CN114298334A - Predictive maintenance system and method for machine tool spindle - Google Patents
Predictive maintenance system and method for machine tool spindle Download PDFInfo
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- 230000036541 health Effects 0.000 claims abstract description 94
- 238000013024 troubleshooting Methods 0.000 claims abstract description 54
- 238000012790 confirmation Methods 0.000 claims abstract description 29
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Abstract
The invention provides a predictive maintenance system and a predictive maintenance method for a machine tool spindle. Based on the analysis of the vibration data, the state monitoring unit displays the health state of the machine tool spindle; and meanwhile, the machine tool spindle fault is diagnosed through the health diagnosis unit, and the diagnosis result is recorded into the system. And on-site operation and maintenance personnel can perform troubleshooting and confirmation on the fault diagnosis result in the diagnosis and confirmation unit and input the troubleshooting result into the system. The diagnosis result query unit may query the historical diagnosis result. In addition, the system configuration unit can flexibly adapt to system acquisition parameters, vibration threshold values and the like. The system can realize predictive maintenance of the main shaft of the machine tool, and avoid the influence on production caused by unplanned shutdown of the machine tool due to a post maintenance method in the traditional method.
Description
Technical Field
The invention relates to the technical field of equipment maintenance, in particular to a predictive maintenance system and method for a machine tool spindle.
Background
The main shaft of the machine tool is the most critical structure of the machine tool, is the most direct bearing part in the machining process of the machine tool, bears complex dynamic load in the machining process of the machine tool, and has faults when the main shaft fails. The health state of the machine tool spindle is directly related to whether the machine tool can be normally produced. Serious sudden failure of the main shaft of the machine tool can even cause safety production accidents.
Currently, in most production and manufacturing enterprises, a method of after-repair is still adopted for maintaining a machine tool spindle, so that unplanned shutdown is caused due to faults of the machine tool spindle, and production is influenced. Even, a serious failure of the main shaft may cause a safety production accident. Meanwhile, the preparation of spare parts for key parts of the main shaft, such as a bearing, cannot be carried out in advance, so that the main shaft often breaks down and then needs to wait for the spare parts to be delivered, the longer spare part delivery period prolongs the downtime of the machine tool, and the production is seriously influenced.
Disclosure of Invention
The invention provides a predictive maintenance system and a predictive maintenance method for a machine tool spindle, which are used for solving the defect that the production progress is influenced because maintenance is carried out after the machine tool spindle breaks down in the prior art.
The invention provides a predictive maintenance system for a machine tool spindle, comprising:
the data acquisition unit is used for identifying the main shaft idling program number based on the Internet of things platform under the main shaft idling program number and acquiring vibration data of the main shaft of the machine tool;
the state monitoring unit is used for determining the health state of the machine tool spindle based on the vibration data;
the health diagnosis unit is used for carrying out fault diagnosis on the machine tool spindle and recording a fault diagnosis result;
the diagnosis confirmation unit is used for carrying out troubleshooting confirmation on the fault diagnosis result and inputting the troubleshooting result into a system;
and the diagnosis result query unit is used for querying the historical diagnosis result.
According to the predictive maintenance system for the main shaft of the machine tool, provided by the invention, the state monitoring unit comprises a calculation module, a state statistics module and a display module;
the calculation module is used for determining a vibration data effective value of the machine tool spindle based on the vibration data and determining the health state of the machine tool spindle based on the vibration data effective value and each health state threshold value; the state counting module is used for counting the number of the machine tool spindles in different health states and the abnormal distribution of the spindles; the display module is used for displaying the statistical result of the state statistical module.
According to the present invention, there is provided a predictive maintenance system for a spindle of a machine tool, the system further comprising: and the system configuration unit is used for configuring the health state threshold values and the bearing fault characteristic frequencies corresponding to different types of machine tool spindles.
According to the predictive maintenance system for the main shaft of the machine tool, provided by the invention, the health diagnosis unit comprises an information overview module and a diagnosis detail module;
the information overview module is used for displaying the overall health state of the machine tool spindle, the effective value of each vibration measuring point of the machine tool spindle and the early warning state change of each vibration measuring point, and carrying out statistical analysis on the early warning times of each vibration measuring point;
the diagnosis detail module is used for analyzing the vibration data based on a preset data analysis algorithm, determining a fault diagnosis result of the machine tool spindle and inputting the fault diagnosis result.
According to the predictive maintenance system for the machine tool spindle, the diagnosis and confirmation unit is further used for displaying the fault diagnosis result so that an operator can perform fault troubleshooting on the machine tool spindle based on the fault diagnosis result.
According to the predictive maintenance system for the machine tool spindle, provided by the invention, the diagnosis confirmation unit further comprises a troubleshooting statistical module, wherein the troubleshooting statistical module is used for counting and displaying the machine tool spindle with the sub-health state and the unhealthy state based on the fault diagnosis result, and counting and displaying the number of the machine tool spindles to be subjected to fault troubleshooting and the accuracy of the fault diagnosis result.
According to the predictive maintenance system for the spindle of the machine tool, provided by the invention, the diagnosis result query unit is used for querying the historical fault diagnosis result of the spindle of the machine tool according to the name of the machine tool and the work center.
According to the predictive maintenance system for the machine tool spindle, provided by the invention, the vibration data are acquired when the machine tool spindle idles at a fixed rotating speed.
The invention also provides a predictive maintenance method for the main shaft of the machine tool, which comprises the following steps:
under the spindle idling program number, identifying the spindle idling program number based on the Internet of things platform, and collecting vibration data of a machine tool spindle;
determining a health state of the machine tool spindle based on the vibration data;
carrying out fault diagnosis on the machine tool spindle, and recording a fault diagnosis result;
carrying out troubleshooting and confirmation on the fault diagnosis result, and inputting the troubleshooting result into a system;
and querying historical diagnosis results.
According to the predictive maintenance method for the machine tool spindle, the health state of the machine tool spindle is determined based on the vibration data, and the method comprises the following steps:
determining a vibration data effective value of the machine tool spindle based on the vibration data, and determining a health state of the machine tool spindle based on the vibration data effective value and each health state threshold value; and counting the number of the machine tool spindles in different health states and the abnormal distribution of the spindles, and displaying the counting result.
According to the predictive maintenance system and method for the machine tool spindle, the data acquisition unit acquires vibration data of the machine tool spindle under the idle working condition. Based on the analysis of the vibration data, the state monitoring unit displays the health state of the machine tool spindle; and meanwhile, the machine tool spindle fault is diagnosed through the health diagnosis unit, and the diagnosis result is recorded into the system. And on-site operation and maintenance personnel can perform troubleshooting and confirmation on the fault diagnosis result in the diagnosis and confirmation unit and input the troubleshooting result into the system. The diagnosis result query unit may query the historical diagnosis result. In addition, the system configuration unit can flexibly adapt to system acquisition parameters, vibration threshold values and the like. The system can realize predictive maintenance of the main shaft of the machine tool, and avoid the influence on production caused by unplanned shutdown of the machine tool due to a post maintenance method in the traditional method.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic structural diagram of a predictive maintenance system for a spindle of a machine tool provided by the present invention;
fig. 2 is a schematic diagram illustrating the variation trend of the effective value of the vibration data provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
At present, in most production and manufacturing enterprises, a method of after-repair is still adopted for maintaining a machine tool spindle, and spindle faults cannot be predicted in advance, so that unplanned shutdown is caused due to the faults of the machine tool spindle, and production is influenced. Even, a serious failure of the main shaft may cause a safety production accident. Meanwhile, the health state of the main shaft cannot be predicted in advance, and spare parts cannot be prepared for key parts of the main shaft, such as a bearing, so that the main shaft often fails and then needs to wait for the spare parts to be delivered, the long spare part delivery period prolongs the downtime of the machine tool, and the production is seriously influenced. In addition, the existing troubleshooting and maintenance of the machine tool spindle fault mainly passes through manual experience, so that a spindle fault part cannot be accurately positioned, the troubleshooting and maintenance time of the spindle fault is prolonged, and the overhauling efficiency of the machine tool spindle is reduced.
In view of the above, the present invention provides a predictive maintenance system for a spindle of a machine tool. Fig. 1 is a schematic structural diagram of a predictive maintenance system for a spindle of a machine tool provided by the present invention, as shown in fig. 1, the system includes: the system comprises a data acquisition unit, a state monitoring unit, a health diagnosis unit, a diagnosis confirmation unit and a diagnosis result query unit.
The data acquisition unit is used for acquiring vibration data of the machine tool spindle. It can be understood that the data acquisition unit comprises a vibration sensor and a data acquisition unit, the vibration sensor is connected with the data acquisition unit, the vibration sensor can be arranged at the position of a main shaft of the machine tool, meanwhile, a main shaft idling program number is arranged in a machine tool controller, and the machine tool program number and the working state of the machine tool are acquired based on an internet of things platform. When the machine tool program number is an idle program number and the machine tool state is a machining state, idle vibration data of the main shaft of the machine tool can be collected through the collector. Meanwhile, the data acquisition unit can be connected with the server through the gateway, so that the vibration data can be uploaded to the server to be stored and analyzed. The vibration sensor can be arranged at the position of the bearing seat or close to the position of the bearing seat, and the arrangement direction of the vibration sensor can comprise the radial direction and the axial direction of the main shaft.
After acquiring the vibration data, the state monitoring unit is used for determining the health state of the machine tool spindle based on the vibration data, and the health state of the machine tool spindle can be divided into: the state monitoring unit can determine the effective value of the vibration data of the machine tool spindle based on the vibration data, and then compares the effective value with each health state threshold value to determine the health state of the machine tool spindle. For example, if the vibration data effective value is less than the sub-health threshold value, the health state of the machine tool spindle is normal. And if the sub-health threshold value is less than or equal to the vibration data effective value and less than the unhealthy threshold value, indicating that the health state of the machine tool spindle is sub-health.
The health diagnosis unit is used for carrying out fault diagnosis on the machine tool spindle and recording a fault diagnosis result; the diagnosis confirmation unit is used for carrying out troubleshooting confirmation on the fault diagnosis result and inputting the troubleshooting result into the system; the diagnosis result query unit is used for querying the historical diagnosis result.
Therefore, the machine tool spindle predictive maintenance system provided by the embodiment of the invention acquires the vibration data of the machine tool spindle under the idle working condition through the data acquisition unit. Based on the analysis of the vibration data, the state monitoring unit displays the health state of the machine tool spindle; and meanwhile, the machine tool spindle fault is diagnosed through the health diagnosis unit, and the diagnosis result is recorded into the system. And on-site operation and maintenance personnel can perform troubleshooting and confirmation on the fault diagnosis result in the diagnosis and confirmation unit and input the troubleshooting result into the system. The diagnosis result query unit may query the historical diagnosis result. The system can realize predictive maintenance of the main shaft of the machine tool, and avoid the influence on production caused by unplanned shutdown of the machine tool due to a post maintenance method in the traditional method.
Based on any one of the above embodiments, the state monitoring unit includes a calculation module, a state statistics module and a display module;
the calculation module is used for determining the effective value of the vibration data of the machine tool spindle based on the vibration data and determining the health state of the machine tool spindle based on the effective value of the vibration data and the threshold values of the health states; the state counting module is used for counting the number of the machine tool spindles corresponding to different health states; the display module is used for displaying the statistical result of the state statistical module.
Specifically, the state monitoring unit may display the health state of the monitored machine spindle from multiple dimensions (such as a group, an enterprise and a subsidiary company) in a hierarchical level, wherein the health state of the machine spindle is the overall health state of the machine spindle, and the health state of the machine spindle can be divided into: normal, sub-healthy, and unhealthy. Different health states may be represented by different colors, such as green for normal, orange for sub-health, and red for unhealthy. The health state of the machine tool spindle is determined by the vibration measuring point with the lowest health grade of the machine tool spindle, if X, Y, Z vibration measuring points exist in the machine tool spindle, the health state of the X-direction vibration measuring point is sub-health, the health state of the Y-direction vibration measuring point is sub-health, and the health state of the Z-direction vibration measuring point is unhealthy, the health state of the whole machine tool spindle is unhealthy.
The calculation module is used for determining a vibration data effective value of the machine tool spindle based on the vibration data, and determining the health state of the machine tool spindle based on the vibration data effective value and each health state threshold, wherein the specific health state calculation method comprises the following steps:
1) calculating the effective value RMS of vibration data of a vibration measuring point of a main shaft of the machine tool;
2) when the RMS is less than the sub-health threshold value, the vibration measuring point state is healthy; when the sub-health threshold value is less than or equal to RMS and less than the unhealthy threshold value, the state of the vibration measuring point is sub-health; and when the unhealthy threshold value is less than or equal to RMS, the state of the vibration measuring point is unhealthy. Wherein, the sub-health threshold and the unhealthy threshold of each vibration measuring point can be configured by a system configuration interface.
When the spindle of the machine tool has no vibration data to upload, the state of the machine tool is defined as an off-line state, and the corresponding color is gray. In addition, the machine tool can be searched and screened according to the machine tool name and the machine tool type on the interface of the state monitoring unit.
In addition, the state statistics module can count the number of the machine tool spindles corresponding to different health states, and also can count and display abnormal distribution of the machine tool spindles, and the state statistics module comprises: abnormal component distribution and abnormal type distribution. The abnormal component distribution can be used for counting the distribution conditions of the fault components involved in multiple historical faults of the machine tool spindle, such as 80% of faults of bearing components, 10% of faults of gears, 5% of faults of lubrication and 5% of faults of other components. The abnormal type distribution can be used for counting the distribution conditions of the machine tool types involved in multiple historical faults of the main shaft of the machine tool, such as 70 percent of the machine tool and 30 percent of the machining center.
After the number of the machine tool spindles corresponding to different health states is counted, the display module can display the counting result of the state counting module.
Based on any of the above embodiments, as shown in fig. 1, the system further includes: and the system configuration unit is used for configuring the health state threshold values and the bearing fault characteristic frequencies corresponding to different types of machine tool spindles.
Specifically, the system configuration unit can configure the vibration acquisition parameters of the main shafts of the same type of machine tools, and the configuration method comprises the following steps: the system comprises a collecting frequency, a collecting time length, sensor sensitivity, a main shaft idling speed and the like. Meanwhile, the system configuration unit can also configure the sub-health threshold and the unhealthy threshold of each vibration measuring point of the machine tool spindle. In addition, the system configuration unit can also configure the fault characteristic frequency of the main shaft bearing of the machine tool, and is used for identifying the fault characteristic frequency position of the main shaft when analyzing the vibration data of the main shaft bearing.
Based on any of the above embodiments, the system further comprises: the health diagnosis unit comprises an information overview module and a diagnosis detail module;
the information overview module is used for displaying the whole health state of the machine tool spindle, the effective value of each vibration measuring point of the machine tool spindle and the early warning state change of each vibration measuring point, and counting and analyzing the early warning times of each vibration measuring point;
the diagnosis detail module is used for carrying out detailed analysis on the vibration data based on a preset data analysis algorithm, determining a fault diagnosis result of the machine tool spindle and recording the fault diagnosis result.
Specifically, as shown in fig. 1, the system further includes a health diagnosis unit including a diagnosis detail module and an information overview module. The diagnosis detail module is used for analyzing the vibration data based on a preset data analysis algorithm (such as a plurality of vibration data analysis algorithms of a time domain, a frequency domain, a time-frequency domain and the like) and determining a fault diagnosis result of the machine tool spindle.
Wherein the time domain analysis comprises: and displaying and filtering an original waveform. The original waveform can show the vibration amplitude and whether the vibration waveform has impact characteristics, so that whether the main shaft vibration is abnormal or not is judged. In addition, the influence of interference frequency can be eliminated by filtering the original vibration data, and effective frequency characteristics are highlighted. The frequency domain analysis includes: FFT spectrum, power spectrum, envelope spectrum. The main shaft fault characteristic frequency can be identified through frequency domain analysis, and then the main shaft key component can be accurately diagnosed. The time-frequency domain analysis comprises: short-time fourier transform, continuous wavelet transform. The time-frequency domain analysis can perform detailed analysis on the vibration data from three dimensions of time-frequency-amplitude, and is particularly suitable for analyzing by using a time-frequency domain analysis method when the main shaft vibration data shows non-stationary characteristics.
In addition, the diagnosis detail module can also provide a fault diagnosis result uploading window, after the machine tool spindle fault is diagnosed by a time domain, frequency domain and time-frequency domain analysis method, the fault diagnosis result and the fault grade can be uploaded, and the fault diagnosis result can be checked in the diagnosis confirmation unit after being uploaded.
In addition, the information overview module is used for showing the health state change trend of the machine tool spindle in a preset time period and can also show the whole health state of the current machine tool spindle, vibration effective values of all measuring points and the like. As shown in FIG. 2, when the effective value of the vibration data of a certain measuring point exceeds the corresponding threshold value, the digital color of the effective value of the vibration data changes (unhealthy-red; sub-healthy-orange; healthy-green). And taking the lowest value of the health grade of each measuring point of the machine tool according to the overall health state of the main shaft of the machine tool, and if the state of the X-direction vibration measuring point of the main shaft is sub-healthy, the state of the Y-direction vibration measuring point is sub-healthy and the state of the Z-direction vibration measuring point is unhealthy, the overall state of the main shaft is unhealthy. Meanwhile, the information overview module can also show the change trend of the vibration characteristic parameters along with the time in the appointed time interval of each vibration measuring point, and the vibration characteristic parameters comprise: vibration effective value, peak-peak value, kurtosis, low-frequency band energy, medium-frequency band energy and high-frequency band energy. The time interval of the characteristic parameter variation trend can be selected, and the vibration characteristic parameter variation trend can be displayed in a switching mode. By analyzing the change trend of a plurality of vibration characteristic parameters of the main shaft, the change of the health state of the main shaft of the machine tool can be judged. As shown in fig. 2, when the effective value of the spindle vibration data has an increasing trend, the health state of the spindle of the machine tool is degraded; when the effective value of the main shaft vibration data is stable, the health state of the main shaft of the machine tool is stable.
Therefore, the machine tool spindle predictive maintenance system integrates the statistics of the early warning state change trend of each vibration measuring point of the machine tool spindle, and is convenient for confirming whether the early warning state of each vibration measuring point of the machine tool spindle is stable or not. Meanwhile, the early warning frequency counting function of each integrated vibration measuring point can show the ratio of the occurrence frequency of different early warning states, and finally the health state of the spindle is determined on site to provide an auxiliary basis.
In addition, the information overview module can also show the change trend of the early warning state of each vibration measuring point of the machine tool spindle in a certain period of time, and the change trend graph of the early warning state of each vibration measuring point is shown in a Gantt chart form. Whether the early warning state of the vibration measuring point is stable or not and whether the early warning level is increased or not can be judged through analyzing the change trend of the early warning state of the vibration measuring point in a certain period of time, and the information overview module can also count the occurrence frequency of different early warning states in a certain period of time and display the occurrence frequency in the form of a histogram, wherein different colors in the histogram represent different early warning states, green is 'healthy', orange is 'sub-healthy', and red is 'unhealthy'. Through counting the early warning times, whether a certain early warning state occurs for multiple times in the period of time can be identified, and an auxiliary basis is provided for finally determining the health state of the spindle on site.
Based on any of the above embodiments, as shown in fig. 1, the system further includes: and the diagnosis and confirmation unit is used for displaying the fault diagnosis result so that an operator can carry out fault troubleshooting on the main shaft of the machine tool based on the fault diagnosis result.
Specifically, the diagnosis confirmation unit is used for displaying a fault diagnosis result, so that an operator can perform fault troubleshooting on the machine tool spindle based on the fault diagnosis result, and fault troubleshooting information can be input into the system. The troubleshooting information may include troubleshooting time, a troubleshooting person, troubleshooting results, a faulty component, whether diagnosis is accurate, a field troubleshooting picture, and the like.
Based on any of the above embodiments, the diagnosis confirmation unit further includes a troubleshooting statistical module, and the troubleshooting statistical module is configured to count and display machine tool spindles whose health states are sub-health and unhealthy, and count and display the number of machine tool spindles to be subjected to troubleshooting and the accuracy of a troubleshooting result based on a troubleshooting result.
Specifically, after troubleshooting of a certain troubleshooting result is completed, the troubleshooting result is classified into a troubleshooting category, and a troubleshooting result that is not troubleshot is classified into a non-troubleshooting category. Meanwhile, the troubleshooting and counting module can count and display the number of the sub-health machine tool spindles and the number of the unhealthy machine tool spindles in the fault diagnosis result. In addition, the troubleshooting statistical module can also be used for counting the number of the machine tool spindles to be subjected to troubleshooting and the accuracy of the failure diagnosis result and displaying the statistics.
Based on any of the above embodiments, as shown in fig. 1, the system further includes: and the diagnosis result query unit is used for querying the historical fault diagnosis result of the main shaft of the machine tool according to the name of the machine tool and the working center.
Specifically, the diagnosis result query unit is used for querying the historical fault diagnosis result of the machine tool spindle. For example, the historical fault diagnosis result can be searched and queried according to the names of the work center and the equipment, and is displayed in a list form. Wherein, the information that each fault diagnosis result can contain includes: health grade, machine tool type, start monitoring time, monitoring duration, abnormal duration, diagnosis result, troubleshooting personnel, troubleshooting result and the like.
According to any of the above embodiments, the vibration data is data collected when the machine tool spindle is idling at a fixed rotational speed.
Specifically, if the machine tool is in a part processing state, the vibration data acquired by the data acquisition unit is mixed with part processing noise, and then the subsequent analysis on the health state of the machine tool spindle is influenced. Therefore, the embodiment of the invention collects the vibration data when the machine tool spindle idles at a fixed rotating speed, so that the noise data can be prevented from being mixed in the vibration data, and the health state of the machine tool spindle can be accurately analyzed and obtained.
For example, a program number MP of a spindle of a machine tool idling at a fixed rotation speed is set, and when the program number MP is operated, the spindle can idle at the fixed rotation speed for a period of time (the idle time can be between 10s and 30 s), and at this time, vibration data can be collected. It can be understood that the machine tool program number and the machine tool state mark can be collected in real time based on the platform of the Internet of things. When the machine tool program number is changed into the idle running program number MP and the machine tool state is running, intercepting and storing a section of idle running vibration data with stable machine tool spindle rotating speed.
Therefore, the machine tool spindle predictive maintenance system integrates a plurality of units of health monitoring, health diagnosis, diagnosis confirmation, diagnosis result inquiry and system configuration, has rich functions, realizes a complete operation and maintenance closed loop from the monitoring diagnosis to the field confirmation, and meets the actual field requirements.
The following describes a machine tool spindle maintenance method provided by the present invention, and the machine tool spindle maintenance method described below and the machine tool spindle predictive maintenance system described above can be referred to correspondingly.
Based on any one of the above embodiments, the present invention provides a machine tool spindle predictive maintenance method based on the machine tool spindle predictive maintenance system according to any one of the above embodiments, the method including:
under the spindle idling program number, identifying the spindle idling program number based on the Internet of things platform, and collecting vibration data of a machine tool spindle;
determining a health state of a machine tool spindle based on the vibration data;
carrying out fault diagnosis on a machine tool spindle, and recording a fault diagnosis result;
checking and confirming the fault diagnosis result, and inputting the checking result into a system;
and querying historical diagnosis results.
The vibration data can be collected through a vibration sensor in the data collection unit and then sent to the data collector, the vibration sensor can be arranged at the position of a machine tool spindle, meanwhile, a spindle idling program number is arranged in a machine tool controller, and the machine tool program number and the working state of the machine tool are collected based on the platform of the Internet of things. When the machine tool program number is an idle program number and the machine tool state is a machining state, idle vibration data of the main shaft of the machine tool can be collected through the collector. Meanwhile, the data acquisition unit can be connected with the server through the gateway, so that the vibration data can be uploaded to the server to be stored and analyzed. The vibration sensor can be arranged at the position of the bearing seat or close to the position of the bearing seat, and the arrangement direction of the vibration sensor can comprise the radial direction and the axial direction of the main shaft.
After obtaining the vibration data, the state monitoring unit may determine a health state of the machine tool spindle based on the vibration data, where the health state of the machine tool spindle may be divided into: the state monitoring unit can determine the effective value of the vibration data of the machine tool spindle based on the vibration data, and then compares the effective value with each health state threshold value to determine the health state of the machine tool spindle. For example, if the vibration data effective value is less than the sub-health threshold value, the health state of the machine tool spindle is normal. And if the sub-health threshold value is less than or equal to the vibration data effective value and less than the unhealthy threshold value, indicating that the health state of the machine tool spindle is sub-health.
After the health state of the machine tool spindle is obtained, fault diagnosis is carried out on the machine tool spindle through the health diagnosis unit, a fault diagnosis result is recorded, troubleshooting confirmation is carried out on the fault diagnosis result through the diagnosis confirmation unit, a troubleshooting result recording system is recorded, and historical diagnosis results are inquired through the diagnosis result inquiry unit. .
Therefore, the predictive maintenance method for the machine tool spindle, provided by the embodiment of the invention, can be used for collecting vibration data of the machine tool spindle under the idle working condition through the data collection unit. Based on the analysis of the vibration data, the state monitoring unit displays the health state of the machine tool spindle; and meanwhile, the machine tool spindle fault is diagnosed through the health diagnosis unit, and the diagnosis result is recorded into the system. And on-site operation and maintenance personnel can perform troubleshooting and confirmation on the fault diagnosis result in the diagnosis and confirmation unit and input the troubleshooting result into the system. The diagnosis result query unit may query the historical diagnosis result. The system can realize predictive maintenance of the main shaft of the machine tool, and avoid the influence on production caused by unplanned shutdown of the machine tool due to a post maintenance method in the traditional method.
Based on any one of the above embodiments, determining the health status of the spindle of the machine tool based on the vibration data includes:
determining a vibration data effective value of the machine tool spindle based on the vibration data, and determining the health state of the machine tool spindle based on the vibration data effective value and each health state threshold value; and counting the number of the machine tool spindles corresponding to different health states, and displaying the counting result.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A predictive machine tool spindle maintenance system, comprising:
the data acquisition unit is used for identifying the main shaft idling program number based on the Internet of things platform under the main shaft idling program number and acquiring vibration data of the main shaft of the machine tool;
the state monitoring unit is used for determining the health state of the machine tool spindle based on the vibration data;
the health diagnosis unit is used for carrying out fault diagnosis on the machine tool spindle and recording a fault diagnosis result;
the diagnosis confirmation unit is used for carrying out troubleshooting confirmation on the fault diagnosis result and inputting the troubleshooting result into a system;
and the diagnosis result query unit is used for querying the historical diagnosis result.
2. The system for predictive maintenance of a spindle of a machine tool of claim 1, wherein said condition monitoring unit comprises a calculation module, a condition statistics module and a display module;
the calculation module is used for determining a vibration data effective value of the machine tool spindle based on the vibration data and determining the health state of the machine tool spindle based on the vibration data effective value and each health state threshold value; the state counting module is used for counting the number of the machine tool spindles in different health states and the abnormal distribution of the spindles; the display module is used for displaying the statistical result of the state statistical module.
3. The system for predictive maintenance of a spindle of a machine tool of claim 2, further comprising: and the system configuration unit is used for configuring the health state threshold values and the bearing fault characteristic frequencies corresponding to different types of machine tool spindles.
4. The system for predictive maintenance of a spindle of a machine tool of claim 1, wherein said health diagnosis unit comprises an information overview module and a diagnosis details module;
the information overview module is used for displaying the overall health state of the machine tool spindle, the effective value of each vibration measuring point of the machine tool spindle and the early warning state change of each vibration measuring point, and carrying out statistical analysis on the early warning times of each vibration measuring point;
the diagnosis detail module is used for analyzing the vibration data based on a preset data analysis algorithm, determining a fault diagnosis result of the machine tool spindle and inputting the fault diagnosis result.
5. The system for predictive maintenance of a machine tool spindle according to claim 4, wherein the diagnosis confirmation unit is further configured to display the fault diagnosis result so that an operator performs troubleshooting on the machine tool spindle based on the fault diagnosis result.
6. The predictive maintenance system for a machine tool spindle according to claim 5, wherein the diagnosis confirmation unit further includes a troubleshooting statistic module configured to count and display machine tool spindles whose health statuses are sub-healthy and unhealthy, and count and display the number of machine tool spindles to be troubleshot and the accuracy of the troubleshooting result, based on the troubleshooting result.
7. The system for predictive maintenance of a machine tool spindle according to any one of claims 1 to 6, wherein the diagnostic result query unit is configured to query historical failure diagnostic results of the machine tool spindle by machine tool name and work center.
8. A machine tool spindle predictive maintenance system according to any one of claims 1 to 6, in which the vibration data is data collected whilst the machine tool spindle is idling at a fixed rotational speed.
9. A machine tool spindle predictive maintenance method based on the machine tool spindle predictive maintenance system according to any one of claims 1 to 8, characterized by comprising:
under the spindle idling program number, identifying the spindle idling program number based on the Internet of things platform, and collecting vibration data of a machine tool spindle;
determining a health state of the machine tool spindle based on the vibration data;
carrying out fault diagnosis on the machine tool spindle, and recording a fault diagnosis result;
carrying out troubleshooting and confirmation on the fault diagnosis result, and inputting the troubleshooting result into a system;
and querying historical diagnosis results.
10. The method of predictive maintenance of a machine tool spindle of claim 9, wherein said determining a health state of the machine tool spindle based on the vibration data comprises:
determining a vibration data effective value of the machine tool spindle based on the vibration data, and determining a health state of the machine tool spindle based on the vibration data effective value and each health state threshold value; and counting the number of the machine tool spindles in different health states and the abnormal distribution of the spindles, and displaying the counting result.
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