Bearing machining equipment operation quality detection method and system based on data analysis
By obtaining current and temperature information in each partition of the roller bottom furnace and calculating the abnormality degree value, the problem of inaccurate quality detection of roller bottom furnace operation in the prior art is solved, and a higher accuracy detection effect is achieved.
Patent Information
- Application Number
- CN202510322209.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-19
- Publication Date
- 2025-04-15
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The prior art is difficult to accurately and quickly detect abnormalities in multiple partitions in the roller bottom furnace, resulting in poor quality detection effect of the roller bottom furnace operation.
By obtaining the current stability degree and temperature information of the power consumption equipment in each partition of the roller bottom furnace, the first abnormality degree value is calculated using the index adjustment method, and the second abnormality degree value is determined based on the temperature information of different partitions, thereby achieving more accurate detection of the operation quality of the roller bottom furnace.
This method can more accurately reflect the abnormal situations of multiple partitions in the roller bottom furnace, and improve the detection accuracy of the operation quality of the roller bottom furnace.
Smart Images
Figure CN119827194A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of data processing technology, and in particular to a method and system for detecting the operating quality of bearing processing equipment based on data analysis. Background Art
[0002] Roller hearth furnace is a kind of equipment used for bearing processing. Roller hearth furnace is mainly composed of furnace body, roller hearth, transmission system and control system. The furnace body of roller hearth furnace is equipped with heating elements. The roller hearth includes a conveyor belt composed of multiple rollers, which is used to carry and transport workpieces. The transmission system is used to control the speed of the roller hearth, and the control system is used to control parameters such as furnace temperature and heating time.
[0003] In the process of bearing processing using a roller hearth furnace, the workpiece is sent into the preheating zone of the roller hearth furnace for preheating treatment. After preheating, the workpiece enters the heating zone and is heated to the required temperature. After heating, the workpiece enters the cooling zone for cooling treatment. After cooling, the workpiece is sent out of the roller hearth furnace to complete the heat treatment.
[0004] A salt tank and a tempering furnace can also be provided in the roller hearth furnace; the salt bath in the salt tank can be used as a medium to transfer carbon or carbon-nitrogen elements to the surface of the workpiece, thereby improving the hardness and wear resistance of the workpiece; the tempering furnace can temper the workpiece during the quenching process, effectively reducing the internal stress of the workpiece, thereby preventing the workpiece from deformation or cracking.
[0005] In the related art, the overall current of the roller hearth furnace is mainly monitored to determine whether there is any abnormality in the working state of the roller hearth furnace, so as to detect the operation quality of the roller hearth furnace. However, the roller hearth furnace may include multiple partitions. It is difficult to accurately and quickly find the abnormality of the roller hearth furnace through the overall current of the roller hearth furnace when it is working. Therefore, it is difficult to effectively detect the operation quality of the roller hearth furnace. Summary of the invention
[0006] In order to overcome the problem in the related art that it is difficult to effectively detect the operating quality of a roller hearth furnace, the present application provides a method and system for detecting the operating quality of bearing processing equipment based on data analysis.
[0007] According to a first aspect of an embodiment of the present application, a method for detecting the operation quality of bearing processing equipment based on data analysis is provided, comprising: for a target partition among multiple partitions of a roller hearth furnace, obtaining a current stability value of electrical equipment in the target partition at a current moment when the roller hearth furnace is working; the current stability value is used to characterize the stability of the current of the electrical equipment in the target partition; obtaining a current fluctuation value of the electrical equipment in the target partition at a historical moment, and using the fluctuation value to perform an exponential adjustment on the current stability value at a current moment, to obtain a first abnormality value of the current of the electrical equipment in the target partition at a current moment; obtaining temperature information of the partitions of the roller hearth furnace in a current time period, and using the temperature information of different partitions of the roller hearth furnace and the first abnormality values of different partitions to determine a second abnormality value of the roller hearth furnace at a current moment; and outputting an operation quality detection result of the roller hearth furnace at a current moment according to the second abnormality value of the roller hearth furnace at a current moment.
[0008] In this way, the temperature information of each partition and the first abnormality degree value are combined to determine the second abnormality degree value of the roller hearth furnace at the current moment. Since the second abnormality degree value is comprehensively derived based on the temperature and current conditions of multiple partitions in the roller hearth furnace, a more accurate detection result of the operation quality of the roller hearth furnace can be obtained.
[0009] Optionally, the current stability value of the electric equipment in the target partition at the current moment is determined by: , where s is the current stability value of the electrical equipment in the target partition at the current moment, and exp is an exponential function with a natural constant as the base. is the standard deviation of the current in the target zone during the ignition preparation phase of the roller hearth furnace, is the standard deviation of the current in the target partition during the period from the ignition time to the current time, is the current of the target partition at the current moment, is the rated current of the target partition.
[0010] Optionally, using the fluctuation degree value to exponentially adjust the current stability degree value at the current moment to obtain a first abnormal degree value of the current of the electrical equipment in the target partition at the current moment includes: ,in, is the first abnormality value of the current of the electric equipment in the target partition at the current moment, s is the current stability value of the electric equipment in the target partition at the current moment, and are respectively the maximum and minimum values of the current stability of the target partition before the current moment, and are respectively the mean and standard deviation of the current stability value of the target partition before the current moment, is the fluctuation degree value.
[0011] Optionally, the second abnormality degree value of the roller hearth furnace at the current moment is determined by: ,in, is the second abnormality value of the roller hearth furnace at the current moment, N is the number of partitions in the roller hearth furnace, is the first abnormality value of the ith partition of the roller hearth furnace at the current moment, is the minimum of the first abnormality degree values of the ith partition of the roller hearth furnace at the historical moment, is the temperature of the ith zone of the roller hearth furnace at the current moment, is the set temperature of the i-th zone of the roller hearth furnace.
[0012] By analyzing the deviation between the temperature of the same partition of the roller hearth furnace at the current moment and the predetermined temperature, and analyzing the difference between the first abnormality degree value of the same partition at the current moment and the lowest first abnormality degree value in the historical record, the obtained second abnormality degree value can better characterize the possibility of abnormality in the roller hearth furnace at the current moment.
[0013] Optionally, the second abnormality degree value of the roller hearth furnace at the current moment is determined by: , is the second abnormality value of the roller hearth furnace at the current moment, N is the number of partitions in the roller hearth furnace, and max is the maximum value. is the first abnormality value of the ith partition of the roller hearth furnace at the current moment, is the minimum of the first abnormality degree values of the ith partition of the roller hearth furnace at the historical moment, is the temperature of the ith zone of the roller hearth furnace at the current moment, is the set temperature of the i-th zone of the roller hearth furnace.
[0014] In this way, since the second abnormality degree value is determined based on the temperature and current data of the partition with the highest abnormality probability among different partitions of the roller hearth furnace, once an abnormality occurs in any partition of the roller hearth furnace, the second abnormality degree value can obtain a higher value, which can guide the user to take corresponding treatment measures for the roller hearth furnace with the abnormality.
[0015] Optionally, based on the second abnormality degree value of the roller hearth furnace at the current moment, an operation quality inspection result of the roller hearth furnace at the current moment is output, including: inputting multiple second abnormality degree values of the roller hearth furnace at the current moment and multiple moments before the current moment into a pre-trained quality inspection model to obtain the operation quality inspection result of the roller hearth furnace at the current moment; wherein the quality inspection model is used to output the operation quality inspection result of the roller hearth furnace based on the input multiple second abnormality degree values.
[0016] The second abnormality degree values of the roller hearth furnace at the current moment and multiple previous moments are input into the pre-trained quality detection model. Since the abnormal conditions at the current moment and its previous moments are taken into consideration, the operation quality of the roller hearth furnace at the current moment can be evaluated more accurately.
[0017] Optionally, the quality detection model is trained in the following manner: obtaining the second abnormality degree value of the roller hearth furnace at a historical moment; for a target historical moment among multiple historical moments, using multiple second abnormality degree values of multiple historical moments before the target historical moment as inputs of a pre-constructed network model, and using the quality detection label of the target historical moment as output of the pre-constructed network model, so that the trained network model is used as the quality detection model.
[0018] By using the second abnormality degree values at multiple historical moments to train the pre-built network model to obtain a quality detection model, it is possible to use the quality detection model to output more accurate detection results on the operation quality of the roller hearth furnace.
[0019] Optionally, based on the second abnormality degree value of the roller hearth furnace at the current moment, a detection result of the operating quality of the roller hearth furnace at the current moment is output, including: when the second abnormality degree value of the roller hearth furnace at the current moment is greater than or equal to a preset threshold value, it is prompted that there is an abnormality in the operating quality of the roller hearth furnace at the current moment; or, when the second abnormality degree value of the roller hearth furnace at the current moment is less than the preset threshold value, it is prompted that the operating quality of the roller hearth furnace at the current moment is normal.
[0020] Optionally, the multiple zones of the roller hearth furnace include: a loading zone, a preheating zone, a heating zone, a salt tank, a tempering furnace, a cooling area and a unloading zone.
[0021] According to the second aspect of an embodiment of the present application, a bearing processing equipment operation quality detection system based on data analysis is provided, comprising: a processor and a memory, the memory storing computer program instructions, and the computer program instructions, when executed by the processor, implement the steps of the bearing processing equipment operation quality detection method based on data analysis provided in the first aspect of the present application.
[0022] The technical solution provided by the embodiments of the present application may include the following beneficial effects: for a target partition among multiple partitions of a roller hearth furnace, a first abnormality degree value of the current of the target partition at the current moment can be determined, and a second abnormality degree value of the roller hearth furnace at the current moment can be determined in combination with the temperature information of different partitions and the first abnormality degree value; since the second abnormality degree value of the roller hearth furnace at the current moment is determined based on the temperature conditions and current conditions of multiple partitions in the roller hearth furnace, compared with the overall current of the roller hearth furnace, the second abnormality degree value can better reflect the abnormality of the roller hearth furnace; therefore, based on the second abnormality degree value of the roller hearth furnace at the current moment, a more accurate operation quality detection result of the roller hearth furnace at the current moment can be output.
[0023] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS
[0024] Figure 1 is a flow chart of a method for detecting the running quality of a bearing processing equipment based on data analysis according to an exemplary embodiment; Figure 2 It is a structural schematic diagram of a bearing processing equipment operation quality detection system based on data analysis according to an exemplary embodiment. DETAILED DESCRIPTION
[0025] First, the application scenario of the embodiment of the present application is briefly introduced. In the application scenario of the present application, the operation quality of the roller hearth furnace can be detected by the change of the overall current of the roller hearth furnace. However, since the roller hearth furnace includes multiple partitions, the overall current passing through the roller hearth furnace is difficult to detect when there is an abnormality in a certain partition. Therefore, it is difficult for the relevant technology to accurately detect the operation quality of the roller hearth furnace.
[0026] In view of the above technical problems, the embodiments of the present application provide a method and system for detecting the operation quality of bearing processing equipment based on data analysis. Figure 1 is a flow chart of a method for detecting the running quality of a bearing processing equipment based on data analysis according to an exemplary embodiment. Figure 1 As shown, the method includes the following steps.
[0027] In step S101, for a target partition among multiple partitions of a roller hearth furnace, a current stability value of electrical equipment in the target partition at the current moment when the roller hearth furnace is operating is obtained.
[0028] The multiple zones of the roller hearth furnace may include a loading zone, a preheating zone, a heating zone, a salt tank, a tempering furnace, a cooling area, and a unloading zone. The heating zone of the roller hearth furnace may be composed of multiple heating furnaces connected in sequence, and the multiple heating furnaces in the heating zone of the roller hearth furnace may belong to different zones.
[0029] By arranging a current sensor in the target partition of the roller hearth furnace, current information of electrical equipment in the target partition when the roller hearth furnace is working can be obtained; different partitions of the roller hearth furnace can correspond to different current sensors.
[0030] The target partition can be any one of the multiple partitions of the roller hearth furnace, and corresponding electrical equipment is arranged in different partitions; for example, electrical equipment such as heating equipment, temperature control equipment and exhaust equipment can be arranged in the heating zone, and the current of the electrical equipment in the target partition can be used to monitor the operating status of the target partition.
[0031] For a roller hearth furnace that processes bearings to be processed after ignition, the electrical equipment in the same partition of the roller hearth furnace has a certain stability, or the stability of the electrical equipment in the same partition is usually within a certain range, and the current stability value is used to characterize the current stability of the electrical equipment in the target partition. Therefore, the current stability value of the electrical equipment in the target partition helps to detect the operating quality of the roller hearth furnace in the target partition.
[0032] In one embodiment, the current stability value of the electrical equipment in the target partition at the current moment is determined in the following manner: , where s is the current stability value of the electrical equipment in the target partition at the current moment, and exp is an exponential function with a natural constant as the base. is the standard deviation of the current in the target zone during the ignition preparation phase of the roller hearth furnace, is the standard deviation of the current in the target partition during the period from the ignition time to the current time, is the current of the target partition at the current moment, is the rated current of the target partition.
[0033] Before the roller-hearth furnace processes workpieces, the operator usually determines whether the various components of the roller-hearth furnace are in a relatively stable state. Therefore, before the roller-hearth furnace is ignited, the various components of the roller-hearth furnace are usually in a more stable state. Therefore, the standard deviation of the current of the target partition in the ignition preparation stage of the roller-hearth furnace can be used as a reference for the current situation of the target partition when it is working.
[0034] Since the target partition has a certain stability during the ignition preparation stage of the roller hearth furnace, by comparing the standard deviation of the current of the target partition during the ignition preparation stage of the roller hearth furnace with the standard deviation of the current of the target partition in the time period from the ignition moment to the current moment, the situation of the electrical equipment in the target partition during the ignition preparation stage can be used as a reference to determine whether there is any abnormality in the current of the electrical equipment in the target partition at the current moment.
[0035] The variables involved in the calculation in the denominator and numerator are all the variance of the current, which can eliminate the influence of the dimension after calculation. The larger the value of is, the lower the current stability of the electrical equipment in the target partition at the current moment is, and a smaller current stability value can be determined.
[0036] During the operation of electrical equipment in the target zone of the roller hearth furnace, the more the current of the electrical equipment exceeds the rated current, the more likely it is that the electrical equipment in the target zone of the roller hearth furnace will be abnormal, or the larger current of the electrical equipment in the target zone of the roller hearth furnace will cause abnormality. Therefore, in the case that the current of the electrical equipment exceeds the rated current more, a smaller stability value can be determined for the electrical equipment in the target zone of the roller hearth furnace, so as to better realize the detection of the operation quality of the roller hearth furnace.
[0037] In this way, during the operation of the electrical equipment in the target zone of the roller hearth furnace, the current conditions of the electrical equipment in the ignition preparation stage can be used as a reference to determine a stability value that can better characterize the stability of the electrical equipment in the target zone of the roller hearth furnace.
[0038] In step S102, the current fluctuation value of the electrical equipment in the target partition at a historical moment is obtained, and the current stability value at the current moment is exponentially adjusted using the fluctuation value to obtain the first abnormality value of the current of the electrical equipment in the target partition at the current moment.
[0039] The fluctuation degree value of the current of the electrical equipment in the target partition at the historical moment is used to characterize the fluctuation degree of the current of the electrical equipment in the target partition at the historical moment; the current stability degree value at the current moment can characterize the stability degree of the electrical equipment in the target partition at the current moment. Therefore, by using the fluctuation degree value to exponentially adjust the current stability degree value at the current moment, it is possible to determine the first abnormality degree value for better characterizing the abnormality degree of the current of the electrical equipment at the current moment.
[0040] In one embodiment, the current stability value at the current moment is exponentially adjusted using the fluctuation degree value to obtain the first abnormal degree value of the current of the electrical equipment in the target partition at the current moment, including: ,in, is the first abnormality value of the current of the electric equipment in the target partition at the current moment, s is the current stability value of the electric equipment in the target partition at the current moment, and are respectively the maximum and minimum values of the current stability of the target partition before the current moment, and are respectively the mean and standard deviation of the current stability value of the target partition before the current moment, or is the fluctuation degree value.
[0041] In the calculation formula of the first abnormality degree value of the current of the electrical equipment in the target partition at the current moment, or It is a fluctuation degree value, which can characterize the fluctuation degree of the current of the electrical equipment in the target partition at a historical moment.
[0042] The current stability values of the electrical equipment in the target zone of the roller hearth furnace at different times are within the range of 0 to 1, so that The value of is in the range of 0 to 1, so The larger the value of , the smaller the value of the first abnormality degree.
[0043] The current stability value of the electrical equipment in the target partition at the current moment is subtracted from the average current stability value of the target partition before the current moment and the absolute value is taken. It can characterize the abnormal degree of current of the electrical equipment in the target partition at the current moment.
[0044] If the fluctuation degree of the current of the electrical equipment in the target partition is large at a historical moment, it means that the current of the electrical equipment in the target partition usually has a large fluctuation degree, which can be specifically manifested as a large amplitude or frequency of current fluctuation. Therefore, when the electrical equipment in the target partition fluctuates with a certain amplitude or frequency at the current moment, it is more likely that the current of the electrical equipment in the target partition fluctuates under normal circumstances.
[0045] When the fluctuation degree of the current of the electrical equipment in the target partition at the historical moment is greater, As an exponential term, and converts the As the base term, exponential operation can be realized A larger adjustment can be made, and the index calculation result obtained can better characterize the abnormal degree of the current of the electrical equipment in the target partition at the current moment.
[0046] On the contrary, if the current fluctuation value of the electrical equipment in the target partition is smaller at the historical moment, it means that the current of the electrical equipment in the target partition usually has a smaller fluctuation degree, which can be specifically manifested as the amplitude or frequency of the current fluctuation is smaller. Therefore, when the electrical equipment in the target partition fluctuates with a certain amplitude or a certain frequency at the current moment, it is more likely that the fluctuation is caused by the abnormality of the current of the electrical equipment in the target partition.
[0047] In this way, due to the fluctuation degree value of the electrical equipment in the target partition at historical moments, the fluctuation of the current of the electrical equipment in the target partition under normal circumstances can be characterized. The current stability degree value at the current moment is exponentially adjusted using the fluctuation degree value to obtain the first abnormal degree value of the current of the electrical equipment in the target partition at the current moment. The first abnormal degree value can better characterize the abnormal degree of the current of the electrical equipment in the target partition at the current moment.
[0048] In step S103, the temperature information of the zones of the roller hearth furnace in the current time period is obtained, and the second abnormality level value of the roller hearth furnace at the current moment is determined by using the temperature information of different zones of the roller hearth furnace and the first abnormality level values of different zones.
[0049] In addition to the current information of the electrical equipment in the roller hearth furnace partitions, the temperature information of the roller hearth furnace partitions is also an important factor affecting the operation quality of the roller hearth furnace partitions; since different partitions of the roller hearth furnace may have different functions, the temperature information of different partitions of the roller hearth furnace is different in the same time period.
[0050] Compared with detecting the operation quality of the roller hearth furnace by the overall temperature of the roller hearth furnace, detecting the operation quality of the roller hearth furnace by using the temperature information of different partitions of the roller hearth furnace can obtain more accurate detection results of the operation quality of the roller hearth furnace.
[0051] By setting corresponding temperature sensors in different partitions of the roller hearth furnace, the temperature information of the different partitions of the roller hearth furnace can be obtained; by using the temperature information of the different partitions of the roller hearth furnace and the first abnormality degree values of the different partitions, the temperature information and the current information of the electrical equipment can be combined to determine the second abnormality degree value of the roller hearth furnace at the current moment.
[0052] In one embodiment, the second abnormality level value of the roller hearth furnace at the current moment is determined by: ,in, is the second abnormality value of the roller hearth furnace at the current moment, N is the number of partitions in the roller hearth furnace, is the first abnormality value of the ith partition of the roller hearth furnace at the current moment, is the minimum of the first abnormality degree values of the ith partition of the roller hearth furnace at the historical moment, is the temperature of the ith zone of the roller hearth furnace at the current moment, is the set temperature of the i-th zone of the roller hearth furnace.
[0053] pass It is possible to compare the temperature of the ith zone of the roller hearth furnace at the current moment with the set temperature of the ith zone of the roller hearth furnace, and to eliminate the dimension of the temperature, which is convenient for comparison with other variables.
[0054] The greater the difference between the temperature of the i-th zone of the roller hearth furnace at the current moment and the set temperature of the i-th zone of the roller hearth furnace, the more likely it is that the i-th zone of the roller hearth furnace is in an abnormal state at the current moment.
[0055] pass It is possible to compare the first abnormality degree value of the i-th partition of the roller hearth furnace at the current moment with the minimum value of the first abnormality degree value of the i-th partition of the roller hearth furnace at historical moments, and to convert the absolute abnormality degree value into a relative abnormality degree value, so as to better characterize the probability of the existence of abnormality in the roller hearth furnace at the current moment through the second difference degree value.
[0056] For example, if the first abnormality degree value of the i-th partition of the roller hearth furnace at the current moment is large, but the minimum value of the first abnormality degree value of the i-th partition of the roller hearth furnace at the historical moment is small, then it means that although the first abnormality degree value of the i-th partition of the roller hearth furnace at the current moment is large, since the minimum value of the first abnormality degree value of the i-th partition of the roller hearth furnace at the historical moment is also large, the larger first abnormality degree value of the i-th partition of the roller hearth furnace at the current moment may belong to the normal situation of the i-th partition of the roller hearth furnace.
[0057] For another example, if the first abnormality degree value of the i-th partition of the roller hearth furnace at the current moment is smaller, but the minimum value of the first abnormality degree value of the i-th partition of the roller hearth furnace at the historical moment is smaller, then it means that although the first abnormality degree value of the i-th partition of the roller hearth furnace at the current moment is smaller, since the minimum value of the first abnormality degree value of the i-th partition of the roller hearth furnace at the historical moment is smaller, the smaller first abnormality degree value of the i-th partition of the roller hearth furnace at the current moment may also be caused by the abnormal situation of the i-th partition of the roller hearth furnace.
[0058] In this way, by comparing the difference between the temperature of the same partition of the same roller hearth furnace at the current moment and the corresponding set temperature, and comparing the difference between the first abnormality degree value of the same partition at the current moment and the minimum first abnormality degree value at the historical moment, the second abnormality degree value obtained can better characterize the probability of an abnormality in the roller hearth furnace at the current moment by comprehensively considering the temperature information and the current information.
[0059] In one embodiment, the second abnormality level value of the roller hearth furnace at the current moment is determined by: , is the second abnormality value of the roller hearth furnace at the current moment, N is the number of partitions in the roller hearth furnace, and max is the maximum value. is the first abnormality value of the ith partition of the roller hearth furnace at the current moment, is the minimum of the first abnormality degree values of the ith partition of the roller hearth furnace at the historical moment, is the temperature of the ith zone of the roller hearth furnace at the current moment, is the set temperature of the i-th zone of the roller hearth furnace.
[0060] In the calculation formula of the second abnormality value of the roller hearth furnace at the current moment, by selecting the corresponding values of different partitions By using the maximum value in , a larger second abnormality degree value can be determined when the temperature of at least one partition of the roller hearth furnace is abnormal, thereby more sensitively capturing the abnormality of the roller hearth furnace.
[0061] By selecting the corresponding The largest of the two can determine a larger second abnormality degree value when the current of the electrical equipment in at least one partition of the roller hearth furnace is abnormal, compared with the corresponding second abnormality degree value for different partitions of the roller hearth furnace. Taking the average value can more keenly discover the abnormality of the current of the electrical equipment in the partitions of the roller hearth furnace, avoid underreporting due to the small number of partitions with abnormalities, and determine more accurate operation quality inspection results for the roller hearth furnace.
[0062] In this way, since the second abnormality degree value is determined based on the temperature information and current information of the partition with the highest probability of abnormality among the different partitions of the roller hearth furnace, the second abnormality degree value can obtain a larger value when at least one partition among the multiple partitions of the roller hearth furnace has an abnormality, so as to guide the user to handle the roller hearth furnace with the abnormality.
[0063] In step S104, the operation quality detection result of the roller hearth furnace at the current moment is outputted according to the second abnormality degree value of the roller hearth furnace at the current moment.
[0064] Since the second abnormality degree value of the roller hearth furnace at the current moment is determined based on the current conditions and temperature conditions of multiple partitions of the roller hearth furnace at the current moment, the second abnormality degree value can better reflect the probability or degree of abnormality in the roller hearth furnace at the current moment. Therefore, the operation quality detection result of the roller hearth furnace at the current moment can be output based on the second abnormality degree value.
[0065] The operation quality detection result may be, for example, that the operation process has an abnormality or that the operation process is normal; or, the operation quality detection result may be that the operation process is normal, that the operation process has a slight abnormality, or that the operation process has a serious abnormality.
[0066] In one embodiment, based on the second abnormality degree value of the roller hearth furnace at the current moment, the detection result of the operation quality of the roller hearth furnace at the current moment is output, including: when the second abnormality degree value of the roller hearth furnace at the current moment is greater than or equal to a preset threshold value, it is prompted that the operation quality of the roller hearth furnace at the current moment is abnormal; or, when the second abnormality degree value of the roller hearth furnace at the current moment is less than the preset threshold value, it is prompted that the operation quality of the roller hearth furnace at the current moment is normal.
[0067] The preset threshold value may be preset according to the value range of the second abnormality level value. For example, when the value range of the second abnormality level value is between 0 and 1, the preset threshold value may be set between 0.4 and 0.6.
[0068] When the second abnormality degree value of the roller hearth furnace at the current moment is greater than or equal to the preset threshold value, it indicates that the probability of the roller hearth furnace having an abnormality at the current moment is relatively high, or that the impact degree of the abnormality existing at the roller hearth furnace at the current moment is relatively high. In order to avoid the loss caused by the abnormality existing at the current moment of the roller hearth furnace, it can be prompted that the operating quality of the roller hearth furnace at the current moment has an abnormality, so that the user can handle the roller hearth furnace with the abnormality.
[0069] The prompting of the abnormality in the operation quality of the roller-hearth furnace at the current moment may be performed by a device such as an audio player, a light prompting device, and a vibration prompting device near the roller-hearth furnace, or may be implemented by sending a prompting message to a terminal device bound to the user so that the terminal device outputs a prompting message. The embodiment of the present application does not limit the method of prompting the information that the operation quality of the roller-hearth furnace at the current moment is abnormal.
[0070] In this way, the output of the running quality detection result of the roller hearth furnace at the current moment can be realized relatively simply and effectively.
[0071] In one embodiment, outputting an operation quality inspection result of the roller hearth furnace at the current moment according to the second abnormality degree value of the roller hearth furnace at the current moment comprises: inputting a plurality of second abnormality degree values of the roller hearth furnace at the current moment and a plurality of moments before the current moment into a pre-trained quality inspection model to obtain an operation quality inspection result of the roller hearth furnace at the current moment; wherein the quality inspection model is used to output an operation quality inspection result of the roller hearth furnace according to the input plurality of second abnormality degree values.
[0072] Referring to the second abnormality degree value of the roller hearth furnace at the current moment, multiple second abnormality degree values of the roller hearth furnace at multiple moments before the current moment can be determined; for example, multiple second abnormality degree values of the roller hearth furnace at the current moment and within 10 seconds before the current moment can be determined.
[0073] By analyzing the second abnormality degree values of the roller hearth furnace at multiple moments, time series data of the second abnormality degree values can be constructed, which helps to identify possible continuous abnormalities of the roller hearth furnace and avoid misidentification of abnormalities of the roller hearth furnace.
[0074] A plurality of second abnormality degree values of the roller hearth furnace at the current moment and at multiple moments before the current moment are input into a pre-trained quality detection model. In addition to considering the abnormal situation of the roller hearth furnace at the current moment, the abnormal situation of the roller hearth furnace at the current moment and at multiple moments before the current moment can be considered, so that the quality detection model can output more accurate operation quality detection results.
[0075] For example, through multiple second abnormality degree values at the current moment and within 10 seconds before the current moment, it may be determined that there is an abnormality in the operation quality of the roller hearth furnace at the current moment, while it may be determined that there is no abnormality in the operation quality of the roller hearth furnace at the current moment based only on the second abnormality degree value at the current moment. The operation quality of the roller hearth furnace at the current moment obtained by using multiple second abnormality degree values at the current moment and within 10 seconds before the current moment is more forward-looking and can achieve predictive detection of the operation quality of the roller hearth furnace at the current moment.
[0076] Since the temperature information of different partitions of the roller hearth furnace and the current information of the electrical equipment at the current moment may be affected by the moments before the current moment, a more accurate operation quality detection result of the roller hearth furnace at the current moment can be determined by using multiple second abnormality degree values at the current moment and within 10 seconds before the current moment.
[0077] The output operation quality detection result can be an operation quality detection label, and different operation quality detection labels correspond to different operation quality states of the roller hearth furnace; for example, the operation quality detection label can include 0 and 1, wherein the operation quality detection label 0 corresponds to the operation quality state when the roller hearth furnace operates normally, and the operation quality detection label 1 corresponds to the operation quality state when there is an abnormality in the operation of the roller hearth furnace.
[0078] Alternatively, the operation quality detection label may include 0, 1 and 2, wherein the operation quality detection label 0 corresponds to the operation quality status when the roller hearth furnace operates normally, the operation quality detection label 1 corresponds to the operation quality status when there is a slight abnormality in the operation of the roller hearth furnace, and the operation quality detection label 2 corresponds to the operation quality status when there is a serious abnormality in the operation of the roller hearth furnace.
[0079] The pre-built network model may be, for example, a LSTM (Long Short-Term Memory) network model or a CNN (Convolutional Neural Network) model.
[0080] Those skilled in the art can adaptively select the correspondence between the operation quality detection label and the operation quality status of the roller hearth furnace according to actual needs. The embodiment of the present application does not limit the operation quality status of the roller hearth furnace corresponding to the operation quality detection label.
[0081] In this way, multiple second abnormality degree values of the roller hearth furnace at the current moment and multiple moments before the current moment are input into the pre-trained quality detection model. Since the abnormal conditions of the roller hearth furnace at the current moment and multiple moments before the current moment are taken into consideration, a more accurate operation quality detection result of the roller hearth furnace at the current moment can be obtained.
[0082] In one embodiment, the quality detection model is trained in the following manner: obtaining the second abnormality degree value of the roller hearth furnace at a historical moment; for a target historical moment among multiple historical moments, using multiple second abnormality degree values of multiple historical moments before the target historical moment as inputs of a pre-constructed network model, and using the quality detection label of the target historical moment as output of the pre-constructed network model, so that the trained network model is used as the quality detection model.
[0083] The target historical moment can be marked with a corresponding quality inspection label according to the operating quality of the roller hearth furnace at the target historical moment; for example, when the operating quality of the roller hearth furnace at the target historical moment is abnormal, the quality inspection label at the target historical moment can be marked as 1; when the operating quality of the roller hearth furnace at the target historical moment is normal, the quality inspection label at the target historical moment can be marked as 0.
[0084] By using the second abnormality degree values of the roller hearth furnace at multiple historical moments as training data, the pre-built network model can learn the characteristics of the roller hearth furnace under different operating conditions, which helps to improve the network model's prediction ability for data at future moments, so that the quality monitoring model obtained after training is better able to output the operating quality detection results of the roller hearth furnace.
[0085] For example, the roller hearth furnace has had an abnormal trend at the historical moments before the current moment, making it highly likely that the operation quality of the roller hearth furnace will be abnormal at the current moment. Prompting that the operation quality of the roller hearth furnace is abnormal at the current moment can prompt the user to check the possible abnormalities of the roller hearth furnace as soon as possible, thereby avoiding further deterioration of the abnormality of the operation quality of the roller hearth furnace. Therefore, training the network model using multiple second abnormality degree values at multiple historical moments before the target historical moment can help the user deal with the abnormal situation of the roller hearth furnace before a serious abnormality occurs in the roller hearth furnace, thereby reducing the use cost of the roller hearth furnace.
[0086] In this way, the pre-constructed network model is trained using multiple second abnormality degree values at multiple historical moments to obtain a quality detection model, which can facilitate the use of the quality detection model to output a more accurate detection result of the operation quality of the roller hearth furnace.
[0087] Figure 2 1 is a structural diagram of a bearing processing equipment operation quality detection system 1000 based on data analysis according to an exemplary embodiment. Figure 2 The bearing processing equipment operation quality detection system 1000 based on data analysis includes: a processor 1100 and a memory 1200, wherein the memory 1200 stores computer program instructions, and when the computer program instructions are executed by the processor 1100, all or part of the steps of the bearing processing equipment operation quality detection method based on data analysis in this application are implemented.
[0088] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary technical means in the art that are not disclosed in the present application, and the specification and embodiments are only considered as exemplary.
[0089] It should be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.
Claims
1. A method for detecting the operation quality of bearing processing equipment based on data analysis, characterized in that: include: For a target partition among multiple partitions of the roller hearth furnace, obtain the current stability value of the electrical equipment in the target partition at the current moment when the roller hearth furnace is working; The current stability value is used to characterize the stability of the current of the electrical equipment in the target partition; Obtain the fluctuation degree value of the current of the electrical equipment in the target partition at the historical moment, and use the fluctuation degree value to exponentially adjust the current stability degree value at the current moment to obtain the first abnormal degree value of the current of the electrical equipment in the target partition at the current moment; Acquire temperature information of the partitions of the roller hearth furnace in the current time period, and determine a second abnormality degree value of the roller hearth furnace at the current moment by using the temperature information of different partitions of the roller hearth furnace and the first abnormality degree values of different partitions; According to the second abnormality degree value of the roller hearth furnace at the current moment, the operation quality detection result of the roller hearth furnace at the current moment is output.
2. The method for detecting the operation quality of bearing processing equipment based on data analysis according to claim 1 is characterized in that: The current stability value of the electrical equipment in the target partition at the current moment is determined by the following method: , where s is the current stability value of the electrical equipment in the target partition at the current moment, and exp is an exponential function with a natural constant as the base. is the standard deviation of the current in the target zone during the ignition preparation phase of the roller hearth furnace, is the standard deviation of the current in the target partition during the period from the ignition time to the current time, is the current of the target partition at the current moment, is the rated current of the target partition.
3. The method for detecting the operation quality of bearing processing equipment based on data analysis according to claim 1 is characterized in that: The current stability value at the current moment is exponentially adjusted by using the fluctuation degree value to obtain the first abnormal degree value of the current of the electrical equipment in the target partition at the current moment, including: ,in, is the first abnormality value of the current of the electric equipment in the target partition at the current moment, s is the current stability value of the electric equipment in the target partition at the current moment, and are respectively the maximum and minimum values of the current stability of the target partition before the current moment, and are respectively the mean and standard deviation of the current stability value of the target partition before the current moment, is the fluctuation degree value.
4. The method for detecting the running quality of bearing processing equipment based on data analysis according to claim 1 is characterized in that: The second abnormality degree value of the roller hearth furnace at the current moment is determined in the following way: ,in, is the second abnormality value of the roller hearth furnace at the current moment, N is the number of partitions in the roller hearth furnace, is the first abnormality value of the ith partition of the roller hearth furnace at the current moment, is the minimum of the first abnormality degree values of the ith partition of the roller hearth furnace at the historical moment, is the temperature of the ith zone of the roller hearth furnace at the current moment, is the set temperature of the i-th zone of the roller hearth furnace.
5. The method for detecting the running quality of bearing processing equipment based on data analysis according to claim 1 is characterized in that: The second abnormality degree value of the roller hearth furnace at the current moment is determined in the following way: , is the second abnormality value of the roller hearth furnace at the current moment, N is the number of partitions in the roller hearth furnace, and max is the maximum value. is the first abnormality value of the ith partition of the roller hearth furnace at the current moment, is the minimum of the first abnormality degree values of the ith partition of the roller hearth furnace at the historical moment, is the temperature of the ith zone of the roller hearth furnace at the current moment, is the set temperature of the i-th zone of the roller hearth furnace.
6. The method for detecting the running quality of bearing processing equipment based on data analysis according to claim 1 is characterized in that: According to the second abnormality degree value of the roller hearth furnace at the current moment, the operation quality detection result of the roller hearth furnace at the current moment is output, including: A plurality of second abnormality degree values of the roller hearth furnace at a current moment and at a plurality of moments before the current moment are input into a pre-trained quality detection model to obtain an operation quality detection result of the roller hearth furnace at the current moment; wherein the quality detection model is used to output an operation quality detection result of the roller hearth furnace according to the input plurality of second abnormality degree values.
7. The method for detecting the running quality of bearing processing equipment based on data analysis according to claim 6 is characterized in that: The quality detection model is trained in the following way: Get the second abnormality value of the roller hearth furnace at a historical moment; For a target historical moment among multiple historical moments, multiple second abnormality degree values of multiple historical moments before the target historical moment are used as inputs of a pre-built network model, and the quality detection label of the target historical moment is used as output of the pre-built network model, so that the trained network model is used as the quality detection model.
8. The method for detecting the running quality of bearing processing equipment based on data analysis according to claim 1 is characterized in that: According to the second abnormality degree value of the roller hearth furnace at the current moment, the operation quality detection result of the roller hearth furnace at the current moment is output, including: When the second abnormality degree value of the roller hearth furnace at the current moment is greater than or equal to the preset threshold value, it is prompted that the operation quality of the roller hearth furnace at the current moment is abnormal; Alternatively, when the second abnormality degree value of the roller hearth furnace at the current moment is less than the preset threshold value, it is prompted that the operation quality of the roller hearth furnace at the current moment is normal.
9. The method for detecting the running quality of bearing processing equipment based on data analysis according to claim 1 is characterized in that: The multiple zones of the roller hearth furnace include: loading zone, preheating zone, heating zone, salt tank, tempering furnace, cooling area and unloading zone.
10. A bearing processing equipment operation quality detection system based on data analysis, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the method for detecting the operating quality of bearing processing equipment based on data analysis according to any one of claims 1 to 9 is implemented.