Oil pollution degree fault simulation device, system, method, equipment and medium

Abstract

The invention relates to the technical field of oil pollution, and discloses an oil pollution degree fault simulation device, system, method and equipment and a medium, and the device comprises a communication module, a micro-processing module, an abrasive particle release module and an abrasive particle adsorption module; the communication module is used for acquiring the current pollution degree and inputting the current pollution degree to the micro-processing module; the micro-processing module is used for determining a current pollution degree error amount based on the current pollution degree, comparing the current pollution degree error amount with a preset target error amount, and controlling the abrasive particle release module or the abrasive particle adsorption module according to a comparison result; the abrasive particle release module is used for controlling abrasive particle release so as to adjust the oil contamination degree; and the abrasive particle adsorption module is used for controlling abrasive particle adsorption so as to adjust the oil contamination degree. The device has the beneficial effects that the oil pollution degree of the hydraulic system is accurately and dynamically simulated, so that the continuous observation of degradation data of the hydraulic system under different oil pollution degrees is realized.

Description

Technical Field

[0001] This application relates to the technical field of oil pollution, and particularly to an oil pollution degree fault simulation device, system, method, equipment and medium. Background Art

[0002] The hydraulic system is a key component of the ship steering system, and its main function is to drive the movement of the steering cylinder to achieve the steering function. However, oil pollution is one of the common fault causes of the hydraulic system. Operating in a polluted oil environment for a long time will accelerate the wear and deterioration of the equipment, ultimately leading to abnormal steering function and affecting the normal operation of the ship. However, due to the lack of long-term and continuous monitoring of the oil pollution degree on the ship and the lack of detailed deterioration data of the hydraulic system under polluted conditions, it is impossible to clarify the specific impact of pollution on the equipment performance, thus increasing the difficulty of fault diagnosis.

[0003] Therefore, how to accurately simulate the oil pollution degree in the hydraulic system and collect the fault data of related equipment in a polluted environment has become a key issue to improve the fault diagnosis ability of the hydraulic system and urgently needs to be solved. Summary of the Invention

[0004] This application provides an oil pollution degree fault simulation device, system, method, equipment and medium, which solves the technical problem of how to accurately simulate the oil pollution degree in the hydraulic system and collect the fault data of related equipment in a polluted environment, and achieves the technical effect of accurately and dynamically simulating the oil pollution degree of the hydraulic system, so as to realize the continuous observation of the deterioration data of the hydraulic system under different oil pollution degrees.

[0005] To achieve the above object, the main technical solutions adopted in this application include: In the first aspect, an embodiment of this application provides an oil pollution degree fault simulation device, which includes a communication module, a microprocessing module, an abrasive particle release module and an abrasive particle adsorption module; The communication module is used to obtain the current pollution degree and input the current pollution degree into the microprocessing module; The microprocessing module is used to determine the current pollution degree error amount based on the current pollution degree, compare the current pollution degree error amount with a preset target error amount, and control the abrasive particle release module or the abrasive particle adsorption module according to the comparison result; The abrasive particle release module is used to control the release of abrasive particles to adjust the oil pollution degree; The abrasive particle adsorption module is used to control the adsorption of abrasive particles to adjust the oil pollution degree.

[0006] An oil contamination fault simulation device provided by this embodiment obtains the current contamination level through a communication module and inputs it into a microprocessing module for comparison. The microprocessing module determines the current contamination error amount based on the current contamination level, compares the difference between the current contamination error amount and a preset target error amount, and controls the abrasive particle release module or the abrasive particle adsorption module, thereby precisely adjusting the oil contamination level. The abrasive particle release module increases the oil contamination level by releasing metal particles, while the abrasive particle adsorption module adsorbs metal particles to reduce the oil contamination level and keep the hydraulic system clean. This process can adaptively adjust the contamination level in real time, ensure that the contamination level remains within the target range, improve the cleaning effect and the operating efficiency of the system. The device has high-efficient contamination adjustment ability, can flexibly cope with different contamination situations, and realizes automatic and precise contamination control. At the same time, the device can be used to study the degradation process of the hydraulic system in a contaminated environment and collect a large amount of fault sample data to provide data support for fault diagnosis.

[0007] In one embodiment, the microprocessing module includes: A contamination level sequence recognition unit, which is used to recognize the contamination level sequence in which the current contamination level is located, and the contamination level sequence includes contamination levels collected at multiple different times; A sequence feature extraction unit, which is used to extract the sequence features of the contamination level sequence and obtain a standard contamination level that matches the sequence features; A residual sequence construction unit, which is used to calculate the residual between any target contamination level in the contamination level sequence and the standard contamination level, and construct a residual sequence based on the calculated residuals; A contamination error amount determination unit, which is used to extract the residual features of the residual sequence and determine the error amount characterized by the residual features as the current contamination error amount.

[0008] For the microprocessing module provided by this embodiment, the contamination level sequence recognition unit ensures the systematization and structuring of data by recognizing the time series range where the contamination level data is located. Then, the sequence feature extraction unit extracts key features from the contamination level sequence and matches them with the standard contamination level, effectively eliminating noise and irrelevant factors and improving the prediction accuracy. Subsequently, the residual sequence construction unit generates a residual sequence by calculating the difference between the target contamination level and the standard contamination level, providing core data for error analysis. Finally, the error amount determination unit accurately characterizes the contamination prediction error by analyzing the residual features and dynamically adjusts the model to optimize the prediction accuracy. Overall, by accurately identifying and analyzing the contamination error, the contamination prediction can be adjusted in real time, thereby improving the prediction accuracy and reducing the error accumulation.

[0009] In one embodiment, both the abrasive particle release module and the abrasive particle adsorption module adopt electromagnetic coil arrays.

[0010] In a second aspect, an oil contamination fault simulation method provided by an embodiment of the present application is applied to the above-mentioned device, and the method includes: Determine the current contamination error amount in response to the target contamination degree input by the user and the monitored current contamination degree; When the current contamination error amount is greater than a preset target error amount, control the abrasive particle release module in the oil contamination fault simulation device to perform release fine-tuning on the current contamination degree; When the current contamination error amount is less than the preset target error amount, control the abrasive particle adsorption module in the oil contamination fault simulation device to perform adsorption fine-tuning on the current contamination degree; Stop fine-tuning until the current contamination error amount is equal to the preset target error amount to achieve oil contamination control.

[0011] An oil contamination fault simulation method provided by this embodiment monitors the error between the target error amount and the current contamination error amount in real time, and ensures that the oil contamination degree is maintained within a preset range through adaptive adjustment. When the current contamination error amount is greater than the preset target error amount, the abrasive particle release module will be automatically activated, and release fine-tuning will be performed by releasing metal particles to increase the pollutant content; while when the current contamination error amount is less than the preset target error amount, the abrasive particle adsorption module will be started to reduce the excess pollutants for adsorption fine-tuning. The whole process requires no manual intervention, realizing automated and intelligent pollution control. This dynamic adjustment mechanism ensures that the hydraulic system maintains the optimal contamination level during long-term operation, thereby reducing the impact of pollution on the performance of the hydraulic system and improving the stability and reliability of the hydraulic system.

[0012] In one embodiment, the determining the current contamination error amount includes: Identify the contamination degree sequence in which the current contamination degree is located, and the contamination degree sequence includes contamination degrees collected at multiple different times; Extract the sequence features of the contamination degree sequence and obtain the standard contamination degree matching the sequence features; For any target contamination degree in the contamination degree sequence, calculate the residual between the target contamination degree and the standard contamination degree, and construct a residual sequence based on the calculated residuals; Extract the residual features of the residual sequence and determine the error amount characterized by the residual features as the current contamination error amount.

[0013] For the microprocessing module provided in this embodiment, the contamination level sequence recognition unit ensures the systematization and structuring of data by identifying the time series range where the contamination data is located. Subsequently, the sequence feature extraction unit extracts key features from the contamination level sequence and matches them with the standard contamination level, effectively eliminating noise and irrelevant factors and improving the prediction accuracy. Then, the residual sequence construction unit generates a residual sequence by calculating the difference between the target contamination level and the standard contamination level, providing core data for error analysis. Finally, the error amount determination unit accurately characterizes the contamination level prediction error by analyzing the residual features and dynamically adjusts the model to optimize the prediction accuracy. Overall, by accurately identifying and analyzing the contamination level error, the contamination level prediction can be adjusted in real time, thereby improving the prediction accuracy and reducing the error accumulation.

[0014] In one embodiment, the current contamination level is determined as follows: Obtain the contamination level sequence data in the hydraulic pipeline after each release fine-tuning or adsorption fine-tuning of the oil fluid; Determine the contamination level standard deviation corresponding to the contamination level sequence data; In the case where the contamination level standard deviation is less than the preset contamination level stability threshold, determine the fine-tuned contamination level data as the current contamination level.

[0015] In this embodiment, by collecting the contamination level sequence data of the oil fluid within a specified time period, a basis for subsequent analysis is provided. Then, by calculating the standard deviation of the contamination level sequence, the contamination level volatility is quantified, thereby determining whether the oil fluid contamination level is stable. When the contamination level standard deviation is less than the preset threshold, the fine-tuned contamination level data is determined as the current contamination level to ensure that the contamination level is in a stable state. This method avoids unnecessary adjustments due to short-term fluctuations, improves the accuracy and efficiency of contamination level control, and ultimately realizes the automation and reliability of the oil fluid contamination level management in the hydraulic system.

[0016] In one embodiment, the contamination level stability threshold is determined as follows: Obtain the average concentration of the oil fluid in the hydraulic pipeline within a specified time period; Determine the product of the average concentration and a preset percentage value as the contamination level stability threshold.

[0017] In this embodiment, by obtaining the average concentration of the oil fluid in the hydraulic pipeline within a specified time period, the change in the contamination level of the oil fluid can be accurately monitored, providing a data basis for subsequent analysis. On this basis, by multiplying the average concentration by a preset percentage value, the contamination stability threshold is dynamically calculated, so as to flexibly adjust the stability judgment criterion according to the actual contamination level. This method avoids unnecessary adjustments caused by short-term fluctuations, improves the accuracy and efficiency of contamination management, ensures the operation of the hydraulic system in a stable state, thus optimizing the contamination control strategy and enhancing the automation and reliability of the system.

[0018] In a third aspect, an oil fluid contamination fault simulation system provided by an embodiment of the present application is applied to the above-mentioned device, and the system includes: An error amount determination unit, configured to determine the current contamination error amount in response to a target contamination level input by a user and the monitored current contamination level; An abrasive particle release unit, configured to control the abrasive particle release module in the oil fluid contamination fault simulation device to perform a release fine adjustment on the current contamination level when the current contamination error amount is greater than a preset target error amount; An abrasive particle adsorption unit, configured to control the abrasive particle adsorption module in the oil fluid contamination fault simulation device to perform an adsorption fine adjustment on the current contamination level when the current contamination error amount is less than the preset target error amount; A stop fine adjustment unit, configured to stop the fine adjustment until the current contamination error amount is equal to the preset target error amount, so as to achieve oil fluid contamination control.

[0019] In a fourth aspect, an embodiment of the present application provides a computer device, including: A memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the above-mentioned oil fluid contamination fault simulation method.

[0020] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to cause a computer to execute the above-mentioned oil fluid contamination fault simulation method. Description of the Drawings

[0021] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0022] Figure 1 Schematic diagram of an oil contamination fault simulation device provided by an embodiment of the present application; Figure 2 Flowchart of an oil contamination fault simulation method provided by an embodiment of the present application; Figure 3 Flowchart of determining the current contamination error amount provided by an embodiment of the present application; Figure 4 Flowchart of the current contamination determination method provided by an embodiment of the present application; Figure 5 Flowchart of the determination method of the contamination stability threshold provided by an embodiment of the present application; Figure 6 Schematic diagram of an oil contamination fault simulation system provided by an embodiment of the present application; Figure 7 Block diagram of an oil contamination fault simulation system provided by an embodiment of the present application; Figure 8 Schematic diagram of the structure of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0023] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0024] The hydraulic system is a key component of the ship steering system, and its main function is to drive the movement of the steering cylinder to achieve the steering function. However, oil contamination is one of the common fault causes of the hydraulic system. Operating in a contaminated oil environment for a long time will accelerate the wear and deterioration of the equipment, and ultimately lead to abnormal steering function, thereby affecting the normal operation of the ship. However, due to the lack of long-term and continuous monitoring of the oil contamination degree on the ship and the lack of detailed deterioration data of the hydraulic system under contaminated conditions, it is impossible to clarify the specific impact of contamination on the equipment performance, thereby increasing the difficulty of fault diagnosis.

[0025] Therefore, how to accurately simulate the oil contamination degree in the hydraulic system and collect the fault data of related equipment in a contaminated environment has become a key issue in improving the fault diagnosis ability of the hydraulic system and urgently needs to be solved.

[0026] According to an embodiment of the present application, an embodiment of an oil contamination fault simulation device is provided. Figure 1The figure is a schematic diagram of an oil contamination fault simulation device provided by an embodiment of the present application. As Figure 1 shown, the device includes a communication module, a microprocessing module, an abrasive particle release module, and an abrasive particle adsorption module; The communication module is used to obtain the current contamination level and input the current contamination level into the microprocessing module; The microprocessing module is used to determine the current contamination error amount based on the current contamination level, compare the current contamination error amount with a preset target error amount, and control the abrasive particle release module or the abrasive particle adsorption module according to the comparison result; The abrasive particle release module is used to control the release of abrasive particles to adjust the oil contamination level; The abrasive particle adsorption module is used to control the adsorption of abrasive particles to adjust the oil contamination level.

[0027] Specifically, the communication module is responsible for collecting data on the current contamination level and transmitting this data to the microprocessing module. The data on the current contamination level is detected by a sensor, and an oil contamination sensor can be used as the sensor. The microprocessing module receives the data from the communication module. The microprocessing module has a built-in closed-loop control algorithm, which continuously monitors and adjusts the contamination level of the oil to ensure that it is maintained within a preset target range. That is, the current contamination error amount is determined based on the data from the communication module and compared with the preset target error amount. If the current contamination error amount does not match the target error amount, the microprocessing module will decide the next action based on this difference, that is, control the abrasive particle release module or the abrasive particle adsorption module.

[0028] When the current contamination error amount is lower than the target error amount, the abrasive particle adsorption module will adsorb metal particles in the oil to reduce the oil contamination level and lower the contamination level. On the contrary, when the current contamination error amount is higher than the target error amount, the abrasive particle release module will release metal particles to increase the oil contamination level.

[0029] The oil contamination fault simulation device further includes a power supply module for power supply. The device can dynamically adjust the oil contamination level to keep it within the target range set by the user. Through automated control, manual intervention can be reduced, and the efficiency and accuracy can be improved. It is applicable to industrial environments that require precise control of the contamination level to ensure the cleanliness and efficiency of equipment operation.

[0030] An oil contamination fault simulation device provided by this embodiment obtains the current contamination level through a communication module and inputs it into a microprocessing module for comparison. The microprocessing module determines the current contamination error amount based on the current contamination level, compares the difference between the current contamination error amount and a preset target error amount, and controls the abrasive particle release module or the abrasive particle adsorption module to precisely adjust the oil contamination level. The abrasive particle release module increases the oil contamination level by releasing metal particles, while the abrasive particle adsorption module adsorbs metal particles to reduce the oil contamination level and keep the hydraulic system clean. This process can adaptively adjust the contamination level in real time to ensure that the contamination level remains within the target range, improving the cleaning effect and the operating efficiency of the system. The device has an efficient contamination level adjustment ability, can flexibly handle different contamination situations, and realizes automatic and precise contamination control. At the same time, the device can be used to study the degradation process of the hydraulic system in a contaminated environment and collect a large amount of fault sample data to provide data support for fault diagnosis.

[0031] In some preferred embodiments, the microprocessing module includes: A contamination level sequence recognition unit for identifying the contamination level sequence in which the current contamination level is located. The contamination level sequence includes contamination levels collected at multiple different times.

[0032] Specifically, the contamination level here is data collected by a sensor or detection device capable of detecting oil contamination at different time points. The set of these data usually forms a time series as the contamination level sequence, that is, these data are arranged in chronological order.

[0033] A sequence feature extraction unit for extracting the sequence features of the contamination level sequence and obtaining the standard contamination level that matches the sequence features.

[0034] Specifically, the sequence features of the contamination level sequence can include mean, variance, peak value, etc. After extracting the sequence features of the contamination level sequence, the standard contamination level that matches the sequence features can be obtained from a pre-matched database. The standard contamination level is defined based on historical data and contamination level standards. By matching historical data with contamination levels, the standard contamination level grade corresponding to each contamination level sequence is determined.

[0035] It is also possible to establish a mapping relationship between sequence features and the standard contamination level through machine learning or deep learning models. For example, if the standard contamination level is discrete categories (such as "low contamination", "medium contamination", "high contamination"), a classification model (such as logistic regression, random forest, K-nearest neighbor, support vector machine (SVM), neural network, etc.) is used for classification. The trained model can be used to predict the contamination level sequence, and based on the extracted sequence features, the corresponding standard contamination level value is output.

[0036] A residual sequence construction unit is used to calculate the residual between a target contamination level in the contamination level sequence and the standard contamination level for any target contamination level, and construct a residual sequence based on the calculated residuals.

[0037] Specifically, the target contamination level is the contamination level value that the user inputs and wants to achieve. For each moment, the difference between the target contamination level and the standard contamination level, that is, the residual, is calculated. By performing the same calculation for all time points, a series of residuals are obtained, which constitute the residual sequence and can serve as the basis for subsequent data analysis.

[0038] A contamination level error amount determination unit is used to extract the residual characteristics of the residual sequence and determine the error amount characterized by the residual characteristics as the current contamination level error amount.

[0039] Specifically, the residual characteristics of the residual sequence are extracted. The residual characteristics can be the mean, variance, or standard deviation. These mean, variance, or standard deviation can be used as the error amount characterized by the residual characteristics. This error amount can be determined as the current contamination level error amount.

[0040] It is also possible to predict the current contamination level error amount through the characteristics of the residual sequence, that is, map the characteristics of the residual sequence to the current contamination level error amount. In order to establish the relationship between the residual characteristics and the contamination level error amount, a model needs to be trained. The input of this model is the characteristics of the residual sequence, and the output is the corresponding current contamination level error amount. Models such as linear regression can be used, and by training this model, this mapping relationship can be learned. After training, the model can be used to predict the current contamination level error amount in real time. When the current residual characteristics are input, the model will output the corresponding current contamination level error amount, thereby helping to identify the size of the contamination level prediction error.

[0041] In the microprocessing module provided in this embodiment, the contamination level sequence recognition unit ensures the systematization and structuring of data by identifying the time sequence range where the contamination level data is located. Then, the sequence feature extraction unit extracts key features from the contamination level sequence and matches them with the standard contamination level, effectively removing noise and irrelevant factors and improving the prediction accuracy. Subsequently, the residual sequence construction unit generates a residual sequence by calculating the difference between the target contamination level and the standard contamination level, providing core data for error analysis. Finally, the error amount determination unit accurately characterizes the contamination level prediction error by analyzing the residual characteristics and dynamically adjusts the model to optimize the prediction accuracy. Overall, by accurately identifying and analyzing the contamination level error, the contamination level prediction can be adjusted in real time, thereby improving the prediction accuracy and reducing the error accumulation.

[0042] In some preferred embodiments, both the abrasive particle release module and the abrasive particle adsorption module adopt electromagnetic coil arrays.

[0043] Specifically, when the electromagnetic coil array is energized, a magnetic field is generated. This magnetic field can attract ferromagnetic metal particles to achieve the adsorption function. When releasing the metal particles, the magnetic field can be reduced or eliminated by changing the current direction of the electromagnetic coil array or turning off the current, so that the metal particles adsorbed on the electromagnetic coil are released. By controlling the on / off and direction of the current, the adsorption and release of the metal particles can be precisely controlled.

[0044] In this embodiment, an oil contamination fault simulation method is provided, which is applied to the above-mentioned oil contamination fault simulation device. Figure 2 The flowchart of an oil contamination fault simulation method provided by an embodiment of the present application is as Figure 2 shown, and the process includes the following steps: Step S1, in response to the target contamination level input by the user and the currently monitored contamination level, determine the current contamination level error amount.

[0045] Specifically, the contamination level is reflected by the data collected by sensors or detection devices capable of detecting the degree of oil contamination at different time points. These data form a time series, usually called the contamination level series, that is, these data are arranged in chronological order. The characteristics of the contamination level series can include mean, variance, peak value, etc. After extracting these series characteristics, the standard contamination level matching the series characteristics can be found through a pre-matched database. The standard contamination level is defined according to historical data and contamination level standards. By matching historical data with contamination levels, the standard contamination level corresponding to each contamination level series is determined.

[0046] In addition, machine learning or deep learning models can also be used to establish the mapping relationship between the series characteristics and the standard contamination level. If the standard contamination level is a discrete category (such as "low contamination", "medium contamination", "high contamination"), a classification model (such as logistic regression, random forest, K-nearest neighbor, support vector machine (SVM), neural network, etc.) can be used for classification. Through the trained model, the contamination level series can be predicted, and the corresponding standard contamination level value can be output according to the extracted series characteristics.

[0047] The target contamination level is the contamination level value that the user hopes to achieve. For each moment, the difference between the target contamination level and the standard contamination level, that is, the residual, can be calculated. By performing the same calculation for all time points, a series of residuals are obtained, forming a residual series, which provides a basis for subsequent data analysis. By extracting the characteristics (such as mean, variance, or standard deviation) of the residual series, the error can be quantified, and then the current contamination level error amount can be obtained.

[0048] In addition, the current contamination error amount can also be predicted based on the characteristics of the residual sequence. To establish the relationship between the residual characteristics and the contamination error amount, a model needs to be trained. The input of this model is the characteristics of the residual sequence, and the output is the corresponding current contamination error amount. Models such as linear regression can be used to learn this mapping relationship through training. After training, the model can predict the current contamination error amount in real time. When the current residual characteristics are input, the model will output the corresponding current contamination error amount, thereby helping to identify the magnitude of the contamination prediction error.

[0049] In some embodiments, the current contamination error amount can also refer to the target contamination w input by the user t and the currently monitored contamination w r The difference between them. This error amount can be calculated by simple subtraction: Δw = w t - w r , and this error amount is a key parameter for the system to adjust the contamination to reach the target contamination.

[0050] Step S3, when the current contamination error amount is greater than the preset target error amount, control the abrasive release module in the oil contamination fault simulation device to perform a fine adjustment of the current contamination by releasing.

[0051] Specifically, the target error amount ε is a threshold set by the user to determine the acceptable difference between the current contamination and the target contamination. This target error amount is usually defined as ε = |w t - w r |. If the current contamination error amount Δw is greater than the target error amount ε, it means that the target contamination w t is greater than the current contamination w r , and it is necessary to increase the contamination to reach the target contamination. At this time, control the abrasive release module in the oil contamination fault simulation device to start. The abrasive release module increases the contamination in the oil by releasing particles such as metal, so that the current contamination w r increases and approaches or reaches the target contamination w t .

[0052] Step S5, when the current contamination error amount is less than the preset target error amount, control the abrasive adsorption module in the oil contamination fault simulation device to perform an adsorption fine adjustment of the current contamination.

[0053] Specifically, if the current contamination error amount Δw is less than the target error amount ε, it means that the target contamination w t is less than the current contamination w r, it is necessary to reduce the contamination level to reach the target contamination level. At this time, the abrasive particle adsorption module in the oil contamination fault simulation device is activated. The abrasive particle adsorption module reduces the contamination level in the oil by adsorbing metal particles in the oil, so that the current contamination level w r decreases and approaches or reaches the target contamination level w t .

[0054] Step S7, until the current contamination error amount is equal to the preset target error amount, stop fine-tuning to achieve oil contamination control.

[0055] Specifically, continuously monitor the current contamination level w r , and repeat Step S4 and Step S5 according to the new current contamination error amount until the current contamination error amount Δw is equal to the target error amount ε. Stop fine-tuning. At this time, the contamination level of the oil is controlled within the range of the target error amount set by the user.

[0056] An oil contamination fault simulation method provided by this embodiment monitors the error between the target error amount and the current contamination error amount in real time, and ensures that the oil contamination level is maintained within the preset range through adaptive adjustment. When the current contamination error amount is greater than the preset target error amount, the abrasive particle release module will be automatically activated, and release fine-tuning is performed by releasing metal particles to increase the pollutant content; while when the current contamination error amount is less than the preset target error amount, the abrasive particle adsorption module is activated to reduce the excess pollutants for adsorption fine-tuning. The whole process requires no manual intervention, realizing automated and intelligent contamination control. This dynamic adjustment mechanism ensures that the hydraulic system maintains the optimal contamination level during long-term operation, thereby reducing the impact of contamination on the performance of the hydraulic system and improving the stability and reliability of the hydraulic system.

[0057] Figure 3 It is a flowchart for determining the current contamination error amount provided by an embodiment of this application. This process may include the following steps: Step S11, identify the contamination level sequence in which the current contamination level is located. The contamination level sequence includes contamination levels collected at multiple different times.

[0058] Specifically, the contamination level here is the data collected by sensors or detection devices capable of detecting oil contamination at different time points. The set of these data is usually a time series as the contamination level sequence, that is, these data are arranged in chronological order.

[0059] Step S13, extract the sequence features of the contamination level sequence and obtain the standard contamination level that matches the sequence features.

[0060] Specifically, the sequence features of the contamination level sequence can include the mean, variance, peak value, etc. After extracting the sequence features of the contamination level sequence, the standard contamination level that matches the sequence features can be obtained from a pre-matched database. The standard contamination level is defined based on historical data and the contamination level standard. By matching the historical data with the contamination level, the standard contamination level corresponding to each contamination level sequence is determined.

[0061] It is also possible to establish a mapping relationship between the sequence features and the standard contamination level through a machine learning or deep learning model. For example, if the standard contamination level is a discrete category (such as "low contamination", "medium contamination", "high contamination"), a classification model (such as logistic regression, random forest, K-nearest neighbor, support vector machine (SVM), neural network, etc.) is used for classification. The trained model can be used to predict the contamination level sequence, and based on the extracted sequence features, the corresponding standard contamination level value is output.

[0062] Step S15: For any target contamination level in the contamination level sequence, calculate the residual between the target contamination level and the standard contamination level, and construct a residual sequence based on the calculated residuals.

[0063] Specifically, the target contamination level is the contamination level value that the user inputs and wants to achieve. For each moment, calculate the difference between the target contamination level and the standard contamination level, that is, the residual. By performing the same calculation for all time points, a series of residuals are obtained, which constitute the residual sequence and can serve as the basis for subsequent data analysis.

[0064] Step S17: Extract the residual features of the residual sequence, and determine the error amount characterized by the residual features as the current contamination level error amount.

[0065] Specifically, extract the residual features of the residual sequence. The residual features can be the mean, variance, or standard deviation. These mean, variance, or standard deviation can be used as the error amount characterized by the residual features. This error amount can be determined as the current contamination level error amount.

[0066] It is also possible to predict the current contamination level error amount through the features of the residual sequence, that is, map the features of the residual sequence to the current contamination level error amount. To establish the relationship between the residual features and the contamination level error amount, a model needs to be trained. The input of this model is the features of the residual sequence, and the output is the corresponding current contamination level error amount. Models such as linear regression can be used, and by training this model, this mapping relationship can be learned. After training, the model can be used to predict the current contamination level error amount in real time. When the current residual features are input, the model will output the corresponding current contamination level error amount, thereby helping to identify the magnitude of the contamination level prediction error.

[0067] For the microprocessing module provided in this embodiment, the contamination degree sequence recognition unit ensures the systematization and structuring of data by identifying the time series range where the contamination degree data is located. Then, the sequence feature extraction unit extracts key features from the contamination degree sequence and matches them with the standard contamination degree, effectively eliminating noise and irrelevant factors and improving the prediction accuracy. Subsequently, the residual sequence construction unit generates a residual sequence by calculating the difference between the target contamination degree and the standard contamination degree, providing core data for error analysis. Finally, the error amount determination unit accurately characterizes the contamination degree prediction error by analyzing the residual features and dynamically adjusts the model to optimize the prediction accuracy. Overall, by accurately identifying and analyzing the contamination degree error, the contamination degree prediction can be adjusted in real time, thereby improving the prediction accuracy and reducing the error accumulation.

[0068] Figure 4 It is a flowchart of the current contamination degree determination method provided by the embodiment of the present application. This process may include the following steps: Step S101: Obtain the contamination degree sequence data in the hydraulic pipeline after each release fine-tuning or adsorption fine-tuning of the oil fluid.

[0069] Step S103: Determine the contamination degree standard deviation corresponding to the contamination degree sequence data.

[0070] Step S105: When the contamination degree standard deviation is less than the preset contamination degree stability threshold, determine the fine-tuned contamination degree data as the current contamination degree.

[0071] It should be noted here that the working mode of the abrasive particle adsorption module / abrasive particle release module is "small amount and multiple times", which means that small amounts of abrasive particles will be released or adsorbed frequently to ensure the stability of the oil fluid contamination degree. By monitoring the inlet pressure signal P i and the outlet pressure signal P o , the flow rate Q can be calculated using the characteristic curve of the pump. Then the working time T of the abrasive particle adsorption module / abrasive particle release module is: T = V÷(Q×100), where V is the medium capacity in the fuel tank and Q is the volume of oil fluid passed by the pump group per unit time. By dividing the fuel tank capacity by the flow rate and dividing by 100, a relatively small working time can be obtained to achieve the "small amount and multiple times" working mode. According to the calculated working time, the abrasive particle adsorption module / abrasive particle release module will release or adsorb a certain amount of metal particles each time it works to adjust the contamination degree of the oil fluid.

[0072] Specifically, after each operation of the abrasive particle adsorption module / abrasive particle release module, the data of the oil fluid contamination degree is continuously collected. That is, the contamination degree of the oil fluid is continuously monitored in the hydraulic pipeline through a contamination degree sensor to obtain the contamination degree data sequence Next, calculate the standard deviation σ of the contamination degree sequence data to quantify the fluctuation of the contamination degree. If the contamination degree standard deviation σ is less than the preset contamination degree stability threshold σ t , it indicates that the oil contamination degree has stabilized. At this time, the fine-tuned contamination degree data is determined as the current contamination degree .

[0073] In this embodiment, by collecting the contamination degree sequence data of the oil fluid within a specified time period, a basis is provided for subsequent analysis. Next, by calculating the standard deviation of the contamination degree sequence, the contamination degree volatility is quantified, thereby determining whether the oil contamination degree is stable. When the contamination degree standard deviation is less than the preset threshold, the fine-tuned contamination degree data is determined as the current contamination degree level, ensuring that the contamination degree is in a stable state. This method avoids unnecessary adjustments due to short-term fluctuations, improves the accuracy and efficiency of contamination degree control, and ultimately realizes the automation and reliability of the oil fluid contamination degree management in the hydraulic system.

[0074] Figure 5 The flowchart of the determination method of the contamination degree stability threshold provided by the embodiment of the present application is as follows, and this process may include the following steps: Step S1341, obtain the average concentration of the oil fluid in the hydraulic pipeline within a specified time period.

[0075] Step S1343, determine the product between the average concentration and the preset percentage value as the contamination degree stability threshold.

[0076] Specifically, continuously monitor the oil fluid contamination degree in the hydraulic pipeline through a contamination degree sensor, and then calculate the average concentration within this time period. The specified time period can be the time interval for one cycle of the oil fluid in the hydraulic pipeline. Multiply the obtained average concentration by a preset percentage value to obtain the contamination degree stability threshold. This preset percentage value can be a fixed value or a value adjusted according to system requirements and operating conditions. σ t = average concentration × preset percentage value, and the preset percentage value is preferably set to 1%.

[0077] In this embodiment, by obtaining the average concentration of the oil fluid in the hydraulic pipeline within a specified time period, the change of the oil fluid contamination degree can be accurately monitored, providing a data basis for subsequent analysis. On this basis, by multiplying the average concentration by the preset percentage value, the contamination degree stability threshold is dynamically calculated, thereby flexibly adjusting the stability judgment standard according to the actual contamination degree level. This method avoids unnecessary adjustments caused by short-term fluctuations, improves the accuracy and efficiency of contamination degree management, ensures the hydraulic system operates in a stable state, thereby optimizing the contamination degree control strategy and enhancing the automation and reliability of the system.

[0078] The following describes the specific implementation of the present invention in combination with a specific application scenario. See Figure 6It is a schematic diagram of an oil contamination fault simulation system provided by an embodiment of the present application. The system includes a hydraulic pipeline, a contamination sensor, and the above-mentioned oil contamination fault simulation device. The hydraulic pipeline is connected in parallel with the contamination sensor, and the oil contamination fault simulation device is connected in series on the hydraulic pipeline; The contamination sensor is used to monitor the current contamination of the hydraulic pipeline through bypass shunting; The oil contamination fault simulation device is used to, according to the current contamination error amount between the target contamination input by the user and the current contamination obtained, when the current contamination error amount is greater than a preset target error amount, use the abrasive particle release module to perform a release fine-tuning on the current contamination; The oil contamination fault simulation device is used to, according to the current contamination error amount between the target contamination input by the user and the current contamination obtained, when the current contamination error amount is less than a preset target error amount, use the abrasive particle adsorption module to perform an adsorption fine-tuning on the current contamination.

[0079] Specifically, in the hydraulic system, the hydraulic pipeline for transmitting hydraulic oil is connected in parallel with the contamination sensor and in series with the oil contamination fault simulation device. The contamination sensor realizes real-time monitoring of the contamination in the hydraulic pipeline and obtains the current contamination through the way of bypass shunting without disturbing the oil flow in the main hydraulic pipeline. Then, according to the current contamination error amount between the target contamination input by the user and the current contamination monitored by the sensor, the contamination is fine-tuned. When the current contamination error amount is greater than the preset target error amount, the abrasive particle release module is used to increase the contamination to make the contamination close to the target value. When the current contamination error amount is less than the preset target error amount, the abrasive particle adsorption module is used to reduce the contamination to make the contamination close to the target value. This embodiment allows users to simulate different contamination situations to test the performance and reliability of the hydraulic system under different contamination degrees. This is of great significance for aspects such as the maintenance, fault diagnosis, and life prediction of the hydraulic system. By precisely controlling the contamination degree, various contamination situations under different working conditions can be simulated, thereby evaluating the anti-contamination ability of the hydraulic system and the effectiveness of the maintenance strategy.

[0080] An oil contamination fault simulation system provided by this embodiment monitors the oil contamination in the hydraulic pipeline in real time through a contamination sensor, compares it with the target contamination set by the user, and automatically adjusts the contamination according to the error. The system has an adaptive adjustment function. When the current contamination error amount is greater than the preset target error amount, the abrasive particle release module is activated to increase the contamination by releasing metal particles; when the current contamination error amount is less than the preset target error amount, the abrasive particle adsorption module is started to reduce the excess contaminants for fine adjustment. Through this adaptive adjustment mechanism, the system can efficiently clean the oil under different contamination levels, ensuring the stable operation of the hydraulic system. At the same time, the system can dynamically respond to the changes in the contaminant concentration in the hydraulic pipeline, achieving flexibility and optimization effects in pollution control, without manual intervention, and improving the automation level. Generally speaking, this system realizes the precise control of oil contamination, effectively ensuring the long-term stability and reliability of the hydraulic system.

[0081] According to an embodiment of the present application, an embodiment of a method for simulating an oil contamination fault is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0082] Correspondingly, please refer to Figure 7 For the block diagram of an oil contamination fault simulation system provided by an embodiment of the present application, the system includes: An error amount determination unit 101, configured to determine the current contamination error amount in response to the target contamination input by the user and the monitored current contamination; An abrasive particle release unit 103, configured to, when the current contamination error amount is greater than the preset target error amount, control the abrasive particle release module in the oil contamination fault simulation device to perform release fine adjustment on the current contamination; An abrasive particle adsorption unit 105, configured to, when the current contamination error amount is less than the preset target error amount, control the abrasive particle adsorption module in the oil contamination fault simulation device to perform adsorption fine adjustment on the current contamination; A stop fine adjustment unit 107, configured to stop fine adjustment until the current contamination error amount is equal to the preset target error amount, so as to achieve oil contamination control.

[0083] In some alternative embodiments, determining the current contamination error amount includes: Identifying the contamination sequence in which the current contamination is located, and the contamination sequence includes contaminations collected at multiple different times; Extracting the sequence features of the contamination sequence and obtaining the standard contamination that matches the sequence features; For any target contamination level in the contamination level sequence, calculate the residual between the target contamination level and the standard contamination level, and construct a residual sequence based on the calculated residuals; Extract the residual features of the residual sequence, and determine the error amount characterized by the residual features as the current contamination error amount.

[0084] In some alternative embodiments, the determination of the current contamination level includes: A data acquisition unit for acquiring contamination level sequence data in the hydraulic pipeline after each release fine-tuning or adsorption fine-tuning of the oil fluid; A standard deviation determination unit for determining the contamination standard deviation corresponding to the contamination level sequence data; A contamination level determination unit for determining the fine-tuned contamination data as the current contamination level when the contamination standard deviation is less than a preset contamination stability threshold.

[0085] In some alternative embodiments, the method for determining the contamination stability threshold is as follows: Obtain the average concentration of the oil fluid in the hydraulic pipeline within a specified time period; Determine the product of the average concentration and a preset percentage value as the contamination stability threshold.

[0086] The further function descriptions of the above-mentioned modules and units are the same as those in the corresponding embodiments above, and will not be elaborated here.

[0087] An oil fluid contamination fault simulation system in this embodiment is presented in the form of functional units. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0088] Please refer to Figure 8 , Figure 8 which is a schematic structural diagram of a computer device provided by an embodiment of the present application, as Figure 8As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting the components, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a set of blade servers, or a multi-processor system). Figure 8 In [description], a processor 10 is taken as an example.

[0089] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above-mentioned hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above-mentioned programmable logic device can be a complex programmable logic device, a field-programmable gate array, a generic array logic, or any combination thereof.

[0090] Among them, the memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiments.

[0091] The memory 20 can include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device, etc. In addition, the memory 20 can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 can optionally include a memory remotely set relative to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above-mentioned network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0092] The memory 20 can include a volatile memory, such as a random access memory; the memory can also include a non-volatile memory, such as a flash memory, a hard disk, or a solid-state drive; the memory 20 can also include a combination of the above types of memories.

[0093] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or communication networks.

[0094] The embodiments of the present application also provide a computer-readable storage medium. The methods according to the embodiments of the present application can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code that is originally stored in a remote storage medium or a non-transitory machine-readable storage medium and downloaded through a network and will be stored in a local storage medium. Thus, the methods described herein can be stored as such software processes on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by the computer, the processor, or the hardware, the methods shown in the above embodiments are implemented.

[0095] The systems, devices, and units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0096] For the convenience of description, when describing the above devices, they are described separately as various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0097] Those skilled in the art should understand that the embodiments of the present application can be provided as methods and systems. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0098] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses, and systems according to embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a means for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or a means for implementing the functions specified in multiple blocks.

[0099] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction means that implements the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or a means for implementing the functions specified in multiple blocks.

[0100] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable devices provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or a means for implementing the functions specified in multiple blocks.

[0101] It should also be noted that the term "comprising", "including", or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity, or device including a series of elements not only includes those elements but also includes other elements not explicitly listed, or elements inherent to such process, method, commodity, or device. Without further limitations, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, commodity, or device including the said element.

[0102] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for system embodiments, since they are basically similar to method embodiments, they are described relatively simply, and the relevant parts can be referred to the corresponding descriptions in the method embodiments.

[0103] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

[0104] Although the embodiments of the present application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations fall within the scope defined by the appended claims.

Claims

1. An oil contamination fault simulation device, characterized in that: The device includes a communication module, a microprocessing module, an abrasive particle releasing module and an abrasive particle adsorption module; The communication module is used to obtain the current pollution level and input the current pollution level to the microprocessing module; The microprocessing module is used to determine a current contamination error based on the current contamination, compare the current contamination error with a preset target error, and control the abrasive release module or the abrasive adsorption module according to the comparison result; An abrasive particle release module is used to control the abrasive particle release to adjust the oil contamination degree; The abrasive particle adsorption module is used to control the abrasive particle adsorption to adjust the oil pollution degree.

2. The device according to claim 1, characterized in that The microprocessing module comprises: A pollution level sequence identification unit, used to identify the pollution level sequence in which the current pollution level is located, wherein the pollution level sequence includes pollution levels collected at multiple different times; A sequence feature extraction unit, used to extract the sequence feature of the contamination degree sequence and obtain the standard contamination degree matching the sequence feature; A residual sequence construction unit, used for calculating the residual between the target pollution level and the standard pollution level for any target pollution level in the pollution level sequence, and constructing a residual sequence based on each calculated residual; The pollution error amount determination unit is used to extract the residual feature of the residual sequence, and determine the error amount represented by the residual feature as the current pollution error amount.

3. The device according to claim 1, characterized in that The abrasive particle releasing module and the abrasive particle adsorption module both adopt electromagnetic coil arrays.

4. A method for simulating oil contamination faults, characterized in that: Applied to the device of claim 1, the method comprises: In response to a target pollution level input by a user and a monitored current pollution level, determining a current pollution level error amount; When the current contamination error is greater than a preset target error, controlling an abrasive release module in the oil contamination fault simulation device to release and fine-tune the current contamination; When the current contamination error is less than the preset target error, controlling the abrasive adsorption module in the oil contamination fault simulation device to perform adsorption fine-tuning on the current contamination; Until the current contamination error is equal to the preset target error, fine-tuning is stopped to achieve oil contamination control.

5. The oil contamination fault simulation method according to claim 4 is characterized in that: The determining of the current pollution error comprises: Identify a pollution level sequence in which the current pollution level is located, wherein the pollution level sequence includes pollution levels collected at multiple different times; Extracting sequence features of the contamination degree sequence, and obtaining a standard contamination degree matching the sequence features; For any target pollution level in the pollution level sequence, calculate the residual between the target pollution level and the standard pollution level, and construct a residual sequence based on the calculated residuals; The residual features of the residual sequence are extracted, and the error amount represented by the residual features is determined as the current contamination error amount.

6. The oil contamination fault simulation method according to claim 4, characterized in that: The current pollution level is determined as follows: Acquiring contamination sequence data of the oil in the hydraulic pipeline after each release fine-tuning or adsorption fine-tuning; Determine the pollution degree standard deviation corresponding to the pollution degree sequence data; When the pollution level standard deviation is smaller than a preset pollution level stability threshold, the finely adjusted pollution level data is determined as the current pollution level.

7. The oil contamination fault simulation method according to claim 6, characterized in that: The contamination stability threshold is determined as follows: Get the average concentration of oil in the hydraulic pipeline within a specified period of time; The product of the average concentration and a preset percentage value is determined as the pollution level stability threshold.

8. An oil contamination fault simulation system, characterized in that: Applied to the device of claim 1, the system comprises: an error amount determination unit, configured to determine a current pollution level error amount in response to a target pollution level input by a user and a monitored current pollution level; an abrasive particle releasing unit, configured to control an abrasive particle releasing module in the oil contamination fault simulation device to release and fine-tune the current contamination degree when the current contamination degree error is greater than a preset target error; An abrasive particle adsorption unit, used for controlling the abrasive particle adsorption module in the oil contamination fault simulation device to perform adsorption fine adjustment on the current contamination degree when the current contamination degree error is less than the preset target error; The stop fine-tuning unit is used to stop fine-tuning until the current contamination error is equal to the preset target error, so as to achieve oil contamination control.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the oil contamination fault simulation method according to any one of claims 4 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the oil contamination fault simulation method according to any one of claims 4 to 7.

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