Oil temperature monitoring method for equipment lubricating system

Abstract

The invention relates to the field of data processing, in particular to an oil temperature monitoring method for an equipment lubrication system, which comprises the following steps: acquiring preprocessed oil temperature data of the lubrication system and operation condition data in the operation process of a press machine in real time; calculating a risk index of the oil temperature data per second according to the operation condition data; taking the current time point as a reference, tracing a preset time window, calculating the mean value and the standard deviation of the risk indexes in the time window, dividing the absolute deviation between the risk index corresponding to the current time point and the mean value of the risk indexes in the time window by the standard deviation of the risk indexes in the time window, and normalizing to obtain the risk index corresponding to the current time point. The change rate of the risk index of the oil temperature data at the current time point is obtained; and abnormal monitoring is conducted on the oil temperature according to the change rate, and an oil temperature monitoring report is generated. According to the invention, through multi-dimensional dynamic analysis and risk quantification, the accuracy of oil temperature abnormity monitoring is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and more particularly to a method for monitoring oil temperature of an equipment lubrication system. Background Art

[0002] The lubrication system, a crucial component of high-speed presses, is specifically designed to lubricate and cool the press's moving parts to ensure their proper operation. For example, the press's moving parts (such as the crankshaft, connecting rod, and slider) require lubricating oil from the lubrication system to reduce friction and wear, ensuring efficient and stable operation. Excessively high oil temperatures accelerate oxidation and deterioration, reducing lubrication performance and causing increased wear on mechanical components. Excessively low oil temperatures increase lubricant viscosity and reduce fluidity, compromising lubrication effectiveness. Therefore, monitoring and maintaining oil temperature within a reasonable range can improve press efficiency and reduce energy consumption.

[0003] However, in the lubrication system of actual production equipment, although an alarm device is installed to automatically detect the oil temperature, it does not take into account that the press has different requirements for the lubricating oil temperature under different working conditions. Since the alarm threshold is fixed, when the press is in a special working condition, the actual oil temperature may not reach a dangerous level but trigger a false alarm (false alarm); or when an alarm is really needed, it is ignored (missed alarm) because the oil temperature does not reach the fixed threshold. Missed alarms and false alarms result in the press not receiving accurate oil temperature protection, affecting normal production or damaging equipment. Summary of the Invention

[0004] In order to solve the above-mentioned technical problem of how to improve the accuracy of oil temperature monitoring, the present invention provides the following technical solution.

[0005] A method for monitoring oil temperature of a lubrication system of an equipment, comprising: Real-time acquisition of pre-processed oil temperature data of the lubrication system and operating condition data of the press during operation; the operating condition data includes the press punching frequency and punching load; Calculate the risk index of oil temperature data per second based on operating condition data; Taking the current time point as the benchmark, trace back a preset time window. Calculate the mean and standard deviation of the risk index within this time window. Divide 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 normalize the result. This yields the rate of change of the risk index for the oil temperature data at the current time point. The oil temperature is monitored for abnormalities according to the change rate, and an oil temperature monitoring report is generated.

[0006] This invention collects real-time data on lubrication system oil temperature and press operating conditions (such as press frequency and load), dynamically calculates an oil temperature risk index, and assesses its abnormal rate of change, enabling precise monitoring of equipment status. Specifically, the risk index, combined with operating condition parameters, quantifies the associated risk between oil temperature and operating load. A normal fluctuation baseline is dynamically established based on mean and standard deviation analysis over a sliding time window. By standardizing the deviation (rate of change) of the current risk index, abnormal trends are identified, enabling early warning before abnormal oil temperature leads to failure.

[0007] Preferably, the oil temperature data of the lubrication system is one or more of the oil temperature of the oil tank, the oil temperature of the oil pump outlet, and the oil temperature of the main bearing.

[0008] Preferably, the real-time acquisition of the pre-processed oil temperature data of the lubrication system and the operating condition data of the press during operation further includes: Obtain the mass and specific heat capacity of the lubricating oil in the lubrication system.

[0009] Lubricating oil quality measures the system's heat capacity buffering ability, while specific heat capacity quantifies the oil temperature's sensitivity to heat. Combining these two with operating condition data allows for the construction of a complete thermodynamic analysis model to avoid misjudgments due to missing parameters (e.g., misdiagnosing insufficient oil as excessive load).

[0010] Preferably, the process of obtaining the risk index includes: Calculate the power fluctuation degree at the current time point, the heat capacity saturation during the press operation, the position weight factor, and the oil temperature deviation factor respectively; The product of the power fluctuation degree, the heat capacity saturation, the position weight factor and the oil temperature deviation factor is used as the risk index.

[0011] Convert multi-dimensional hidden dangers into a single value. The degree of power fluctuation indicates that the press is unstable or abnormal. Excessive heat capacity saturation may cause equipment overheating or even burning. Oil temperature deviation reflects lubrication failure or system blockage. The position weight factor distinguishes the risk contribution differences between critical components (such as the spindle) and non-critical components.

[0012] Preferably, the process of obtaining the power fluctuation degree includes: Calculate the average stamping frequency and average stamping load of all collected time points, calculate the instantaneous power at the current time point and the average instantaneous power of all collected time points; the instantaneous power at the current time point is the product of the stamping frequency, stamping load and press power corresponding to the current time point; construct a local time window, calculate the standard deviation and average value of the instantaneous power in the local time window; take the relative deviation between the average value of the instantaneous power in the local time window and the average instantaneous power corresponding to all collected time points as the first parameter, and take the ratio of the standard deviation of the instantaneous power in the local time window to the average instantaneous power corresponding to all collected time points as the second parameter; convert the first parameter and the second parameter using an exponential function and then perform weighted summation to obtain the power fluctuation degree.

[0013] 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 the local time period, that is, the amplitude of power fluctuation around the mean value; the mean value reflects the concentration trend of power within the local time period. These two indicators can focus on the power fluctuation characteristics within a specific time period and provide a more detailed analysis of power fluctuations at different stages.

[0014] Preferably, the process of obtaining the heat capacity saturation includes: Calculate the heat accumulation factor and heat capacity factor during the operation of the press; The negative exponent of the ratio of the heat accumulation factor to the heat capacity factor is calculated, and the heat capacity saturation during the operation of the press is obtained by subtracting the negative exponent value from 1.

[0015] The heat accumulation factor reflects the degree of heat accumulation during press operation, while the heat capacity factor reflects the press's ability to accommodate heat. By calculating the ratio of the two and performing a negative exponential operation, the heat capacity saturation index is derived. This indicator comprehensively reflects the balance between heat accumulation and heat accommodation in the press's current operating state, helping operators intuitively understand the press's thermal status.

[0016] Preferably, the process of obtaining the oil temperature deviation factor includes: The oil temperature collected at any position at the current moment is compared with the mean of the oil temperature data of all time points corresponding to the position, the oil temperature deviation is calculated, and the oil temperature deviation is converted using a hyperbolic tangent function to obtain the oil temperature deviation factor.

[0017] Preferably, the abnormality monitoring of the oil temperature according to the change rate and generating an oil temperature monitoring report includes: If the rate of change of the risk index of the oil temperature data exceeds the preset change rate 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 recording the value and change rate of the oil temperature change, marking the monitoring location where the abnormality occurred, and the time when the alarm was issued.

[0018] The beneficial effects of the present invention are: This method comprehensively assesses the thermal state of the equipment by integrating operating conditions (pressing frequency, load), thermodynamic parameters (lubricant 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 assessment method more accurately reflects the risk level of oil temperature fluctuations and avoids misjudgments caused by a single factor. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a method flow chart of steps S1 to S4 in a method for monitoring oil temperature of an equipment lubrication system according to an embodiment of the present invention.

[0020] Figure 2 The present invention is a schematic diagram of a process for calculating a 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 DESCRIPTION

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0022] Reference Figure 1 A method for monitoring oil temperature of a lubrication system of an equipment includes steps S1 to S4, specifically as follows: S1: Real-time acquisition of pre-processed oil temperature data of the lubrication system and operating condition data of the press during operation.

[0023] In one embodiment, by acquiring and pre-processing the press operating condition data and the lubrication system oil temperature data, a basis is provided for dynamic monitoring and effective management of the lubrication system oil temperature.

[0024] For example, high-precision temperature sensors are installed at various monitoring locations in the lubrication system (oil tanks, main oil pipelines, and key lubrication areas such as the oil chamber near the main bearing). Oil temperature data is collected once per second to ensure continuous and timely data. Oil temperature data at different times and locations is recorded using specific symbols. The number of presses per second, press load, and press run time are obtained from the press control system communication interface. This generates a set of press frequencies and press loads, which together determine the input power, the primary source of oil temperature rise.

[0025] Furthermore, filtering algorithms (such as Kalman filtering and median filtering) are used to perform real-time denoising on the collected oil temperature data and press operating condition data, removing abnormal data points caused by sensor fluctuations or electromagnetic interference. Data from different sensors is standardized to make them comparable, for example, by standardizing the temperature scale of oil temperature data and converting press operating condition data into dimensionless indicators.

[0026] In addition, the mass and specific heat capacity of the lubricating oil need to be obtained.

[0027] S2: Calculate the risk index of oil temperature data per second based on operating condition data.

[0028] It's important to consider that analyzing oil temperature data alone only reveals numerical temperature changes, but doesn't determine the cause of the abnormal oil temperature. During press operation, the load directly affects internal friction. Increased load intensifies friction between moving parts, generating more frictional heat. This excess heat is transferred to the oil, causing it to heat up. 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, generating more heat.

[0029] Therefore, in this application, the oil temperature data is combined with the working condition data such as stamping load and stamping frequency to evaluate the real-time oil temperature risk. Figure 2 , Figure 2 The diagram is a schematic diagram of the calculation process of the risk index of oil temperature data, including steps S20 to S22, as follows: S20: Calculate the power fluctuation degree per second based on the punching frequency and punching load.

[0030] First, the average punching frequency and the average punching load of the punching frequency set and the punching load set are calculated respectively, and the instantaneous power at the current time point and the average instantaneous power corresponding to all collected time points are calculated.

[0031] The instantaneous power at the current time is the product of the stamping frequency, stamping load, and press power at the current time. The press power can be read via the PLC and is in the range of 0 < press power ≤ 1. The average instantaneous power is the average of the instantaneous power values ​​collected at all time points.

[0032] Then, a local time window is constructed, and the standard deviation and mean value of the instantaneous power within the local time window are calculated. The local time window is constructed by taking a sliding window of m seconds before and after the t second as the center.

[0033] Finally, the relative deviation between the average value of the instantaneous power in the local time window and the average instantaneous power corresponding to all the time points collected above is taken as the first parameter (reflecting the short-term deviation), and the ratio of the standard deviation of the instantaneous power in the local time window to the average instantaneous power corresponding to all the time points collected above is taken as the second parameter (reflecting the fluctuation intensity). An exponential function is further used to amplify the influence of the first and second parameters, thereby constructing a comprehensive indicator, namely the power fluctuation degree, to evaluate the impact of power fluctuation on oil temperature stability.

[0034] For example, the power fluctuation degree per second satisfies the following relationship: Where, For the The power fluctuation degree per second, is the first parameter, is the second parameter, For the first The average value of the instantaneous power in the local time window constructed with 1 second as the center, is the average value of the instantaneous power corresponding to all collected time points, For the first The standard deviation of the instantaneous power within a local time window constructed with the second as the center. The first and second parameters each have a 50% weight, integrating the effects of short-term trend deviations and instantaneous fluctuations.

[0035] when When it is large, it means that the power demand of the press at the current moment deviates significantly from the long-term average level, and the power fluctuates violently, which may increase the risk of abnormal oil temperature.

[0036] S21: Calculate the heat capacity saturation of the system during operation.

[0037] Heat capacity saturation is an indicator of a system's ability to accumulate heat. It reflects how close the system is to its heat capacity limit under its current operating conditions. As the system accumulates more heat over time, the heat capacity saturation gradually increases until it approaches saturation.

[0038] First, the heat accumulation factor and heat capacity factor during press operation are calculated respectively.

[0039] The heat accumulation factor reflects the speed and degree of heat accumulation during operation. The heat accumulation factor is the product of the operating time, the proportional coefficient, and the adjustment factor. The operating time is the total time during which S1 collects data. The proportional coefficient is used to adjust the impact of operating time on heat accumulation. For example, the proportional coefficient is 0.1, which can be determined based on experimental data or historical operating data of the equipment. The adjustment factor, for example, is 20, which controls the growth rate of heat capacity saturation. A larger adjustment factor value results in a faster growth of heat capacity saturation, and the system reaches heat capacity saturation more quickly.

[0040] The heat capacity factor reflects the contribution of the lubricant to the heat capacity of the system, that is, the ability of the system to absorb and store heat, that is, the product of the mass of the lubricant and the specific heat capacity of the lubricant. The greater the mass of the lubricant, the stronger the ability of the system to absorb and store heat, the larger the specific heat capacity, the smaller the temperature change when the lubricant absorbs the same amount of heat, the greater the heat capacity of the system, and the larger the heat capacity factor, the greater the heat capacity of the system and the slower the growth of heat capacity saturation.

[0041] Then, the negative exponent of the ratio of the above heat accumulation factor to the heat capacity factor is calculated, and the heat capacity saturation of the system during operation is obtained by subtracting the negative exponent value from 1.

[0042] The exponential decay model shows that when the operating time is short, the heat capacity saturation increases rapidly, which means that the system can quickly accumulate heat when it starts running. As the operating time increases, the growth rate of the heat capacity saturation gradually slows down and eventually tends to saturation (close to 1). This reflects the process in which the system heat capacity gradually reaches saturation as heat accumulates.

[0043] S22: Calculate the risk index of the oil temperature data per second based on the power fluctuation degree, heat capacity saturation, and the impact of different monitoring positions and oil temperature deviation on the oil temperature risk.

[0044] First, consider the impact of different locations on oil temperature risk. Different parts of a press have varying sensitivities to oil temperature fluctuations. For example, oil temperature fluctuations at a critical lubrication point may have a greater impact on the equipment than changes in oil temperature at the oil tank. Therefore, a location weighting factor is introduced to adjust the risk contribution of different locations.

[0045] For example, the position weight factor is set by performing risk level assessment on different monitoring locations (lubrication parts such as bearings and oil tanks). For example, if the risk level of the bearing is extremely high through assessment, the position weight factor of the bearing can be set in the range of 0.9-1.0.

[0046] Secondly, consider the impact of power fluctuations on oil temperature risk. Power may fluctuate during equipment operation. 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 degree (calculated using the S20 parameter above) is introduced.

[0047] Furthermore, consider the impact of operating time on oil temperature risk. As equipment operating time increases, accumulated heat gradually increases, increasing the system's sensitivity to thermal disturbances and leading to an increase in oil temperature risk. Therefore, a heat capacity saturation (calculated using S21 above) is introduced to reflect the impact of operating time on oil temperature risk. The longer the operating time, the greater the heat capacity saturation value.

[0048] Finally, consider the impact of oil temperature deviation on risk. Compare the oil temperature collected at any location at the current moment with the expected steady-state oil temperature at that location (i.e., the mean of the oil temperature data at all time points corresponding to that location) to calculate the oil temperature deviation. In order to quantify the impact of this deviation on risk, the hyperbolic tangent function is used to map the oil temperature deviation to a range of 0 to 1, thereby 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 little impact on the risk; when the oil temperature deviation is large, the tanh function value is close to 1, indicating that the oil temperature deviation has a greater impact on the risk. At the same time, in order to consider the safety requirements of the equipment, the oil temperature deviation is divided by the maximum allowable oil temperature deviation (set according to the actual operating characteristics and safety requirements of the equipment) to ensure that the deviation is evaluated within a reasonable range.

[0049] Then, the above four parts are combined to calculate the risk index of the complete oil temperature data. That is, the following relationship is satisfied: Where, For the The risk index of the oil temperature data at any monitoring location in a second, is the position weight factor of any monitoring location, For the The power fluctuation degree per second, is the heat capacity saturation, is the oil temperature deviation factor at any monitoring position, For the The oil temperature data of any monitoring position in seconds, is the mean value of the oil temperature data at all time points corresponding to any monitoring location, is the maximum allowable deviation of the oil temperature. Finally, 1 is added to the tanh result to ensure that the risk index still has a base value (such as 1) when there is no deviation, and the risk is linearly amplified as the deviation increases.

[0050] S3: Taking the current time point as the 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, and obtain the rate of change of the risk index of the oil temperature data at the current time point.

[0051] In one embodiment, Take seconds as the benchmark, and trace back a fixed length time window (such as L = 5 seconds), calculate the mean and standard deviation of the risk index within the time window, and The absolute value of the difference between the risk index corresponding to the second and the mean of the risk index in the time window (i.e., the absolute deviation) is divided by the standard deviation of the risk index in the time window and normalized (such as the sigmoid function) to obtain the second The rate of change of the risk index of the oil temperature data per second.

[0052] 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), this indicates possible abnormal oil temperature fluctuations. Conversely, if the deviation is small or within the fluctuation range, the current oil temperature fluctuations are relatively normal. A larger standardized rate of change indicates a more dramatic change in the current risk index, suggesting possible abnormal oil temperature fluctuations. Through normalization, this rate of change can be standardized to a uniform range, facilitating comparisons across different locations and time periods.

[0053] S4: Abnormal monitoring of the oil temperature is performed according to the change rate, and an oil temperature monitoring report is generated.

[0054] In one embodiment, the first The change rate of the risk index of the oil temperature data at all time points can be calculated by the same operation as that of the oil temperature data at each second.

[0055] Based on the operating characteristics and safety requirements of the equipment, a standardized risk index change rate threshold (such as 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 (such as 10 consecutive seconds), the oil temperature change is considered abnormal. When the oil temperature changes abnormally, an alarm is triggered to remind the operator to check the equipment status and automatically generate a monitoring report. The report content includes recording the specific value and change rate of the oil temperature change, marking the specific monitoring location where the abnormality occurred, and the time when the alarm was issued, so as to be used for subsequent analysis and maintenance, helping technicians understand the operating status of the equipment, analyze the cause of the abnormality, and take appropriate maintenance measures.

[0056] In general, the present 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 press load) in real time, providing basic data for subsequent calculations and analysis. Lubricant mass and specific heat capacity are also acquired to aid in the calculation. Next, the power fluctuation degree, heat capacity saturation, location weight factor, and oil temperature deviation factor are calculated, and their product is used as the risk index. The power fluctuation degree reflects the fluctuation of press power; heat capacity saturation reflects the relationship between heat accumulation and heat capacity during press operation; and the oil temperature deviation factor represents the deviation of the current oil temperature from the historical mean. Furthermore, starting from the current time point, a preset time window is traced back, and the mean and standard deviation of the risk index within the window are calculated. This results in the rate of change of the oil temperature data risk index at the current time point, which is used to measure 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, the oil temperature change is determined to be abnormal, triggering an alarm and generating a monitoring report. The oil temperature change value, rate of change, location of the abnormality, and alarm issuance time are recorded, allowing for timely identification and action.

[0057] It should be noted that those skilled in the art may make a number of modifications and improvements without departing from the scope of the present invention, and these modifications and improvements fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be based on the appended claims.

Claims

1. A method for monitoring oil temperature of an equipment lubrication system, characterized in that: include: Real-time acquisition of pre-processed oil temperature data of the lubrication system and operating condition data of the press during operation; The operating condition data includes the pressing frequency and pressing load of the press; Calculate the risk index of oil temperature data per second based on operating condition data; Taking the current time point as the benchmark, trace back a preset time window. Calculate the mean and standard deviation of the risk index within this time window. Divide 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 normalize the result. This yields the rate of change of the risk index for the oil temperature data at the current time point. The oil temperature is monitored for abnormalities according to the change rate, and an oil temperature monitoring report is generated.

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

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

4. The oil temperature monitoring method of an equipment lubrication system according to claim 3, characterized in that: The process of obtaining the risk index includes: Calculate the power fluctuation degree at the current time point, the heat capacity saturation during the press operation, the position weight factor, and the oil temperature deviation factor respectively; The product of the power fluctuation degree, the heat capacity saturation, the position weight factor and the oil temperature deviation factor is used as the risk index.

5. The oil temperature monitoring method of an equipment lubrication system according to claim 4, characterized in that: The process of obtaining the power fluctuation degree includes: Calculate the average stamping frequency and average stamping load of all collected time points, calculate the instantaneous power at the current time point and the average instantaneous power of all collected time points; the instantaneous power at the current time point is the product of the stamping frequency, stamping load and press power corresponding to the current time point; construct a local time window, calculate the standard deviation and average value of the instantaneous power in the local time window; take the relative deviation between the average value of the instantaneous power in the local time window and the average instantaneous power corresponding to all collected time points as the first parameter, and take the ratio of the standard deviation of the instantaneous power in the local time window to the average instantaneous power corresponding to all collected time points as the second parameter; convert the first parameter and the second parameter using an exponential function and then perform weighted summation to obtain the power fluctuation degree.

6. The oil temperature monitoring method of an equipment lubrication system according to claim 4, characterized in that: The process of obtaining the heat capacity saturation includes: Calculate the heat accumulation factor and heat capacity factor during the operation of the press; The negative exponent of the ratio of the heat accumulation factor to the heat capacity factor is calculated, and the heat capacity saturation during the operation of the press is obtained by subtracting the negative exponent value from 1.

7. The oil temperature monitoring method of an equipment lubrication system according to claim 4, characterized in that: The process of obtaining the oil temperature deviation factor includes: The oil temperature collected at any position at the current moment is compared with the mean of the oil temperature data of all time points corresponding to the position, the oil temperature deviation is calculated, and the oil temperature deviation is converted using a hyperbolic tangent function to obtain the oil temperature deviation factor.

8. The oil temperature monitoring method of an equipment lubrication system according to claim 1, characterized in that: The abnormal monitoring of the oil temperature according to the change rate and generating an oil temperature monitoring report includes: If the rate of change of the risk index of the oil temperature data exceeds the preset change rate 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 recording the value and change rate of the oil temperature change, marking the monitoring location where the abnormality occurred, and the time when the alarm was issued.

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