A crane lubricating oil water content on-line detection method and system

Abstract

This invention belongs to the field of online detection, specifically a method and system for online detection of moisture content in crane lubricating oil. The method includes simultaneously acquiring oil temperature, humidity, the imaginary part of the complex dielectric constant, and the main lifting load signal; performing time-series differentiation on the oil temperature signal to obtain the temperature change rate, and constructing a thermal inertia weighting function accordingly to weight the temperature and obtain the equilibrium temperature; constructing a polar aging factor through the deviation of the imaginary part of the complex dielectric constant; converting the real-time load into microscopic equivalent contact stress based on the gear pair parameters and load-stress mapping relationship, and constructing a shear stress solubilization correction coefficient; establishing a generalized saturated solubility model and correcting the characteristic constants with the polar aging factor; weighting the temperature sensitivity index with the shear stress coefficient; substituting the equilibrium temperature into the model to calculate the oil saturated water concentration limit under the current operating conditions; converting the relative humidity into a water activity value and multiplying it by the saturated water concentration limit to output the absolute moisture content of the lubricating oil in real time.

Description

Technical Field

[0001] This application belongs to the field of online detection, and in particular relates to an online detection method and system for moisture content in crane lubricating oil. Background Technology

[0002] The lubrication system, as the "blood" of crane mechanical transmission, is directly related to the operational safety and lifespan of the equipment. Moisture contamination is one of the contributing factors to lubrication failure. Moisture not only weakens the oil film, causing corrosion and wear on gears and bearings, but also accelerates oil oxidation and deterioration, leading to additive precipitation and emulsification. Traditional lubricant moisture testing relies on periodic offline laboratory sampling and analysis, which has drawbacks such as long sampling cycles, significant time lag, and inability to detect sudden water ingress failures. Online moisture sensors based on the principle of polymer thin-film capacitance measure the relative humidity and temperature of the oil and, combined with a pre-set oil saturation solubility model, calculate the absolute water content in the oil. However, frequent start-stop and load-changing operations during crane operation cause drastic fluctuations in oil temperature. Due to limitations in the packaging structure, the temperature-sensing element of the online sensor exhibits thermal inertia, resulting in a lag between the measured temperature signal and the actual oil temperature. Furthermore, the saturation solubility model is calibrated based on new oil with constant parameters; the detection model is based on static experimental calibration. Under heavy-load crane conditions, the high contact stress in the gear meshing area generates mechanical shearing, which alters the microscopic arrangement and free volume of oil molecules, thus affecting the dissolution behavior of water molecules. Existing online detection methods struggle to provide absolute moisture readings under conditions of drastic temperature changes, oil aging, and heavy-load shearing, necessitating an optimized detection method capable of compensating for multi-source errors. Summary of the Invention

[0003] To address the problem that existing online detection methods struggle to provide absolute moisture readings under conditions of drastic temperature changes, oil aging, and heavy-duty shearing.

[0004] In the first aspect, the present invention proposes an online method for detecting moisture content in crane lubricating oil, comprising: The system synchronously acquires real-time signals of oil temperature, relative humidity, imaginary part of complex dielectric constant, and load of the main hoisting mechanism from the crane lubrication system. It performs time-series differential calculations on the acquired real-time oil temperature signals to obtain the temperature change rate, constructs a thermal inertia hysteresis weighting function based on the temperature change rate, and uses this weighting function to weight the real-time oil temperature signals to calculate the oil equilibrium temperature to compensate for the sensor's thermal hysteresis effect. Based on the stored new oil data, a polarity aging factor is constructed using the deviation rate of the imaginary part signal of the complex dielectric constant. Based on the preset crane gear pair parameters and load-stress mapping relationship, the real-time load signal is converted into the micro equivalent contact stress of the lubrication contact area, and the shear stress dissolution correction coefficient is constructed using the micro equivalent contact stress. A generalized saturated solubility model is constructed. In the model, the polar aging factor is used to fit and correct the solubility characteristic baseline constant. The shear stress solubilization correction coefficient is used to weight and correct the solubility temperature sensitivity index. The calculated equilibrium temperature is substituted into the generalized saturated solubility model to calculate the oil saturated water concentration limit under the current operating conditions and oil quality. The real-time oil relative humidity signal is converted into a water activity value, and the saturated water concentration limit is multiplied by the water activity value to output the online absolute water content of the crane lubricating oil.

[0005] In another aspect, the present invention also proposes an online detection system for moisture content in crane lubricating oil, comprising the following modules: The calculation module is used to synchronously acquire real-time signals of oil temperature, oil relative humidity, imaginary part of oil complex dielectric constant, and real-time load signal of the main hoisting mechanism of the crane lubrication system; perform time-series differential operation on the acquired real-time oil temperature signal to obtain the temperature change rate; construct a thermal inertia hysteresis weighting function based on the temperature change rate; and use the weighting function to weight the real-time oil temperature signal to calculate the oil equilibrium temperature to compensate for the thermal hysteresis effect of the sensor. The module is used to construct a polar aging factor based on the deviation rate of the imaginary part signal of the complex dielectric constant, using the stored new oil data as a reference; based on the preset crane gear pair parameters and load-stress mapping relationship, the real-time load signal is converted into the micro equivalent contact stress of the lubrication contact area, and the shear stress dissolution correction coefficient is constructed using the micro equivalent contact stress. The correction module is used to construct a generalized saturated solubility model. In the model, the polar aging factor is used to fit and correct the solubility characteristic baseline constant; and the shear stress solubilization correction coefficient is used to weight and correct the solubility temperature sensitivity index. The output module is used to substitute the calculated equilibrium temperature into the generalized saturated solubility model, calculate the oil saturated water concentration limit under the current working conditions and oil quality, convert the real-time oil relative humidity signal into a water activity value, and multiply the saturated water concentration limit by the water activity value to output the online absolute moisture content of the crane lubricating oil.

[0006] This invention addresses the thermal hysteresis effect of temperature sensors under drastic temperature changes. By analyzing the rate of temperature change and constructing a thermal inertia hysteresis weighting function, the original temperature signal is weighted and calculated to obtain the equilibrium temperature inside the oil, eliminating measurement errors caused by sensor response delay. A polar aging factor is constructed by monitoring the change in the imaginary part of the oil's complex dielectric constant, representing the increased hydrophilicity of the oil due to increased oxidation products. A shear stress solubilization correction coefficient, combined with real-time load and gear contact parameters, represents the specific changes in the microscopic water-soluble structure of the oil under high-pressure shearing conditions. The established generalized saturated solubility model achieves dual calibration of the solubility characteristic reference constant and the temperature sensitivity index, improving the accuracy of absolute water content calculation under oil aging and heavy-load shearing conditions. Attached Figure Description

[0007] Figure 1 A flowchart of the first embodiment; Figure 2 This is a schematic diagram showing the relationship between the acquired signal and the sampling time. Detailed Implementation

[0008] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0009] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0010] In the first embodiment, the present invention proposes an online method for detecting moisture content in crane lubricating oil, such as... Figure 1 As shown, it includes: S1, synchronously acquire real-time signals of oil temperature, oil relative humidity, imaginary part of oil complex dielectric constant, and real-time load signal of main hoisting mechanism of crane lubrication system; perform time-series differential operation on the acquired real-time oil temperature signal to obtain temperature change rate, construct thermal inertia hysteresis weight function based on temperature change rate, use the weight function to weight the real-time oil temperature signal, and calculate the oil equilibrium temperature to compensate for sensor thermal hysteresis effect; Specifically, an integrated oil condition monitoring sensor is installed in the oil return line of the crane gearbox or at the outlet of the circulating pump. This sensor integrates a platinum resistance temperature probe, a capacitive polymer film humidity probe, and an interdigitated electrode dielectric constant probe. Simultaneously, it connects to the crane's main PLC control system via a fieldbus communication interface to read the real-time load value at the drum end of the main hoisting mechanism. An industrial control computer is used as the host computer, synchronously reading the sensor data and PLC load data via RS485 bus using the Modbus RTU protocol. The sampling frequency is set to 1Hz to 10Hz. The four acquired signals are timestamped to ensure all signals correspond in the same time dimension. High-frequency noise in the signals is preprocessed using a five-point moving average filtering method to obtain synchronized real-time oil temperature signals, real-time oil relative humidity signals, imaginary part signals of the oil complex dielectric constant, and real-time load signals of the main hoisting mechanism. Figure 2 As shown.

[0011] The discrete derivative of the real-time oil temperature signal T(t) is calculated using the first-order backward difference method. Specifically, the temperature rate is obtained by subtracting the previous temperature value from the current temperature value and dividing by the sampling time interval. A temperature rate threshold is set; when the absolute value of the temperature rate is less than the threshold, it is considered steady state; when the absolute value of the temperature rate is greater than the threshold, it is considered transient state. A thermal inertia hysteresis weighting function based on the sigmoid logistic function is constructed, with the absolute value of the temperature rate as the independent variable, and the value range being between 0 and 1. Where k is the preset slope gain, The steady-state threshold for the temperature change rate is defined as follows: when the temperature change rate is positive and its absolute value increases, the output value of the weighting function tends to 1, indicating that compensation for the lag trend needs to be strengthened. The sliding window length is adjusted according to the weighting function value; the larger the temperature change rate, the shorter the window. The specific formula is as follows: ,in The preset minimum window length, The preset maximum window length, The current temperature is measured by the sensor. An exponential decay weight is applied to the historical temperature data within the window, with the weight increasing as the time approaches the current moment. The linear regression slope of the weighted data within the window is calculated. The current sensor temperature value is then added to the product of the slope and the sensor's thermal time constant, thereby calculating the oil equilibrium temperature that eliminates the influence of thermal conduction hysteresis in the sensor's packaging shell.

[0012] In an optional embodiment, constructing the thermal inertia hysteresis weighting function based on the temperature change rate includes: When the sign of the temperature change rate is positive, a preset heating time constant is selected to construct a heating weight sequence based on the exponential decay law. When the sign of the temperature change rate is negative, a preset cooling time constant is selected to construct a cooling weight sequence based on the exponential decay law, wherein the cooling time constant is greater than the heating time constant. The selected weight sequence is normalized to obtain the thermal inertia hysteresis weight function used for weighted calculation.

[0013] Set the length N of the sliding window, and calculate the current time t and the historical times within the window. The time difference. First-order difference calculation is performed on the real-time temperature signal to determine the temperature change rate. If the temperature change rate is greater than 0, a smaller heating time constant is selected. This reflects the sensor's rapid response characteristics when heated; if the temperature change rate is less than 0, a larger cooling time constant should be selected. This simulates the thermal inertia of the sensor and the natural cooling process of the oil. Based on a selected time constant... Using the formula An unnormalized weight sequence is constructed, where temperature data closer to the current moment has a higher weight, and the weight decays exponentially with time intervals. The weight sequence is then normalized to ensure that the sum of all weights is 1. This normalized weight sequence is then used... A weighted linear regression is performed on the raw temperature data within the sliding window to obtain an estimate of the temperature change rate at the current moment. Then, the current sensor temperature value is added to the product of the estimated temperature change rate and the corresponding time constant to obtain the equilibrium temperature after eliminating thermal hysteresis error.

[0014] S2, using the stored new oil data as a reference, a polarity aging factor is constructed using the deviation rate of the imaginary part signal of the complex dielectric constant; based on the preset crane gear pair parameters and load-stress mapping relationship, the real-time load signal is converted into the micro equivalent contact stress of the lubrication contact area, and the shear stress dissolution correction coefficient is constructed using the micro equivalent contact stress. Specifically, the imaginary part of the complex dielectric constant of this type of lubricating oil in its new state is stored in advance in the host computer database. The imaginary part signal of the complex dielectric constant of the oil at the current moment is read, the difference between the current value and the reference value is calculated, and the difference is divided by the reference value to obtain the dielectric loss deviation rate. The deviation rate is normalized and defined as the polarity aging factor, which is used to represent the degree of polarity enhancement caused by the increase of oil oxidation products. At the same time, the module, pressure angle, tooth width and material elastic modulus parameters of the key gear pair of the crane gearbox are pre-entered. According to the Hertzian contact stress calculation formula, the normal pressure between the tooth surfaces is calculated using the real-time load signal, and then the microscopic equivalent contact stress in the tooth surface contact area is calculated. An empirical formula with the microscopic equivalent contact stress as the independent variable is established. The formula adopts an exponential form with the natural constant as the base, and maps the calculated microscopic equivalent contact stress to a dimensionless shear stress solubilization correction coefficient. The coefficient is used to represent the influence of the change in the gap between oil molecules under high pressure shear field on the water molecule accommodation capacity.

[0015] In an optional embodiment, the construction of a polarity aging factor based on the deviation rate of the imaginary part signal of the complex permittivity, using stored new oil data as a reference, includes: Calculate the difference between the currently acquired imaginary part signal value of the complex dielectric constant of the oil and the stored imaginary part value of the new complex dielectric constant of the oil, and divide the difference by the imaginary part value of the new complex dielectric constant of the oil to obtain the relative deviation rate; Multiply the relative deviation rate by a preset dielectric sensitivity coefficient, and add 1 to the product to obtain the dimensionless polar aging factor.

[0016] Pre-store the imaginary part of the complex permittivity of the same type of new oil under standard conditions. During online testing, the imaginary part of the complex permittivity of the oil sensor is read in real time. This parameter reflects the degree of accumulation of polar oxidation products and contaminants in the oil. The relative deviation rate between the two is calculated. This ratio represents the relative increase in oil polarity. It utilizes a preset dielectric sensitivity coefficient. This is used to adjust the weight of the effect of polarity changes on hydrophilic properties. (Using the formula...) Calculate the polar aging factor. A dimensionless value greater than or equal to 1 maps the microscopic changes in dielectric properties to a correction factor in the macroscopic solubility model, representing the phenomenon that the oil's ability to dissolve similar substances is enhanced due to oxidative aging.

[0017] In an optional embodiment, the step of converting the real-time load signal into the microscopic equivalent contact stress of the lubrication contact area based on preset crane gear pair parameters and load-stress mapping relationship includes: The equivalent radius of curvature and material elastic modulus parameters of the gear pair of the main hoisting mechanism are read, and the Hertzian contact coefficient is calculated using Hertzian contact theory. The square root of the real-time load signal is performed, and the result is multiplied by the Hertz contact coefficient to obtain the microscopic equivalent contact stress representing the stress state of the lubrication contact area.

[0018] Retrieve the geometric and material parameters of the gear pair in the reduction gearbox of the crane's main hoisting mechanism, including the equivalent radius of curvature. and the comprehensive elastic modulus of gear materials The contact line length L is also considered, which can be approximated by the tooth width. Based on Hertzian line contact theory, the Hertzian contact coefficient is pre-calculated. This coefficient will be stored as a constant in the system. During the real-time monitoring phase, the load signal F of the main hoisting mechanism is acquired and its square root is calculated. This reflects the relationship between contact stress and load. Contact coefficient with Hertz Multiplying these yields the maximum Hertzian contact stress in the tooth surface contact area. Microscopic equivalent contact stress represents the mechanical compression state experienced by the lubricating oil film.

[0019] In an optional embodiment, constructing the shear stress dissolution correction coefficient using the micro-equivalent contact stress includes: Divide the microscopic equivalent contact stress by the preset critical shear yield strength of the lubricating oil film to obtain a dimensionless stress ratio. Calculate the value of an exponential function with the natural constant e as the base and the stress ratio as the exponent, and use the value of the exponential function as the shear stress dissolution correction coefficient.

[0020] Setting the critical shear yield strength parameter of the lubricating oil film The micro equivalent contact stress calculated in the preceding steps is then... and Perform ratio calculations to obtain the dimensionless stress ratio. Considering that the change in molecular free volume due to mechanical stress usually follows exponential dynamics, the formula is used... Calculate the shear stress solubilization correction factor, where This is a stress-sensitive factor. (Coefficient) A value greater than 1 indicates that under heavy load conditions, the high-pressure shear field forces the oil molecule spacing to change, thereby increasing the space for water molecules to be contained.

[0021] S3, Construct a generalized saturated solubility model; In the model, the polar aging factor is used to fit and correct the solubility characteristic benchmark constant; The shear stress solubilization correction coefficient is used to weight and correct the solubility temperature sensitivity index. Specifically, the Arrhenius equation is used as the basic saturated solubility model. This model includes two core parameters: a solubility characteristic baseline constant and a solubility temperature sensitivity index. A polar aging correction term is constructed using a power function. The solubility characteristic baseline constant of the basic model is multiplied by the polar aging correction term, causing the baseline constant to increase with the increase of the polar aging factor, thus reflecting the enhanced adsorption capacity of polar substances for water molecules. A shear stress correction term is constructed using a linear weighted formula. The solubility temperature sensitivity index of the basic model is divided by the shear stress solubilization correction coefficient, causing the temperature sensitivity index to adjust with changes in shear stress, thus reflecting the influence of changes in the molecular chain arrangement of oil under high-pressure shear on the solubility variation with temperature. The corrected baseline constant and the corrected temperature sensitivity index are then recombined to obtain a generalized saturated solubility model suitable for the current aging degree and stress state. In an optional embodiment, the fitting correction of the solubility characteristic baseline constant using the polar aging factor in the model includes: Read the initial solubility characteristic reference constant in the new oil state; The polar aging factor is subjected to a power exponent calculation, and the power exponent is a preset hydrophilicity correlation index. The corrected solubility characteristic reference constant is obtained by multiplying the initial solubility characteristic reference constant by the polar aging factor calculated by power exponentiation.

[0022] Read the initial solubility characteristic reference constant of the new oil in the database. A preset hydrophilicity correlation index *n* is used to represent the enhancing effect of polar substance accumulation on solubility. The polar aging factor obtained in the preceding steps... Perform exponentiation, that is Using formulas The corrected solubility characteristic baseline constant was calculated. By amplification, it was shown that as the oil ages, the number of adsorption sites for water molecules by the internally generated polar functional groups increases exponentially, thereby enhancing the oil's saturated water absorption capacity at the same temperature.

[0023] In an optional embodiment, the weighted correction of the solubility temperature sensitivity index using the shear stress solubilization correction coefficient includes: Read the initial solubility temperature sensitivity index in the new oil state; Divide the initial solubility temperature sensitivity index by the shear stress solubilization correction coefficient to obtain the corrected solubility temperature sensitivity index.

[0024] Read the initial solubility temperature sensitivity index from the storage The shear stress dissolution correction factor obtained from the aforementioned calculation is used. Using the formula Weighted corrections were applied. The high-intensity mechanical shear stress field altered the arrangement of oil molecules and the energy barrier of the dissolution process, shifting the dissolution equilibrium that was originally controlled solely by temperature. Because shear stress increased the free volume between oil molecules, it lowered the activation energy required for water molecule dissolution, thus reducing the temperature sensitivity index. By adjusting the stress correction coefficient through division, the exponential parameters in the Arrhenius model were modified to adapt the model to the actual impact of changes in the internal microstructure of the oil on the temperature sensitivity of water solubility under different crane operating conditions (stationary, lightly loaded, and heavily loaded).

[0025] S4. Substitute the calculated equilibrium temperature into the generalized saturated solubility model to calculate the oil saturated water concentration limit under the current working conditions and oil quality state. Convert the real-time oil relative humidity signal into a water activity value, and multiply the saturated water concentration limit with the water activity value to output the online absolute water content of the crane lubricating oil.

[0026] Specifically, the calculated oil equilibrium temperature is used as an input variable and substituted into the constructed generalized saturated solubility model. An exponential calculation is performed to determine the current limit of saturated water concentration that the lubricating oil can accommodate, expressed in parts per million (ppm). Simultaneously, the real-time relative humidity signal of the collected oil is divided by 100 to convert it into a water activity value between 0 and 1. The calculated saturated water concentration limit is multiplied by this water activity value to obtain the actual water content of the oil under the current temperature, current aging state, and current load stress. This value is then displayed in real time on the host computer interface as the online absolute water content of the crane lubricating oil and simultaneously stored in the historical database for trend analysis.

[0027] In an optional embodiment, substituting the calculated equilibrium temperature into the generalized saturated solubility model to calculate the oil saturated water concentration limit under the current operating conditions and oil quality status includes: Calculate the ratio of the corrected solubility temperature sensitivity index to the thermodynamic temperature value of the equilibrium temperature, and take a negative sign for the ratio; Calculate the value of the exponential function with the natural constant e as the base and the negative ratio as the exponent; Multiplying the exponential function value by the corrected solubility characteristic reference constant yields the limit value of the oil saturated water concentration.

[0028] The compensated equilibrium temperature Convert to thermodynamic absolute temperature. Construct the modified generalized saturated solubility model formula: Calculate the ratios within the exponential terms. Calculate the natural exponent of this ratio, which represents the proportion of solubility decay under the current thermodynamic state. Compare the exponent result with the corrected reference constant. Multiplying these values ​​yields the current time, current aging level, and the limit value for the oil saturated water concentration under the current load conditions. The collected oil relative humidity (RH) signal was converted into a water activity value. Through formula The online absolute water content of the crane lubricating oil was calculated. The water content was output by considering the triple effects of thermal hysteresis, increased polarity due to chemical aging, and solubilization due to mechanical stress.

[0029] In a second embodiment, the present invention also proposes an online detection system for moisture content in crane lubricating oil, comprising the following modules: The calculation module is used to synchronously acquire real-time signals of oil temperature, oil relative humidity, imaginary part of oil complex dielectric constant, and real-time load signal of the main hoisting mechanism of the crane lubrication system; perform time-series differential operation on the acquired real-time oil temperature signal to obtain the temperature change rate; construct a thermal inertia hysteresis weighting function based on the temperature change rate; and use the weighting function to weight the real-time oil temperature signal to calculate the oil equilibrium temperature to compensate for the thermal hysteresis effect of the sensor. The module is used to construct a polar aging factor based on the deviation rate of the imaginary part signal of the complex dielectric constant, using the stored new oil data as a reference; based on the preset crane gear pair parameters and load-stress mapping relationship, the real-time load signal is converted into the micro equivalent contact stress of the lubrication contact area, and the shear stress dissolution correction coefficient is constructed using the micro equivalent contact stress. The correction module is used to construct a generalized saturated solubility model. In the model, the polar aging factor is used to fit and correct the solubility characteristic baseline constant; and the shear stress solubilization correction coefficient is used to weight and correct the solubility temperature sensitivity index. The output module is used to substitute the calculated equilibrium temperature into the generalized saturated solubility model, calculate the oil saturated water concentration limit under the current working conditions and oil quality, convert the real-time oil relative humidity signal into a water activity value, and multiply the saturated water concentration limit by the water activity value to output the online absolute moisture content of the crane lubricating oil.

[0030] In an optional embodiment, constructing the thermal inertia hysteresis weighting function based on the temperature change rate includes: When the sign of the temperature change rate is positive, a preset heating time constant is selected to construct a heating weight sequence based on the exponential decay law. When the sign of the temperature change rate is negative, a preset cooling time constant is selected to construct a cooling weight sequence based on the exponential decay law, wherein the cooling time constant is greater than the heating time constant. The selected weight sequence is normalized to obtain the thermal inertia hysteresis weight function used for weighted calculation.

[0031] In an optional embodiment, the construction of a polarity aging factor based on the deviation rate of the imaginary part signal of the complex permittivity, using stored new oil data as a reference, includes: Calculate the difference between the currently acquired imaginary part signal value of the complex dielectric constant of the oil and the stored imaginary part value of the new complex dielectric constant of the oil, and divide the difference by the imaginary part value of the new complex dielectric constant of the oil to obtain the relative deviation rate; Multiply the relative deviation rate by a preset dielectric sensitivity coefficient, and add 1 to the product to obtain the dimensionless polar aging factor.

[0032] In an optional embodiment, the step of converting the real-time load signal into the microscopic equivalent contact stress of the lubrication contact area based on preset crane gear pair parameters and load-stress mapping relationship includes: The equivalent radius of curvature and material elastic modulus parameters of the gear pair of the main hoisting mechanism are read, and the Hertzian contact coefficient is calculated using Hertzian contact theory. The square root of the real-time load signal is performed, and the result is multiplied by the Hertz contact coefficient to obtain the microscopic equivalent contact stress representing the stress state of the lubrication contact area.

[0033] In an optional embodiment, constructing the shear stress dissolution correction coefficient using the micro-equivalent contact stress includes: Divide the microscopic equivalent contact stress by the preset critical shear yield strength of the lubricating oil film to obtain a dimensionless stress ratio. Calculate the value of an exponential function with the natural constant e as the base and the stress ratio as the exponent, and use the value of the exponential function as the shear stress dissolution correction coefficient.

[0034] In an optional embodiment, the fitting correction of the solubility characteristic benchmark constant using a polar aging factor in the model includes: Read the initial solubility characteristic reference constant in the new oil state; The polar aging factor is subjected to a power exponent calculation, and the power exponent is a preset hydrophilicity correlation index. The corrected solubility characteristic reference constant is obtained by multiplying the initial solubility characteristic reference constant by the polar aging factor calculated by power exponentiation.

[0035] In an optional embodiment, the weighted correction of the solubility temperature sensitivity index using the shear stress solubilization correction coefficient includes: Read the initial solubility temperature sensitivity index in the new oil state; Divide the initial solubility temperature sensitivity index by the shear stress solubilization correction coefficient to obtain the corrected solubility temperature sensitivity index.

[0036] In an optional embodiment, substituting the calculated equilibrium temperature into the generalized saturated solubility model to calculate the oil saturated water concentration limit under the current operating conditions and oil quality status includes: Calculate the ratio of the corrected solubility temperature sensitivity index to the thermodynamic temperature value of the equilibrium temperature, and take a negative sign for the ratio; Calculate the value of the exponential function with the natural constant e as the base and the negative ratio as the exponent; Multiplying the exponential function value by the corrected solubility characteristic reference constant yields the limit value of the oil saturated water concentration.

[0037] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0038] The functional modules shown in the above-described block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0039] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0040] The aspects of this application have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0041] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for online detection of moisture content in crane lubricating oil, characterized in that, Includes the following steps: The system synchronously acquires real-time signals of oil temperature, relative humidity, imaginary part of complex dielectric constant, and load of the main hoisting mechanism from the crane lubrication system. It performs time-series differential calculations on the acquired real-time oil temperature signals to obtain the temperature change rate, constructs a thermal inertia hysteresis weighting function based on the temperature change rate, and uses this weighting function to weight the real-time oil temperature signals to calculate the oil equilibrium temperature to compensate for the sensor's thermal hysteresis effect. Based on the stored new oil data, a polarity aging factor is constructed using the deviation rate of the imaginary part signal of the complex permittivity. Based on the preset crane gear pair parameters and load-stress mapping relationship, the real-time load signal is converted into the micro equivalent contact stress in the lubrication contact area, and the shear stress dissolution correction coefficient is constructed using the micro equivalent contact stress. Construct a generalized saturated solubility model; In the model, the polar aging factor is used to fit and correct the solubility characteristic baseline constant; the shear stress solubilization correction coefficient is used to weight and correct the solubility temperature sensitivity index. The calculated equilibrium temperature is substituted into the generalized saturated solubility model to calculate the oil saturated water concentration limit under the current operating conditions and oil quality. The real-time oil relative humidity signal is converted into a water activity value, and the saturated water concentration limit is multiplied by the water activity value to output the online absolute water content of the crane lubricating oil.

2. The method according to claim 1, characterized in that, The construction of the thermal inertia hysteresis weight function based on the temperature change rate includes: When the sign of the temperature change rate is positive, a preset heating time constant is selected to construct a heating weight sequence based on the exponential decay law. When the sign of the temperature change rate is negative, a preset cooling time constant is selected to construct a cooling weight sequence based on the exponential decay law, wherein the cooling time constant is greater than the heating time constant. The selected weight sequence is normalized to obtain the thermal inertia hysteresis weight function used for weighted calculation.

3. The method according to claim 1, characterized in that, The method of constructing a polarity aging factor based on stored new oil data and utilizing the deviation rate of the imaginary part signal of the complex permittivity includes: Calculate the difference between the currently acquired imaginary part signal value of the complex dielectric constant of the oil and the stored imaginary part value of the new complex dielectric constant of the oil, and divide the difference by the imaginary part value of the new complex dielectric constant of the oil to obtain the relative deviation rate; Multiply the relative deviation rate by a preset dielectric sensitivity coefficient, and add 1 to the product to obtain the dimensionless polar aging factor.

4. The method according to claim 2, characterized in that, The method, based on preset crane gear pair parameters and load-stress mapping relationships, converts real-time load signals into microscopic equivalent contact stresses in the lubrication contact area, including: The equivalent radius of curvature and material elastic modulus parameters of the gear pair of the main hoisting mechanism are read, and the Hertzian contact coefficient is calculated using Hertzian contact theory. The square root of the real-time load signal is performed, and the result is multiplied by the Hertz contact coefficient to obtain the microscopic equivalent contact stress representing the stress state of the lubrication contact area.

5. The method according to claim 1, characterized in that, The method of constructing the shear stress dissolution correction coefficient using the microscopic equivalent contact stress includes: Divide the microscopic equivalent contact stress by the preset critical shear yield strength of the lubricating oil film to obtain a dimensionless stress ratio. Calculate the value of an exponential function with the natural constant e as the base and the stress ratio as the exponent, and use the value of the exponential function as the shear stress dissolution correction coefficient.

6. The method according to claim 1, characterized in that, In the model, the solubility characteristic baseline constant is fitted and corrected using a polar aging factor, including: Read the initial solubility characteristic reference constant in the new oil state; The polar aging factor is subjected to a power exponent calculation, and the power exponent is a preset hydrophilicity correlation index. The corrected solubility characteristic reference constant is obtained by multiplying the initial solubility characteristic reference constant by the polar aging factor calculated by power exponentiation.

7. The method according to claim 1 or 4, characterized in that, The weighted correction of the solubility temperature sensitivity index using the shear stress solubilization correction coefficient includes: Read the initial solubility temperature sensitivity index in the new oil state; Divide the initial solubility temperature sensitivity index by the shear stress solubilization correction coefficient to obtain the corrected solubility temperature sensitivity index.

8. The method according to claim 1, characterized in that, The step of substituting the calculated equilibrium temperature into the generalized saturated solubility model to calculate the oil saturated water concentration limit under the current operating conditions and oil quality status includes: Calculate the ratio of the corrected solubility temperature sensitivity index to the thermodynamic temperature value of the equilibrium temperature, and take a negative sign for the ratio; Calculate the value of the exponential function with the natural constant e as the base and the negative ratio as the exponent; Multiplying the exponential function value by the corrected solubility characteristic reference constant yields the limit value of the oil saturated water concentration.

9. An online detection system for moisture content in crane lubricating oil, characterized in that, Includes the following modules: The calculation module is used to synchronously acquire real-time signals of oil temperature, oil relative humidity, imaginary part of oil complex dielectric constant, and real-time load signal of the main hoisting mechanism of the crane lubrication system; perform time-series differential operation on the acquired real-time oil temperature signal to obtain the temperature change rate; construct a thermal inertia hysteresis weighting function based on the temperature change rate; and use the weighting function to weight the real-time oil temperature signal to calculate the oil equilibrium temperature to compensate for the thermal hysteresis effect of the sensor. A module is built to construct a polar aging factor based on stored new oil data and using the deviation rate of the imaginary part of the complex permittivity signal. Based on the preset crane gear pair parameters and load-stress mapping relationship, the real-time load signal is converted into the micro equivalent contact stress in the lubrication contact area, and the shear stress dissolution correction coefficient is constructed using the micro equivalent contact stress. The correction module is used to construct a generalized saturated solubility model; In the model, the polar aging factor is used to fit and correct the solubility characteristic benchmark constant; The solubility temperature sensitivity index is weighted and corrected using a shear stress solubilization correction factor. The output module is used to substitute the calculated equilibrium temperature into the generalized saturated solubility model, calculate the oil saturated water concentration limit under the current working conditions and oil quality, convert the real-time oil relative humidity signal into a water activity value, and multiply the saturated water concentration limit by the water activity value to output the online absolute moisture content of the crane lubricating oil.

10. The system according to claim 9, characterized in that, The construction of the thermal inertia hysteresis weight function based on the temperature change rate includes: When the sign of the temperature change rate is positive, a preset heating time constant is selected to construct a heating weight sequence based on the exponential decay law. When the sign of the temperature change rate is negative, a preset cooling time constant is selected to construct a cooling weight sequence based on the exponential decay law, wherein the cooling time constant is greater than the heating time constant. The selected weight sequence is normalized to obtain the thermal inertia hysteresis weight function used for weighted calculation.

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