CN118143740A - Spindle detection method and system of numerical control machine tool - Google Patents
Spindle detection method and system of numerical control machine tool Download PDFInfo
- Publication number
- CN118143740A CN118143740A CN202410586384.3A CN202410586384A CN118143740A CN 118143740 A CN118143740 A CN 118143740A CN 202410586384 A CN202410586384 A CN 202410586384A CN 118143740 A CN118143740 A CN 118143740A
- Authority
- CN
- China
- Prior art keywords
- data
- fault
- frequency
- parameters
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 49
- 230000006855 networking Effects 0.000 claims abstract description 63
- 238000000034 method Methods 0.000 claims abstract description 47
- 238000004458 analytical method Methods 0.000 claims abstract description 45
- 230000001360 synchronised effect Effects 0.000 claims abstract description 29
- 230000008569 process Effects 0.000 claims abstract description 21
- 238000012216 screening Methods 0.000 claims abstract description 17
- 238000012545 processing Methods 0.000 claims abstract description 11
- 238000012423 maintenance Methods 0.000 claims description 13
- 238000004364 calculation method Methods 0.000 claims description 11
- 238000003860 storage Methods 0.000 claims description 8
- 230000002159 abnormal effect Effects 0.000 description 9
- 238000007405 data analysis Methods 0.000 description 4
- 238000003754 machining Methods 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005461 lubrication Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000009529 body temperature measurement Methods 0.000 description 1
- 239000008358 core component Substances 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000006698 induction Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012502 risk assessment Methods 0.000 description 1
- 238000004804 winding Methods 0.000 description 1
Landscapes
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The application discloses a method and a system for detecting a main shaft of a numerical control machine tool, which belong to the technical field of the numerical control machine tool and comprise the following steps: receiving first data; establishing a networking operation database, and carrying out synchronous processing on the first data to generate synchronous data; performing fault analysis according to the stored data, calculating fault influence weights of different types of data, and screening out at least two high-frequency fault parameters; establishing a first model, and inputting at least two high-frequency fault parameters into the first model; and overhauling and maintaining the main shaft according to the output result of the first model. In the implementation process of the technical scheme, the networking operation database is established, the first data is synchronously processed to generate synchronous data, the synchronous data is stored in the networking operation database, fault analysis is carried out through the data in the database, high-frequency fault parameters are screened out, relevance analysis is carried out on the high-frequency fault parameters through the first model, and an analysis result is fed back to the acquisition end, so that the detection efficiency is improved.
Description
Technical Field
The application relates to the technical field of numerical control machine tools, in particular to a main shaft detection method and a main shaft detection system of a numerical control machine tool.
Background
The numerical control machine tool is widely used as a working machine in the field of machine manufacturing, and the main shaft system is one of core components of the machine tool, and the running state of the main shaft system can directly influence the overall machining performance of the machine tool, so that the main shaft system of the numerical control machine tool is usually required to be detected and overhauled in time in order to ensure the normal running of the numerical control machine tool.
In the prior art, various parameters, such as vibration data, sound data, power data, image data and the like, in the operation process of a main shaft are generally collected through a collection device, and are uploaded to an industrial personal computer or a computer, the operation state of the main shaft is judged through analysis of the collected parameters, and the main shaft is overhauled and maintained according to a judging result, so that the main shaft is prevented from being abnormal in the operation process, and the normal operation of a numerical control machine tool is prevented from being influenced.
However, in practical application, because the main shaft structure of the numerical control machine tool is complex and compact, the internal heat source is more, the collected data is influenced, and then the collected data is abnormal, and the detection result of the main shaft is directly influenced, in the prior art, the collected data can be monitored in real time by adding the monitoring equipment, but the cost is increased, and another set of data analysis scheme is needed for detecting the operation of the collected data, so that when the number of the collected data is more, the normal operation of the collected data and the validity of the data are difficult to ensure, and meanwhile, the detection result of the main shaft only can reflect the operation state of the main shaft, and the data utilization rate is lower.
It is therefore necessary to provide a spindle detection method and a spindle detection system for a numerical control machine tool to solve the above problems.
It should be noted that the above information disclosed in this background section is only for understanding the background of the inventive concept and, therefore, it may contain information that does not constitute prior art.
Disclosure of Invention
Based on the above problems existing in the prior art, the present application aims to solve the problems: the main shaft detection method and the main shaft detection system of the numerical control machine tool can be used for reasonably distributing acquisition equipment corresponding to high-frequency fault parameters through screening out the high-frequency fault parameters, and the main shaft detection efficiency is improved.
The technical scheme adopted for solving the technical problems is as follows: a spindle detection method of a numerical control machine, the method comprising:
receiving first data acquired by acquisition equipment, wherein the acquisition equipment is arranged in an effective detection range of a numerical control machine tool, and the first data is operation data in the working process of a main shaft;
Establishing a networking operation database, carrying out synchronous processing on the first data to generate synchronous data, and storing the synchronous data into the networking operation database, wherein the networking operation database has a cloud storage function, and other spindle operation data with the same model are stored in the networking operation database;
performing fault analysis according to stored data in the networking operation database, calculating fault influence weights of different types of data according to fault analysis results, and screening out at least two high-frequency fault parameters;
Establishing a first model, wherein the first model is provided with an input end and an output end, a first program is operated in the first model, the first program is used for judging the relevance between data according to input information, outputting a judging result and inputting at least two high-frequency fault parameters into the first model;
And overhauling and maintaining the main shaft according to the output result of the first model, and uploading data generated in the maintenance process and the fault detection result to a networking operation database.
In the implementation process of the technical scheme, the networking operation database is established, the first data is synchronously processed to generate synchronous data, the synchronous data is stored in the networking operation database, fault analysis is carried out through the data in the database, high-frequency fault parameters are screened out, relevance analysis is carried out on the high-frequency fault parameters through the first model, and an analysis result is fed back to the acquisition end, so that the detection efficiency is improved.
Further, the synchronization processing of the first data includes the following steps:
Setting a public acquisition length, wherein the public acquisition length is determined by the acquisition frequency of the acquisition equipment, and the public acquisition length takes time as a unit;
grouping the first data according to the public acquisition length, and naming the grouped data according to the category of the acquisition equipment;
The grouped first data is stored in the form of individual objects in a networked operation database.
Further, the acquisition frequency refers to a minimum data segmentation interval determined according to the acquired data type.
Furthermore, the common acquisition length is the acquisition frequency corresponding to the acquisition equipment with the maximum acquisition frequency in all the acquisition equipment.
Further, the fault analysis result includes a fault reason and an operation parameter type causing the fault.
Further, the method for calculating the fault influence weight comprises the following steps: traversing the fault analysis result in the networking operation database, obtaining the frequency of occurrence of one of the operation parameters in the fault result, dividing the frequency by the total number of fault cases, and obtaining data which is the fault influence weight of the operation parameter.
Further, the screening method of the high-frequency fault parameters comprises the following steps:
Calculating fault influence weights of all operation parameters, and sorting the calculated fault influence weights to generate a weight set, wherein the weight set is updated when a new fault case exists;
setting a first weight threshold value, and screening out high-frequency fault parameters according to the first weight threshold value;
Wherein, the operation parameters with the fault influence weight larger than the first weight threshold value are listed as high-frequency fault parameters;
when the high-frequency fault parameters are more, the high-frequency fault parameters are screened among groups, and the specific screening method comprises the following steps:
Obtaining fault influence weights of high-frequency fault parameters, performing intra-group absolute difference calculation, setting a second weight threshold, comparing the second weight threshold with a calculation result, taking the high-frequency fault parameters with absolute differences closer to the second weight threshold as effective high-frequency fault parameters, and waiting for next comparison when the high-frequency fault parameters are not compared.
Further, the operation steps of the first program are as follows:
after receiving the input high-frequency fault parameters, automatically traversing a networking operation database, and acquiring the times of the high-frequency fault parameters in the same fault case at the same time;
calculating the relevance between the high-frequency fault parameters according to the times that the high-frequency fault parameters are simultaneously in the same fault case and the total number of the fault cases;
And outputting the calculation result.
Further, the method for calculating the correlation between the high-frequency fault parameters comprises the following steps:
The correlation between the high frequency fault parameters is equal to the ratio of the number of times the high frequency fault parameters are simultaneously present in the same fault case to the total number of fault cases.
A spindle detection system for a numerically controlled machine tool, the system comprising:
The data receiving module is used for receiving first data acquired by acquisition equipment, the acquisition equipment is arranged in an effective detection range of the numerical control machine tool, and the first data is operation data in the working process of the main shaft;
The networking operation database establishing module is used for establishing a networking operation database, carrying out synchronous processing on the first data, generating synchronous data, storing the synchronous data into the networking operation database, wherein the networking operation database has a cloud storage function, and other spindle operation data with the same model are stored in the networking operation database;
The fault analysis module is used for carrying out fault analysis according to stored data in the networking operation database, calculating fault influence weights of different types of data according to fault analysis results, and screening out at least two high-frequency fault parameters;
The model building module is used for building a first model, the first model is provided with an input end and an output end, a first program is operated in the first model, the first program is used for judging the relevance between data according to input information, outputting a judging result and inputting at least two high-frequency fault parameters into the first model;
and the overhaul maintenance module is used for overhauling and maintaining the main shaft according to the output result of the first model and uploading data generated in the maintenance process and the fault detection result to the networking operation database.
The beneficial effects of the application are as follows: according to the spindle detection method and the spindle detection system for the numerical control machine tool, the networking operation database is established, the first data are synchronously processed to generate the synchronous data, the synchronous data are stored in the networking operation database, fault analysis is conducted through the data in the database, high-frequency fault parameters are screened out, relevance analysis is conducted on the high-frequency fault parameters through the first model, an analysis result is fed back to the acquisition end, and detection efficiency is improved.
In addition to the objects, features and advantages described above, the present application has other objects, features and advantages. The present application will be described in further detail with reference to the drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
FIG. 1 is a schematic overall flow chart of a spindle detection method of a numerical control machine tool according to the present application;
FIG. 2 is a schematic diagram of a first data distribution after grouping;
Fig. 3 is a schematic diagram of a module configuration of a spindle detection system of a numerically-controlled machine tool according to the present application.
Detailed Description
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
Embodiment one: as shown in fig. 1, the application provides a spindle detection method of a numerically-controlled machine tool, which is applied to spindle system detection of the numerically-controlled machine tool, wherein the spindle system of the numerically-controlled machine tool comprises a spindle, a spindle bearing, a spindle motor, a driving mechanism and the like, the spindle system plays a key role in the numerically-controlled machine tool and is responsible for tool movement and workpiece rotation in the machining process, and because the spindle system needs to move continuously, fatigue, damage and the like are easy to generate and need to be detected, and the production and machining process is prevented from being influenced by spindle faults, and the method comprises the following steps:
Step 101: receiving first data acquired by acquisition equipment, wherein the acquisition equipment is arranged in an effective detection range of a numerical control machine tool, and the first data is operation data in the working process of a main shaft;
In order to detect the main shaft, the main shaft needs to be subjected to operation data acquisition, so that the operation state of the main shaft is analyzed according to the acquired operation data to realize the detection of the main shaft, wherein the acquisition equipment comprises (but is not limited to) a vibration sensor for acquiring vibration information in the operation process of the main shaft, and the vibration information comprises the vibration acceleration of the main shaft, the vibration displacement of the main shaft, the vibration speed of the main shaft and the like; the temperature sensor is used for collecting the temperature in the running process of the main shaft, such as a thermocouple, is mainly used for detecting the temperature of a main shaft bearing and a motor winding, and the infrared thermometer is used for carrying out non-contact temperature measurement on the main shaft, and the collected temperature data are all in the unit of the temperature; a force sensor for detecting a cutting force of the spindle system; a sound sensor for acquiring noise generated during the operation of the spindle so as to identify abnormal vibration according to the noise information; the current sensor is used for acquiring current data of the spindle motor, the devices form acquisition devices in the embodiment, more types of acquisition devices can be set according to the needs in practical application, and the current sensor is not exemplified one by one in the embodiment;
It should be noted that, when the different collecting devices have corresponding effective collecting distances, the effective collecting distances of the collecting devices should be considered for reasonable layout, for example, the effective collecting distances of the collecting devices can be obtained by referring to technical manuals of the corresponding devices, so that the abnormal occurrence of collected data caused by the unreasonable layout of the collecting devices is prevented, and the detection of the main shaft is prevented from being influenced;
Step 102: establishing a networking operation database, carrying out synchronous processing on the first data to generate synchronous data, and storing the synchronous data into the networking operation database, wherein the networking operation database has a cloud storage function, and other spindle operation data with the same model are stored in the networking operation database;
In order to improve the efficiency of spindle detection, spindle operation data of the same type are stored into a networking database through a wireless network in a mode of establishing the networking operation database, so that transverse data induction is facilitated, the networking operation database has a cloud storage function, local storage is not required to be occupied, and after the spindle operation data are uploaded into the networking operation database, any user obtaining access rights can inquire, and the utilization efficiency of the data is improved;
Because the difference of the acquisition frequencies exists between different acquisition devices, the scale of the first data is not uniform, and therefore synchronous processing is needed to be performed, and synchronous data is generated, wherein the synchronous processing of the first data comprises the following steps:
Step A: setting a public acquisition length, wherein the public acquisition length is determined by the acquisition frequency of the acquisition equipment, and the public acquisition length takes time as a unit;
In the prior art, with the development of technology, the acquisition device can realize uninterrupted acquisition, but the acquired data is huge and complicated, which is unfavorable for data analysis work, and not all data need to be acquired continuously, so that when data analysis is performed, the data often need to be segmented, and the acquisition device is defined to have an acquisition frequency, wherein the acquisition frequency refers to a minimum data segmentation interval determined according to the acquired data type, for example, the acquisition frequency of the temperature sensor in the operation process of the main shaft is fifteen minutes, and refers to that the temperature data acquired by the temperature sensor is a group every fifteen minutes, but not the temperature data acquired by the temperature sensor every fifteen minutes; taking a vibration sensor as an example, the acquisition frequency of the vibration sensor in the running process of the spindle is five minutes, which means that the vibration sensor acquires a group of vibration data bits every five minutes instead of acquiring vibration data every five minutes, so that the data are grouped at the acquisition end, and the follow-up operation is convenient;
Since different devices generally have different acquisition frequencies, in order to make the acquisition scale of each group consistent, a common acquisition length needs to be set, where the common acquisition length is the acquisition frequency corresponding to the acquisition device with the largest acquisition frequency among all the acquisition devices, for example, the acquisition frequencies of different acquisition devices in a certain group of acquisition devices are respectively five minutes, ten minutes and fifteen minutes, fifteen minutes are taken as the common acquisition length, and since for the acquisition device with the shorter acquisition frequency, even if the acquisition frequency is increased, the integrity of acquired data is not affected, and for the acquisition device with the longer acquisition frequency, if the acquisition frequency is reduced, the integrity of acquired data is affected, and therefore, the common acquisition length of the acquisition frequency seat corresponding to the acquisition device with the largest acquisition frequency among the acquisition devices is selected, so that different data have the same scale;
Because in the numerical control machine tools with different models, even if the same type of main shaft is adopted, the types of data to be collected are not necessarily consistent, so that the common collection length is required to be adjusted according to the distributed collection equipment, and in order to ensure the consistency of the data in the networking operation database, the same type of collection equipment is selected;
And (B) step (B): grouping the first data according to the public acquisition length, and naming the grouped data according to the category of the acquisition equipment;
After setting the common acquisition length, the first data can be grouped, and for convenience of maintenance of the networked operation database, the grouped data are named according to the type of the acquisition equipment and the main shaft, for example, (temperature data, main shaft a), (noise data, main shaft b) and the like;
Step C: the grouped first data is stored in the form of individual objects in a networked operation database.
The form of the single object refers to the situation that the detection result is abnormal in one or more pieces of first data after grouping, as shown in fig. 2, the first data after grouping is data 1 and data 2 … … and data 9 respectively, each single data may be the cause of the detection result being abnormal, and the combination of different data may also be the cause of the detection result being abnormal, instead of directly uploading the first data after grouping to the networking operation database, so that subsequent fault analysis is facilitated.
Step 103: performing fault analysis according to stored data in the networking operation database, calculating fault influence weights of different types of data according to fault analysis results, and screening out at least two high-frequency fault parameters;
After the operation data of the main shaft is collected, the operation data is analyzed, so that the fault detection of the main shaft of the numerical control machine tool can be realized, in the prior art, the fault detection of the main shaft of the numerical control machine tool can be usually performed through the data such as the main shaft rotating speed, main shaft noise, temperature change, vibration data and the like, for example, the invention patent with the publication number of CN114054785A is used for carrying out the fault analysis through collecting the operation data of the main shaft and the operation data of each feeding shaft in the three-dimensional direction, the invention patent with the publication number of CN117784711A is used for analyzing the operation risk assessment index of the main shaft through analyzing the operation rotating speed change condition of the main shaft, so that the fault analysis is realized, and various methods can be selected for the fault detection of the main shaft, and the fault analysis is performed on the main shaft from a plurality of operation data;
After the fault analysis of the main shaft is completed by the above methods, a fault analysis result is also generated, the analysis result is uploaded to a networking operation database, and the analysis result contains a fault reason and an operation parameter type causing the fault, for example, after the fault analysis of a main shaft of a certain model, the fault is determined, the fault reason is that the main shaft is overheated to cause lubrication failure in the main shaft, and meanwhile, the main shaft bearing wear causes unbalanced operation of the main shaft and generates an abnormal vibration signal, so that the analysis result of the main shaft is that: the operation parameters of faults are temperature data and vibration data;
As the fault analysis results in the networking operation database are more and more, more fault cases are generated, and the fault influence weights of different types of data are calculated according to the fault cases, and specifically, the calculation method of the fault influence weights is as follows:
Traversing fault analysis results in a networking operation database, obtaining the frequency of occurrence of one of the operation parameters in the fault results, dividing the frequency by the total number of fault cases, and obtaining data which is the fault influence weight of the operation parameter;
For example, the number of times the temperature data appears in the fault result is 8, the total number of fault cases is 10, and then the fault influence weight of the temperature data is 0.8;
calculating fault influence weights of all operation parameters according to the same calculation method, and sorting the calculated fault influence weights to generate a weight set, wherein the weight set is updated when a new fault case exists;
setting a first weight threshold value, and screening out high-frequency fault parameters according to the first weight threshold value;
In the fault detection of the spindle, the high-frequency fault parameters refer to that in the fault case, the occurrence times of the running parameters of the faults exceeds a certain threshold, for example, a first weight threshold is set to be 0.7, the running parameters with the fault influence weight being greater than the first weight threshold are listed as the high-frequency fault parameters, the setting of the first weight threshold is not unique, the adjustment can be carried out according to actual needs, and only the set first weight threshold is required to be ensured to generate at least two high-frequency fault parameters, so that the correlation analysis on the running parameters of the spindle is facilitated;
Step 104: establishing a first model, wherein the first model is provided with an input end and an output end, a first program is operated in the first model, the first program is used for judging the relevance between data according to input information, outputting a judging result and inputting at least two high-frequency fault parameters into the first model;
The operation steps of the first program are as follows:
after receiving the input high-frequency fault parameters, automatically traversing a networking operation database, and acquiring the times of the high-frequency fault parameters in the same fault case at the same time;
calculating the relevance between the high-frequency fault parameters according to the times that the high-frequency fault parameters are simultaneously in the same fault case and the total number of the fault cases;
And outputting the calculation result.
After the high-frequency fault parameters are screened out, the relevance among the high-frequency fault parameters needs to be calculated, at least two high-frequency fault parameters need to be input, when the number of the high-frequency fault parameters is smaller than 2, a first weight threshold needs to be reset, or the screening is continued after a fault case is expanded, the high-frequency fault parameters are operation data which cause the high occurrence frequency of the main shaft fault, therefore, at an acquisition end, the acquired operation data quality needs to be reasonably laid out and maintained, and when the number of the high-frequency fault parameters is at least two groups, the situation that whether a plurality of fault parameters occur frequently and simultaneously in the main shaft operation process is also considered, so that the daily maintenance of the main shaft is facilitated, and the relevance among the high-frequency fault parameters is calculated through a first program;
wherein, the relevance between the high-frequency fault parameters is equal to the ratio of the times that the high-frequency fault parameters are simultaneously present in the same fault case to the total number of the fault cases;
For example, in the case of ten times of faults, the temperature data and the vibration data are both high-frequency fault parameters, and the number of times of the two simultaneous occurrence in the case of ten times of faults is 8, the relevance of the temperature data and the vibration data is 0.8, the relevance can be directly output through the first model, a worker can take the output relevance as a reference, and judge the running state of the acquisition equipment at the same time, for example, when the temperature data of the main shaft are abnormal, if the relevance of the temperature data and the vibration data is higher, the main shaft bearing and the lubrication condition can be simultaneously checked, and the related acquisition equipment is synchronously overhauled and maintained, and fault analysis is not required to be performed after all the data are acquired, or all the acquisition equipment is directly checked and maintained, so that the maintenance procedure of the main shaft of the numerical control machine tool is reduced, the monitoring of the acquisition equipment is realized, the data validity is improved, and the production efficiency is improved;
Along with the continuous increase of fault cases in the networking operation database, the number of high-frequency fault parameters is increased, and in order to improve the effectiveness of data and optimize the combination of associated data, the high-frequency fault parameters are required to be screened among groups, and the specific screening method is as follows:
Obtaining fault influence weights of high-frequency fault parameters, performing intra-group absolute difference calculation, setting a second weight threshold, comparing the second weight threshold with a calculation result, taking the high-frequency fault parameters with absolute differences closer to the second weight threshold as effective high-frequency fault parameters, and waiting for next comparison if the high-frequency fault parameters are not compared;
after the high-frequency fault parameters are compared by the method, the effectiveness of data can be improved, a plurality of high-frequency fault parameters are screened, and the data analysis efficiency is improved.
Step 105: and overhauling and maintaining the main shaft according to the output result of the first model, and uploading data generated in the maintenance process and the fault detection result to a networking operation database.
After the first model outputs the relevance of the high-frequency fault parameters, a worker overhauls the corresponding parts, if the faults are removed after the overhauling is finished, the overhauling result is uploaded by filling in a form of a networking operation database; if the fault is not removed after the maintenance is finished, continuing to perform fault analysis, uploading an analysis result to a networking operation database, and expanding a data source.
Embodiment two: as shown in fig. 3, the present application also provides a spindle detection system of a numerically-controlled machine tool, which operates the detection method as in the first embodiment, the detection system comprising:
The data receiving module is used for receiving first data acquired by acquisition equipment, the acquisition equipment is arranged in an effective detection range of the numerical control machine tool, and the first data is operation data in the working process of the main shaft;
The networking operation database establishing module is used for establishing a networking operation database, carrying out synchronous processing on the first data, generating synchronous data, storing the synchronous data into the networking operation database, wherein the networking operation database has a cloud storage function, and other spindle operation data with the same model are stored in the networking operation database;
The fault analysis module is used for carrying out fault analysis according to stored data in the networking operation database, calculating fault influence weights of different types of data according to fault analysis results, and screening out at least two high-frequency fault parameters;
The model building module is used for building a first model, the first model is provided with an input end and an output end, a first program is operated in the first model, the first program is used for judging the relevance between data according to input information, outputting a judging result and inputting at least two high-frequency fault parameters into the first model;
and the overhaul maintenance module is used for overhauling and maintaining the main shaft according to the output result of the first model and uploading data generated in the maintenance process and the fault detection result to the networking operation database.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. A method for detecting a main shaft of a numerical control machine tool is characterized by comprising the following steps of: the method comprises the following steps: receiving first data acquired by acquisition equipment, wherein the acquisition equipment is arranged in an effective detection range of a numerical control machine tool, and the first data is operation data in the working process of a main shaft; establishing a networking operation database, carrying out synchronous processing on the first data to generate synchronous data, and storing the synchronous data into the networking operation database, wherein the networking operation database has a cloud storage function, and other spindle operation data with the same model are stored in the networking operation database; performing fault analysis according to stored data in the networking operation database, calculating fault influence weights of different types of data according to fault analysis results, and screening out at least two high-frequency fault parameters; establishing a first model, wherein the first model is provided with an input end and an output end, a first program is operated in the first model, the first program is used for judging the relevance between data according to input information, outputting a judging result and inputting at least two high-frequency fault parameters into the first model; and overhauling and maintaining the main shaft according to the output result of the first model, and uploading data generated in the maintenance process and the fault detection result to a networking operation database.
2. The method for detecting a spindle of a numerical control machine according to claim 1, characterized in that: the synchronization processing of the first data comprises the following steps: setting a public acquisition length, wherein the public acquisition length is determined by the acquisition frequency of the acquisition equipment, and the public acquisition length takes time as a unit; grouping the first data according to the public acquisition length, and naming the grouped data according to the category of the acquisition equipment; the grouped first data is stored in the form of individual objects in a networked operation database.
3. The method for detecting a spindle of a numerical control machine according to claim 2, characterized in that: the acquisition frequency refers to a minimum data segment interval determined according to the type of data acquired.
4. The method for detecting a spindle of a numerical control machine according to claim 2, characterized in that: the public acquisition length is the acquisition frequency corresponding to the acquisition equipment with the maximum acquisition frequency in all the acquisition equipment.
5. The method for detecting a spindle of a numerical control machine according to claim 1, characterized in that: the fault analysis result comprises a fault reason and a fault-causing operation parameter type.
6. The method for detecting a spindle of a numerical control machine according to claim 1, characterized in that: the method for calculating the fault influence weight comprises the following steps: traversing the fault analysis result in the networking operation database, obtaining the frequency of occurrence of one of the operation parameters in the fault result, dividing the frequency by the total number of fault cases, and obtaining data which is the fault influence weight of the operation parameter.
7. The method for detecting a spindle of a numerical control machine according to claim 6, wherein: the screening method of the high-frequency fault parameters comprises the following steps: calculating fault influence weights of all operation parameters, and sorting the calculated fault influence weights to generate a weight set, wherein the weight set is updated when a new fault case exists; setting a first weight threshold value, and screening out high-frequency fault parameters according to the first weight threshold value; wherein, the operation parameters with the fault influence weight larger than the first weight threshold value are listed as high-frequency fault parameters; when the high-frequency fault parameters are more, the high-frequency fault parameters are screened among groups, and the specific screening method comprises the following steps: obtaining fault influence weights of high-frequency fault parameters, performing intra-group absolute difference calculation, setting a second weight threshold, comparing the second weight threshold with a calculation result, taking the high-frequency fault parameters with absolute differences closer to the second weight threshold as effective high-frequency fault parameters, and waiting for next comparison when the high-frequency fault parameters are not compared.
8. The method for detecting a spindle of a numerical control machine according to claim 7, wherein: the operation steps of the first program are as follows: after receiving the input high-frequency fault parameters, automatically traversing a networking operation database, and acquiring the times of the high-frequency fault parameters in the same fault case at the same time; calculating the relevance between the high-frequency fault parameters according to the times that the high-frequency fault parameters are simultaneously in the same fault case and the total number of the fault cases; and outputting the calculation result.
9. The method for detecting a spindle of a numerical control machine according to claim 8, wherein: the method for calculating the relevance between the high-frequency fault parameters comprises the following steps: the correlation between the high frequency fault parameters is equal to the ratio of the number of times the high frequency fault parameters are simultaneously present in the same fault case to the total number of fault cases.
10. A spindle detection system of a numerical control machine tool for carrying out the spindle detection method according to any one of claims 1 to 9, characterized in that: the system comprises: the data receiving module is used for receiving first data acquired by acquisition equipment, the acquisition equipment is arranged in an effective detection range of the numerical control machine tool, and the first data is operation data in the working process of the main shaft; the networking operation database establishing module is used for establishing a networking operation database, carrying out synchronous processing on the first data, generating synchronous data, storing the synchronous data into the networking operation database, wherein the networking operation database has a cloud storage function, and other spindle operation data with the same model are stored in the networking operation database; the fault analysis module is used for carrying out fault analysis according to stored data in the networking operation database, calculating fault influence weights of different types of data according to fault analysis results, and screening out at least two high-frequency fault parameters; the model building module is used for building a first model, the first model is provided with an input end and an output end, a first program is operated in the first model, the first program is used for judging the relevance between data according to input information, outputting a judging result and inputting at least two high-frequency fault parameters into the first model; and the overhaul maintenance module is used for overhauling and maintaining the main shaft according to the output result of the first model and uploading data generated in the maintenance process and the fault detection result to the networking operation database.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410586384.3A CN118143740B (en) | 2024-05-13 | Spindle detection method and system of numerical control machine tool |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410586384.3A CN118143740B (en) | 2024-05-13 | Spindle detection method and system of numerical control machine tool |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN118143740A true CN118143740A (en) | 2024-06-07 |
| CN118143740B CN118143740B (en) | 2024-07-02 |
Family
ID=
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060089743A1 (en) * | 2004-10-25 | 2006-04-27 | Ford Motor Company | Data management and networking system and method |
| JP2014148294A (en) * | 2013-02-04 | 2014-08-21 | Denso Corp | Diagnostic device |
| CN105081879A (en) * | 2015-08-27 | 2015-11-25 | 成都飞机工业（集团）有限责任公司 | Fault diagnosis and prediction method for numerical control machine tool spindle |
| CN107358300A (en) * | 2017-06-19 | 2017-11-17 | 北京至信普林科技有限公司 | A kind of intelligent O＆M alarm filtering method and system based on multi-platform Autonomic prediction |
| CN109318053A (en) * | 2018-09-12 | 2019-02-12 | 基准精密工业（惠州）有限公司 | For monitoring the terminal installation, far end device and monitoring method of main shaft of numerical control machine tool |
| CN110039373A (en) * | 2019-04-04 | 2019-07-23 | 华中科技大学 | A kind of main shaft of numerical control machine tool thermal deformation prediction technique and system |
| CN111026642A (en) * | 2019-11-14 | 2020-04-17 | 山东中创软件商用中间件股份有限公司 | Database operation detection system, method and device and computer readable storage medium |
| CN112182068A (en) * | 2020-09-30 | 2021-01-05 | 重庆市海普软件产业有限公司 | Remote fault judgment system and method based on Internet of things technology |
| CN113176986A (en) * | 2021-04-28 | 2021-07-27 | 一汽解放汽车有限公司 | Internet of vehicles data quality determination method and device, computer equipment and storage medium |
| US20210248482A1 (en) * | 2020-02-07 | 2021-08-12 | International Business Machines Corporation | Maintaining a knowledge database based on user interactions with a user interface |
| CN114389359A (en) * | 2021-12-23 | 2022-04-22 | 广西电网有限责任公司 | Intelligent operation and maintenance method of centralized control type relay protection equipment based on cloud edge cooperation |
| CN114894479A (en) * | 2022-02-11 | 2022-08-12 | 上海电机学院 | Fan main shaft bearing fault diagnosis method based on VMD parameter optimization |
| CN114928529A (en) * | 2022-07-22 | 2022-08-19 | 广州市盛望信息科技有限公司 | Information system and information system fault detection method |
| CN115220396A (en) * | 2022-09-20 | 2022-10-21 | 南通韦俐数控机床有限公司 | Intelligent monitoring method and system for numerical control machine tool |
| US20230358596A1 (en) * | 2020-10-29 | 2023-11-09 | Ricoh Company, Ltd. | Diagnostic apparatus, machining system, diagnostic method, and recording medium |
| CN117349502A (en) * | 2023-10-11 | 2024-01-05 | 四川通信科研规划设计有限责任公司 | Operation and maintenance data query analysis method and system based on internet data center |
| CN117784711A (en) * | 2023-12-26 | 2024-03-29 | 山东富克斯精密机床有限公司 | Fault maintenance and reliability analysis system for numerical control machine tool |
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060089743A1 (en) * | 2004-10-25 | 2006-04-27 | Ford Motor Company | Data management and networking system and method |
| JP2014148294A (en) * | 2013-02-04 | 2014-08-21 | Denso Corp | Diagnostic device |
| CN105081879A (en) * | 2015-08-27 | 2015-11-25 | 成都飞机工业（集团）有限责任公司 | Fault diagnosis and prediction method for numerical control machine tool spindle |
| CN107358300A (en) * | 2017-06-19 | 2017-11-17 | 北京至信普林科技有限公司 | A kind of intelligent O＆M alarm filtering method and system based on multi-platform Autonomic prediction |
| CN109318053A (en) * | 2018-09-12 | 2019-02-12 | 基准精密工业（惠州）有限公司 | For monitoring the terminal installation, far end device and monitoring method of main shaft of numerical control machine tool |
| CN110039373A (en) * | 2019-04-04 | 2019-07-23 | 华中科技大学 | A kind of main shaft of numerical control machine tool thermal deformation prediction technique and system |
| CN111026642A (en) * | 2019-11-14 | 2020-04-17 | 山东中创软件商用中间件股份有限公司 | Database operation detection system, method and device and computer readable storage medium |
| US20210248482A1 (en) * | 2020-02-07 | 2021-08-12 | International Business Machines Corporation | Maintaining a knowledge database based on user interactions with a user interface |
| CN112182068A (en) * | 2020-09-30 | 2021-01-05 | 重庆市海普软件产业有限公司 | Remote fault judgment system and method based on Internet of things technology |
| US20230358596A1 (en) * | 2020-10-29 | 2023-11-09 | Ricoh Company, Ltd. | Diagnostic apparatus, machining system, diagnostic method, and recording medium |
| CN113176986A (en) * | 2021-04-28 | 2021-07-27 | 一汽解放汽车有限公司 | Internet of vehicles data quality determination method and device, computer equipment and storage medium |
| CN114389359A (en) * | 2021-12-23 | 2022-04-22 | 广西电网有限责任公司 | Intelligent operation and maintenance method of centralized control type relay protection equipment based on cloud edge cooperation |
| CN114894479A (en) * | 2022-02-11 | 2022-08-12 | 上海电机学院 | Fan main shaft bearing fault diagnosis method based on VMD parameter optimization |
| CN114928529A (en) * | 2022-07-22 | 2022-08-19 | 广州市盛望信息科技有限公司 | Information system and information system fault detection method |
| CN115220396A (en) * | 2022-09-20 | 2022-10-21 | 南通韦俐数控机床有限公司 | Intelligent monitoring method and system for numerical control machine tool |
| CN117349502A (en) * | 2023-10-11 | 2024-01-05 | 四川通信科研规划设计有限责任公司 | Operation and maintenance data query analysis method and system based on internet data center |
| CN117784711A (en) * | 2023-12-26 | 2024-03-29 | 山东富克斯精密机床有限公司 | Fault maintenance and reliability analysis system for numerical control machine tool |
Non-Patent Citations (1)
| Title |
|---|
| 王贵学;马勇;: "基于网络的数控机床设备远程故障诊断技术研究", 科技与企业, no. 06, 22 March 2016 (2016-03-22) * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110900307B (en) | Numerical control machine tool cutter monitoring system driven by digital twin | |
| CN108620949B (en) | Cutter wear monitoring and predicting method | |
| US6845340B2 (en) | System and method for machining data management | |
| CN104808585B (en) | A kind of quick inspection method of lathe health status | |
| CN108241343A (en) | A kind of intelligent plant management platform system | |
| EP2916187B1 (en) | Improved database for chatter predictions | |
| CN102929210A (en) | Control and optimization system for feature-based numerical control machining process and control and optimization method therefor | |
| US7571022B2 (en) | System and method for monitoring machine health | |
| CN102441817B (en) | Duty history management method and duty history management devices | |
| JP6752866B2 (en) | Cutting tool condition inspection system and method | |
| CN110045679B (en) | Edge calculation-based machine tool multi-source data acquisition and data quality evaluation method | |
| CN105629920B (en) | Processing monitoring system and method | |
| CN111708798A (en) | Wind turbine generator fault diagnosis and processing method and system | |
| Godreau et al. | Continuous improvement of HSM process by data mining | |
| CN107866696A (en) | Management system and device, main shaft failure detection method, computer-readable medium | |
| CN116757648B (en) | Production and manufacturing management system and method based on artificial intelligence | |
| Quintana et al. | Prediction, monitoring and control of surface roughness in high-torque milling machine operations | |
| CN112692646A (en) | Intelligent assessment method and device for tool wear state | |
| CN113126563A (en) | Numerical control machine tool data management system and method | |
| CN113627304A (en) | Machine tool spindle health monitoring method and system based on artificial intelligence | |
| Kang et al. | An integrated energy data analytics approach for machine tools | |
| Zheng et al. | Exploring the effectiveness of using internal CNC system signals for chatter detection in milling process | |
| Euldji et al. | Decision-making based on decision tree for ball bearing monitoring | |
| CN118143740B (en) | Spindle detection method and system of numerical control machine tool | |
| CN118143740A (en) | Spindle detection method and system of numerical control machine tool |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |