A heat dissipation monitoring and early warning method and system for ultra-thin reducers

Abstract

The present disclosure provides a heat dissipation monitoring and early warning method and system for an ultra-thin reducer, which relates to the field of heat dissipation monitoring technology. The method includes: performing simulation modeling to generate a reduction twin model; performing correlation analysis between a high-temperature warning threshold and environmental parameters; matching to obtain a high-temperature correlation warning threshold; judging the box sensor temperature and vibration sensor parameters; when the box correlation warning threshold or the vibration warning threshold is not met, performing infrared image acquisition; judging the temperature distribution information and generating a heat dissipation early warning signal; performing heat dissipation analysis and outputting a first heat dissipation control measure for heat dissipation treatment. The present disclosure can solve the technical problem in the prior art that the accuracy and precision of ultra-thin reducer temperature monitoring are low due to the small size and volume of the ultra-thin reducer, achieve the goal of improving the accuracy and precision of ultra-thin reducer temperature monitoring, and achieve the technical effect of taking targeted and effective measures for heat dissipation treatment in a timely manner.

Description

Technical Field

[0001] The present disclosure relates to the technical field of heat dissipation monitoring, and in particular to a heat dissipation monitoring and early warning method and system for an ultra-thin reducer. Background Art

[0002] The reducer is a reduction transmission device between the prime mover and the working machine. Among them, the ultra-thin reducer is small in size and volume, and it is difficult to install sensors inside the ultra-thin reducer for temperature monitoring, which makes it difficult to dissipate heat through temperature monitoring. At the same time, it is difficult to determine the position of abnormally high-temperature components in time, which may cause the temperature to be too high and the viscosity of the lubricating oil to decrease, and the components inside the ultra-thin reducer cannot be effectively lubricated, thereby causing wear and damage to the components; it may also cause the temperature to be too high and the gear rigidity to decrease, and the concentricity of the shaft to also decrease, and may even cause excessive evaporation or oil leakage, resulting in dry operation of the equipment, thereby causing the burning of bearings and gears, and may also lead to reduced equipment life or equipment scrapping, and may even cause safety accidents such as fire and endanger safety.

[0003] To sum up, there is a technical problem in the prior art that due to the small size and volume of the ultra-thin reducer, it is difficult to install a sensor inside the ultra-thin reducer for temperature monitoring, resulting in low accuracy and precision of ultra-thin reducer temperature monitoring, and poor heat dissipation effect of the ultra-thin reducer. Summary of the Invention

[0004] The present disclosure provides a heat dissipation monitoring and early warning method and system for an ultra-thin reducer, which is used to solve the technical problems in the prior art that, due to the small size and volume of the ultra-thin reducer, it is difficult to install a sensor inside the ultra-thin reducer for temperature monitoring, resulting in low accuracy and precision of ultra-thin reducer temperature monitoring, and poor heat dissipation effect of the ultra-thin reducer.

[0005] According to the first aspect of the present disclosure, a heat dissipation monitoring and early warning method for an ultra-thin reducer is provided, comprising: using the equipment parameters and operating data of the target reducer for simulation modeling to generate a reduction twin model; based on the reduction twin model, performing correlation analysis between the high temperature warning threshold and the environmental parameters of the target reducer, and constructing an environment-high temperature threshold correlation library; collecting the real-time environmental parameters of the target reducer, inputting the environment-high temperature threshold correlation library for matching to obtain the high temperature correlation warning threshold, the high temperature correlation warning threshold including the case correlation warning threshold, the gear correlation warning threshold, the bearing correlation warning threshold and the lubricating oil correlation warning threshold; through the case correlation warning threshold and the vibration warning threshold, the high temperature correlation warning threshold is respectively The box sensor temperature and vibration sensor parameters are judged; when the box sensor temperature does not meet the box-related warning threshold or the vibration sensor parameter does not meet the vibration warning threshold, the infrared thermal imager is activated to collect infrared images of the target reducer to obtain target temperature distribution information; the temperature distribution information is judged according to the gear-related warning threshold, the bearing-related warning threshold, and the lubricating oil-related warning threshold to generate a heat dissipation warning signal, wherein the heat dissipation warning signal includes the components to be dissipated and the corresponding heat dissipation temperature deviation; the components to be dissipated and the corresponding heat dissipation temperature deviation are input into the heat dissipation control channel for heat dissipation analysis, and the first heat dissipation control measure is output for heat dissipation processing.

[0006] According to a second aspect of the present disclosure, a heat dissipation monitoring and early warning system for an ultra-thin reducer is provided, comprising: a reduction twin model generation module, the reduction twin model generation module being used to perform simulation modeling using the equipment parameters and operating data of the target reducer to generate a reduction twin model; an environment-high temperature threshold value association library construction module, the environment-high temperature threshold value association library construction module being used to perform an association analysis between the high temperature warning threshold and the environmental parameters of the target reducer based on the reduction twin model, and to construct an environment-high temperature threshold value association library; a high temperature associated warning threshold acquisition module, the high temperature associated warning threshold acquisition module being used to collect the real-time environmental parameters of the target reducer, input the environmental-high temperature threshold value association library to match and obtain the high temperature associated warning threshold, the high temperature associated warning threshold including the case associated warning threshold, the gear associated warning threshold, the bearing associated warning threshold and the lubricating oil associated warning threshold; a case sensor temperature judgment module, the case sensor temperature judgment module being used to judge the temperature of the case through the case switch The gear-related warning threshold and the vibration warning threshold are used to judge the box sensor temperature and the vibration sensor parameters respectively; the target temperature distribution information acquisition module is used to activate the infrared thermal imager to collect infrared images of the target reducer when the box sensor temperature does not meet the box-related warning threshold or the vibration sensor parameters do not meet the vibration warning threshold, so as to obtain the target temperature distribution information; the heat dissipation warning signal generation module is used to judge the temperature distribution information according to the gear-related warning threshold, the bearing-related warning threshold, and the lubricating oil-related warning threshold, and generate a heat dissipation warning signal, wherein the heat dissipation warning signal includes the components to be dissipated and the corresponding heat dissipation temperature deviation; the first heat dissipation control measure output module is used to input the components to be dissipated and the corresponding heat dissipation temperature deviation into the heat dissipation control channel for heat dissipation analysis, and output the first heat dissipation control measure for heat dissipation processing.

[0007] One or more technical solutions provided in the present disclosure have at least the following technical effects or advantages: according to the method adopted in the present disclosure, simulation modeling is performed by utilizing the equipment parameters and operating data of the target reducer to generate a reduction twin model; based on the reduction twin model, the correlation analysis between the high temperature warning threshold and the environmental parameters of the target reducer is performed, and an environment-high temperature threshold correlation library is constructed; the real-time environmental parameters of the target reducer are collected, and the environment-high temperature threshold correlation library is matched to obtain the high temperature correlation warning threshold, and the high temperature correlation warning threshold includes the case-related warning threshold, the gear-related warning threshold, the bearing-related warning threshold and the lubricating oil-related warning threshold; the case sensor temperature and the vibration sensor parameter are judged respectively by the case-related warning threshold and the vibration warning threshold; when the case sensor temperature does not meet the case-related warning threshold or the vibration sensor parameter does not meet the vibration warning threshold, the infrared thermal imager is activated to capture infrared images of the target reducer. , obtain target temperature distribution information; judge the temperature distribution information according to the gear-related warning threshold, the bearing-related warning threshold, and the lubricating oil-related warning threshold, and generate a heat dissipation warning signal, wherein the heat dissipation warning signal includes the component to be dissipated and the corresponding heat dissipation temperature deviation; input the component to be dissipated and the corresponding heat dissipation temperature deviation into the heat dissipation control channel for heat dissipation analysis, and output a first heat dissipation control measure for heat dissipation treatment, which solves the technical problem in the prior art that due to the small size and volume of the ultra-thin reducer, it is difficult to install a sensor inside the ultra-thin reducer for temperature monitoring, resulting in low accuracy and precision of ultra-thin reducer temperature monitoring, and poor heat dissipation effect of the ultra-thin reducer, achieves the goal of improving the accuracy and precision of ultra-thin reducer temperature monitoring, timely determines the position of abnormally high-temperature components, improves the efficiency of taking targeted and effective measures for heat dissipation treatment, and achieves the technical effect of improving the operation safety of the ultra-thin reducer.

[0008] It should be understood that the contents described in this section are not intended to indicate the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the present disclosure or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without any creative work.

[0010] Figure 1 A schematic flow chart of a heat dissipation monitoring and early warning method for an ultra-thin reducer provided in an embodiment of the present disclosure;

[0011] Figure 2 A schematic structural diagram of a heat dissipation monitoring and early warning system for an ultra-thin reducer provided in an embodiment of the present disclosure.

[0012] Explanation of the accompanying symbols: deceleration twin model generation module 11, environment-high temperature threshold association library construction module 12, high temperature association warning threshold acquisition module 13, box sensor temperature judgment module 14, target temperature distribution information acquisition module 15, heat dissipation warning signal generation module 16, first heat dissipation control measure output module 17. DETAILED DESCRIPTION

[0013] The following description of exemplary embodiments of the present disclosure is provided in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0014] Example 1

[0015] The embodiment of the present disclosure provides a heat dissipation monitoring and early warning method for an ultra-thin reducer, which is referred to Figure 1 Illustratively, the method comprises:

[0016] Use the equipment parameters and operating data of the target reducer for simulation modeling to generate a twin reducer model;

[0017] Specifically, the target reducer is the reducer to be monitored for heat dissipation. The device parameters and operating data of the target reducer are obtained. The device parameters refer to parameters such as the reduction ratio, torque, and power, while the operating data refers to operating parameters such as the input and output speeds. The target reducer's device parameters and operating data are used to simulate and model the target reducer, generating a simulation twin model of the target reducer, i.e., a reduction twin model.

[0018] Based on the twin model of the reduction gear, the correlation analysis between the high temperature warning threshold and environmental parameters of the target reduction gear is carried out to build an environment-high temperature threshold correlation library;

[0019] Specifically, based on the twin reduction model, multiple environmental parameters (temperature and pressure) are derived by combining the target reducer's normal operating temperature and pressure ranges. These environmental parameters are fed into the twin reduction model for simulation, resulting in high-temperature warning thresholds that vary with these parameters. The high-temperature warning thresholds are determined by referencing the correlation between operating temperature and pressure. This library of environmental-high-temperature threshold associations is then constructed to improve the accuracy of warning threshold settings.

[0020] Collecting real-time environmental parameters of the target reducer, inputting them into the environment-high temperature threshold correlation library for matching to obtain high temperature correlation warning thresholds, wherein the high temperature correlation warning thresholds include the housing correlation warning threshold, the gear correlation warning threshold, the bearing correlation warning threshold, and the lubricating oil correlation warning threshold;

[0021] Specifically, the real-time environmental parameters of the target reducer are collected in real time through temperature metering equipment and air pressure collection equipment, and the real-time environmental parameters are input into the environment-high temperature threshold association library for matching to obtain the high-temperature associated warning threshold corresponding to the real-time environmental parameters, wherein the high-temperature associated warning threshold includes the warning thresholds of the housing, gears, bearings and lubricating oil, namely, the housing associated warning threshold, the gear associated warning threshold, the bearing associated warning threshold and the lubricating oil associated warning threshold.

[0022] The box body sensor temperature and vibration sensor parameters are judged respectively by the box body associated warning threshold and vibration warning threshold;

[0023] Specifically, the box sensor temperature and vibration sensor parameters outside the box are collected and acquired, the box-related warning threshold and the vibration warning threshold are extracted, and the box sensor temperature and vibration sensor parameters are respectively judged to see whether they meet the box-related warning threshold and the vibration warning threshold.

[0024] When the box sensor temperature does not meet the box-related warning threshold or the vibration sensor parameter does not meet the vibration warning threshold, activating the infrared thermal imager to collect infrared images of the target reducer to obtain target temperature distribution information;

[0025] Specifically, when the box sensor temperature does not meet the box-related warning threshold or the vibration sensor parameter does not meet the vibration warning threshold, that is, when either of these conditions is not met, the infrared thermal imager is activated to capture infrared images of the target reducer and obtain target temperature distribution information. Activating the infrared thermal imager for internal monitoring when a warning is detected ensures monitoring quality while conserving infrared monitoring resources.

[0026] The temperature distribution information is judged according to the gear-related warning threshold, the bearing-related warning threshold, and the lubricating oil-related warning threshold to generate a heat dissipation warning signal, wherein the heat dissipation warning signal includes the heat dissipation component and the corresponding heat dissipation temperature deviation;

[0027] Specifically, the temperature distribution information is judged based on the gear-related warning threshold, the bearing-related warning threshold, and the lubricating oil-related warning threshold, and it is judged whether the gear temperature, the bearing temperature, and the lubricating oil temperature are greater than or equal to the gear-related warning threshold, the bearing-related warning threshold, and the lubricating oil-related warning threshold respectively. When it is judged that the gear temperature, the bearing temperature, and the lubricating oil temperature are greater than or equal to the gear-related warning threshold, the bearing-related warning threshold, and the lubricating oil-related warning threshold respectively, corresponding heat dissipation warning signals are generated respectively, and the heat dissipation warning signals are obtained by combination, wherein the heat dissipation warning signal includes the component to be dissipated and the corresponding heat dissipation temperature deviation. The component to be dissipated refers to the component whose temperature reaches the warning threshold, generates a heat dissipation warning signal, and then dissipates heat. The heat dissipation temperature deviation refers to the temperature of the component to be dissipated that is greater than the corresponding warning threshold, which is generated when the temperature is greater than the corresponding warning threshold, and is used for heat dissipation.

[0028] The component to be cooled and the corresponding cooling temperature deviation are input into a cooling control channel for cooling analysis, and a first cooling control measure is output for cooling processing.

[0029] Specifically, a search is performed based on big data to obtain a set of heat dissipation control measures. A heat dissipation control channel is constructed for training heat dissipation control. The components to be dissipated, the corresponding heat dissipation temperature deviation, and the heat dissipation control measures set are input into the heat dissipation control channel as heat dissipation input data, and the heat dissipation input data is divided into heat dissipation training data and heat dissipation verification data. The division ratio is obtained by a technician in this field according to the actual situation. For example, the division ratio of heat dissipation training data and heat dissipation verification data is 6:4. The heat dissipation control channel is trained with the heat dissipation training data. When the output data of the heat dissipation control channel tends to be stable, the heat dissipation control channel is verified with the heat dissipation verification data. The heat dissipation verification accuracy is compared with the heat dissipation verification accuracy threshold. When the heat dissipation verification accuracy is greater than or equal to the heat dissipation verification accuracy threshold, the heat dissipation control channel is obtained, and the obtained heat dissipation control measures are output as the first heat dissipation control measures corresponding to the components to be dissipated for heat dissipation processing.

[0030] Among them, this embodiment can solve the technical problems in the prior art that due to the small size and volume of the ultra-thin reducer, it is difficult to install sensors inside the ultra-thin reducer for temperature monitoring, resulting in low accuracy and precision of ultra-thin reducer temperature monitoring, and poor heat dissipation effect of the ultra-thin reducer. The goal of improving the accuracy and precision of ultra-thin reducer temperature monitoring can be achieved, the location of abnormally high-temperature components can be determined in time, the efficiency of taking targeted and effective measures for heat dissipation can be improved, and the technical effect of improving the operation safety of the ultra-thin reducer can be achieved.

[0031] The method provided in the embodiment of the present disclosure also includes:

[0032] Obtain the temperature range and air pressure range for normal operation of the target reducer;

[0033] Selecting a first temperature value within the temperature range, performing parameter expansion within the pressure range according to a preset step size to obtain multiple pressure values, and combining them to generate multiple environmental parameters;

[0034] Based on the multiple environmental parameters and the deceleration twin model, a simulation operation is performed according to preset operating data to obtain multiple high-temperature warning thresholds, including a box warning threshold, a gear warning threshold, a bearing warning threshold, and a lubricating oil warning threshold;

[0035] With the environmental parameters as the root node and the corresponding high temperature warning threshold as the child node, an environment-high temperature threshold association library is generated.

[0036] Specifically, the historical working records of the target reducer are obtained, and the temperature range and air pressure range for normal operation are extracted. When the target reducer does not generate an early warning, it indicates that the target reducer is operating normally.

[0037] Furthermore, a temperature value is randomly extracted within the temperature range as the first temperature value, and parameter expansion is performed within the air pressure range according to a preset step size to obtain multiple air pressure values, which are combined to generate multiple environmental parameters. The preset step size is the adjustment range for controlling the temperature. For example, if the preset step size is 5 to 10 steps, the larger the preset step size, the more the temperature adjustment, and vice versa. The higher the temperature, the higher the air pressure, and vice versa. Based on the first temperature value as the adjustment basis, the temperature is adjusted to obtain multiple air pressure values ​​that change with the temperature. The adjusted temperature value and the corresponding air pressure value are combined to obtain multiple environmental parameters.

[0038] Furthermore, the reduction twin model is fed with multiple environmental parameters and preset operating data for simulation. The preset operating data can be the average of daily operating load and power. This generates multiple high-temperature warning thresholds, including those for the housing, gears, bearings, and lubricant. Under normal circumstances, the following temperature thresholds are set: housing temperature warning threshold: 50°C; gear temperature warning threshold: 90°C; bearing temperature warning threshold: 80°C; and lubricant temperature warning threshold: 60°C. The warning temperatures can be determined through a comprehensive assessment based on lubricant type, reducer type, and operating conditions. Under different operating environments, the housing, gear, bearing, and lubricant warning thresholds may vary. For example, as the ambient temperature increases, the warning thresholds decrease. For an ambient temperature of 25°C, the gear warning threshold is 90°C; and for an ambient temperature of 30°C, the gear warning threshold decreases to 88°C.

[0039] Furthermore, with the environmental parameter as the root node and the corresponding high temperature warning threshold as the child node, the environmental parameter and the high temperature warning threshold are associated to generate an environmental-high temperature threshold association library. Obtaining the environmental-high temperature threshold association library can improve the efficiency of obtaining the corresponding high temperature warning threshold from the environmental parameter.

[0040] The method provided in the embodiment of the present disclosure also includes:

[0041] Updating the cabinet-related warning threshold according to the first preset tolerance interval to obtain an updated cabinet-related warning threshold;

[0042] Data is collected through temperature sensors and vibration sensors placed on the periphery of the target reducer housing to obtain the housing sensing temperature and vibration sensing parameters;

[0043] judging the cabinet sensor temperature according to the updated cabinet association threshold;

[0044] The vibration sensing parameter is judged according to the vibration warning threshold.

[0045] Specifically, a first preset tolerance interval is obtained. The first preset tolerance interval refers to the buffer range of the cabinet-related warning threshold. That is, when the cabinet temperature approaches the cabinet-related warning threshold, a temperature warning is issued if the buffer temperature is met. The first preset tolerance interval can be customized by those skilled in the art based on actual conditions. For example, if the cabinet-related warning threshold is 50 degrees Celsius, the cabinet-related warning threshold is updated to 48 degrees Celsius. The cabinet-related warning threshold is updated based on the first preset tolerance interval to obtain an updated cabinet-related warning threshold.

[0046] Furthermore, a temperature sensor and a vibration sensor are arranged on the periphery of the target reducer housing, and temperature data and vibration data are collected by the temperature sensor and the vibration sensor to obtain the housing sensing temperature and vibration sensing parameters.

[0047] Furthermore, the cabinet sensor temperature is evaluated based on the updated cabinet-related threshold to determine whether the cabinet sensor temperature meets the updated cabinet-related threshold. Furthermore, the vibration sensor parameter is evaluated based on the vibration warning threshold to determine whether the vibration sensor parameter meets the vibration warning threshold. Evaluating both the cabinet sensor temperature and the vibration sensor parameter can improve the efficiency of determining whether the warning threshold is met.

[0048] The method provided in the embodiment of the present disclosure also includes:

[0049] Among them, there are four temperature sensors, which are respectively arranged in the upper, lower, left and right directions of the target reducer housing. The housing sensor temperature is the average of the temperature data collected by the four temperature sensors;

[0050] There are two vibration sensors, which are respectively arranged in the upper and lower directions of the target reducer housing. The vibration sensing parameter is the average value of the vibration sensing parameters collected by the two vibration sensors.

[0051] Specifically, there are four temperature sensors located on the periphery of the target reducer housing, one located in the top, bottom, left, and right directions of the target reducer housing. The top and bottom directions are relative directions, and the left and right directions are relative directions. Furthermore, the housing sensor temperature is the average of the four temperature data collected by the four temperature sensors.

[0052] Furthermore, there are two vibration sensors arranged on the periphery of the target reducer housing, one in the upper and lower directions of the target reducer housing, and the upper and lower directions are two opposite directions, which are the same as the upper and lower directions of the temperature sensors. The vibration sensing parameter is the average of the two vibration sensing parameters collected by the two vibration sensors. Among them, the vibration sensing parameter refers to the amplitude and vibration frequency. Among them, the deployment of multiple temperature sensors and vibration sensors can improve the accuracy of obtaining the housing sensor temperature and vibration sensor, and avoid inaccurate data caused by external factors.

[0053] The method provided in the embodiment of the present disclosure also includes:

[0054] Based on industrial big data, the target reducer is used as the search condition to obtain multiple sample vibration parameters of the reducer under abnormal high temperature conditions;

[0055] Performing cluster analysis on the plurality of sample vibration parameters, extracting sample vibration parameters that meet a preset number threshold in the clustering results, and obtaining a plurality of valid vibration parameter sets;

[0056] Setting weights based on the number of valid vibration parameters in the valid vibration parameter set, performing weighted calculation on the multiple valid vibration parameter sets to obtain an initial vibration warning threshold;

[0057] The initial vibration warning threshold is updated according to the second preset tolerance interval to obtain the vibration warning threshold.

[0058] Specifically, industrial big data refers to data such as the operating parameters of industrial equipment. Based on this data, a search is performed using the target reducer as the search criteria to obtain multiple sample vibration parameters under abnormal high-temperature conditions. Specifically, the vibration parameters of the reducer are obtained when the reducer meets the high-temperature threshold. The abnormal high-temperature condition of the reducer includes the high-temperature warning threshold of the target reducer.

[0059] Furthermore, cluster analysis is performed on the multiple sample vibration parameters, where clustering can be performed based on a clustering threshold. For example, the clustering threshold can be customized by a person skilled in the art based on actual conditions, thereby obtaining clustering results. From the clustering results, clustering results that meet a preset number threshold, i.e., sample vibration parameters that meet the preset number threshold, are extracted to obtain multiple valid vibration parameter sets. The preset number threshold can be customized by a person skilled in the art based on actual conditions.

[0060] Furthermore, the number of valid vibration parameters in the valid vibration parameter set is counted. Weights are set based on the number of valid vibration parameters in the valid vibration parameter set, and a weighted calculation is performed on the multiple valid vibration parameter sets according to the corresponding weights to obtain an initial vibration warning threshold. The greater the number counted, the higher the weight set, and vice versa.

[0061] Furthermore, the initial vibration warning threshold is updated based on a second preset tolerance interval to obtain the vibration warning threshold. The second preset tolerance interval refers to a buffer zone for vibration warnings. When the vibration value approaches the vibration warning threshold and the vibration parameter meets the buffered vibration interval, a vibration warning is issued. The second preset tolerance interval is customizable by those skilled in the art based on actual conditions. Obtaining the vibration warning threshold under high temperature anomalies can improve the accuracy of vibration warnings.

[0062] The method provided in the embodiment of the present disclosure also includes:

[0063] The infrared image of the target reducer is collected by an infrared thermal imager to obtain the target infrared image;

[0064] performing denoising processing on the target infrared image to obtain a denoised infrared image;

[0065] Performing dimensionality reduction processing on the denoised infrared image to obtain an infrared dimensionality reduced image;

[0066] Temperature information is extracted based on the infrared dimension reduction image to obtain target temperature distribution information.

[0067] Specifically, an infrared image of the target reducer is captured using an infrared thermal imager to obtain an infrared image as the target infrared image. The target infrared image is denoised to obtain a denoised infrared image. Denoising can be performed using methods such as wavelet threshold denoising and median filtering. For example, the signal of the target infrared image is decomposed using wavelet transform, and then the wavelet coefficients are thresholded to remove the noisy wavelet coefficients, and finally the signal is reconstructed. When processing infrared images, noise can be effectively removed while retaining the image's detailed information. Accordingly, median filtering is a nonlinear signal processing technique. For target infrared images, median filtering can remove noise caused by abnormal pixels while maintaining the image's edge details.

[0068] Furthermore, the denoised infrared image is subjected to dimensionality reduction processing to obtain an infrared reduced-dimensional image. Dimensionality reduction processing reduces the amount of data processing, improves the efficiency of identifying abnormally high-temperature components, and thus achieves a timely response effect.

[0069] Furthermore, temperature information is extracted from the reduced-dimensional infrared image to obtain target temperature distribution information. The reduced-dimensional infrared image can be interpolated using a bilinear interpolation algorithm to obtain target temperature distribution information. Processing the captured infrared image to obtain target temperature distribution information can improve the accuracy of temperature warnings.

[0070] The method provided in the embodiment of the present disclosure also includes:

[0071] Acquire a gear temperature extreme value, a bearing temperature extreme value, and a lubricating oil temperature extreme value based on the target temperature distribution information;

[0072] When the gear temperature extreme value is greater than or equal to the gear-related warning threshold, a gear heat dissipation warning signal is generated, wherein the gear heat dissipation warning signal includes a gear heat dissipation temperature deviation;

[0073] When the extreme value of the bearing temperature is greater than or equal to the bearing-related warning threshold, a bearing heat dissipation warning signal is generated;

[0074] When the extreme value of the lubricating oil temperature is greater than or equal to the lubricating oil associated warning threshold, generating a lubricating oil heat dissipation warning signal;

[0075] A heat dissipation warning signal is composed according to the gear heat dissipation warning signal, the bearing heat dissipation warning signal, and the lubricating oil heat dissipation warning signal.

[0076] Specifically, based on the target temperature distribution information, the extreme values ​​of the temperatures of the gear, the bearing, and the lubricating oil are respectively acquired as the gear temperature extreme value, the bearing temperature extreme value, and the lubricating oil temperature extreme value.

[0077] Furthermore, when the extreme gear temperature is greater than or equal to the gear-related warning threshold, indicating high gear temperature, a gear heat dissipation warning signal is generated. The gear heat dissipation warning signal includes a gear heat dissipation temperature deviation. The gear heat dissipation temperature deviation refers to the difference between the gear temperature after continuing to rise after reaching the gear-related warning threshold and the temperature after lowering to the gear-related warning threshold.

[0078] Furthermore, when the extreme bearing temperature is greater than or equal to the bearing-related warning threshold, indicating high bearing temperature, a bearing heat dissipation warning signal is generated. This bearing heat dissipation warning signal includes a bearing heat dissipation temperature deviation. The bearing heat dissipation temperature deviation refers to the difference between the bearing temperature after continuing to rise after reaching the bearing-related warning threshold and the temperature after lowering to the bearing-related warning threshold.

[0079] Furthermore, when the extreme lubricating oil temperature is greater than or equal to the lubricating oil-associated warning threshold, indicating that the lubricating oil temperature is high, a lubricating oil heat dissipation warning signal is generated. The lubricating oil heat dissipation warning signal includes a lubricating oil heat dissipation temperature deviation. The lubricating oil heat dissipation temperature deviation refers to the difference between the lubricating oil temperature after continuing to rise after reaching the lubricating oil-associated warning threshold and the temperature after lowering to the lubricating oil-associated warning threshold.

[0080] Furthermore, the gear heat dissipation warning signal, the bearing heat dissipation warning signal, and the lubricating oil heat dissipation warning signal are combined to form a heat dissipation warning signal. Acquiring the heat dissipation warning signal can improve the accuracy of heat dissipation warning.

[0081] Example 2

[0082] Based on the same inventive concept as the heat dissipation monitoring and early warning method of an ultra-thin reducer in the aforementioned embodiment, Figure 2 As shown, the present disclosure also provides a heat dissipation monitoring and early warning system for an ultra-thin reducer, the system comprising:

[0083] A reduction twin model generation module 11 is used to perform simulation modeling using equipment parameters and operating data of a target reducer to generate a reduction twin model;

[0084] An environment-high temperature threshold correlation library construction module 12 is used to perform correlation analysis between high temperature warning thresholds and environmental parameters on a target reducer based on a reduction twin model, and to construct an environment-high temperature threshold correlation library;

[0085] A high-temperature associated warning threshold acquisition module 13 is used to collect real-time environmental parameters of the target reducer, input the environmental-high-temperature threshold association library for matching, and obtain high-temperature associated warning thresholds. The high-temperature associated warning thresholds include a box-related warning threshold, a gear-related warning threshold, a bearing-related warning threshold, and a lubricating oil-related warning threshold.

[0086] The box body sensor temperature judgment module 14 is used to judge the box body sensor temperature and vibration sensor parameters respectively according to the box body associated warning threshold and vibration warning threshold;

[0087] a target temperature distribution information obtaining module 15, configured to activate an infrared thermal imager to capture an infrared image of the target reducer to obtain target temperature distribution information when the box sensor temperature does not meet the box-related warning threshold or the vibration sensor parameter does not meet the vibration warning threshold;

[0088] a heat dissipation warning signal generating module 16, configured to determine the temperature distribution information based on the gear-related warning threshold, the bearing-related warning threshold, and the lubricant-related warning threshold, and generate a heat dissipation warning signal, wherein the heat dissipation warning signal includes the heat dissipation component and the corresponding heat dissipation temperature deviation;

[0089] The first heat dissipation control measure output module 17 is used to input the component to be dissipated and the corresponding heat dissipation temperature deviation into the heat dissipation control channel for heat dissipation analysis, and output the first heat dissipation control measure for heat dissipation processing.

[0090] Furthermore, the environment-high temperature threshold value association library construction module 12 further includes:

[0091] Obtain the temperature range and pressure range for normal operation of the target reducer;

[0092] Selecting a first temperature value within the temperature range, performing parameter expansion within the pressure range according to a preset step size to obtain multiple pressure values, and combining them to generate multiple environmental parameters;

[0093] Based on the multiple environmental parameters and the deceleration twin model, a simulation operation is performed according to preset operating data to obtain multiple high-temperature warning thresholds, including a box warning threshold, a gear warning threshold, a bearing warning threshold, and a lubricating oil warning threshold;

[0094] With the environmental parameters as the root node and the corresponding high temperature warning threshold as the child node, an environment-high temperature threshold association library is generated.

[0095] Furthermore, the box body temperature sensing judgment module 14 also includes:

[0096] Updating the cabinet-related warning threshold according to the first preset tolerance interval to obtain an updated cabinet-related warning threshold;

[0097] Data is collected through temperature sensors and vibration sensors placed on the periphery of the target reducer housing to obtain the housing sensing temperature and vibration sensing parameters;

[0098] judging the cabinet sensor temperature according to the updated cabinet association threshold;

[0099] The vibration sensing parameter is judged according to the vibration warning threshold.

[0100] Furthermore, the environment-high temperature threshold value association library construction module 12 further includes:

[0101] Among them, there are four temperature sensors, which are respectively arranged in the upper, lower, left and right directions of the target reducer housing. The housing sensor temperature is the average of the temperature data collected by the four temperature sensors;

[0102] There are two vibration sensors, which are respectively arranged in the upper and lower directions of the target reducer housing. The vibration sensing parameter is the average value of the vibration sensing parameters collected by the two vibration sensors.

[0103] Furthermore, the environment-high temperature threshold value association library construction module 12 further includes:

[0104] Based on industrial big data, the target reducer is used as the search condition to obtain multiple sample vibration parameters of the reducer under abnormal high temperature conditions;

[0105] Performing cluster analysis on the plurality of sample vibration parameters, extracting sample vibration parameters that meet a preset number threshold in the clustering results, and obtaining a plurality of valid vibration parameter sets;

[0106] Setting weights based on the number of valid vibration parameters in the valid vibration parameter set, performing weighted calculation on the multiple valid vibration parameter sets to obtain an initial vibration warning threshold;

[0107] The initial vibration warning threshold is updated according to the second preset tolerance interval to obtain the vibration warning threshold.

[0108] Furthermore, the target temperature distribution information obtaining module 15 further includes:

[0109] The infrared image of the target reducer is collected by an infrared thermal imager to obtain the target infrared image;

[0110] performing denoising processing on the target infrared image to obtain a denoised infrared image;

[0111] Performing dimensionality reduction processing on the denoised infrared image to obtain an infrared dimensionality reduced image;

[0112] Temperature information is extracted based on the infrared dimension reduction image to obtain target temperature distribution information.

[0113] Furthermore, the heat dissipation warning signal generating module 16 further includes:

[0114] Acquire a gear temperature extreme value, a bearing temperature extreme value, and a lubricating oil temperature extreme value based on the target temperature distribution information;

[0115] When the gear temperature extreme value is greater than or equal to the gear-related warning threshold, a gear heat dissipation warning signal is generated, wherein the gear heat dissipation warning signal includes a gear heat dissipation temperature deviation;

[0116] When the extreme value of the bearing temperature is greater than or equal to the bearing-related warning threshold, a bearing heat dissipation warning signal is generated;

[0117] When the extreme value of the lubricating oil temperature is greater than or equal to the lubricating oil associated warning threshold, generating a lubricating oil heat dissipation warning signal;

[0118] A heat dissipation warning signal is composed according to the gear heat dissipation warning signal, the bearing heat dissipation warning signal, and the lubricating oil heat dissipation warning signal.

[0119] The specific example of the heat dissipation monitoring and early warning method for an ultra-thin reducer in the aforementioned embodiment 1 is also applicable to the heat dissipation monitoring and early warning system for an ultra-thin reducer in this embodiment. Through the detailed description of the heat dissipation monitoring and early warning method for an ultra-thin reducer, those skilled in the art can clearly understand the heat dissipation monitoring and early warning system for an ultra-thin reducer in this embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail here. As for the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant details, please refer to the method description.

[0120] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions of this disclosure can be achieved. This is not a limitation herein.

[0121] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A heat dissipation monitoring and early warning method for an ultra-thin reducer, characterized in that: The method comprises: Use the equipment parameters and operating data of the target reducer for simulation modeling to generate a twin reducer model; Based on the twin model of the reduction gear, the correlation analysis between the high temperature warning threshold and environmental parameters of the target reduction gear is carried out to build an environment-high temperature threshold correlation library; Collecting real-time environmental parameters of the target reducer, inputting them into the environment-high temperature threshold correlation library for matching to obtain high temperature correlation warning thresholds, wherein the high temperature correlation warning thresholds include the housing correlation warning threshold, the gear correlation warning threshold, the bearing correlation warning threshold, and the lubricating oil correlation warning threshold; The box body sensor temperature and vibration sensor parameters are judged respectively by the box body associated warning threshold and vibration warning threshold; When the box sensor temperature does not meet the box-related warning threshold or the vibration sensor parameter does not meet the vibration warning threshold, activating the infrared thermal imager to collect infrared images of the target reducer to obtain target temperature distribution information; The temperature distribution information is judged according to the gear-related warning threshold, the bearing-related warning threshold, and the lubricating oil-related warning threshold to generate a heat dissipation warning signal, wherein the heat dissipation warning signal includes the heat dissipation component and the corresponding heat dissipation temperature deviation; Inputting the heat dissipation component and the corresponding heat dissipation temperature deviation into the heat dissipation control channel for heat dissipation analysis, and outputting a first heat dissipation control measure for heat dissipation processing; Based on the reduction twin model, the correlation analysis between the high temperature warning threshold and the environmental parameters of the target reducer is performed to build an environment-high temperature threshold correlation library, including: Obtain the temperature range and pressure range for normal operation of the target reducer; Selecting a first temperature value within the temperature range, performing parameter expansion within the pressure range according to a preset step size to obtain multiple pressure values, and combining them to generate multiple environmental parameters; Based on the multiple environmental parameters and the deceleration twin model, a simulation operation is performed according to preset operating data to obtain multiple high-temperature warning thresholds, including a box warning threshold, a gear warning threshold, a bearing warning threshold, and a lubricating oil warning threshold; With the environmental parameters as the root node and the corresponding high temperature warning threshold as the child node, an environment-high temperature threshold association library is generated.

2. The method according to claim 1, wherein The step of judging the box body sensor temperature and vibration sensor parameters respectively by using the box body associated warning threshold and the vibration warning threshold comprises: Updating the cabinet-related warning threshold according to the first preset tolerance interval to obtain an updated cabinet-related warning threshold; Data is collected through temperature sensors and vibration sensors placed on the periphery of the target reducer housing to obtain the housing sensing temperature and vibration sensing parameters; judging the box sensor temperature according to the updated box-related warning threshold; The vibration sensing parameter is judged according to the vibration warning threshold.

3. The method according to claim 2, wherein The method also include: Among them, there are four temperature sensors, which are respectively arranged in the upper, lower, left and right directions of the target reducer housing. The housing sensor temperature is the average of the temperature data collected by the four temperature sensors; There are two vibration sensors, which are respectively arranged in the upper and lower directions of the target reducer housing. The vibration sensing parameter is the average value of the vibration sensing parameters collected by the two vibration sensors.

4. The method according to claim 2, wherein The method further comprises: Based on industrial big data, the target reducer is used as the search condition to obtain multiple sample vibration parameters of the reducer under abnormal high temperature conditions; Performing cluster analysis on the plurality of sample vibration parameters, extracting sample vibration parameters that meet a preset number threshold in the clustering results, and obtaining a plurality of valid vibration parameter sets; Setting weights based on the number of valid vibration parameters in the valid vibration parameter set, performing weighted calculation on the multiple valid vibration parameter sets to obtain an initial vibration warning threshold; The initial vibration warning threshold is updated according to the second preset tolerance interval to obtain the vibration warning threshold.

5. The method according to claim 1, wherein The activating the infrared thermal imager to collect infrared images of the target reducer to obtain target temperature distribution information includes: The infrared image of the target reducer is collected by an infrared thermal imager to obtain the target infrared image; performing denoising processing on the target infrared image to obtain a denoised infrared image; Performing dimensionality reduction processing on the denoised infrared image to obtain an infrared dimensionality reduced image; Temperature information is extracted based on the infrared dimension reduction image to obtain target temperature distribution information.

6. The method according to claim 1, wherein The determining the temperature distribution information based on the gear-related warning threshold, the bearing-related warning threshold, and the lubricating oil-related warning threshold to generate a heat dissipation warning signal includes: Acquire a gear temperature extreme value, a bearing temperature extreme value, and a lubricating oil temperature extreme value based on the target temperature distribution information; When the gear temperature extreme value is greater than or equal to the gear-related warning threshold, a gear heat dissipation warning signal is generated, wherein the gear heat dissipation warning signal includes a gear heat dissipation temperature deviation; When the extreme value of the bearing temperature is greater than or equal to the bearing-related warning threshold, a bearing heat dissipation warning signal is generated; When the extreme value of the lubricating oil temperature is greater than or equal to the lubricating oil associated warning threshold, generating a lubricating oil heat dissipation warning signal; A heat dissipation warning signal is composed according to the gear heat dissipation warning signal, the bearing heat dissipation warning signal, and the lubricating oil heat dissipation warning signal.

7. A heat dissipation monitoring and early warning system for an ultra-thin reducer, characterized in that: The system is used to perform the method according to claims 1 to 6, and the system includes: A reduction twin model generation module, which is used to perform simulation modeling using equipment parameters and operating data of a target reducer to generate a reduction twin model; An environment-high temperature threshold correlation library construction module is used to perform correlation analysis between high temperature warning thresholds and environmental parameters for a target reducer based on a reduction twin model, and to construct an environment-high temperature threshold correlation library; A high-temperature associated warning threshold acquisition module is used to collect real-time environmental parameters of the target reducer, input the environmental-high-temperature threshold association library for matching, and obtain high-temperature associated warning thresholds. The high-temperature associated warning thresholds include a box-related warning threshold, a gear-related warning threshold, a bearing-related warning threshold, and a lubricating oil-related warning threshold. A box body sensor temperature judgment module, which is used to judge the box body sensor temperature and vibration sensor parameters respectively through the box body associated warning threshold and vibration warning threshold; a target temperature distribution information acquisition module, configured to activate an infrared thermal imager to capture an infrared image of a target reducer to obtain target temperature distribution information when the box sensor temperature does not meet the box-related warning threshold or the vibration sensor parameter does not meet the vibration warning threshold; a heat dissipation warning signal generation module, configured to determine the temperature distribution information based on the gear-related warning threshold, the bearing-related warning threshold, and the lubricant-related warning threshold, and generate a heat dissipation warning signal, wherein the heat dissipation warning signal includes a component to be dissipated and a corresponding heat dissipation temperature deviation; The first heat dissipation control measure output module is used to input the component to be dissipated and the corresponding heat dissipation temperature deviation into the heat dissipation control channel for heat dissipation analysis, and output the first heat dissipation control measure for heat dissipation processing.

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