A method for monitoring oil temperature in a device lubrication system

Abstract

This invention relates to the field of data processing, specifically to a method for monitoring the oil temperature of a lubrication system. The method includes: acquiring pre-processed oil temperature data of the lubrication system and operating condition data during the operation of a press in real time; calculating a risk index for the oil temperature data per second based on the operating condition data; using the current time point as a baseline, tracing back a preset time window, calculating the mean and standard deviation of the risk index within that time window, dividing the absolute deviation between the risk index at the current time point and the mean of the risk index within the time window by the standard deviation of the risk index within the time window and normalizing the result to obtain the rate of change of the risk index of the oil temperature data at the current time point; monitoring oil temperature anomalies based on the rate of change, and generating an oil temperature monitoring report. This invention effectively improves the accuracy of oil temperature anomaly monitoring through multi-dimensional dynamic analysis and risk quantification.

Description

Technical Field

[0001] This invention relates to the field of data processing. More specifically, this invention relates to a method for monitoring the oil temperature of a device lubrication system. Background Technology

[0002] The lubrication system, a crucial component of high-speed presses, is specifically designed to provide lubrication and cooling for all moving parts of the press, ensuring its normal operation. For example, the various moving parts of the press (such as crankshafts, connecting rods, and slides) require lubricating oil from the lubrication system to reduce friction and wear, ensuring efficient and stable operation. Excessive oil temperature accelerates lubricant oxidation and deterioration, reducing lubrication performance and leading to increased wear on mechanical parts. Conversely, excessively low oil temperature increases oil viscosity, reduces fluidity, and affects lubrication effectiveness. Therefore, monitoring and maintaining the oil temperature within a reasonable range can improve press operating efficiency and reduce energy consumption.

[0003] However, in the lubrication system of actual production equipment, although an alarm device for automatically detecting oil temperature is provided, the different requirements of the press for lubricating oil temperature under different operating conditions are not taken into account. Since the alarm threshold is fixed, when the press is under special operating conditions, the actual oil temperature may not have reached a dangerous level, but a false alarm is triggered (false alarm); or when an alarm is really needed, it is ignored because the oil temperature has not reached the fixed threshold (missed alarm). Missed alarms and false alarms result in the press not receiving accurate oil temperature protection, affecting normal production or damaging the equipment. Summary of the Invention

[0004] To address the aforementioned technical problem of improving the accuracy of oil temperature monitoring, this invention provides the following technical solution.

[0005] A method for monitoring the oil temperature of a device lubrication system includes:

[0006] Real-time acquisition of pre-processed oil temperature data of the lubrication system and operating condition data during press operation; the operating condition data includes press stamping frequency and stamping load;

[0007] Calculate the risk index of oil temperature data per second based on operating condition data;

[0008] Using the current time point as a benchmark, trace back a preset time window, calculate the mean and standard deviation of the risk index within the time window, divide the absolute deviation between the risk index corresponding to the current time point and the mean of the risk index within the time window by the standard deviation of the risk index within the time window and normalize it to obtain the rate of change of the risk index of the oil temperature data at the current time point.

[0009] The oil temperature is monitored for anomalies based on the rate of change, and an oil temperature monitoring report is generated.

[0010] This invention achieves precise monitoring of equipment status by real-time acquisition of lubrication system oil temperature and press operating condition data (such as stamping frequency and load), dynamically calculating the oil temperature risk index and assessing its abnormal change rate. Specifically, the risk index, combined with operating parameters, quantifies the correlation risk between oil temperature and operating load. Based on the mean and standard deviation analysis of the sliding time window, a normal fluctuation benchmark can be dynamically established. By standardizing the deviation (change rate) of the current risk index, abnormal trends are identified, thereby issuing an early warning before abnormal oil temperature leads to failure.

[0011] Preferably, the oil temperature data of the lubrication system is one or more of the following: oil tank temperature, oil pump outlet temperature, and main bearing temperature.

[0012] Preferably, the real-time acquisition of pre-processed oil temperature data of the lubrication system and operating condition data during press operation further includes:

[0013] Obtain the quality and specific heat capacity of the lubricating oil in the lubrication system.

[0014] The heat capacity buffering capacity of the lubricating oil quality measurement system and the sensitivity of the oil temperature to heat by specific heat capacity are combined with operating data to construct a complete thermodynamic analysis model, avoiding misjudgments caused by missing parameters (such as misdiagnosing insufficient oil as excessive load).

[0015] Preferably, the process of obtaining the risk index includes:

[0016] Calculate the power fluctuation level at the current time point, the heat saturation during press operation, the position weight factor, and the oil temperature deviation factor, respectively.

[0017] The risk index is the product of the power fluctuation level, heat capacity saturation, location weighting factor, and oil temperature deviation factor.

[0018] Multi-dimensional hidden dangers are converted into a single value. The degree of power fluctuation indicates that the press is unstable or abnormal. Excessive heat capacity saturation may lead to overheating or even burnout of the equipment. Oil temperature deviation reflects lubrication failure or system blockage. The position weight factor distinguishes the risk contribution difference between critical components (such as the spindle) and non-critical components.

[0019] Preferably, the process of obtaining the power fluctuation level includes:

[0020] Calculate the average stamping frequency and average stamping load at all time points, and calculate the instantaneous power at the current time point and the average instantaneous power at all time points. The instantaneous power at the current time point is the product of the stamping frequency, stamping load, and press power at the current time point. Construct a local time window and calculate the standard deviation and average value of the instantaneous power within this local time window. Use the relative deviation between the average instantaneous power within the local time window and the average instantaneous power at all time points as the first parameter, and the ratio of the standard deviation of the instantaneous power within the local time window to the average instantaneous power at all time points as the second parameter. After transforming the first and second parameters using an exponential function, perform a weighted sum to obtain the power fluctuation degree.

[0021] A local time window is constructed, and the standard deviation and mean of the instantaneous power within that window are calculated. The standard deviation measures the dispersion of instantaneous power within a local time, i.e., the amplitude of power fluctuation around the mean; the mean reflects the central tendency of power within a local time. Using these two indicators, we can focus on the power fluctuation characteristics within a specific time period and analyze the power fluctuations at different stages in more detail.

[0022] Preferably, the process of obtaining the heat capacity saturation includes:

[0023] Calculate the heat accumulation factor and heat capacity factor during the operation of the press;

[0024] Calculate the negative exponent of the ratio of heat accumulation factor to heat capacity factor, and subtract the negative exponent value from 1 to obtain the heat capacity saturation during the operation of the press.

[0025] The heat accumulation factor reflects the degree of heat accumulation during the operation of the press, while the heat capacity factor reflects the press's ability to hold heat. By calculating the ratio of the two and performing a negative exponential operation, the heat capacity saturation is finally obtained. This index can comprehensively reflect the balance between heat accumulation and heat holding in the current operating state of the press, helping operators to intuitively understand the thermal state of the press.

[0026] Preferably, the process of obtaining the oil temperature deviation factor includes:

[0027] The oil temperature collected at any location at the current moment is compared with the mean of the oil temperature data at all time points corresponding to that location. The oil temperature deviation is calculated, and the oil temperature deviation factor is obtained by converting the oil temperature deviation using the hyperbolic tangent function.

[0028] Preferably, the step of monitoring oil temperature anomalies based on the rate of change and generating an oil temperature monitoring report includes:

[0029] If the rate of change of the risk index of oil temperature data exceeds the preset rate of change threshold at multiple consecutive time points, the oil temperature change is determined to be abnormal, an alarm is triggered and a monitoring report is generated. The report includes the recorded value and rate of change of oil temperature, the monitoring location where the abnormality occurred, and the time when the alarm was issued.

[0030] The beneficial effects of this invention are:

[0031] This invention comprehensively assesses the thermal state of equipment by integrating operating conditions (pressing frequency, load), thermodynamic parameters (lubricating oil quality, specific heat capacity), historical data, and real-time oil temperature. It incorporates factors such as power fluctuation, heat capacity saturation, and location weighting to calculate a risk index for the oil temperature data. This comprehensive evaluation method more accurately reflects the risk level of oil temperature changes, avoiding misjudgments caused by a single factor. Attached Figure Description

[0032] Figure 1 This is a flowchart of steps S1-S4 in an oil temperature monitoring method for a device lubrication system according to an embodiment of the present invention.

[0033] Figure 2 This is a schematic diagram illustrating the calculation process of the risk index of oil temperature data in an oil temperature monitoring method for an equipment lubrication system according to an embodiment of the present invention. Detailed Implementation

[0034] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0035] Reference Figure 1 A method for monitoring the oil temperature of a device lubrication system includes steps S1-S4, as detailed below:

[0036] S1: Real-time acquisition of oil temperature data of the pre-processed lubrication system and operating condition data of the press during operation.

[0037] In one embodiment, by acquiring and preprocessing press operating condition data and lubrication system oil temperature data, a foundation is provided for achieving dynamic monitoring and effective management of lubrication system oil temperature.

[0038] For example, high-precision temperature sensors are installed at different monitoring locations in the lubrication system (oil tank, main oil pipelines, and critical lubrication points such as the oil cavity near the main bearing) to collect oil temperature data once per second, ensuring continuous and timely data collection. Specific symbols are used to record the oil temperature data at different times and locations. The number of press strokes per second, press load, and continuous press operation time are obtained from the press control system's communication interface. This yields a set of press frequency and a set of press load data, which together determine the input power and are the primary source of oil temperature rise.

[0039] Furthermore, filtering algorithms (such as Kalman filtering and median filtering) are used to perform real-time noise reduction on the collected oil temperature data and press operating condition data, eliminating abnormal data points caused by sensor fluctuations or electromagnetic interference. Data from different sensors are standardized to make the data comparable; for example, the temperature scale of the oil temperature data is standardized, and the press operating condition data is converted into dimensionless indicators.

[0040] In addition, it is necessary to obtain the quality and specific heat capacity of the lubricating oil.

[0041] S2: Calculate the risk index of oil temperature data per second based on operating condition data.

[0042] It's important to consider that analyzing oil temperature data alone only shows the numerical change in temperature, but cannot determine the cause of the abnormal oil temperature. During the operation of the press, the load directly affects the internal friction. When the load increases, the friction between the moving parts intensifies, leading to increased frictional heat. This extra heat is transferred to the oil, causing the oil temperature to rise. Furthermore, the stamping frequency reflects the number of times the press performs work per unit time. The higher the frequency, the more energy the press consumes per unit time, and the more heat it generates.

[0043] Therefore, in this application, oil temperature data is combined with operating condition data such as stamping load and stamping frequency to assess real-time oil temperature risk. (Refer to...) Figure 2 , Figure 2 The diagram illustrates the calculation process of the risk index for oil temperature data, including steps S20-S22, as follows:

[0044] S20: Calculate the power fluctuation per second based on the stamping frequency and stamping load.

[0045] First, calculate the average stamping frequency and average stamping load of the stamping frequency set and the stamping load set respectively, and calculate the instantaneous power at the current time point and the average instantaneous power corresponding to all collected time points.

[0046] The instantaneous power at the current time point is the product of the stamping frequency, stamping load, and press power at that time point. The press power can be read from the PLC terminal, and its range is 0 < press power ≤ 1. The average instantaneous power is the mean of the instantaneous power collected over all time points.

[0047] Then, a local time window is constructed, and the standard deviation and average value of the instantaneous power within this local time window are calculated. Specifically, the local time window is constructed by taking a sliding window of m seconds before and after the t-th second as the center.

[0048] Finally, the relative deviation between the average instantaneous power within the local time window and the average instantaneous power corresponding to all the time points collected above is used as the first parameter (reflecting short-term deviation), and the ratio of the standard deviation of instantaneous power within the local time window to the average instantaneous power corresponding to all the time points collected above is used as the second parameter (reflecting fluctuation intensity). An exponential function is then used to amplify the influence of the first and second parameters, thereby constructing a comprehensive index, namely the degree of power fluctuation, to assess the impact of power fluctuation on oil temperature stability.

[0049] For example, the power fluctuation per second described above satisfies the following relationship:

[0050]

[0051] In the formula, For the first The degree of power fluctuation per second, As the first parameter, For the second parameter, For the first The average instantaneous power within a local time window centered on the second. This represents the average instantaneous power at all collected time points. For the first The standard deviation of instantaneous power within a local time window centered on the second. The first and second parameters each account for 50% of the weight, taking into account the influence of short-term trend deviations and instantaneous fluctuations.

[0052] when A large value indicates that the power demand of the press at the current moment deviates significantly from the long-term average level, resulting in drastic power fluctuations and potentially increasing the risk of abnormal oil temperature.

[0053] S21: Calculate the heat capacity saturation of the system during operation.

[0054] Heat saturation is an indicator used to describe a system's ability to accumulate heat. It reflects how close the system is to its heat capacity limit under current operating conditions. As operating time increases, the system accumulates more and more heat, and the heat saturation gradually rises until it approaches saturation.

[0055] First, calculate the heat accumulation factor and heat capacity factor during the operation of the press.

[0056] The heat accumulation factor reflects the rate and extent of heat accumulation during operation; it is the product of the operating time, the proportionality coefficient, and the adjustment factor. The operating time is the total time for data collection in S1. The proportionality coefficient adjusts the impact of operating time on heat accumulation; for example, it is set to 0.1, which can be determined using experimental data or historical operating data of the equipment. The adjustment factor is set to an example value of 20, used to control the rate of increase in heat capacity saturation. The larger the adjustment factor value, the faster the heat capacity saturation increases, and the faster the system reaches heat capacity saturation.

[0057] The heat capacity factor reflects the contribution of lubricating oil to the system's heat capacity, that is, the system's ability to absorb and store heat. It is the product of the mass of the lubricating oil and its specific heat capacity. The greater the mass of the lubricating oil, the stronger the system's ability to absorb and store heat. The greater the specific heat capacity, the smaller the temperature change when the lubricating oil absorbs the same amount of heat. The greater the system's heat capacity, the larger the heat capacity factor, indicating that the system's heat capacity is greater and the heat capacity saturation increases more slowly.

[0058] Then, calculate the negative exponent of the ratio of the above heat accumulation factor to the heat capacity factor, and subtract the negative exponent value from 1 to obtain the heat capacity saturation of the system during operation.

[0059] The above exponential decay model shows that when the running time is short, the heat capacity saturation increases rapidly. This means that the system can quickly accumulate heat when it starts running. As the running time increases, the growth rate of heat capacity saturation gradually slows down and eventually tends to saturate (close to 1). This reflects the process of the system's heat capacity gradually reaching saturation as heat accumulates.

[0060] S22: Calculate the risk index of oil temperature data per second based on the degree of power fluctuation, heat capacity saturation, and the impact of different monitoring locations and oil temperature deviations on oil temperature risk.

[0061] First, the impact of different locations on oil temperature risk is considered. Different parts of the press are sensitive to oil temperature changes to varying degrees. For example, oil temperature changes in critical lubrication points may have a greater impact on the equipment than changes in the oil temperature in the oil tank. Therefore, a location weighting factor is introduced to adjust the risk contribution of different locations.

[0062] For example, a location weight factor can be set by assessing the risk level of different monitoring locations (bearings, oil tanks, and other lubrication parts). For instance, if the risk level of the bearing is found to be extremely high through assessment, the location weight factor of the bearing can be set to a range of 0.9-1.0.

[0063] Secondly, the impact of power fluctuations on oil temperature risk is considered. During equipment operation, power may change. The greater the power fluctuation, the more difficult it is for the system to maintain a stable temperature, and the higher the risk of abnormal oil temperature. Therefore, a power fluctuation level (calculated from S20 above) is introduced.

[0064] Furthermore, the impact of operating time on oil temperature risk is considered. As equipment operating time increases, the accumulated heat gradually increases, and the system's sensitivity to thermal disturbances also increases, leading to a rise in oil temperature risk. Therefore, a heat capacity saturation (calculated from S21 above) is introduced to reflect the impact of operating time on oil temperature risk. The longer the operating time, the greater the value of the heat capacity saturation.

[0065] Finally, the impact of oil temperature deviation on risk is considered. The oil temperature collected at any location at the current moment is compared with the expected steady-state oil temperature at that location (i.e., the average oil temperature data at all time points corresponding to that location), and the oil temperature deviation is calculated. To quantify the impact of this deviation on risk, the hyperbolic tangent function is used to map the oil temperature deviation to the range of 0 to 1, thus obtaining the oil temperature deviation factor. When the oil temperature deviation is small, the tanh function value is close to 0, indicating that the oil temperature deviation has a small impact on risk; when the oil temperature deviation is large, the tanh function value is close to 1, indicating that the oil temperature deviation has a large impact on risk. At the same time, to consider the safety requirements of the equipment, the oil temperature deviation is divided by the maximum permissible oil temperature deviation (set according to the actual operating characteristics and safety requirements of the equipment) to ensure that the deviation is assessed within a reasonable range.

[0066] Then, by combining the above four parts, the risk index of the complete oil temperature data is calculated. This satisfies the following relationship:

[0067]

[0068] In the formula, For the first The risk index of oil temperature data at any monitoring location per second. For any monitoring location, the location weight factor is... For the first The degree of power fluctuation per second, For heat capacity saturation, Let the oil temperature deviation factor be any monitoring location. For the first Oil temperature data at any monitoring location per second. The average oil temperature data at any given monitoring location across all time points. This represents the maximum permissible deviation of oil temperature. Finally, add 1 to the tanh result to ensure that the risk index still has a base value (such as 1) when there is no deviation, and that the risk is linearly amplified as the deviation increases.

[0069] S3: Using the current time point as a benchmark, trace back a preset time window, calculate the mean and standard deviation of the risk index within the time window, divide the absolute deviation between the risk index corresponding to the current time point and the mean of the risk index within the time window by the standard deviation of the risk index within the time window and normalize it to obtain the rate of change of the risk index of the oil temperature data at the current time point.

[0070] In one embodiment, with the first Using a second as a baseline, trace back a fixed-length time window (e.g., L = 5 seconds), calculate the mean and standard deviation of the risk index within that time window, and then... The absolute value of the difference between the risk index corresponding to the second and the mean of the risk index within the time window (i.e., the absolute deviation) is divided by the standard deviation of the risk index within the time window and then normalized (e.g., using the sigmoid function) to obtain the value of the second. The rate of change of the risk index of oil temperature data per second.

[0071] If the current risk index deviates significantly from the recent average, and this deviation exceeds the recent fluctuation range (i.e., a large standard deviation), it suggests that the current oil temperature may be experiencing abnormal changes. Conversely, if the deviation is small or within the fluctuation range, it indicates that the current oil temperature change is relatively normal. A larger standardized rate of change indicates a more drastic change in the current risk index, suggesting a possible anomaly in the oil temperature. Normalization can standardize this rate of change to a uniform range, facilitating comparisons across different locations and time periods.

[0072] S4: Monitor oil temperature anomalies based on the rate of change and generate an oil temperature monitoring report.

[0073] In one embodiment, the calculation of the first step according to S3 above is performed. The same method can be used to calculate the rate of change of the risk index for oil temperature data at all time points, based on the rate of change of the risk index for oil temperature data at each second.

[0074] Based on the equipment's operating characteristics and safety requirements, a standardized risk index change rate threshold (e.g., 0.6) is set. If the change rate of the risk index of the oil temperature data exceeds the change rate threshold at multiple consecutive time points (e.g., 10 consecutive seconds), the oil temperature change is considered abnormal. When the oil temperature change is abnormal, an alarm is triggered to remind the operator to check the equipment status and an automatic monitoring report is generated. The report includes the specific value and change rate of the oil temperature, the specific monitoring location where the abnormality occurred, and the alarm issuance time, so as to be used for subsequent analysis and maintenance, helping technicians understand the equipment's operating status, analyze the cause of the abnormality, and take corresponding maintenance measures.

[0075] In summary, this invention first acquires pre-processed lubrication system oil temperature data (one or more of the oil tank, oil pump outlet, and main bearing oil temperatures) and press operating condition data (pressing frequency and pressing load) in real time, providing basic data for subsequent calculations and analyses. Simultaneously, it acquires lubricating oil quality and specific heat capacity to aid in calculations. Then, it calculates the power fluctuation degree, heat capacity saturation, location weighting factor, and oil temperature deviation factor, and multiplies them as a risk index. Specifically, the power fluctuation degree reflects the power fluctuation of the press; the heat capacity saturation reflects the relationship between heat accumulation and heat capacity during press operation; and the oil temperature deviation factor indicates the deviation of the current oil temperature from the historical average. Furthermore, using the current time point as a benchmark, it traces back a preset time window, calculating the mean and standard deviation of the risk index within the window, thereby obtaining the rate of change of the oil temperature data risk index at the current time point. This rate of change measures the dynamic changes in the oil temperature risk index. If the rate of change of the oil temperature data risk index exceeds a preset threshold at multiple consecutive time points, an abnormal oil temperature change is determined, triggering an alarm and generating a monitoring report. The report records the oil temperature change value, rate of change, location of the anomaly, and alarm issuance time to facilitate timely problem detection and corrective action.

[0076] It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the scope of protection of this invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for monitoring the oil temperature of an equipment lubrication system, characterized in that, include: Real-time acquisition of oil temperature data of the pre-processed lubrication system and operating condition data of the press during operation; The operating condition data includes the press stamping frequency and stamping load; Calculate the power fluctuation level at the current time point, the heat saturation during press operation, the position weight factor, and the oil temperature deviation factor respectively; and use the product of the power fluctuation level, heat saturation, position weight factor, and oil temperature deviation factor as the risk index of the oil temperature data per second. The process of obtaining the power fluctuation level includes: calculating the average stamping frequency and average stamping load at all time points; calculating the instantaneous power at the current time point and the average instantaneous power at all time points; the instantaneous power at the current time point is the product of the stamping frequency, stamping load, and press power at the current time point; constructing a local time window and calculating the standard deviation and average value of the instantaneous power within the local time window; using the relative deviation between the average instantaneous power within the local time window and the average instantaneous power at all time points as the first parameter, and using the ratio of the standard deviation of the instantaneous power within the local time window to the average instantaneous power at all time points as the second parameter; and then weighted summing the first and second parameters using an exponential function to obtain the power fluctuation level. The process of obtaining the heat capacity saturation includes: calculating the heat accumulation factor and heat capacity factor during the operation of the press; calculating the negative exponent of the ratio of the heat accumulation factor to the heat capacity factor; and subtracting the negative exponent from 1 to obtain the heat capacity saturation during the operation of the press. The process of obtaining the oil temperature deviation factor includes: comparing the oil temperature collected at any location at the current moment with the mean of the oil temperature data at all time points corresponding to that location, calculating the oil temperature deviation, and using the hyperbolic tangent function to transform the oil temperature deviation to obtain the oil temperature deviation factor. Using the current time point as a benchmark, trace back a preset time window, calculate the mean and standard deviation of the risk index within the time window, divide the absolute deviation between the risk index corresponding to the current time point and the mean of the risk index within the time window by the standard deviation of the risk index within the time window and normalize it to obtain the rate of change of the risk index of the oil temperature data at the current time point. The oil temperature is monitored for anomalies based on the rate of change, and an oil temperature monitoring report is generated.

2. The method for monitoring oil temperature in a lubrication system according to claim 1, characterized in that, The oil temperature data of the lubrication system includes one or more of the following: oil tank temperature, oil pump outlet temperature, and main bearing temperature.

3. The method for monitoring oil temperature in a lubrication system of an equipment according to claim 1, characterized in that, The real-time acquisition of pre-processed oil temperature data of the lubrication system and operating condition data during the operation of the press also includes: Obtain the quality and specific heat capacity of the lubricating oil in the lubrication system.

4. The method for monitoring oil temperature in a lubrication system of an equipment according to claim 1, characterized in that, The step of monitoring oil temperature anomalies based on the rate of change and generating an oil temperature monitoring report includes: If the rate of change of the risk index of oil temperature data exceeds the preset rate of change threshold at multiple consecutive time points, the oil temperature change is determined to be abnormal, an alarm is triggered and a monitoring report is generated. The report includes the recorded value and rate of change of oil temperature, the monitoring location where the abnormality occurred, and the time when the alarm was issued.

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