A Method for Monitoring the Operating Temperature Status of Electromechanical Equipment Based on Big Data

Abstract

The present invention discloses a method for monitoring the operating temperature state of electromechanical equipment based on big data, belonging to the technical field of equipment analysis, and specifically includes the following steps: collecting infrared images of the overall electromechanical equipment in real time based on an infrared sensor; generating an infrared image sequence according to time, identifying the regions with temperature changes in the electromechanical equipment within a unit time, and determining whether the temperature of the temperature change regions exceeds a preset threshold. If so, an alarm is issued; screening out abnormal regions in the temperature change regions, sending a maintenance signal, and calculating the temperature change rate of the abnormal regions. If the temperature change rate is greater than the preset threshold, the power consumption of the current electromechanical equipment is obtained. If the change rate of the power consumption is less than the preset power value, it is determined that there is an abnormality in the temperature control of the current electromechanical equipment; the present invention realizes the effective analysis of electromechanical equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment analysis, and particularly to a method for monitoring the operating temperature status of electromechanical equipment based on big data. Background Art

[0002] With the continuous development of the times and the continuous improvement of the industrialization level, the types of electromechanical equipment are also increasing. Electromechanical equipment is widely used in various fields and is one of the essential important equipment in modern industrial production. With the continuous progress of technology, electromechanical equipment is also constantly developing and has now become an indispensable part of the modern industrialization process. Electromechanical equipment can be divided into transmission devices, control systems, and other auxiliary equipment according to functions, such as hydraulic systems, pneumatic systems, etc.; it can be divided into manufacturing, transportation, construction, municipal, etc. according to application fields; and it can be divided into traditional electromechanical equipment and intelligent electromechanical equipment according to technical levels.

[0003] With the development of industrial automation and informatization, electromechanical equipment plays an increasingly important role in the production process. However, the problem of operating energy consumption of electromechanical equipment has always been one of the difficult problems in industrial production. Traditional energy consumption monitoring methods mainly rely on manual inspections and regular maintenance. This method is not only inefficient but also prone to missed inspections and misjudgments. Therefore, how to achieve real-time monitoring and early warning of the operating energy consumption of electromechanical equipment has become an urgent problem to be solved.

[0004] When electromechanical equipment operates for a long time, each component is in an energized state for a long time. When component failures or material aging occur, it will affect the circuit, increase energy consumption, generate excess heat, and thus affect the operation and energy consumption of electromechanical equipment. Therefore, it is necessary to monitor the temperature of electromechanical equipment and give early warnings or alarms in a timely manner. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for monitoring the operating temperature status of electromechanical equipment based on big data, and solve the following technical problems:

[0006] When electromechanical equipment operates for a long time, each component is in an energized state for a long time. When component failures or material aging occur, it will affect the circuit, increase energy consumption, generate excess heat, and thus affect the operation and energy consumption of electromechanical equipment. Therefore, it is necessary to monitor the temperature of electromechanical equipment and give early warnings or alarms in a timely manner.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A method for monitoring the operating temperature status of electromechanical equipment based on big data includes the following steps:

[0009] Real-time collect the infrared image of the whole electromechanical equipment based on an infrared sensor;

[0010] Generate an infrared image sequence according to the time sequence, identify the area with temperature change in the electromechanical equipment within the unit time t based on the infrared image sequence, and determine whether the temperature of the temperature change area exceeds the preset threshold. If so, issue an alarm; if not, continue to identify;

[0011] Screen the abnormal areas in the temperature change area, issue a maintenance signal, and calculate the temperature change speed v1 of the abnormal area within the unit time t. If the temperature change speed is greater than the preset threshold, obtain the power consumption of the current electromechanical equipment, collect the change speed v2 of the power consumption of the electromechanical equipment. If the change speed v2 is less than the corresponding preset power value, it is determined that there is an abnormality in the temperature control of the current electromechanical equipment;

[0012] Calculate the early warning time of the electromechanical equipment according to the temperature change speed. When the early warning time is less than the preset threshold, issue an early warning; otherwise, continue to identify.

[0013] As a further solution of the present invention: The specific process of identifying the temperature change area is as follows:

[0014] Perform gray-scale processing on the infrared image sequence to obtain a gray-scale image sequence. Establish a rectangular coordinate system with the central pixel point of the gray-scale image as the origin, obtain the coordinates (x, y) of all pixel points in the gray-scale image, and respectively obtain the gray-scale values G i (x, y), G represents the gray-scale value, i represents the order of the gray-scale image corresponding to the current pixel point in the gray-scale image sequence, i = 1,..., n. Calculate the gray-scale difference D between the pixel points with the same coordinates in adjacent gray-scale images. If the gray-scale difference D > d, d is the natural temperature change threshold, then the corresponding pixel point is the temperature change area; if the gray-scale difference D ≤ d, then the corresponding pixel point is the non-change area.

[0015] As a further solution of the present invention: The setting process of the natural temperature change threshold d is as follows:

[0016] Obtain the generation date and time of the current infrared image, estimate the current temperature change speed v, calculate the temperature change value ΔT within the unit time t according to the temperature change speed, calculate the corresponding background radiation intensity change according to the temperature change value ΔT and the temperature starting point, calculate the corresponding gray-scale change value according to the background radiation intensity change, and mark the gray-scale change value as the natural temperature change threshold.

[0017] As a further solution of the present invention: number and sort the temperature change regions, sequentially obtain the temperature values T1, T2, …, Tn of each temperature change region, and sequentially calculate the proportions P1, P2, …, Pn of each temperature change region in the overall electromechanical equipment. Calculate the temperature warning value Y according to the formula Y = (T1*P1 + T2*P2 + … + Tn*Pn) / n. When the temperature warning value exceeds the preset threshold, an alarm is issued.

[0018] As a further solution of the present invention: the process of screening the abnormal region is as follows:

[0019] Select the temperature of one of the temperature change regions as the value to be detected, mark the temperatures of the remaining temperature change regions as the detection values, calculate the differences between the value to be detected and all the detection values respectively, and calculate the average value of the differences. If the average value of the differences is greater than the preset threshold and the variance of all the detection values is less than the preset threshold, determine the temperature change region corresponding to the value to be detected as the abnormal region, otherwise continue to identify.

[0020] As a further solution of the present invention: the process of screening the abnormal region is as follows:

[0021] If the average value of the differences between the value to be measured and the detection values is less than the preset threshold and the variance of all the detection values is greater than the preset threshold, calculate the average temperature of the detection values, and select the detection value with the largest difference from the average temperature. Determine the temperature change region corresponding to the detection value as the abnormal region, otherwise continue to identify.

[0022] As a further solution of the present invention: if the average value of the differences between the value to be measured and the detection values is less than the preset threshold and the variance of all the detection values is less than the preset threshold, calculate the average value of the temperature change values of all the temperature change regions. If the average value is greater than the preset threshold, an alarm is issued.

[0023] As a further solution of the present invention: for the electromechanical equipment determined to have temperature control abnormalities, generate a fault report, including the exact location of the abnormal region, temperature change data, and power consumption change situation, and upload the fault report to the central monitoring system.

[0024] The beneficial effects of the present invention:

[0025] The present invention collects infrared images of the overall electromechanical equipment through an infrared sensor, thereby facilitating the monitoring of the temperature state of the overall electromechanical equipment, facilitating the real-time control of possible abnormal conditions in various parts of the electromechanical equipment, and performing comprehensive management and control; and by identifying the temperature change regions in the infrared images through the frame difference sequence, the regions where the temperature changes in the electromechanical equipment can be quickly located, an alarm is issued in a timely manner, and further screening is performed on the temperature change regions to screen out abnormal regions that are significantly different from the temperatures of most regions, and timely maintenance alarms are issued for the abnormal regions, thereby eliminating potential safety hazards in advance, achieving comprehensive safety protection for the electromechanical equipment. At the same time, by monitoring the change in power consumption, the temperature control performance of the equipment can be further evaluated, providing data support for energy-saving optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The present invention will be further described below with reference to the accompanying drawings.

[0027] Figure 1 is a schematic flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0029] Please refer to Figure 1 shown. The present invention is a method for monitoring the operating temperature state of electromechanical equipment based on big data, including the following steps:

[0030] 1. Use a highly sensitive infrared sensor to monitor the electromechanical equipment in real time and capture the temperature distribution on the surface of the equipment. These infrared images can reflect the temperature changes of various parts of the equipment during operation, providing basic data for subsequent analysis and judgment. Through the infrared images, it is possible to directly observe whether there are overheated or temperature-abnormal regions on the equipment.

[0031] 2. Arrange the continuously collected infrared images in chronological order to form a series of image sequences. Use image processing algorithms to analyze these sequences and identify the regions where the temperature changes significantly within a unit time. Monitor the temperatures of these regions. If it is found that the temperature exceeds the preset safety threshold, the system will immediately issue an alarm to remind the operator to take corresponding measures.

[0032] 3. In the identified temperature change regions, further screen out the regions where faults or anomalies may exist. For these abnormal regions, the system will issue a maintenance signal to notify the maintenance personnel to conduct inspections and repairs. At the same time, calculate the temperature change rate v1 of these regions per unit time to evaluate the severity and urgency of the problem.

[0033] 4. If the temperature change rate v1 of the abnormal region exceeds the preset threshold, it indicates that there may be serious faults or performance degradation. At this time, the system will obtain the power consumption of the current electromechanical equipment and collect the change rate v2 of the power consumption. Through the analysis of the power consumption, the operating state and energy efficiency of the equipment can be further understood.

[0034] 5. Compare the collected change rate v2 of the power consumption with the preset power value. If v2 is less than the preset value, it means that there may be problems with the temperature control system of the electromechanical equipment and it cannot effectively control the temperature of the equipment. In this case, it is necessary to check and repair the temperature control system to ensure the safe and stable operation of the equipment.

[0035] 6. Use the temperature change rate v1, combined with the thermodynamic characteristics and historical operation data of the equipment, to calculate the possible warning time. This warning time refers to the time point when the equipment may have faults or performance degradation under the current operating state. By giving an early warning, more time can be gained for the maintenance and servicing of the equipment, avoiding the impact of sudden faults on production.

[0036] 7. If the calculated warning time is less than the set safety threshold, it means that the equipment has a high risk and may have faults in a short time. At this time, the system will immediately issue a warning signal to alert the operator and take necessary measures to prevent the occurrence of faults. This warning mechanism helps to improve the reliability and stability of the equipment and ensure the smooth progress of production.

[0037] The present invention collects the infrared image of the overall electromechanical equipment through an infrared sensor, thereby facilitating the monitoring of the overall temperature state of the electromechanical equipment, facilitating the real-time control of possible anomalies in various parts of the electromechanical equipment, and conducting all-round management and control; and identifies the temperature change regions in the infrared image through the frame difference sequence, thereby quickly locating the regions where the temperature changes in the electromechanical equipment, promptly issuing an alarm, and further screening the temperature change regions, screening out the abnormal regions with obvious differences in temperature from most regions, and giving timely maintenance alarms for the abnormal regions, thereby eliminating potential safety hazards in advance and realizing comprehensive safety protection for the electromechanical equipment.

[0038] In a preferred embodiment of the present invention, the specific process of identifying the temperature change regions is as follows:

[0039] 1. Grayscale processing: Perform grayscale processing on the collected infrared image sequence to obtain a grayscale image sequence. This step is to convert complex color images into simple black-and-white images for subsequent image processing and analysis.

[0040] 2. Establish a coordinate system: Take the central pixel point of the grayscale image as the origin to establish a rectangular coordinate system. Through this coordinate system, the position of each pixel point can be accurately located.

[0041] Obtain pixel coordinates and grayscale values: Obtain the coordinates (x, y) of all pixel points in the grayscale image, and record the grayscale value Gi(x, y) of each pixel point in the grayscale image sequence respectively. Here, G represents the grayscale value, and i represents the order of the grayscale image corresponding to the current pixel point in the grayscale image sequence, i = 1, …, n.

[0042] 3. Calculate the grayscale difference: Calculate the grayscale difference D between pixel points with the same coordinates in adjacent grayscale images. This grayscale difference reflects the brightness change of pixel points between two consecutive frames of images.

[0043] 4. Determine the temperature change area: If the grayscale difference D is greater than the preset natural temperature change threshold d, then it is considered that the pixel point belongs to the temperature change area. This means that within this time interval, the temperature in this area has changed significantly. On the contrary, if the grayscale difference D is less than or equal to the threshold d, then it is considered that the pixel point belongs to the non-change area.

[0044] In a preferred case of this embodiment, the setting process of the natural temperature change threshold d is as follows:

[0045] 1. The system will first record the generation date and time of the current infrared image. This information is crucial for subsequent estimation of the temperature change rate because it provides a reference point for the change of environmental conditions over time. For example, if the image is collected at noon in summer, the expected temperature may be higher than in the early morning or evening. By accurately recording these timestamps, the accuracy and relevance of subsequent analysis can be ensured.

[0046] 2. Using historical meteorological data and known environmental parameters, such as season, weather conditions, equipment operating load, etc., the system will estimate the current temperature change rate v. This estimated value is obtained based on the statistical analysis of past temperature changes under similar conditions. For example, if historical data shows that the surface temperature of the equipment usually rises rapidly in the hot summer afternoon, then the system will predict a higher temperature change rate. This estimation helps to set a reasonable natural temperature change threshold to distinguish normal temperature fluctuations from possible anomalies.

[0047] 3. Based on the estimated temperature change rate v, the system calculates the expected temperature change ΔT within a given time interval t. This calculation takes into account the thermal inertia of the device and the influence of environmental factors, thus providing a dynamic temperature change range. For example, if the device usually takes some time to respond to changes in the ambient temperature, then even in the case of rapid heating, there will be a certain delay in the actual temperature change. In this way, the temperature change trend within a short period can be predicted more accurately.

[0048] 4. Next, the system determines the change in background radiation intensity based on the calculated temperature change value ΔT and the initial temperature reading. Since an infrared image is generated by capturing the infrared radiation on the surface of an object, any temperature change will cause a change in the radiation intensity. By understanding this relationship, the subtle differences in the infrared image can be interpreted more accurately and related to the actual temperature change.

[0049] 5. Once the change in background radiation intensity is known, the system can convert it into a gray-scale change value on the image. This is because the infrared camera captures the energy distribution of infrared radiation, and radiation at different energy levels will appear as different gray levels on the image. In this way, the abstract temperature data can be converted into intuitive visual information, enabling the operator to more easily identify the temperature change area.

[0050] 6. The last step is to set the previously calculated gray-scale change value as the natural temperature change threshold d. This threshold represents the maximum expected temperature change caused by natural environmental factors in the absence of external interference. Any temperature change exceeding this threshold will be regarded as abnormal and may require further inspection or intervention. In this way, the system can automatically adjust its sensitivity to adapt to the changing external environmental conditions while reducing the possibility of false alarms.

[0051] In another preferred embodiment of the present invention, the temperature change regions are numbered and sorted, and the temperature values T1, T2,..., Tn of each temperature change region are obtained in sequence, and the proportions P1, P2,..., Pn of each temperature change region in the overall electromechanical device are calculated in sequence. The temperature warning value Y is calculated according to the formula Y = (T1 * P1 + T2 * P2 +... + Tn * Pn) / n. When the temperature warning value exceeds the preset threshold, a warning is issued.

[0052] In another preferred embodiment of the present invention, the process of screening abnormal regions is as follows:

[0053] The temperature of one of the temperature change regions is marked as the value to be detected, and the temperatures of the remaining temperature change regions are marked as detection values.

[0054] Calculate the differences between the value to be detected and all the detected values respectively, and calculate the mean value of these differences.

[0055] If the mean value of the differences is greater than a preset threshold and the variance of all the detected values is less than the preset threshold, then determine the temperature change area corresponding to the value to be detected as an abnormal area. Otherwise, continue to identify other possible abnormal areas.

[0056] In a preferred case of this embodiment, the process of screening abnormal areas further includes:

[0057] If the mean value of the differences between the value to be measured and the detected values is less than the preset threshold, but the variance of all the detected values is greater than the preset threshold, then calculate the temperature mean value of the detected values.

[0058] Select the detected value with the largest difference from the temperature mean value from all the detected values, and determine the temperature change area corresponding to this detected value as an abnormal area. Otherwise, continue to identify other possible abnormal areas.

[0059] In another preferred case of this embodiment, if the mean value of the differences between the value to be measured and the detected values is less than the preset threshold and the variance of all the detected values is also less than the preset threshold, then calculate the mean value of the temperature change values of all the temperature change areas. If the mean value is greater than the preset threshold, then issue an alarm, indicating that there may be an overall temperature control problem or abnormal operation of the electromechanical equipment.

[0060] In another preferred case of this embodiment, for the electromechanical equipment determined to have a temperature control abnormality, generate a fault report, including the exact location of the abnormal area, temperature change data, and power consumption change situation, and upload the fault report to the central monitoring system.

[0061] The above has described a detailed description of an embodiment of the present invention, but the content described is only a preferred embodiment of the present invention and cannot be considered as used to limit the implementation scope of the present invention. All equal changes and improvements made according to the scope of the application of the present invention should still fall within the patent coverage scope of the present invention.

Claims

1. A method for monitoring the operating temperature status of electromechanical equipment based on big data, characterized in that, The steps include: Based on an infrared sensor, collect infrared images of the overall electromechanical equipment in real time; Generate a sequence of infrared images according to the time sequence, identify the area with temperature change in the electromechanical equipment within a unit time t based on the infrared image sequence, and determine whether the temperature of the temperature change area exceeds a preset threshold. If so, issue an alarm; if not, continue to identify; Screen the abnormal areas in the temperature change area, issue a maintenance signal, and calculate the temperature change speed v1 of the abnormal area within a unit time t. If the temperature change speed is greater than the preset threshold, obtain the power consumption of the current electromechanical equipment, collect the change speed v2 of the power consumption of the electromechanical equipment. If the change speed v2 is less than the corresponding preset power value, it is determined that there is an abnormality in the temperature control of the current electromechanical equipment; Calculate the warning time of the electromechanical equipment according to the temperature change speed. When the warning time is less than the preset threshold, issue a warning; otherwise, continue to identify.

2. The method for monitoring the operating temperature state of electromechanical equipment based on big data according to claim 1, characterized in that The specific process of identifying the temperature change area is: Perform grayscale processing on the infrared image sequence to obtain a grayscale image sequence. Establish a rectangular coordinate system with the central pixel point of the grayscale image as the origin, and obtain the coordinates (x, y) of all pixel points in the grayscale image. Respectively obtain the grayscale values G of all pixel points in the grayscale image sequence i (x, y), where G represents the grayscale value and i represents the order of the grayscale image corresponding to the current pixel point in the grayscale image sequence, i = 1, …, n. Calculate the grayscale difference D between the pixel points with the same coordinates in adjacent grayscale images. If the grayscale difference D > d, where d is the natural temperature change threshold, then the corresponding pixel point is a temperature change area. If the grayscale difference D ≤ d, then the corresponding pixel point is an unchanged area.

3. A method for monitoring the operating temperature status of electromechanical equipment based on big data according to claim 2, characterized in that, The setting process of the natural temperature change threshold d is: Obtain the generation date and time of the current infrared image, estimate the current temperature change speed v, calculate the temperature change value ΔT within a unit time t according to the temperature change speed, calculate the corresponding background radiation intensity change according to the temperature change value ΔT and the temperature starting point, calculate the corresponding gray scale change value according to the background radiation intensity change, and mark the gray scale change value as the natural temperature change threshold.

4. A method for monitoring the operating temperature status of electromechanical equipment based on big data according to claim 1, characterized in that, Number and sort the temperature change areas, sequentially obtain the temperature values T1, T2,..., Tn of each temperature change area, and sequentially calculate the proportions P1, P2,..., Pn of each temperature change area in the overall electromechanical equipment. Calculate the temperature warning value Y according to the formula Y = (T1*P1 + T2*P2 +... + Tn*Pn) / n. When the temperature warning value exceeds the preset threshold, issue a warning.

5. A method for monitoring the operating temperature status of electromechanical equipment based on big data according to claim 1, characterized in that, The process of screening abnormal areas is: Select the temperature of one of the temperature change areas as the value to be detected, mark the temperatures of the remaining temperature change areas as the detection values, calculate the differences between the value to be detected and all detection values respectively, and calculate the average value of the differences. If the average value of the differences is greater than the preset threshold and the variance of all detection values is less than the preset threshold, determine the temperature change area corresponding to the value to be detected as an abnormal area; otherwise, continue to identify.

6. A method for monitoring the operating temperature state of electromechanical equipment based on big data according to claim 5, characterized in that, The process of screening abnormal areas also includes: If the average value of the differences between the value to be measured and the detection values is less than the preset threshold and the variance of all detection values is greater than the preset threshold, calculate the average temperature of the detection values, and select the detection value with the largest difference from the average temperature. Determine the temperature change area corresponding to the detection value as an abnormal area; otherwise, continue to identify.

7. A method for monitoring the operating temperature status of electromechanical equipment based on big data according to claim 6, characterized in that, If the average value of the differences between the value to be measured and the detection values is less than the preset threshold and the variance of all detection values is less than the preset threshold, calculate the average value of the temperature change values of all temperature change areas. If the average value is greater than the preset threshold, issue an alarm.

8. A method for monitoring the operating temperature status of electromechanical equipment based on big data according to claim 1, characterized in that, For the electromechanical equipment determined to have a temperature control abnormality, generate a fault report, including the exact location of the abnormal area, temperature change data, and power consumption change situation, and upload the fault report to the central monitoring system.

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