connecting rods, crankshafts, and cylinders.

[0024] The diesel engine operating state diagnosis model is established by detecting the dynamic change of the metal particle content in the lubricating oil sample, establishing the corresponding relationship between the engine wear state and the metal particle concentration, and the metal particles are Fe, Cu and Al particles. Specifically, the diesel engine operating state diagnosis model is based on the principle of combining spectrometric oil analysis program (SOAP) with engine wear mechanism, using the dynamic change of the metal element content in the lubricating oil sample to establish the corresponding relationship between the engine wear state and the metal element concentration. Mainly for Fe, Cu and Al particles generated by friction pair wear, through quantitative analysis of Fe, Cu and Al metal elements, the engine operating state diagnosis model is established.

[0025] The diesel engine operating state diagnosis model includes the following models: (1) Metal Fe and metal Cu, Al particle concentration model: ; ; Wherein, C Fe (t) is the concentration of Fe element at time t; C Fe0 is the initial concentration of Fe element; k Fe is the wear rate coefficient of Fe element; C (Cu / Al) (t) is the concentration of Cu and Al elements at time t; C (Cu / Al)0 is the initial concentration of Cu and Al elements; k (Cu / Al) is the wear rate coefficient of Cu and Al elements.

[0026] The metal Fe and metal Cu, Al particle concentration model is established by the following principles: Assume that the volume of the lubricating oil in the lubricating oil system is fixed V O, the distribution of metal particles in lubricating oil is uniform, the detection sample can represent the overall concentration level, the generation rate of metal particles is a function of the load, speed and temperature of the worn parts; the concentration of metal particles changes in accordance with the quasi-linear growth or exponential growth law, and the basic formula is: ; wherein, C i (t) is the concentration of metal particles i elements at time t, in ppm, i.e. mg / L; C i0 is the initial concentration, in ppm, i.e. mg / L; V O is the volume of lubricating oil, in L; G i (τ) is the generation rate of metal particles, in mg / h; L i (τ) is the loss rate of metal particles, in mg / h; i is the element category of metal particles. L i (τ) includes the metal particles intercepted and settled by the filter; the metal particles intercepted by the filter represent the mass of metal particles filtered out by the filter per unit time, in mg / h. The lubricating oil system is generally provided with a lubricating oil filter to maintain the cleanliness of the lubricating oil. When the lubricating oil carrying metal particles flows through the filter, the metal particles will be intercepted and captured by the filter element, thereby being physically removed from the circulating engine oil. The metal particles settled represent the mass of metal particles that disappear per unit time due to sedimentation, in mg / h. Due to the action of gravity, the heavier metal particles in the lubricating oil will gradually sink out of the flowing oil and eventually settle in the oil pan, the inner wall of the crankcase, the oil passage and other low-flow parts. These settled metal particles no longer participate in circulation and therefore cannot be detected by the sensor installed in the circulating oil circuit; the model introduces the metal particles intercepted and settled by the filter into the model, thereby improving the accuracy of the model prediction results.

[0027] Assuming that the filtering effect is constant and weak, the model can be approximated as: ; wherein, k i is the wear rate coefficient, in ppm / h, which can reflect the process that the element concentration gradually tends to be stable over time.

[0028] (2) Wear feature vector model: .

[0029] (3) Wear particle concentration model: ; wherein: C i(t) is the concentration of the metal abrasive element i at time t in ppm, i.e. mg / L; C i 0 is the initial concentration of the metal abrasive element i in ppm, i.e. mg / L; V0 is the volume of the lubricating oil in L; a i is the material wear coefficient in ; N(τ) is the diesel engine speed in rpm; P(τ) is the diesel engine load expressed as a percentage of the rated load; β i is the settling coefficient, C i (τ) is the concentration-dependent non-linear settling in ppm, i.e. mg / L.

[0030] Specifically, the fault prediction of the diesel engine operating state by the model calculation module includes calculating the current metal abrasive concentration theoretical prediction value based on the real-time collected metal abrasive concentration data, using the diesel engine operating state diagnosis model, comparing the metal abrasive concentration measured value with the theoretical prediction value, triggering a warning for the metal abrasive exceeding the threshold concentration, and triggering a warning and grading according to the degree of the metal abrasive concentration exceeding the threshold.

[0031] The fault diagnosis module has a fault rule base, specifically, the fault rule base is embedded in the diesel engine abnormal wear diagnosis system in the form of SQLite database, and the fault rule base includes a wear safety baseline and a wear grade table and fault matching rules; specifically, the fault rule base includes the fault component name, fault mode and fault reason corresponding to the metal element exceeding the threshold.

[0032] Table 1 is the wear safety baseline and wear grade table (unit: ppm): .

[0033] The system gives the following maintenance suggestions according to the diagnosis results: Mild wear: increase oil monitoring frequency, sample every 100h; use anti-wear additives to reduce wear rate growth; moderate wear: local repair of wear parts, replace lubricating oil; adjust engine operating parameters to reduce load and wear rate; severe wear: immediately stop, overhaul key parts and replace lubricating oil; retest three metal concentrations and vibration amplitude after repair to ensure that the wear particle detection value returns to normal or mild level.

[0034] The fault matching rules are as follows: When only the Fe concentration in the lubricating oil exceeds the threshold, and the Fe concentration C Fe > 15 ppm, the fault component is determined to be the cylinder, the fault mode is cylinder pulling, and the fault reason is that the engine is run at high load without warming up after cold start; the gap between the piston and the cylinder sleeve is too small to damage the oil film; overloading operation causes local overheating.

[0035] When only the Al concentration in the lubricating oil exceeds the threshold value, and the Al concentration C Al > 16 ppm, the faulty component is determined to be the piston connecting rod set; the change rate K Al of the Al concentration is further calculated, and if K Al < 0.5 ppm / h, it is determined that the large end bearing bush of the connecting rod is worn, and the cause of the failure is impurities in the oil, insufficient oil pressure, abnormal assembly gap of the bearing bush, fatigue wear of the bearing bush material, blockage of the lubricating oil channel, insufficient pre-tightening force of the connecting rod bolt, or serious failure of the crankshaft and connecting rod; if K Fe ≥ 0.5 ppm / h, it is determined that the small end bearing bush of the connecting rod is worn, and the cause of the failure is poor lubrication caused by poor oil quality or insufficient oil supply; or accelerated fatigue cracks caused by overload or improper assembly gap.

[0036] When the Fe and Cu concentrations in the lubricating oil exceed the threshold value, and the Fe concentration C Fe > 30 ppm and the Cu concentration C Cu > 10 ppm, the faulty component is determined to be the crankshaft flywheel set; the ratio C Cu / C Fe is further calculated, and if C Cu / C Fe ≤ 0.33, it is determined that the main bearing bush is worn, and the cause of the failure is poor lubrication, improper bearing installation, or shaft misalignment; if C Cu / C Fe > 0.33, it is determined that the gear tooth is broken, and the cause of the failure is poor lubrication, improper assembly, or abnormal gear meshing.

[0037] Specifically, when the metal abrasive particle concentration exceeds the threshold value, the fault diagnosis module calls the fault rule library, matches the current wear characteristic vector with the fault modes in the fault rule library, and generates a fault candidate set.

[0038] The fault diagnosis module outputs the final diagnosis result based on the fault prediction result of the model calculation module, the fault rule library, the real-time working condition of the diesel engine, and the simulation model data of the diesel engine, and sends it to the human-computer interaction module. The human-computer interaction module displays the fault prediction result of the model calculation module and the diagnosis result of the fault diagnosis module for the user to view.

[0039] More specifically, the model calculation module compares the real-time collected metal abrasive particle concentration data with the current metal abrasive particle concentration theoretical prediction value calculated by the diagnosis model of the diesel engine operating state, triggers a warning for metal abrasive particles exceeding the safety baseline in Table 1, and classifies the warning according to the range of the metal abrasive particle concentration exceeding the threshold value in Table 1, i.e., preliminarily determines the wear level. At the same time, the fault modes are matched according to the fault matching rules in the fault rule library, and a fault candidate set is generated.

[0040] The fault diagnosis module sets confidence levels for the fault prediction results, fault rule base matching results, and diesel engine simulation model verification results from the model calculation module. The module then obtains a comprehensive confidence level by weighted averaging these confidence levels. The system categorizes and displays the comprehensive confidence level into different levels based on a set threshold range. Preferably, the system can output different levels of certainty based on the confidence level, such as "Confirmed," "Suspected," or "Abnormal and pending investigation."

[0041] More specifically, the confidence level for matching according to Table 1 is defined as the feature threshold determination confidence level C. 阈值 When the concentration of metal abrasive particles is within a safe baseline range, C 阈值 =0; When the concentration of metal abrasive particles is within the range of mild wear, C 阈值 =0.6; When the concentration of metal abrasive particles is within the range of moderate wear, C 阈值 =0.8; When the concentration of metal abrasive particles is within the range of severe wear, C 阈值 =0.95.

[0042] The confidence level of matching based on fault matching rules is defined as the rule base matching confidence level C. 规则 If the system determines that the metal element exceeds the threshold, then C 规则 =0.5; If the system calculates and compares the concentration ratios of the metal elements that simultaneously exceed the standard, then C 规则 =0.3; If the system calculates the rate of change of metal concentration with time, then C 规则 =0.2.

[0043] The deviation between the measured frictional work loss value and the frictional work loss value in the AVL-EXCITE simulation is defined as the simulation verification confidence level C. 仿真 If the deviation is less than 10%, then C 仿真 =0.9; if 10% ≤ deviation < 20%, then the confidence level is C. 仿真 =0.7; if the deviation is ≥20%, then it is C. 仿真 =0.3.

[0044] The fault diagnosis module will perform C 阈值 C 规则 C 仿真 We perform weighted fusion to obtain the final overall confidence level C. 综合 C 综合 =W1×C 阈值 +W2×C 规则 +W3×C 仿真 The weighting coefficients are W1=0.2, W2=0.6, and W3=0.2.

[0045] Comprehensive confidence level classification: when 0.8≤C 综合 ≤1.0, it belongs to high confidence, and the specific diagnosis result is directly output, and "confirmed diagnosis" is displayed; when 0.5≤C 综合 ≤0.8, it belongs to medium confidence, the diagnosis result is output, and "suspected" is displayed; when C 综合 <0.5, it belongs to low confidence, and no specific diagnosis result is displayed, only "abnormal to be checked" is displayed, that is, it is warned that the engine has a wear state.

[0046] The system adopts a combination mode of three-layer judgment mechanisms of feature threshold judgment, fault matching rule judgment and simulation verification judgment, and improves the accuracy of the system on the diagnosis result of the abnormal wear of the diesel engine.

[0047] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application, and the modifications, modifications, replacements and variations of the above embodiments by those skilled in the art are within the scope of the present application.

Claims

1. A diesel engine abnormal wear diagnosis system, characterized in that: It includes a data acquisition and processing module, a model calculation module, a fault diagnosis module, and a human-computer interaction module; The data acquisition and processing module is used to collect data on the temperature of the diesel engine lubricating oil and the concentration of metal abrasive particles in the lubricating oil. After preprocessing the collected data, it is sent to the model calculation module. The model calculation module has a diesel engine simulation model and a diesel engine operating status diagnostic model. The model calculation module is used to run the diesel engine operating status diagnostic model and fault prediction. The diesel engine simulation model contains three-dimensional geometric data of key diesel engine components; the diagnostic model for diesel engine operating conditions establishes the correlation between engine wear state and metal abrasive particle concentration by detecting the dynamic changes in the content of metal abrasive particles in lubricating oil samples. The metal abrasive particles are Fe, Cu, and Al particles; including the following models: Abrasive particle concentration models for Fe, Cu, and Al metals: ； ； Among them, C Fe (t) represents the concentration of Fe at time t; C Fe0 The initial concentration of Fe; k Fe The wear rate coefficient for Fe; C (Cu / Al) (t) represents the concentrations of Cu and Al elements at time t; C (Cu / Al)0 The initial concentrations of Cu and Al elements; k (Cu / Al) For Cu and Al elements, represents the wear rate coefficients. Wear feature vector Model: ； Wear particle concentration model: ； Where: C i (t) represents the concentration of metal abrasive element i at time t, C i 0 represents the initial concentration of metal abrasive element i, V0 represents the volume of lubricating oil, and α represents the initial concentration of abrasive element i. i Where is the material wear coefficient, N(τ) is the diesel engine speed, P(τ) is the diesel engine load, and β is the wear coefficient. i C is the settlement coefficient. i (τ) represents concentration-dependent nonlinear sedimentation; The fault diagnosis module has a fault rule base. Based on the fault prediction results of the model calculation module and the fault rule base, the fault diagnosis module integrates the real-time operating conditions of the diesel engine and the simulation model data of the diesel engine, outputs the final diagnosis result, and sends it to the human-machine interaction module. The human-machine interaction module displays the fault prediction results of the model calculation module and the diagnosis results of the fault diagnosis module.

2. The diesel engine abnormal wear diagnosis system according to claim 1, characterized in that: The data acquisition and processing module preprocesses the acquired data, including denoising, filtering, and correcting the raw acquired signals, and performing time alignment on the acquired data.

3. A diesel engine abnormal wear diagnosis system according to claim 1 or 2, characterized in that: The data acquisition and processing module preprocesses the acquired data, including extracting real-time concentration values ​​as key features for abrasive particle concentration data and performing moving average processing on the results of multiple consecutive measurements.

4. The diesel engine abnormal wear diagnosis system according to claim 1, characterized in that: The diesel engine simulation model of the model calculation module is based on the inherent design parameters of the diesel engine under test. The diesel engine model is built in the AVL-EXCITE platform, and the inherent design parameters of the diesel engine are entered into the database module of the AVL-EXCITE platform. Import the 3D geometric data of key components through the AVL-EXCITE geometry modeling interface.

5. The diesel engine abnormal wear diagnosis system according to claim 1, characterized in that: Key components of diesel engines: pistons, connecting rods, crankshafts, and cylinders.

6. The diesel engine abnormal wear diagnosis system according to claim 1, characterized in that: The model calculation module predicts faults in the operating status of diesel engines by using real-time collected metal wear particle concentration data and a diagnostic model of the operating status of the diesel engine to calculate the theoretical predicted value of the current metal wear particle concentration. The measured value of the metal wear particle concentration is compared with the theoretical predicted value, and an early warning is triggered for metal wear particles that exceed the threshold concentration. The early warning is triggered and graded according to the degree to which the metal wear particle concentration exceeds the threshold.

7. The diesel engine abnormal wear diagnosis system according to claim 1, characterized in that: The fault rule base includes the faulty component name, fault mode, and fault cause corresponding to metal elements that exceed the threshold.

8. The diesel engine abnormal wear diagnosis system according to claim 1, characterized in that: When the concentration of metal abrasive particles exceeds the threshold, the fault diagnosis module calls the fault rule base, matches the current wear feature vector with the fault mode in the fault rule base, and generates a fault candidate set.

9. The diesel engine abnormal wear diagnosis system according to claim 1, characterized in that: The fault diagnosis module sets confidence levels for the fault prediction results, fault rule base matching results, and diesel engine simulation model verification results from the model calculation module. The fault diagnosis module obtains a comprehensive confidence level by weighted averaging of the confidence levels of the fault prediction results, fault rule base matching results, and diesel engine simulation model verification results. Based on the comprehensive confidence level, the module outputs the final diagnosis result and the corresponding fault mode.

10. A diesel engine abnormal wear diagnosis system according to claim 9, characterized in that: The system categorizes and displays the overall confidence level according to the set overall confidence level threshold range.

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