Intelligent lubricating system and method for open gear equipment

Abstract

The invention relates to the technical field of equipment lubrication control, and particularly discloses an intelligent lubrication system and method for open gear equipment. Key working condition parameters such as the operation rotating speed and the body temperature of an open gear are collected in real time, and a reverse solution model based on the target oil film thickness is constructed; and calculating the target kinematic viscosity corresponding to the optimal lubrication effect under the current dynamic working condition on line. And further, on the basis of the target viscosity calculated on line, a double-base oil dynamic mixing proportion solver is driven, so that an accurate proportioning instruction for the high-viscosity base lubricating oil and the low-viscosity base lubricating oil is generated. And finally, operation of the corresponding oil pump is directly regulated and controlled through the matching instruction, and online blending and accurate supply of the effective viscosity of the lubricant are achieved. In this way, it can be ensured that the optimal protection oil film is always maintained under the widely-changing working condition, and therefore precise and intelligent lubrication of the split gear equipment is achieved.

Description

Technical Field

[0001] This application relates to the field of equipment lubrication control technology, and more specifically, to an intelligent lubrication system and method for open gear equipment. Background Technology

[0002] Open gear transmission systems, as key power transmission components, are widely used in heavy industries such as mining, cement, metallurgy, and power generation. These devices typically operate under harsh conditions including heavy loads, low speeds, high dust levels, and drastic temperature fluctuations. Their safe and stable operation largely depends on effective and reliable lubrication. The core objective of lubrication is to form a stable, continuous elastohydrodynamic lubricating film between the meshing gear surfaces to isolate direct metal-to-metal contact, thereby significantly reducing wear, frictional power consumption, and extending equipment lifespan. The kinematic viscosity of the lubricant is the most critical physical parameter determining the oil film thickness and load-bearing capacity, and viscosity itself is highly sensitive to temperature. In actual operation, seasonal changes in ambient temperature, along with the temperature rise caused by equipment start-up and shutdown and load fluctuations, result in dynamic variations in the gear body temperature over a wide range. This makes it difficult to maintain ideal lubrication conditions under all operating conditions using a single lubricant with a fixed viscosity grade.

[0003] However, in existing technologies, traditional open gear lubrication solutions often employ timed, metered, or automatic jet lubrication systems based on equipment operating cycles. These systems typically use one or more pre-selected fixed-grade lubricants, supplied according to a set program. However, the inherent drawback of this approach lies in the static nature of its lubrication strategy. The viscosity of the lubricant is selected based on the equipment's requirements under typical or rated operating conditions, which is essentially a compromise. When the equipment faces atypical conditions such as cold starts, low-speed heavy loads, or significant increases in gear body temperature due to sudden changes in ambient temperature and high-load operation, this fixed-viscosity lubricant becomes unsuitable. For example, at low temperatures, excessively high lubricant viscosity results in poor flowability, making it difficult to effectively pump to the meshing area, potentially leading to starting wear; while at high temperatures, its viscosity may become too low, failing to form a sufficiently thick protective oil film, thus increasing the risk of failures such as tooth surface scuffing or pitting.

[0004] Therefore, there is a need for an optimized intelligent lubrication system and method for open gear equipment. Summary of the Invention

[0005] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide an intelligent lubrication system and method for open gear equipment. It collects key operating parameters such as the operating speed and body temperature of the open gear in real time and constructs a reverse-engineering model based on the target oil film thickness to calculate online the target kinematic viscosity corresponding to the optimal lubrication effect under the current dynamic operating conditions. Furthermore, based on the online calculated target viscosity, a dual-base oil dynamic mixing ratio calculator is driven to generate precise mixing instructions for high and low viscosity base lubricating oils. Finally, the operation of the corresponding oil pump is directly controlled through these mixing instructions to achieve online adjustment and precise supply of the effective viscosity of the lubricant. This ensures that the optimal protective oil film is maintained under a wide range of changing operating conditions, thereby achieving precise and intelligent lubrication of open gear equipment.

[0006] According to one aspect of this application, an intelligent lubrication method for an open gear device is provided, comprising:

[0007] Obtain the gear body temperature and gear speed;

[0008] Calculate the pitch velocity of the gear based on the gear speed and the gear pitch circle diameter;

[0009] The target kinematic viscosity is obtained by back-calculating the oil film thickness based on the pitch velocity and gear body temperature of the gear.

[0010] Based on the target kinematic viscosity and gear body temperature, the base oil mixing ratio is calculated by analyzing the kinematic viscosity of low-viscosity oil and the kinematic viscosity of high-viscosity oil to obtain the high-viscosity oil mixing ratio.

[0011] Based on the high viscosity oil mixing ratio and total lubrication metering, control commands for low viscosity oil pumps and high viscosity oil pumps are generated.

[0012] According to another aspect of this application, an intelligent lubrication system for open gear equipment is provided, comprising:

[0013] The gear data acquisition module is used to acquire gear body temperature and gear speed;

[0014] The pitch line speed calculation module is used to calculate the pitch line speed of a gear based on its rotational speed and pitch circle diameter.

[0015] The oil film thickness back calculation module is used to back calculate the oil film thickness based on the pitch linear velocity and gear body temperature of the gear to obtain the target kinematic viscosity;

[0016] The base oil mixing ratio calculation module is used to calculate the base oil mixing ratio based on the target kinematic viscosity and gear body temperature, for low viscosity oil kinematic viscosity and high viscosity oil kinematic viscosity, in order to obtain the high viscosity oil mixing ratio.

[0017] The lubrication pump control command generation module is used to generate low-viscosity oil pump control commands and high-viscosity oil pump control commands based on the high-viscosity oil mixing ratio and total lubrication metering.

[0018] Compared with existing technologies, the intelligent lubrication system and method for open gear equipment provided in this application collects key operating parameters such as the operating speed and body temperature of the open gear in real time, and constructs a reverse-engineering model based on the target oil film thickness to calculate online the target kinematic viscosity corresponding to the optimal lubrication effect under the current dynamic operating conditions. Then, based on the online calculated target viscosity, a dual-base oil dynamic mixing ratio calculator is driven to generate precise mixing instructions for high and low viscosity base lubricating oils. Finally, the operation of the corresponding oil pump is directly controlled through these mixing instructions to achieve online adjustment and precise supply of the effective viscosity of the lubricant. This ensures that the optimal protective oil film is maintained under a wide range of changing operating conditions, thereby achieving precise and intelligent lubrication of open gear equipment. Attached Figure Description

[0019] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0020] Figure 1 This is a flowchart of an intelligent lubrication method for an open gear device according to an embodiment of this application.

[0021] Figure 2 This is a data flow diagram of an intelligent lubrication method for open gear equipment according to an embodiment of this application.

[0022] Figure 3 This is a flowchart of sub-step S3 of the intelligent lubrication method for open gear equipment according to an embodiment of this application.

[0023] Figure 4 This is a flowchart of sub-step S4 of the intelligent lubrication method for open gear equipment according to an embodiment of this application.

[0024] Figure 5 This is a flowchart of sub-step S41 of the intelligent lubrication method for open gear equipment according to an embodiment of this application.

[0025] Figure 6 This is a block diagram of an intelligent lubrication system for an open gear device according to an embodiment of this application. Detailed Implementation

[0026] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0027] While this application makes various references to certain modules of the systems according to embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The modules described are merely illustrative, and different aspects of the systems and methods may use different modules.

[0028] Flowcharts are used in this application to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0029] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0030] It is worth noting that all data acquisition actions in this application were carried out in compliance with the relevant data protection laws and policies of the country where the application is located, and with the authorization granted by the owner of the relevant device.

[0031] To address the technical problems described in the background, this application proposes an intelligent lubrication method for open gear equipment. This method collects key operating parameters such as the operating speed and body temperature of the open gear in real time and constructs a reverse-engineering model based on the target oil film thickness to calculate online the target kinematic viscosity corresponding to the optimal lubrication effect under the current dynamic operating conditions. Then, based on this online calculated target viscosity, a dual-base oil dynamic mixing ratio calculator is driven to generate precise mixing instructions for high and low viscosity base lubricating oils. Finally, the operation of the corresponding oil pump is directly controlled through these mixing instructions, achieving online adjustment and precise supply of the effective viscosity of the lubricant. This ensures that the optimal protective oil film is maintained under a wide range of changing operating conditions, thereby achieving precise and intelligent lubrication of open gear equipment.

[0032] Figure 1 This is a flowchart of an intelligent lubrication method for an open gear device according to an embodiment of this application.

[0033] Figure 2 This is a data flow diagram of an intelligent lubrication method for an open gear device according to an embodiment of this application. Figure 1 and Figure 2 As shown, the intelligent lubrication method for open gear equipment includes the following steps: S1, obtaining the gear body temperature and gear speed; S2, calculating the pitch line velocity of the gear based on the gear speed and the gear pitch circle diameter; S3, back-calculating the oil film thickness based on the gear pitch line velocity and gear body temperature to obtain the target kinematic viscosity; S4, calculating the base oil mixing ratio based on the target kinematic viscosity and gear body temperature to obtain the high viscosity oil mixing ratio; S5, generating low viscosity oil pump control commands and high viscosity oil pump control commands based on the high viscosity oil mixing ratio and total lubrication metering.

[0034] In the aforementioned intelligent lubrication method for open gear equipment, step S1 involves acquiring the gear body temperature and gear speed. It should be understood that, due to the harsh operating conditions of open gear equipment—heavy loads, high dust levels, and drastic temperature fluctuations—the gear body temperature directly determines the actual rheological properties of the lubricant. Increased temperature leads to decreased lubricant viscosity, while decreased temperature results in excessively high viscosity. The gear speed, related to the relative motion speed of the meshing area of ​​the gear teeth, directly affects the efficiency of lubricating oil being carried into the meshing area. Both factors jointly determine the thickness and stability of the lubricating oil film. Therefore, this application uses dedicated sensing equipment to collect gear body temperature and gear speed in real time, providing accurate and real-time core operating condition input for subsequent target kinematic viscosity calculation. This avoids the problem of lubricant viscosity mismatch with actual requirements caused by using static operating condition parameters, ensuring that all subsequent lubrication decisions are based on the current actual operating state of the equipment. This ensures the effectiveness of the adaptive lubrication strategy from the data source, preventing oil film rupture or over-lubrication caused by misjudgment of operating conditions.

[0035] In the specific implementation process, a non-contact infrared thermometer is used to collect the temperature of the gear body. The thermometer is fixed on a metal bracket on the upstream side of the gear meshing area. The detection lens is focused on the middle area of ​​the gear tooth width and is kept at a fixed distance of 150-200mm from the gear surface to avoid interference from wear marks or contaminants on the tooth surface on the temperature measurement accuracy. An incremental rotary encoder is used to collect the gear speed. The encoder is directly connected to the gear shaft through a coupling. If there is no installation space on the gear shaft, the speed data collected by the encoder at the motor end is converted into the actual gear speed based on the known transmission ratio of the equipment's transmission system. The collected temperature and speed data are transmitted to the edge computing controller in real time via the PROFINET protocol. The controller performs 50Hz low-pass filtering on the raw data to filter out high-frequency noise generated by mechanical vibration or electromagnetic interference. The processed data is stored in the real-time operating condition buffer for direct use in subsequent linear velocity calculation steps.

[0036] In the aforementioned intelligent lubrication method for open gear equipment, step S2 calculates the pitch velocity of the gear based on the gear rotational speed and the gear pitch circle diameter. It should be understood that since gear rotational speed only reflects the number of rotations per unit time and is a discrete rotational motion parameter, it cannot be directly used to calculate oil film thickness. Therefore, this application further combines the inherent pitch circle diameter of the gear with the real-time acquired gear rotational speed to calculate the pitch velocity of the gear using kinematic principles, thereby providing key input parameters that meet the theoretical requirements of the EHL model for subsequent back-calculation of the target dynamic viscosity. In a specific example of this application, step S2 includes: calculating the pitch velocity of the gear using the following formula:

[0037]

[0038] Where, d p The pitch circle diameter of the gear, RPM gear v is the gear speed. p This represents the pitch velocity of the gear. This allows the rotational motion parameters to be converted into the linear motion parameters required for fluid lubrication calculations. The EHL model can then perform oil film thickness analysis based on the actual tooth surface motion state, ensuring that the subsequent target kinematic viscosity calculation results are highly consistent with the current lubrication requirements of the meshing area, thus avoiding oil film thickness calculation errors caused by parameter type mismatches.

[0039] In the aforementioned intelligent lubrication method for open gear equipment, step S3 involves back-calculating the oil film thickness based on the gear's pitch speed and gear body temperature to obtain the target kinematic viscosity. It should be understood that the gear's pitch speed directly determines the entrainment efficiency of lubricating oil in the meshing zone, while the gear body temperature significantly affects the actual rheological properties of the lubricant. Although these are key operating parameters, they cannot be directly used to guide lubricant formulation. A correlation with the target kinematic viscosity needs to be established through oil film thickness back-calculation, and kinematic viscosity is the core indicator determining oil film thickness and load-bearing capacity. Therefore, this application further combines elastohydrodynamic lubrication theory to back-calculate the oil film thickness based on the pitch speed and gear body temperature to obtain the target kinematic viscosity that can form a stable protective oil film under the current operating conditions. This transforms discrete operating parameters into concrete lubricant performance requirements, avoiding problems such as excessively thin (at high temperatures and high speeds) or excessively thick (at low temperatures and low speeds) oil films caused by directly using lubricants of fixed viscosity. This ensures that subsequent base oil mixing ratio calculations have a clear target basis, fundamentally guaranteeing the operating condition adaptability of the lubrication strategy. in, Figure 3 This is a flowchart of sub-step S3 of the intelligent lubrication method for open gear equipment according to an embodiment of this application. Figure 3As shown, step S3 includes the following steps: S31, determining the target minimum oil film thickness based on the target oil film thickness ratio and the overall roughness of the tooth surface; S32, performing back-calculation of the target dynamic viscosity based on the target minimum oil film thickness, the suction speed, the unit linear load, and the gear geometry and material parameters to obtain the target dynamic viscosity, wherein the suction speed is calculated based on the pitch linear velocity of the gear; S33, performing target kinematic viscosity conversion on the target dynamic viscosity based on the gear body temperature, reference density, reference temperature, and volumetric thermal expansion coefficient to obtain the target kinematic viscosity.

[0040] Specifically, step S31 determines the target minimum oil film thickness based on the target oil film thickness ratio and the overall tooth surface roughness. It should be understood that the oil film thickness ratio is a core indicator for judging the lubrication state (a ratio greater than 3 is required for complete fluid lubrication). This ratio needs to be combined with the actual tooth surface roughness to be converted into a physically meaningful effective oil film thickness. If only the theoretical oil film thickness ratio is relied upon, ignoring the microscopic protrusions on the tooth surface, the actual oil film may fail to cover the rough surface, leading to metal-to-metal contact. Therefore, this application further combines the target oil film thickness ratio with the overall tooth surface roughness, and determines the target minimum oil film thickness through mathematical calculations. This transforms the abstract lubrication state indicator into a specific and quantifiable physical oil film thickness. This ensures that the calculated oil film thickness not only meets the theoretical lubrication requirements but also actually covers the rough protrusions on the tooth surface, avoiding insufficient oil film thickness design due to neglecting roughness, and effectively preventing tooth surface wear, scuffing, and other failure problems under boundary lubrication or mixed lubrication conditions.

[0041] In the specific implementation process, the root mean square roughness of the tooth surfaces of the meshing gears is first measured using a laser confocal microscope to obtain the roughness data of the driving and driven gears. The controller calculates the comprehensive roughness of the tooth surfaces based on the superposition principle, that is, the square root of the sum of the squares of the roughness of the two gears. Then, a preset target oil film thickness ratio (a value greater than 3, determined according to the equipment load level) is retrieved from the system parameter library, and the comprehensive roughness of the tooth surfaces is multiplied by the target oil film thickness ratio to obtain the target minimum oil film thickness. After the calculation is completed, the controller compares the thickness data with the gear meshing clearance. If the target minimum oil film thickness exceeds the reasonable range of the meshing clearance, the target oil film thickness ratio is automatically corrected and recalculated until the result meets the requirements. The finally determined target minimum oil film thickness is transmitted to the target dynamic viscosity back-calculation stage.

[0042] Specifically, in step S32, the target dynamic viscosity is calculated by back-calculating the target dynamic viscosity based on the target minimum oil film thickness, suction speed, unit linear load, and gear geometry and material parameters. The suction speed is calculated based on the pitch speed of the gear. It should be understood that dynamic viscosity is a core physical property parameter determining whether a lubricant can form the target oil film thickness. This application uses the target minimum oil film thickness as a benchmark, combined with the suction speed calculated based on the pitch speed, the measured unit linear load, and the preset gear geometry and material parameters, to back-calculate the target dynamic viscosity using the elastohydrodynamic lubrication theory formula. This yields the lubricant dynamic viscosity index required to form the target oil film under the current operating conditions. This organically combines oil film requirements, operating conditions, and gear characteristics, ensuring that the back-calculated dynamic viscosity accurately matches the actual lubrication needs, providing a reliable input for subsequent kinematic viscosity conversion, and avoiding insufficient or excessive oil film formation capacity due to deviations in dynamic viscosity estimation.

[0043] In the specific implementation process, the suction speed is first calculated based on the gear pitch linear velocity. For external meshing gears, the average pitch linear velocity of the driving and driven gears is taken as the suction speed. Then, unit linear load data (converted from the motor torque sensor) is retrieved from the real-time operating condition database, and the equivalent radius of curvature, equivalent elastic modulus, and lubricant pressure-viscosity coefficient of the gears are retrieved from the equipment parameter database. Finally, the controller takes the target minimum oil film thickness, suction speed, unit linear load, and the above parameters as known quantities, substitutes them into the standard Dowson-Higginson EHL oil film thickness calculation formula, and treats this formula as an equation with dynamic viscosity as the only unknown. It is then solved through algebraic transformation or numerical solution methods (such as Newton's iteration method) to obtain the target dynamic viscosity.

[0044] Specifically, step S33 involves converting the target dynamic viscosity to target kinematic viscosity based on the gear body temperature, reference density, reference temperature, and volumetric thermal expansion coefficient to obtain the target kinematic viscosity. It should be understood that since the base oil mixing ratio calculation requires kinematic viscosity as a direct input parameter, and the lubricant density changes with gear body temperature (density decreases as temperature increases), ignoring the effect of temperature on density and directly using the density at the reference temperature for conversion would lead to errors in the kinematic viscosity calculation. Therefore, this application further combines gear body temperature, lubricant reference density, reference temperature, and volumetric thermal expansion coefficient to first correct the lubricant density, and then converts the target dynamic viscosity to kinematic viscosity to obtain the accurate target kinematic viscosity at the current temperature. In a specific example of this application, step S33 includes: converting the target dynamic viscosity to target kinematic viscosity using the following formula:

[0045]

[0046] Where, η 0_targetFor the target dynamic viscosity, ρ(T) gear To correct for lubricant density, ρ ref For reference density, β T T is the coefficient of volumetric thermal expansion. ref For reference temperature, T gear The gear body temperature, ν target The target kinematic viscosity is set. This eliminates the influence of temperature on density, ensuring that the converted kinematic viscosity matches the lubricant properties under actual operating conditions. This provides a reliable basis for the accurate calculation of the subsequent base oil mixing ratio, avoiding issues such as insufficient oil film thickness or over-lubrication caused by kinematic viscosity deviations leading to unsatisfactory mixed lubricant viscosity.

[0047] In the aforementioned intelligent lubrication method for open gear equipment, step S4 involves calculating the base oil mixing ratio based on the target kinematic viscosity and gear body temperature, using the kinematic viscosity of the low-viscosity oil and the kinematic viscosity of the high-viscosity oil. It should be understood that since the target kinematic viscosity is the core indicator for maintaining an ideal oil film under current operating conditions, and the standard viscosities of the low-viscosity and high-viscosity oils (usually measured at 40°C) change significantly with gear body temperature, directly using the standard viscosity to calculate the mixing ratio would cause the actual viscosity of the mixed lubricant to deviate from the target value, failing to meet lubrication requirements. Therefore, this application uses the target kinematic viscosity as a benchmark, corrects the viscosity of the two base oils based on the gear body temperature, and then calculates the high-viscosity oil mixing ratio using a mixing rule. This transforms the abstract viscosity requirement into an executable base oil mixing instruction. This ensures that the actual viscosity of the mixed lubricant at the current gear temperature accurately matches the target value, avoiding insufficient oil film thickness or over-lubrication due to viscosity deviation caused by temperature. This provides a precise proportional basis for subsequent oil pump control, ensuring stable lubrication of the open gear under dynamic operating conditions. Figure 4 This is a flowchart of sub-step S4 of the intelligent lubrication method for open gear equipment according to an embodiment of this application. Figure 4 As shown, step S4 includes the following steps: S41, based on the gear body temperature, predicting the effective viscosity of the low-viscosity oil and the high-viscosity oil under the current working conditions to obtain the effective viscosity of the low-viscosity oil and the high-viscosity oil; S42, based on the target kinematic viscosity, calculating the optimal mixing ratio of the effective viscosity of the low-viscosity oil and the high-viscosity oil to obtain the mixing ratio of the high-viscosity oil.

[0048] Specifically, in step S41, based on the gear body temperature, the effective viscosity under the current operating conditions is predicted for both the low-viscosity oil and the high-viscosity oil to obtain their effective viscosities. It should be understood that since the gear body temperature fluctuates widely with load and ambient temperature during open gear operation, temperature changes can cause exponential changes in lubricant viscosity. To avoid deviations from the actual lubricant properties under operating conditions, this application uses the gear body temperature as a basis and corrects the standard viscosity of the two base oils using a viscosity-temperature model to predict their effective viscosity under the current operating conditions, thereby obtaining base oil viscosity data that matches the actual operating temperature. This eliminates the influence of temperature on base oil viscosity, ensuring that the base oil viscosity used in subsequent mixing ratio calculations is the true value under the current operating conditions, avoiding viscosity deviations in the mixed lubricant due to inaccurate viscosity data, and guaranteeing the accuracy of the base oil mixing process from the source. Figure 5 This is a flowchart of sub-step S41 of the intelligent lubrication method for an open gear device according to an embodiment of this application. Figure 5 As shown, step S41 includes the following steps: S411, extracting coefficients A and B from the Walther-ASTM viscosity-temperature equation at the previous moment to obtain the posterior state estimation vector at the previous moment composed of coefficients A and B; S412, performing model adaptive correction based on online sensing of lubricant aging state on the posterior state estimation vector at the previous moment to obtain the posterior state estimation vector at the current moment; S413, extracting coefficients A and B from the posterior state estimation vector at the current moment from the Walther-ASTM viscosity-temperature equation at the current moment; S414, calculating the effective viscosity of the low viscosity oil based on the coefficients A and B at the current moment.

[0049] More specifically, step S411 involves extracting coefficients A and B from the Walther-ASTM viscosity-temperature equation of the previous moment to obtain the posterior state estimation vector of the previous moment, composed of coefficients A and B. It should be understood that since coefficients A and B of the Walther-ASTM viscosity-temperature equation are core parameters characterizing the viscosity-temperature properties of the lubricant, and the posterior state estimation vector of the previous moment is the optimal set of parameters that, after Bayesian correction, closely matches the actual state of the lubricant at that time, in order to continue tracking the aging state of the lubricant and avoid starting the correction process from scratch, this application further extracts coefficients A and B from the stored parameters of the Walther-ASTM viscosity-temperature equation of the previous moment and integrates them into the posterior state estimation vector of the previous moment. This provides a continuous and accurate historical parameter starting point for the adaptive correction of the model at the current moment. This ensures the continuity of the model correction process, avoids the failure of aging state tracking due to parameter disconnection, and ensures that the current correction is based on historically optimal parameters, improving the accuracy of the correction results and providing a reliable parameter source for subsequent effective viscosity prediction.

[0050] In the specific implementation process, the timestamp of the previous moment is first determined based on the system clock, and the corresponding A coefficients and B coefficients are retrieved from the parameter storage module using this timestamp. Then, the controller constructs the posterior state estimation vector of the previous moment according to a preset data structure, using the A coefficients as the first dimension and the B coefficients as the second dimension. After construction, the controller performs an integrity check on the vector, confirming that both the A and B coefficients are valid values ​​(not empty, not outliers). If the check fails, the most recent valid posterior state estimation vector from historical backup is automatically retrieved, and the vector that passes the check is transmitted to the model adaptive correction stage.

[0051] It is understandable that existing viscosity prediction mechanisms are based on a static viscosity-temperature model established using the standard Walther-ASTM equations. Once this model is calculated and deployed according to the technical parameters of a new lubricant, its core coefficients characterizing the viscosity-temperature properties of the oil are fixed. The fundamental flaw of this method is that it treats the lubricant as an ideal fluid with constant physical properties, completely ignoring the inevitable performance degradation that lubricants undergo in harsh industrial applications due to factors such as thermal oxidation, mechanical shearing, and contaminant intrusion. As service time increases, the actual rheological properties of the lubricant will gradually deviate from its initial state, leading to an ever-widening error between the effective viscosity predicted by the static model and the actual viscosity of the oil. Subsequent calculations of lubricant mixing ratios will lose accuracy, not only failing to achieve precise lubrication but also potentially causing accelerated equipment wear or even failure under critical operating conditions due to the provision of incorrect oil film protection.

[0052] In a preferred embodiment of this application, to address the limitations of the aforementioned static model, this application proposes a technical means capable of online sensing of lubricant aging status and adaptive model correction. More specifically, in step S412, the posterior state estimation vector of the previous moment is adaptively corrected based on online sensing of lubricant aging status to obtain the posterior state estimation vector of the current moment. That is, the key parameters of the viscosity-temperature model are considered as latent states evolving over time, and easily monitored equipment operating data (such as vibration and temperature) are used as indirect observation signals. Through a recursive Bayesian inference framework, continuous optimization of the model parameters is achieved.

[0053] First, the viscosity-temperature model parameters are predicted using the posterior state estimate vector and posterior error covariance matrix from the previous time step to obtain the prior state estimate vector and prior error covariance matrix for the current time step. It should be understood that lubricant aging is a slow and continuous degradation process, not an instantaneous abrupt change; therefore, the model parameter state at the current time step is highly correlated with that at the previous time step. Based on this physical understanding, a state transition model is established to predict the natural evolution of the parameters. During execution, the system uses the corrected optimal parameter estimates from the previous time step, i.e., the posterior state vector... Where T is the transpose operation, A k-1 B k-1 The coefficients A and B in the Walther-ASTM equations from the previous time step are projected onto the state transition matrix F to obtain the prior state estimate for the current time step. Meanwhile, its uncertainty is determined by the posterior error covariance matrix P. k-1 By passing in and superimposing a process noise covariance Q, we obtain the prior error covariance matrix P. k|k-1 In this way, a preliminary prediction value based on historical evolution patterns is provided for the parameter estimation at the current moment. The system obtains an initial judgment on the current parameter state, which includes a quantitative consideration of the randomness of the aging process, laying the foundation for subsequent fusion of new observation information. The above process can be expressed by the following formula:

[0054]

[0055] P k|k-1 =FP k-1 F T +Q

[0056] in, This is the posterior state estimate vector from the previous time step. Let F be the prior state estimate vector at the current moment, F be the state transition matrix, Q be the process noise covariance matrix, and P be the vector of the prior state estimate vector. k|k-1 Let be the prior error covariance matrix at the current time.

[0057] Next, based on the vibration signal characteristics and gear body temperature at the current moment, the posterior state estimation vector from the previous moment is calculated using aging indices based on indirect observation to obtain the innovation vector. Specifically, considering the technically challenging nature of directly measuring lubricant viscosity online, it is necessary to use indirect physical quantities closely related to the lubrication state to infer changes in lubricant performance. Lubricant performance degradation directly leads to a decrease in lubricating oil film quality, which in turn causes deterioration of equipment vibration characteristics or abnormal operating temperature. During execution, the system collects current vibration and temperature sensor data to form the actual observation vector z. k Simultaneously, using a nonlinear observation function h(·), the prior state vector predicted in the previous step is... This is mapped to the theoretically expected observations. Subtracting the theoretical observations from the actual observations yields the innovation vector y. k In this way, the difference between the quantified system's prediction and objective reality is generated, thus producing a key error signal, namely the innovation vector. The magnitude and direction of this vector profoundly reveal the degree of deviation in the prior prediction, constituting the core driving force for model correction. The above process can be expressed by the formula:

[0058]

[0059] Among them, z k Let y be the actual observation vector at the current moment, h(·) be the nonlinear observation function, and y be the actual observation vector at the current moment. k For innovation vectors.

[0060] Finally, the posterior state estimate vector from the previous time step is updated using Bayesian algorithm based on the innovation vector to obtain the posterior state estimate vector for the current time step. In other words, a rigorous mathematical framework is needed to integrate previous prediction information and newly acquired observation error information to generate a more accurate parameter estimate that more closely approximates the true state. This framework must be able to intelligently balance the reliability of predictions with the reliability of observations. During execution, the system first calculates the Kalman gain K. k As a dynamic weight, its magnitude is determined by both the uncertainty of prediction and measurement. Subsequently, the prior state estimate... By adding the innovation vector weighted by the Kalman gain, we obtain the optimal posterior state estimate that incorporates the new information. At the same time, the error covariance matrix is ​​also updated to P. kIts value is usually smaller than the prior value, reflecting the reduction of uncertainty brought about by information fusion. In this way, the online correction of the viscosity-temperature model parameters is completed, enabling it to reflect the real health condition of the lubricant in real time. The system then outputs a set of dynamically adaptively corrected model parameters. The effective viscosity calculated using these new parameters will be much closer to physical reality than the results of the static model, thus providing high-fidelity input for subsequent lubricant mixing decisions. The above process can be expressed by the formula:

[0061]

[0062] P k =(IK k H k )P k|k-1

[0063] Among them, H k Let R be the observation matrix at the current time, and K be the observation noise covariance matrix. k For Kalman gain, y k Let this be the residual vector at the current time step. Let I be the posterior state estimate vector at the current time step, and P be the identity matrix. k Let be the posterior error covariance matrix at the current time.

[0064] In summary, this preferred embodiment revolutionizes the original open-loop, static viscosity prediction model into a closed-loop, adaptive dynamic estimation system. This system continuously learns from the equipment's operating status and infers changes in the lubricant's intrinsic performance, ensuring that the calculated effective viscosity closely tracks the actual degradation trajectory of the lubricant due to aging and contamination. This fundamentally improves the decision-making accuracy and reliability of the intelligent lubrication system. By providing a viscosity input that reflects the lubricant's true health condition, it ensures that subsequent mixing ratio calculations are based on a solid data foundation, thereby achieving truly condition-adaptive and precise lubrication. This not only maximizes equipment protection throughout its entire lifecycle and effectively avoids potential failures caused by overestimation of lubricant performance, but also provides a scientific basis for implementing condition-based lubricant replacement strategies, ultimately improving the overall system's intelligence, operational safety, and economic efficiency.

[0065] More specifically, step S413 involves extracting the A and B coefficients of the Walther-ASTM viscosity-temperature equation for the current moment from the posterior state estimation vector at the current moment. It should be understood that since the posterior state estimation vector at the current moment is structured data integrating the A and B coefficients, without extracting individual coefficients, it is impossible to directly substitute them into the Walther-ASTM viscosity-temperature equation for effective viscosity calculation. This results in a lack of core parameters in the subsequent viscosity prediction process, hindering its execution. Therefore, this application further performs dimensionality analysis on the posterior state estimation vector at the current moment, separating and extracting the A and B coefficients required for the Walther-ASTM viscosity-temperature equation at the current moment. This transforms the structured state vector into independent parameters that can be directly used for viscosity-temperature calculation. This ensures that the Walther-ASTM viscosity-temperature equation obtains core parameters that closely match the current lubricant state, avoiding calculation interruptions due to parameter mismatch, and ensuring that effective viscosity prediction is based on the current optimal viscosity-temperature parameters, thus improving the consistency between the prediction results and the actual viscosity of the lubricant.

[0066] In the specific implementation process, the controller clearly defines the dimensions of the posterior state estimation vector at the current moment: the first dimension is the A coefficient, and the second dimension is the B coefficient. After obtaining this vector, the controller uses the vector dimension indexing function to read the values ​​of the first and second dimensions respectively, obtaining the A coefficient and B coefficient at the current moment. Subsequently, the controller performs a reasonableness check on the extracted coefficients, comparing the A coefficient and B coefficient with the preset normal range (calibrated according to the lubricant type). If the coefficient exceeds the range, an alarm signal is triggered to indicate that the lubricant may have abnormal aging, and the backed-up historical correction coefficients are automatically activated. The verified A coefficient and B coefficient are transmitted to the effective viscosity calculation stage and simultaneously stored in the temporary parameter cache for subsequent calculations.

[0067] More specifically, step S414 calculates the effective viscosity of the low-viscosity oil based on the A coefficient and B coefficient at the current moment. It should be understood that the effective viscosity of the low-viscosity oil needs to be determined by considering both the current gear body temperature and the actual viscosity-temperature characteristics of the lubricant. The A coefficient and B coefficient at the current moment are viscosity-temperature parameters that correspond to the current aging state of the lubricant. If these coefficients are not used for calculation, relying solely on the viscosity value at the standard temperature will result in a significant deviation between the effective viscosity and the actual value due to temperature changes and aging effects. Therefore, this application further substitutes the A coefficient and B coefficient at the current moment into the Walther-ASTM viscosity-temperature equation, and calculates the effective viscosity of the low-viscosity oil based on the current gear body temperature, thereby obtaining the true viscosity value of the low-viscosity oil under the current operating conditions. In a specific example of this application, the effective viscosity of the low-viscosity oil is calculated using the following formula:

[0068]

[0069] Among them, T gear v represents the temperature of the gear body. eff This is the effective viscosity of the low-viscosity oil. This ensures that the effective viscosity data accurately reflects the actual physical properties under current temperature and aging conditions, providing precise viscosity input for subsequent base oil mixing ratio calculations. It avoids mixing ratio deviations caused by inaccurate viscosity data, ensuring that the viscosity of the mixed lubricant meets the lubrication requirements of the open gear under current operating conditions.

[0070] Specifically, in step S42, based on the target kinematic viscosity, the optimal mixing ratio of the effective viscosity of the low-viscosity oil and the effective viscosity of the high-viscosity oil is calculated to obtain the mixing ratio of the high-viscosity oil. It should be understood that since the effective viscosity of the low-viscosity oil is lower than the target kinematic viscosity and the effective viscosity of the high-viscosity oil is higher than the target kinematic viscosity, they need to be mixed in a specific ratio to achieve the target viscosity of the mixed oil. If a linear mixing rule or empirical ratio is used, the non-linear mixing characteristics of the lubricant viscosity will lead to deviations in the results, failing to meet the requirements for precise lubrication. Therefore, this application further uses the target kinematic viscosity as the core and adopts a logarithmic mixing rule that conforms to the mixing characteristics of the lubricant. Substituting the effective viscosities of the low-viscosity oil and the high-viscosity oil, the optimal mixing ratio of the high-viscosity oil is calculated to ensure that the viscosity of the mixed lubricant accurately matches the target value. In a specific example of this application, step S42 includes: calculating the optimal mixing ratio of the effective viscosity of the low-viscosity oil and the effective viscosity of the high-viscosity oil using the following formula:

[0071]

[0072] Among them, v target For the target kinematic viscosity, v LV_eff and v HV_eff Let L be the effective viscosity of low-viscosity oil and L be the effective viscosity of high-viscosity oil, ln(·) be a logarithmic function with base e, and Ratio be the effective viscosity of high-viscosity oil. HV This is a high-viscosity oil mixing ratio. This fully considers the non-linear mixing characteristics of lubricant viscosity, avoiding ratio deviations caused by linear calculations. It ensures that the actual viscosity of the mixed oil under current operating conditions strictly meets the target requirements, providing stable oil film protection for open gears while avoiding base oil waste and balancing lubrication performance and economy.

[0073] In the aforementioned intelligent lubrication method for open gear equipment, step S5 generates control commands for both low-viscosity and high-viscosity oil pumps based on the high-viscosity oil mixing ratio and total lubrication metering. It should be understood that the high-viscosity oil mixing ratio only determines the volume percentage of the two base oils, while the total lubrication metering specifies the total amount of lubricant required for a single lubrication cycle. These two factors must be combined to calculate the specific delivery dosage of the low-viscosity and high-viscosity oils. Since the oil pump cannot directly identify the ratio and total quantity data, they must be converted into executable control commands to achieve precise delivery. Therefore, this application further uses the high-viscosity oil mixing ratio and total lubrication metering as inputs to calculate the individual dosages of the two base oils and convert them into corresponding control commands for the oil pumps. This translates the ratio and total quantity requirements of the base oil mixing into specific operating parameters for the oil pumps. This ensures that the low-viscosity oil pump and the high-viscosity oil pump deliver the base oil synchronously with precise dosage, avoiding uncontrolled mixing ratios or insufficient / excessive total lubrication due to dosage deviations. It also ensures that the viscosity and total amount of the mixed lubricant meet the lubrication requirements of the current working conditions, preventing gear wear, galling, and other failures caused by improper lubrication. At the same time, it avoids base oil waste and balances lubrication effect and operating economy.

[0074] In the specific implementation process, the edge computing controller first retrieves the high-viscosity oil mixing ratio from the base oil mixing ratio calculation module and the preset total lubrication metering from the lubrication strategy parameter library (this metering is pre-calibrated based on gear speed, unit linear load, and meshing frequency). Then, the controller calculates the single delivery dose of the high-viscosity oil based on the high-viscosity oil mixing ratio and the total lubrication metering, and then subtracts the high-viscosity oil dose from the total lubrication metering to obtain the single delivery dose of the low-viscosity oil. The system uses a precision metering pump driven by a stepper motor to perform delivery. The controller uses a pre-calibrated delivery dose-stepper motor step number mapping relationship to convert the individual doses of the two base oils into corresponding stepper motor rotation steps, and generates a synchronous trigger signal to ensure that the two metering pumps start simultaneously. After generating the control command, the controller performs boundary verification on the step number. If the step number exceeds the maximum stroke range of the metering pump, the total lubrication metering is automatically reduced and the individual doses are recalculated. The control command that passes the verification is transmitted to the oil pump drive unit via the industrial Ethernet protocol. The oil pump drive unit drives the stepper motor to operate according to the command, completing the precise and synchronous delivery of the two base oils.

[0075] In summary, the intelligent lubrication method for open gear equipment based on the embodiments of this application is explained. It collects key operating parameters such as the operating speed and body temperature of the open gear in real time and constructs a reverse-engineering model based on the target oil film thickness to calculate online the target kinematic viscosity corresponding to the optimal lubrication effect under the current dynamic operating conditions. Then, based on the online calculated target viscosity, a dual-base oil dynamic mixing ratio calculator is driven to generate precise mixing instructions for high and low viscosity base lubricating oils. Finally, the operation of the corresponding oil pump is directly controlled through these mixing instructions to achieve online adjustment and precise supply of the effective viscosity of the lubricant. This ensures that the optimal protective oil film is maintained under a wide range of changing operating conditions, thereby achieving precise and intelligent lubrication of open gear equipment.

[0076] Furthermore, an intelligent lubrication system for open gear equipment is also provided.

[0077] Figure 6 This is a block diagram of an intelligent lubrication system for an open gear device according to an embodiment of this application. Figure 6 As shown, the intelligent lubrication system 100 for open gear equipment according to an embodiment of this application includes: a gear data acquisition module 110 for acquiring gear body temperature and gear speed; a pitch line speed calculation module 120 for calculating the pitch line speed of the gear based on the gear speed and the pitch circle diameter of the gear; an oil film thickness back calculation module 130 for back-calculating the oil film thickness based on the pitch line speed and gear body temperature to obtain the target kinematic viscosity; a base oil mixing ratio calculation module 140 for calculating the base oil mixing ratio based on the target kinematic viscosity and gear body temperature to obtain the high viscosity oil mixing ratio; and a lubrication pump control command generation module 150 for generating low viscosity oil pump control commands and high viscosity oil pump control commands based on the high viscosity oil mixing ratio and total lubrication metering.

[0078] Here, those skilled in the art will understand that the specific operation of each module in the intelligent lubrication system for open gear equipment described above has been referenced. Figures 1 to 5 The intelligent lubrication method for open gear equipment is described in detail therein, and therefore its repeated description will be omitted.

[0079] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details of the above embodiments are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the specific details described above.

[0080] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. In the several embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for example, the unit division is only a logical functional division, and other division methods may exist in actual implementation. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0081] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the present invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0082] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units stated in a system claim may also be implemented by a single unit through software or hardware.

[0083] Finally, it should be noted that the above description has been given for illustrative and descriptive purposes. Furthermore, the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although modifications or equivalent substitutions may be made to the technical solutions with reference to preferred embodiments, they will not depart from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent lubrication method for open gear equipment, characterized in that, include: Obtain the gear body temperature and gear speed; Calculate the pitch velocity of the gear based on the gear speed and the gear pitch circle diameter; The target kinematic viscosity is obtained by back-calculating the oil film thickness based on the pitch linear velocity and gear body temperature of the gear. Based on the target kinematic viscosity and gear body temperature, the base oil mixing ratio is calculated by analyzing the kinematic viscosity of low-viscosity oil and the kinematic viscosity of high-viscosity oil to obtain the high-viscosity oil mixing ratio. Based on the high viscosity oil mixing ratio and total lubrication metering, control commands for low viscosity oil pumps and high viscosity oil pumps are generated.

2. The intelligent lubrication method for open gear equipment according to claim 1, characterized in that, The pitch velocity of a gear is calculated based on its rotational speed and pitch circle diameter, including using the following formula: Where, d p The pitch circle diameter of the gear, RPM gear v is the gear speed. p Let be the pitch velocity of the gear.

3. The intelligent lubrication method for open gear equipment according to claim 1, characterized in that, The target kinematic viscosity is obtained by back-calculating the oil film thickness based on the gear's pitch velocity and gear body temperature, including: The target minimum oil film thickness is determined based on the target oil film thickness ratio and the overall surface roughness. The target dynamic viscosity is obtained by back-calculating based on the target minimum oil film thickness, suction speed, unit linear load, and gear geometry and material parameters. The suction speed is calculated based on the pitch linear velocity of the gear. Based on the gear body temperature, reference density, reference temperature, and volumetric thermal expansion coefficient, the target dynamic viscosity is converted into the target kinematic viscosity to obtain the target kinematic viscosity.

4. The intelligent lubrication method for open gear equipment according to claim 3, characterized in that, Based on the gear body temperature, reference density, reference temperature, and volumetric thermal expansion coefficient, the target dynamic viscosity is converted to target kinematic viscosity to obtain the target kinematic viscosity, including: converting the target dynamic viscosity to target kinematic viscosity using the following formula: Where, η 0_target For the target dynamic viscosity, ρ(T) gear To correct for lubricant density, ρ ref For reference density, β T T is the coefficient of volumetric thermal expansion. ref For reference temperature, T gear v is the temperature of the gear body. target The target kinematic viscosity.

5. The intelligent lubrication method for open gear equipment according to claim 1, characterized in that, Based on the target kinematic viscosity and gear body temperature, the base oil mixing ratio is calculated by analyzing the kinematic viscosity of low-viscosity oil and high-viscosity oil to obtain the high-viscosity oil mixing ratio, including: Based on the gear body temperature, the effective viscosity under the current working conditions is predicted for the kinematic viscosity of low viscosity oil and the kinematic viscosity of high viscosity oil to obtain the effective viscosity of low viscosity oil and the effective viscosity of high viscosity oil. Based on the target kinematic viscosity, the optimal mixing ratio of the effective viscosity of low-viscosity oil and the effective viscosity of high-viscosity oil is calculated to obtain the mixing ratio of high-viscosity oil.

6. The intelligent lubrication method for open gear equipment according to claim 5, characterized in that, Based on gear body temperature, the effective viscosity under current operating conditions is predicted for both low-viscosity and high-viscosity oils, including: Extract the A and B coefficients from the Walther-ASTM viscosity-temperature equation of the previous moment to obtain the posterior state estimation vector of the previous moment composed of the A and B coefficients. The posterior state estimation vector of the previous moment is adaptively corrected based on the online sensing of the lubricant aging state to obtain the posterior state estimation vector of the current moment. Extract the A and B coefficients of the Walther-ASTM viscosity-temperature equation for the current moment from the posterior state estimation vector at the current moment; Based on the current A coefficient and B coefficient, the effective viscosity of the low-viscosity oil is calculated using the following formula: Among them, T gear Let A be the temperature of the gear body, B be the coefficient of A, and v be the coefficient of B. eff This is the effective viscosity of low-viscosity oil.

7. The intelligent lubrication method for open gear equipment according to claim 6, characterized in that, The posterior state estimation vector at the previous time step is adaptively modified based on the online sensing of lubricant aging state to obtain the posterior state estimation vector at the current time step, including: The viscosity-temperature model parameters are predicted by using the posterior state estimation vector and the posterior error covariance matrix of the previous time step to obtain the prior state estimation vector and the prior error covariance matrix of the current time step. Based on the vibration signal characteristics and gear body temperature at the current moment, the aging index based on indirect observation is used to calculate the innovation vector from the posterior state estimation vector of the previous moment. The posterior state estimate vector at the previous time step is updated using Bayesian model parameters based on the innovation vector to obtain the posterior state estimate vector at the current time step.

8. The intelligent lubrication method for open gear equipment according to claim 5, characterized in that, Based on the target kinematic viscosity, the optimal mixing ratio of the effective viscosity of the low-viscosity oil and the effective viscosity of the high-viscosity oil is calculated to obtain the mixing ratio of the high-viscosity oil. This includes: calculating the optimal mixing ratio of the effective viscosity of the low-viscosity oil and the effective viscosity of the high-viscosity oil using the following formula: Among them, v target For the target kinematic viscosity, v LV_eff and v HV_eff Let L be the effective viscosity of low-viscosity oil and L be the effective viscosity of high-viscosity oil, ln(·) be a logarithmic function with base e, and Ratio be the effective viscosity of high-viscosity oil. HV This is a high viscosity oil mixing ratio.

9. An intelligent lubrication system for open gear equipment, characterized in that, include: The gear data acquisition module is used to acquire gear body temperature and gear speed; The pitch line speed calculation module is used to calculate the pitch line speed of a gear based on its rotational speed and pitch circle diameter. The oil film thickness back calculation module is used to back calculate the oil film thickness based on the pitch linear velocity and gear body temperature of the gear to obtain the target kinematic viscosity; The base oil mixing ratio calculation module is used to calculate the base oil mixing ratio based on the target kinematic viscosity and gear body temperature, for low viscosity oil kinematic viscosity and high viscosity oil kinematic viscosity, in order to obtain the high viscosity oil mixing ratio. The lubrication pump control command generation module is used to generate low-viscosity oil pump control commands and high-viscosity oil pump control commands based on the high-viscosity oil mixing ratio and total lubrication metering.

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