An online monitoring method for hydraulic mechanism oil level

Abstract

This invention provides an online monitoring method for hydraulic oil levels, comprising: determining whether the hydraulic oil level exceeds a preset level height; if so, generating an alarm signal; if not, determining the corresponding desired oil volume; determining whether the temperature is within a preset hydraulic oil temperature range; if not, generating an alarm signal; if so, determining the temperature difference and time difference between the current time and the previous time point; determining the rate of temperature change and determining whether the rate of change is greater than a preset rate of change threshold; if so, generating an alarm signal; if not, obtaining a corrected temperature; determining the current actual hydraulic oil volume; determining the difference between the desired oil volume and the actual oil volume, and determining the corresponding target oil volume based on the comparison result; when the target oil volume is less than a preset oil volume threshold, determining to replenish hydraulic oil. This invention effectively eliminates the expansion or contraction of hydraulic oil volume caused by temperature changes, preventing discrepancies between the actual oil level and the oil level indicated by the level gauge, thus avoiding errors.

Description

Technical Field

[0001] This invention relates to the field of online monitoring technology for hydraulic mechanism oil levels, and in particular to an online monitoring method for hydraulic mechanism oil levels. Background Technology

[0002] As hydraulic circuit breakers age, their seals may age and break, and the hydraulic oil may contain more impurities, leading to oil leaks. Insufficient hydraulic oil can prevent pressure reduction, and in severe cases, the circuit breaker may fail to operate, affecting its safe and stable operation. Therefore, regular inspections of the hydraulic mechanism are necessary to ensure the oil level is normal and to filter the hydraulic oil to remove impurities. Currently, oil level gauges are used to detect the oil level. However, changes in temperature can cause the hydraulic oil to expand or contract, resulting in a discrepancy between the actual oil level and the level indicated by the gauge, thus introducing errors. Summary of the Invention

[0003] The purpose of this invention is to propose an online monitoring method for hydraulic system oil level, thereby solving the technical problem of how to reduce the error caused by the discrepancy between the actual oil level and the oil level indicated by the oil level gauge.

[0004] On the one hand, an online monitoring method for the oil level of a hydraulic mechanism is provided, including:

[0005] The system acquires hydraulic oil level information, hydraulic oil temperature information, and ambient temperature information in real time through multiple preset sensors.

[0006] The system determines whether the hydraulic oil level exceeds a preset level. If so, an alarm signal is generated; otherwise, the corresponding expected oil quantity is determined based on the level information. The expected oil quantity is the oil quantity predicted for each level value.

[0007] Determine whether the hydraulic oil temperature information is within the preset hydraulic oil temperature range. If not, generate an alarm signal; if yes, determine the current time of the collected temperature and the temperature difference between the collected hydraulic oil temperature at the previous time point, as well as the time difference between the current time and the previous time point.

[0008] The rate of temperature change is determined based on the temperature difference and the time difference, and it is determined whether the rate of change is greater than a preset rate of change threshold. If so, an alarm signal is generated; if not, the temperature information of the hydraulic oil and the ambient temperature information are input into a pre-trained temperature prediction model to obtain a corrected temperature. The temperature prediction model is a model that uses historically collected actual hydraulic oil temperature, top hydraulic oil temperature, and ambient temperature as training samples to train a preset neural network model to predict the actual temperature of the hydraulic oil.

[0009] The density of the hydraulic oil is calculated based on the corrected temperature, and the actual amount of hydraulic oil at present is determined based on the current volume and density of the hydraulic oil.

[0010] The difference between the desired oil volume and the current actual hydraulic oil volume is determined, and the corresponding target oil volume is determined based on the comparison result between the difference and a preset threshold range; when the target oil volume is less than the preset oil volume threshold, it is determined that hydraulic oil needs to be added.

[0011] Preferably, the sensor includes at least,

[0012] A level sensor is installed on the level window of the hydraulic mechanism to measure the level information of the hydraulic oil.

[0013] The first temperature sensor is used to measure the real-time temperature information of the hydraulic oil;

[0014] The second temperature sensor is used to measure the ambient temperature information of the hydraulic mechanism during operation.

[0015] Preferably, it also includes,

[0016] When an alarm signal is received, the corresponding alarm action is executed according to the preset alarm method, and the corresponding abnormal information is output.

[0017] The abnormal information includes at least the following: the temperature is too high / too low, the liquid level is too high / too low, or the temperature changes too rapidly.

[0018] Preferably, the rate of temperature change is the ratio between the temperature difference and the time difference.

[0019] Preferably, the temperature prediction model is pre-trained through the following process:

[0020] The actual hydraulic oil temperature, the top layer temperature of the hydraulic oil, and the ambient temperature collected in history were used as training samples.

[0021] The hydraulic oil top temperature sample and the ambient temperature sample are used as inputs to a preset neural network model. The actual temperature of the hydraulic oil is predicted by the neural network model to obtain the predicted temperature.

[0022] The predicted temperature and the actual hydraulic oil temperature samples are calculated using a preset loss function. The parameters of the neural network model are optimized based on the calculation results until a preset number of iterations or a preset accuracy is reached, at which point the trained temperature prediction model is output.

[0023] Preferably, the density of the hydraulic oil is calculated using the following formula:

[0024] ρ=ρ0 / (1+αT1)

[0025] Where ρ is the density of hydraulic oil, ρ0 is the density of hydraulic oil at 0℃, α is the coefficient of thermal expansion of hydraulic oil, and T1 is the corrected temperature.

[0026] Preferably, the current volume of hydraulic oil is determined based on the liquid level information collected by the liquid level sensor and the cross-sectional area of ​​the oil storage container inside the hydraulic mechanism.

[0027] Preferably, the actual quantity of the current hydraulic oil is the product of the current volume of the hydraulic oil and the density of the hydraulic oil.

[0028] Preferably, determining the corresponding target oil quantity based on the comparison result between the difference and a preset threshold range specifically includes:

[0029] If the difference is within the preset threshold range, the expected oil quantity will be used as the target oil quantity.

[0030] If the difference is not within the preset threshold range, the current actual hydraulic oil volume will be used as the target oil volume.

[0031] Preferably, it also includes,

[0032] When hydraulic oil needs to be replenished, the hydraulic mechanism is replenished according to the target oil level.

[0033] In summary, implementing the embodiments of the present invention has the following beneficial effects:

[0034] The online monitoring method for hydraulic mechanism oil level provided by this invention comprehensively monitors the hydraulic oil temperature and the actual oil volume determined based on the hydraulic oil temperature by using liquid level information, hydraulic oil temperature information, and ambient temperature information. It then determines an accurate target oil volume based on the actual oil volume, thereby achieving oil level monitoring. This effectively eliminates the expansion or contraction of hydraulic oil volume caused by temperature changes, eliminates discrepancies between the actual oil level and the oil level indicated by the oil level gauge, and avoids the generation of errors. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, obtaining other drawings based on these drawings without creative effort still falls within the scope of the present invention.

[0036] Figure 1 This is a schematic diagram of the main process of an online monitoring method for the oil level of a hydraulic mechanism according to an embodiment of the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings.

[0038] like Figure 1 The diagram shown is a schematic representation of an embodiment of an online monitoring method for hydraulic mechanism oil level provided by the present invention. In this embodiment, the method includes the following steps:

[0039] Step S1 involves acquiring real-time information on the hydraulic oil level, hydraulic oil temperature, and ambient temperature using multiple pre-set sensors. Understandably, level sensors are installed within the hydraulic mechanism, ensuring proper positioning to prevent interference from mechanism movement and guaranteeing sensor reliability and stability. For example, a level sensor can be installed on the hydraulic mechanism's level window to collect data. High-precision level sensors suitable for high-pressure hydraulic environments are selected. The type of sensor chosen is a non-contact sensor, such as infrared, ultrasonic, or capacitive, depending on the specific circumstances.

[0040] In one embodiment, the sensor includes at least: a level sensor, disposed on the level window of the hydraulic mechanism, for measuring the hydraulic oil level; a first temperature sensor, for measuring the real-time temperature of the hydraulic oil; and a second temperature sensor, for measuring the ambient temperature of the hydraulic mechanism during operation. Understandably, two temperature sensors are provided: one for collecting the hydraulic oil temperature, for example, by placing the temperature sensor on the top layer of the hydraulic oil. The other temperature sensor is used to collect the ambient temperature of the hydraulic mechanism and can be installed on the hydraulic mechanism, such as at any position next to the level sensor. The process involves acquiring the hydraulic oil temperature collected by the first temperature sensor, acquiring the hydraulic oil level on the level window using the level sensor, and acquiring the ambient temperature collected by the second temperature sensor.

[0041] Step S2: Determine whether the hydraulic oil level exceeds a preset level. If so, generate an alarm signal; otherwise, determine the expected oil volume based on the level information. The expected oil volume is a predicted volume for each level. Essentially, determining whether the hydraulic oil level exceeds the preset level triggers an alarm module. This alarm module can use a buzzer or an indicator light to send abnormal information (too high or too low level) to the operator and display the information on the screen. If not, the corresponding oil volume is pre-measured for each level. Therefore, after determining the hydraulic oil level, the expected oil volume can be found.

[0042] Step S3: Determine if the hydraulic oil temperature is within the preset hydraulic oil temperature range. If not, generate an alarm signal. If yes, determine the temperature difference between the current and previous timestamps, as well as the time difference between the current and previous timestamps. Essentially, determining if the collected hydraulic oil temperature is within the preset range involves calculating the temperature difference between the collected and previous timestamps, and the time difference between the current and previous timestamps. If not, it indicates that the hydraulic oil temperature cannot be compensated, and the alarm module will sound an alarm. The alarm module can use a buzzer or an indicator light, and send abnormal information about excessively high or low hydraulic oil temperature to the operator. The preset hydraulic oil temperature range is determined based on the properties of the hydraulic oil.

[0043] Step S4: Determine the rate of temperature change based on the temperature difference and the time difference, and determine whether the rate of change exceeds a preset rate of change threshold. If so, generate an alarm signal; otherwise, input the hydraulic oil temperature information and ambient temperature information into a pre-trained temperature prediction model to obtain a corrected temperature. The temperature prediction model is a model that predicts the actual temperature of hydraulic oil by training a preset neural network model using historically collected actual hydraulic oil temperatures, top hydraulic oil temperatures, and ambient temperatures as training samples. Understandably, determining whether the rate of change exceeds the preset rate of change threshold indicates that the hydraulic oil temperature is changing too rapidly, and the hydraulic oil volume may expand rapidly, leading to a malfunction in the hydraulic mechanism. Therefore, the alarm module issues an alarm, which can be a buzzer, and sends abnormal information about the rapid temperature change to the operator. If not, input the hydraulic oil temperature collected by the first temperature sensor and the ambient temperature collected by the second temperature sensor into the temperature prediction model to output the corrected temperature.

[0044] In one embodiment, the rate of temperature change is the ratio between the temperature difference and the time difference.

[0045] In one embodiment, the temperature prediction model is pre-trained through the following process: Historically collected actual hydraulic oil temperature, hydraulic oil top layer temperature, and ambient temperature are used as training samples; the hydraulic oil top layer temperature sample and ambient temperature sample are used as input to a preset neural network model, which predicts the actual hydraulic oil temperature to obtain the predicted temperature; a preset loss function is used to calculate the predicted temperature against the actual hydraulic oil temperature samples, and the parameters of the neural network model are optimized based on the calculation results until a preset number of iterations or a preset accuracy is achieved, at which point the trained temperature prediction model is output. It is understandable that the temperature of hydraulic oil is affected by the external operating environment, and simply collecting the hydraulic oil top layer temperature is insufficient to determine the true oil temperature. If a temperature sensor is placed in the hydraulic oil, it needs to be replaced periodically, resulting in high costs. Therefore, the actual hydraulic oil temperature collected by the sensor in the past, the top layer temperature of the hydraulic oil collected by the sensor, and the ambient temperature collected by the sensor are used as training samples. The top layer temperature sample of the hydraulic oil and the ambient temperature sample are used as input to the neural network model to predict the actual hydraulic oil temperature. The loss function is used to calculate the predicted temperature and the actual hydraulic oil temperature sample to optimize the parameters of the neural network model. This process is repeated iteratively to train the model and finally output the trained temperature prediction model.

[0046] Step S5: Calculate the density of the hydraulic oil based on the corrected temperature, and determine the actual quantity of hydraulic oil based on the current volume and density. Understandably, assume the hydraulic oil container is a uniform cylinder or cuboid, etc., and calculate the current volume of hydraulic oil based on the oil level sensor's data and the container's cross-sectional area. Then, calculate the actual quantity of hydraulic oil based on the formula.

[0047] In one embodiment, the density of the hydraulic oil is calculated using the following formula:

[0048] ρ=ρ0 / (1+αT1)

[0049] Where ρ is the density of hydraulic oil, ρ0 is the density of hydraulic oil at 0℃, α is the coefficient of thermal expansion of hydraulic oil, and T1 is the corrected temperature.

[0050] In one embodiment, the current volume of hydraulic oil is determined based on the liquid level information collected by the liquid level sensor and the cross-sectional area of ​​the oil storage container within the hydraulic mechanism. The actual current volume of hydraulic oil is the product of its current volume and its density.

[0051] Step S6: Determine the difference between the expected oil quantity and the current actual hydraulic oil quantity, and determine the corresponding target oil quantity based on the comparison result between this difference and a preset threshold range; if the target oil quantity is less than the preset oil quantity threshold, it is determined that hydraulic oil needs to be replenished. In essence, the expected oil quantity is subtracted from the current actual hydraulic oil quantity; if the difference is within a preset range, the expected oil quantity is used as the target oil quantity; if the difference is not within the preset range, the current actual hydraulic oil quantity is used as the target oil quantity. This ensures the accuracy of the oil quantity measurement, making the replenished hydraulic oil quantity more accurate. If the target oil quantity is less than the oil quantity threshold, hydraulic oil needs to be replenished.

[0052] In one embodiment, determining the target oil quantity based on the comparison between the difference and a preset threshold range specifically includes: if the difference is within the preset threshold range, the desired oil quantity is used as the target oil quantity; if the difference is not within the preset threshold range, the current actual hydraulic oil quantity is used as the target oil quantity. When hydraulic oil needs to be replenished, the hydraulic mechanism is replenished according to the target oil quantity.

[0053] In summary, implementing the embodiments of the present invention has the following beneficial effects:

[0054] The online monitoring method for hydraulic mechanism oil level provided by this invention comprehensively monitors the hydraulic oil temperature and the actual oil volume determined based on the hydraulic oil temperature by using liquid level information, hydraulic oil temperature information, and ambient temperature information. It then determines an accurate target oil volume based on the actual oil volume, thereby achieving oil level monitoring. This effectively eliminates the expansion or contraction of hydraulic oil volume caused by temperature changes, eliminates discrepancies between the actual oil level and the oil level indicated by the oil level gauge, and avoids the generation of errors.

[0055] The above description discloses only preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. Therefore, equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for online monitoring of oil level in a hydraulic mechanism, characterized in that, include: The system acquires hydraulic oil level information, hydraulic oil temperature information, and ambient temperature information in real time through multiple preset sensors. The system determines whether the hydraulic oil level exceeds a preset level. If so, an alarm signal is generated; otherwise, the corresponding expected oil quantity is determined based on the level information. The expected oil quantity is the oil quantity predicted for each level value. Determine whether the hydraulic oil temperature information is within the preset hydraulic oil temperature range. If not, generate an alarm signal; if yes, determine the current time of the collected temperature and the temperature difference between the collected hydraulic oil temperature at the previous time point, as well as the time difference between the current time and the previous time point. The rate of temperature change is determined based on the temperature difference and the time difference, and it is determined whether the rate of change is greater than a preset rate of change threshold. If so, an alarm signal is generated; if not, the temperature information of the hydraulic oil and the ambient temperature information are input into a pre-trained temperature prediction model to obtain a corrected temperature. The temperature prediction model is a model that uses historically collected actual hydraulic oil temperature, top hydraulic oil temperature, and ambient temperature as training samples to train a preset neural network model to predict the actual temperature of the hydraulic oil. The density of the hydraulic oil is calculated based on the corrected temperature, and the actual amount of hydraulic oil at present is determined based on the current volume and density of the hydraulic oil. The difference between the desired oil volume and the current actual hydraulic oil volume is determined, and the corresponding target oil volume is determined based on the comparison result between the difference and a preset threshold range; when the target oil volume is less than the preset oil volume threshold, it is determined that hydraulic oil needs to be added.

2. The method as described in claim 1, characterized in that, The sensor includes at least the following: A level sensor is installed on the level window of the hydraulic mechanism to measure the level information of the hydraulic oil. The first temperature sensor is used to measure the real-time temperature information of the hydraulic oil; The second temperature sensor is used to measure the ambient temperature information of the hydraulic mechanism during operation.

3. The method as described in claim 1, characterized in that, It also includes, When an alarm signal is received, the corresponding alarm action is executed according to the preset alarm method, and the corresponding abnormal information is output. The abnormal information includes at least the following: the temperature is too high / too low, the liquid level is too high / too low, or the temperature changes too rapidly.

4. The method as described in claim 1, characterized in that, The rate of temperature change is the ratio between the temperature difference and the time difference.

5. The method as described in claim 1, characterized in that, The temperature prediction model is pre-trained through the following process. The actual hydraulic oil temperature, the top layer temperature of the hydraulic oil, and the ambient temperature collected in history were used as training samples. The hydraulic oil top temperature sample and the ambient temperature sample are used as inputs to a preset neural network model. The actual temperature of the hydraulic oil is predicted by the neural network model to obtain the predicted temperature. The predicted temperature and the actual hydraulic oil temperature samples are calculated using a preset loss function. The parameters of the neural network model are optimized based on the calculation results until a preset number of iterations or a preset accuracy is reached, at which point the trained temperature prediction model is output.

6. The method as described in claim 1, characterized in that, The density of hydraulic oil can be calculated using the following formula. ρ=ρ0 / (1+αT1) Where ρ is the density of hydraulic oil, ρ0 is the density of hydraulic oil at 0℃, α is the coefficient of thermal expansion of hydraulic oil, and T1 is the corrected temperature.

7. The method as described in claim 2, characterized in that, The current volume of hydraulic oil is determined based on the liquid level information collected by the liquid level sensor and the cross-sectional area of ​​the oil storage container inside the hydraulic mechanism.

8. The method as described in claim 7, characterized in that, The actual current hydraulic oil volume is the product of the current hydraulic oil volume and the hydraulic oil density.

9. The method as described in claim 1, characterized in that, The step of determining the corresponding target oil quantity based on the comparison result between the difference and the preset threshold range specifically includes: If the difference is within the preset threshold range, the expected oil quantity will be used as the target oil quantity. If the difference is not within the preset threshold range, the current actual hydraulic oil volume will be used as the target oil volume.

10. The method as described in claim 1, characterized in that, It also includes, When hydraulic oil needs to be replenished, the hydraulic mechanism is replenished according to the target oil level.

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