CN102825504A - State detection method for main shaft of numerically-controlled machine tool - Google Patents
State detection method for main shaft of numerically-controlled machine tool Download PDFInfo
- Publication number
- CN102825504A CN102825504A CN2012103467859A CN201210346785A CN102825504A CN 102825504 A CN102825504 A CN 102825504A CN 2012103467859 A CN2012103467859 A CN 2012103467859A CN 201210346785 A CN201210346785 A CN 201210346785A CN 102825504 A CN102825504 A CN 102825504A
- Authority
- CN
- China
- Prior art keywords
- signal
- domain
- time
- main shaft
- diagnosis result
- 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
Images
Landscapes
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The invention discloses a state detection method for a main shaft of a numerically-controlled machine tool, belonging to the field of machine tool failure diagnosis. The method comprises the following steps of: acquiring a signal transmitted by a sensor system through a host; and analyzing the signal acquired according to the step 1, determining a failure characteristic parameter, processing the obtained failure characteristic parameter through a specialist system, an artificial nerve network system and a support vector respectively to obtain three failure diagnosis results, and fusing the three failure diagnosis results through a genetic algorithm. By fusing the diagnosis results of the specialist system, the artificial nerve network system and the support vector, an artificial intelligence effect can be achieved during field failure diagnosis, so that the failure diagnosis accuracy of the numerically-controlled machine tool is increased; moreover, a displacement signal is detected by adopting a CCD (Charge Coupled Device) laser displacement sensor, so that the obtained run-out accuracy of the main shaft can be up to 0.5 micron, and the failure diagnosis accuracy of the numerically-controlled machine tool is further ensured.
Description
Technical field
The invention belongs to the machine failure diagnostic field, particularly relate to a kind of main shaft of numerical control machine tool condition detection method.
Background technology
Digit Control Machine Tool is a kind of automated machine tool that program control system is housed, and this program control system can logically be handled has control coding or other symbolic instruction regulated procedures, and with its decoding, thereby the lathe action is processed machine components.Main shaft of numerical control machine tool refers to the axle that drives workpiece or cutter rotation on the lathe; Usually form spindle unit by main shaft, bearing and driving member such as gear or belt wheel etc.; Except main motions such as planer, broaching machine are straight-line lathe; Most of lathes all have spindle unit, and the kinematic accuracy of spindle unit and the rigidity of structure are the key factors of decision crudy and stock-removing efficiency.The index of weighing the spindle unit performance mainly is rotating accuracy, rigidity and velocity adaptive property, beats influencing occur on the direction of machining accuracy radial and axial when rotating accuracy is meant main axis rotation, depends mainly on the manufacturing and the assembly quality of main shaft and bearing; Dynamic and static rigidity depends mainly on the bending stiffness of main shaft, the rigidity and the damping of bearing; Velocity adaptive property is meant the maximum speed and the range of speeds of permission, and the structure that depends mainly on bearing is with lubricated, and radiating condition.
The quality of main shaft runnability is the important key factor of decision Digit Control Machine Tool crudy, and during as if Installation and Debugging or maintenance, drive disk assembly (like tooth form synchronous belt, gear and the shaft coupling etc.) parameter adjustment in the main spindle box is improper; Or, all can cause the main shaft performance decrease owing to the part distortion that causes in using, wearing and tearing etc., cause main shaft to beat overproof in the machining accuracy direction; Make crudy descend; Machining accuracy exceeds standard, even fully can not processing parts, therefore; Guarantee that the main shaft runnability is the prerequisite that Digit Control Machine Tool carries out normal process at kilter; And the running status of main shaft is monitored accurately, can accurately diagnose when going wrong, its running precision is predicted it is to guarantee main shaft runnability good basis condition.
Current domestic and international monitoring and diagnostic techniques to Digit Control Machine Tool; Mainly concentrate on the aspects such as driver part, PLC control logic and electric fault of Digit Control Machine Tool; To the research of the diagnosis of main shaft and prediction aspect with use less; Be because the jerk value of main shaft own is very little on the one hand, generally between 0.5～1 micron, need the very high displacement transducer of precision; The antijamming capability that need on hardware and software, guarantee test on the other hand is strong.
The running status of main shaft can be through main shaft jerk value and vibration situation, the vibration and the temperature conditions of main spindle box reflect; Actual conditions to above-mentioned main tapping; Main in the prior art through ANN (Artificial Neural Networks; Artificial neural network), ES (Expert System, expert system) or SVM (support vector machine, SVMs) carry out Analysis on Fault Diagnosis to Digit Control Machine Tool.
The characteristics of ANN are that to set up model relatively easy, distinguish but need carry out parameter, and the layering of its neutral net still do not have guidance method at present, are not easy to obtain optimized level, and whether restrain and need carry out computational analysis.
The birth of SVM has been opened up new approach for the solution that solves the failure modes problem, and Statistical Learning Theory is on the basis of structural risk minimization, the new theoretical system of setting up to the machine learning problem of small sample specially of a cover.At present, the many sorting techniques of SVM successfully have been applied in many practical problems, like text identification, recognition of face, three-dimensional body identification, speech recognition, image classification, gene sequencing etc.
Obtained in the process failure diagnosis field using widely based on the expert system of sign computation and reasoning, expert system can high efficiency, carry out work accurately, thoughtfully, rapidly and tirelessly, but expert system can not obtain new knowledge from the instance of diagnosis; The bottleneck that has knowledge acquisition; And in the reasoning of knowledge, traditional expert systems adopts serial mode, and inference method is single; Control strategy is dumb, is prone to problems such as matching conflict, multiple shot array, infinite recurrence.
SVMs has extensive preferably popularization ability than neutral net, and for the mapping of each higher dimensional space in this space, kernel function does not also have suitable method now how to confirm this shining upon just; So for general problem, SVM has just transferred the difficulty of the complexity of higher dimensional space to ask kernel function difficulty, even and after the definite kernel function; Divide time-like in the problem of finding the solution; Find the solution the quadratic programming of kernel function, this just needs a large amount of memory spaces, and this also is the problem of SVM; And the artificial neural network system just can head it off; Therefore, how dexterously expert system, artificial neural network system and SVMs being joined together to carry out intelligent diagnostics, is the key problem of this Project Study.
Therefore those skilled in the art are devoted to develop a kind of main shaft of numerical control machine tool condition detection method, can Fusion Expert System, the diagnostic result of artificial neural network system and SVMs, thus improve the accuracy of diagnosing faults of numerical control machine.
Summary of the invention
Because the above-mentioned defective of prior art, technical problem to be solved by this invention provide a kind of can Fusion Expert System, the diagnostic result of artificial neural network system and SVMs, thereby improve the accuracy of diagnosing faults of numerical control machine.
For realizing above-mentioned purpose, the invention provides a kind of main shaft of numerical control machine tool condition detection method, may further comprise the steps:
The signal that step 1, host computer pick-up transducers system send;
Step 3, the fault signature parameter that step 2 is obtained are handled through expert system, draw first fault diagnosis result; The fault signature parameter that step 2 obtains is handled through the artificial neural network system, drawn second fault diagnosis result; The fault signature parameter that step 2 obtains is handled through SVMs, drawn the 3rd fault diagnosis result;
Step 4, said first fault diagnosis result, second fault diagnosis result and the 3rd fault diagnosis result are merged through genetic algorithm.
In the said step 4 said first fault diagnosis result, second fault diagnosis result and the 3rd fault diagnosis result merged according to the following steps through genetic algorithm and carry out:
A1, said first fault diagnosis result, second fault diagnosis result and the 3rd fault diagnosis result are encoded, each fault diagnosis result is converted to the genotype colony that forms by chromosome;
A2, calculate all chromosomal fitness values;
A2, form based on each chromosomal fitness value and to select wheel disc;
A3, from wheel disc, select the individual pairing individuality of each genotype to carry out that single-point intersects and variation is handled at random, form new individuality;
A4, the fitness value of said genotype colony not have to change or the fitness value of said genotype colony when being positioned at the precision threshold of setting the genetic algorithm end in setting algebraically.
Preferable, said sensing system comprises temperature sensor, said temperature sensor sends temperature value signal and gives said host computer; Said host computer also comprises the step of the temperature value signal that collects being carried out time-domain analysis after gathering the signal that said sensing system sends;
The temperature value signal that collects is carried out time-domain analysis comprise the peak-to-peak value of analyzing this signal, value, kurtosis and waveform, through the temperature value signal that collects being carried out draw after the time-domain analysis fault signature parameter of this temperature value signal.
Further, said sensing system also comprises acceleration transducer, and said acceleration transducer sends acceleration signal and gives said host computer; Said host computer also comprises the step of the acceleration signal that collects being carried out time-domain analysis and frequency-domain analysis after gathering the signal that said sensing system sends;
The acceleration signal that collects is carried out time-domain analysis comprise the peak-to-peak value of analyzing this signal, value, kurtosis and waveform; Time-domain analysis transforms to frequency domain with time domain data after accomplishing, and the acceleration signal that collects is carried out frequency-domain analysis comprise single channel and multichannel stable state spectrum analysis and time frequency analysis; The acceleration signal that collects is carried out drawing after time-domain analysis and the frequency-domain analysis fault signature parameter of this acceleration signal.
Further, said sensing system also comprises displacement transducer, and is preferable, and said displacement transducer is the CCD laser displacement sensor; Said displacement transducer sends displacement signal and gives said host computer; Said host computer is gathered and is also comprised jerk value that calculates main shaft and the step that displacement signal is carried out time domain, frequency-domain analysis behind the signal that said sensing system sends:
Said calculating main axis jerking amount is carried out according to the following steps:
The time domain data of said displacement signal is transformed to frequency domain, carry out High frequency filter at frequency domain then, and calculate the jerk value that main shaft rotates a week through the time domain point in the monocycle;
The displacement signal that collects is carried out time-domain analysis comprise the peak-to-peak value of analyzing this signal, value, kurtosis and waveform; Time-domain analysis transforms to frequency domain with time domain data after accomplishing, and the displacement signal that collects is carried out frequency-domain analysis comprise single channel and multichannel stable state spectrum analysis and time frequency analysis; The displacement signal that collects is carried out drawing after time-domain analysis and the frequency-domain analysis fault signature parameter of this displacement signal.Still brought great interfering signal because the repeatable accuracy of CCD laser displacement sensor has reached 0.01 micron, the signal that therefore records seriously drifts about, and the main axis jerking accuracy of measurement that obtains through above method can reach 0.05 micron.
The invention has the beneficial effects as follows: the present invention is through the diagnostic result of Fusion Expert System, artificial neural network system and SVMs; The effect of fault diagnosis time performance artificial intelligence at the scene; Thereby improve the accuracy of diagnosing faults of numerical control machine; The present invention adopts CCD laser displacement sensor detecting position shifting signal in addition, and the main axis jerking accuracy of measurement that obtains can reach 0.05 micron, assurance further the accuracy of diagnosing faults of numerical control machine.
Description of drawings
Fig. 1 is the structural representation that is used for the device of main shaft of numerical control machine tool state-detection.
Fig. 2 is the schematic flow sheet of the embodiment of the invention one.
Fig. 3 is the schematic flow sheet of the embodiment of the invention two.
The specific embodiment
Below in conjunction with accompanying drawing and embodiment the present invention is described further:
Embodiment one: as shown in Figure 1, a kind of device that is used for the main shaft of numerical control machine tool state-detection comprises host computer 1 and sensing system 2; Said sensing system 2 comprises temperature sensor 201, acceleration transducer 202 and displacement transducer 203; The output of said temperature sensor 201 connects the first input end of said host computer 1 through the RS232 data wire; The output of said acceleration transducer 202 is through second input of the said host computer 1 of capture card 204 connections, and the output of said displacement transducer 203 connects the 3rd input of said host computer 1 through displacement controller 205.
As shown in Figure 2, a kind of main shaft of numerical control machine tool condition detection method may further comprise the steps:
S1, said host computer 1 are gathered the signal that said sensing system 2 sends.
S2, the temperature value signal that collects is carried out time-domain analysis; The temperature value signal that collects is carried out time-domain analysis comprise the peak-to-peak value of analyzing this signal, value, kurtosis and waveform, through the temperature value signal that collects being carried out draw after the time-domain analysis fault signature parameter index of this temperature value signal.
S3, the acceleration signal that collects is carried out time-domain analysis and frequency-domain analysis; The acceleration signal that collects is carried out time-domain analysis comprise the peak-to-peak value of analyzing this signal, value, kurtosis and waveform; Time-domain analysis transforms to frequency domain with time domain data after accomplishing, and the acceleration signal that collects is carried out frequency-domain analysis comprise single channel and multichannel stable state spectrum analysis and time frequency analysis; The acceleration signal that collects is carried out drawing after time-domain analysis and the frequency-domain analysis fault signature parameter index of this acceleration signal.
The jerk value of S4, calculating main shaft reaches displacement signal is carried out time domain, frequency-domain analysis; Said calculating main axis jerking amount is carried out according to the following steps:
The time domain data of said displacement signal is transformed to frequency domain, carry out High frequency filter at frequency domain then, and calculate the jerk value that main shaft rotates a week through the time domain point in the monocycle; Repeatedly calculating main shaft in the present embodiment rotates the jerk value in a week and obtains the jerk value mean value that main shaft rotates a week; The displacement signal that collects is carried out time-domain analysis comprise the peak-to-peak value of analyzing this signal, value, kurtosis and waveform; Time-domain analysis transforms to frequency domain with time domain data after accomplishing, and the displacement signal that collects is carried out frequency-domain analysis comprise single channel and multichannel stable state spectrum analysis and time frequency analysis; The displacement signal that collects is carried out drawing after time-domain analysis and the frequency-domain analysis fault signature parameter index of this displacement signal.
S5, the fault signature parameter index through jerk value, acceleration signal, the fault signature parameter index of temperature value signal and the fault signature parameter index of displacement signal obtain the fault signature parameter, i.e. the temperature value of the vibratory output of the vibratory output of the jerk value of main shaft of numerical control machine tool, main shaft of numerical control machine tool, main shaft of numerical control machine tool casing and main shaft of numerical control machine tool casing.
S6, the fault signature parameter that step S5 is obtained are handled through expert system, draw first fault diagnosis result; The fault signature parameter that step 2 obtains is handled through the artificial neural network system, drawn second fault diagnosis result; The fault signature parameter that step 2 obtains is handled through SVMs, drawn the 3rd fault diagnosis result.
S7, said first fault diagnosis result, second fault diagnosis result and the 3rd fault diagnosis result are merged through genetic algorithm.
Among the said step S7 said first fault diagnosis result, second fault diagnosis result and the 3rd fault diagnosis result merged according to the following steps through genetic algorithm and carry out:
A1, said first fault diagnosis result, second fault diagnosis result and the 3rd fault diagnosis result are encoded, each fault diagnosis result is converted to the genotype colony that forms by chromosome;
A2, calculate all chromosomal fitness values;
A2, form based on each chromosomal fitness value and to select wheel disc;
A3, from wheel disc, select the individual pairing individuality of each genotype to carry out that single-point intersects and variation is handled at random, form new individuality;
A4, the fitness value of said genotype colony not have to change or the fitness value of said genotype colony when being positioned at the precision threshold of setting the genetic algorithm end in setting algebraically.
Be provided with two acceleration transducers 202 in the present embodiment, acceleration transducer 202 adopts the ICP acceleration transducers, carries out becoming data signal and getting into host computer 1 after the signal anti-aliasing filtering changes with AD through capture card 204.Temperature sensor 201 adopts infrared temperature sensor; The CCD laser displacement sensor that the Japanese Keyemce of said displacement transducer 203 employings company produces; Its repeatable accuracy is 0.05 micron, and displacement transducer 203 is given host computer 1 through displacement controller 205 transmission data, and host computer 1 adopts computer realization in the present embodiment; Certainly, said host computer 1 also can adopt realizations such as notebook computer, panel computer.
The artificial neural network system's model that adopts in the present embodiment is the BP model; Certain said artificial neural network system's model can also be RBF model and Elman model; Be the assurance convergence of genetic algorithm, the method for genetic algorithms use roulette in the present embodiment, the individuality that fitness is the highest directly copies in the follow-on population; Both can improve pace of learning, the optimal solution that can keep genotype colony again exempts from degeneration.
The order that it should be noted that step S2, S3 and S4 can be changed arbitrarily, as carrying out S3, S4 and S2 behind the execution in step S1 successively, and execution in step S5, S6 and S7 successively then.
Embodiment two, as shown in Figure 3, the flow process of present embodiment and embodiment one is basic identical, and different is:
S1, said host computer 1 are gathered the signal that said sensing system 2 sends.
S2, the temperature value signal that collects is carried out time-domain analysis; Acceleration signal to collecting carries out time-domain analysis and frequency-domain analysis; Calculate the jerk value of main shaft and displacement signal is carried out time domain, frequency-domain analysis; The temperature value signal that collects is carried out time-domain analysis comprise the peak-to-peak value of analyzing this signal, value, kurtosis and waveform, through the temperature value signal that collects being carried out draw after the time-domain analysis fault signature parameter index of this temperature value signal.
The acceleration signal that collects is carried out time-domain analysis comprise the peak-to-peak value of analyzing this signal, value, kurtosis and waveform; Time-domain analysis transforms to frequency domain with time domain data after accomplishing, and the acceleration signal that collects is carried out frequency-domain analysis comprise single channel and multichannel stable state spectrum analysis and time frequency analysis; The acceleration signal that collects is carried out drawing after time-domain analysis and the frequency-domain analysis fault signature parameter index of this acceleration signal.
Said calculating main axis jerking amount is carried out according to the following steps: the time domain data of said displacement signal is transformed to frequency domain, carry out High frequency filter at frequency domain then, and calculate the jerk value that main shaft rotates a week through the time domain point in the monocycle; Repeatedly calculating main shaft in the present embodiment rotates the jerk value in a week and obtains the jerk value mean value that main shaft rotates a week.
The displacement signal that collects is carried out time-domain analysis comprise the peak-to-peak value of analyzing this signal, value, kurtosis and waveform; Time-domain analysis transforms to frequency domain with time domain data after accomplishing, and the displacement signal that collects is carried out frequency-domain analysis comprise single channel and multichannel stable state spectrum analysis and time frequency analysis; The displacement signal that collects is carried out drawing after time-domain analysis and the frequency-domain analysis fault signature parameter index of this displacement signal.
Obtain the fault signature parameter through the fault signature parameter index of main axis jerking amount, acceleration signal, the fault signature parameter index of temperature value signal and the fault signature parameter index of displacement signal, i.e. the temperature value of the vibratory output of the vibratory output of the jerk value of main shaft of numerical control machine tool, main shaft of numerical control machine tool, main shaft of numerical control machine tool casing and main shaft of numerical control machine tool casing.
S3, the fault signature parameter that S2 is obtained are handled through expert system, draw first fault diagnosis result; The fault signature parameter that step 2 obtains is handled through the artificial neural network system, drawn second fault diagnosis result; The fault signature parameter that step 2 obtains is handled through SVMs, drawn the 3rd fault diagnosis result.
S4, said first fault diagnosis result, second fault diagnosis result and the 3rd fault diagnosis result are merged through genetic algorithm.
More than describe preferred embodiment of the present invention in detail.Should be appreciated that those of ordinary skill in the art need not creative work and just can design according to the present invention make many modifications and variation.Therefore, all technical staff in the art all should be in the determined protection domain by claims under this invention's idea on the basis of existing technology through the available technical scheme of logical analysis, reasoning, or a limited experiment.
Claims (6)
1. a main shaft of numerical control machine tool condition detection method comprises host computer (1) and sensing system (2); The output of said sensing system (2) connects the input of said host computer (1); It is characterized in that may further comprise the steps:
Step 1, said host computer (1) are gathered the signal that said sensing system (2) sends;
Step 2, analysis are confirmed the fault signature parameter according to the signal that step 1 collects;
Step 3, the fault signature parameter that step 2 is obtained are handled through expert system, draw first fault diagnosis result; The fault signature parameter that step 2 obtains is handled through the artificial neural network system, drawn second fault diagnosis result; The fault signature parameter that step 2 obtains is handled through SVMs, drawn the 3rd fault diagnosis result;
Step 4, said first fault diagnosis result, second fault diagnosis result and the 3rd fault diagnosis result are merged through genetic algorithm.
2. main shaft of numerical control machine tool condition detection method as claimed in claim 1 is characterized in that: in the said step 4 said first fault diagnosis result, second fault diagnosis result and the 3rd fault diagnosis result merged according to the following steps through genetic algorithm and carry out:
A1, said first fault diagnosis result, second fault diagnosis result and the 3rd fault diagnosis result are encoded, each fault diagnosis result is converted to the genotype colony that forms by chromosome;
A2, calculate all chromosomal fitness values;
A2, form based on each chromosomal fitness value and to select wheel disc;
A3, from wheel disc, select the individual pairing individuality of each genotype to carry out that single-point intersects and variation is handled at random, form new individuality;
A4, the fitness value of said genotype colony not have to change or the fitness value of said genotype colony when being positioned at the precision threshold of setting the genetic algorithm end in setting algebraically.
3. main shaft of numerical control machine tool condition detection method as claimed in claim 1 is characterized in that: said sensing system (2) comprises temperature sensor (201), and said temperature sensor (201) sends temperature value signal and gives said host computer (1); Said host computer (1) also comprises the step of the temperature value signal that collects being carried out time-domain analysis after gathering the signal that said sensing system (2) sends;
The temperature value signal that collects is carried out time-domain analysis comprise the peak-to-peak value of analyzing this signal, value, kurtosis and waveform, through the temperature value signal that collects being carried out draw after the time-domain analysis fault signature parameter of this temperature value signal.
4. like claim 1 or 3 described main shaft of numerical control machine tool condition detection methods, it is characterized in that: said sensing system (2) also comprises acceleration transducer (202), and said acceleration transducer (202) sends acceleration signal and gives said host computer (1); Said host computer (1) also comprises the step of the acceleration signal that collects being carried out time-domain analysis and frequency-domain analysis after gathering the signal that said sensing system (2) sends;
The acceleration signal that collects is carried out time-domain analysis comprise the peak-to-peak value of analyzing this signal, value, kurtosis and waveform; Time-domain analysis transforms to frequency domain with time domain data after accomplishing, and the acceleration signal that collects is carried out frequency-domain analysis comprise single channel and multichannel stable state spectrum analysis and time frequency analysis; The acceleration signal that collects is carried out drawing after time-domain analysis and the frequency-domain analysis fault signature parameter of this acceleration signal.
5. main shaft of numerical control machine tool condition detection method as claimed in claim 4 is characterized in that: said sensing system (2) also comprises displacement transducer (203), and said displacement transducer (203) sends displacement signal and gives said host computer (1); Said host computer (1) is gathered and is also comprised jerk value that calculates main shaft and the step that displacement signal is carried out time domain, frequency-domain analysis behind the signal that said sensing system (2) sends:
Said calculating main axis jerking amount is carried out according to the following steps:
The time domain data of said displacement signal is transformed to frequency domain, carry out High frequency filter at frequency domain then, and calculate the jerk value that main shaft rotates a week through the time domain point in the monocycle;
The displacement signal that collects is carried out time-domain analysis comprise the peak-to-peak value of analyzing this signal, value, kurtosis and waveform; Time-domain analysis transforms to frequency domain with time domain data after accomplishing, and the displacement signal that collects is carried out frequency-domain analysis comprise single channel and multichannel stable state spectrum analysis and time frequency analysis; The displacement signal that collects is carried out drawing after time-domain analysis and the frequency-domain analysis fault signature parameter of this displacement signal.
6. main shaft of numerical control machine tool condition detection method as claimed in claim 5 is characterized in that: said displacement transducer (203) is the CCD laser displacement sensor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210346785.9A CN102825504B (en) | 2012-09-18 | 2012-09-18 | State detection method for main shaft of numerically-controlled machine tool |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210346785.9A CN102825504B (en) | 2012-09-18 | 2012-09-18 | State detection method for main shaft of numerically-controlled machine tool |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102825504A true CN102825504A (en) | 2012-12-19 |
| CN102825504B CN102825504B (en) | 2014-12-24 |
Family
ID=47328924
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201210346785.9A Expired - Fee Related CN102825504B (en) | 2012-09-18 | 2012-09-18 | State detection method for main shaft of numerically-controlled machine tool |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102825504B (en) |
Cited By (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103624633A (en) * | 2013-12-09 | 2014-03-12 | 大连理工大学 | Micro-milling vibration precision measurement system taking laser micro-displacement sensor as measuring element |
| CN104742153A (en) * | 2015-04-01 | 2015-07-01 | 中国计量学院 | Fault predication device of six-axis multi-joint industrial robot |
| CN104942655A (en) * | 2015-07-20 | 2015-09-30 | 徐文欢 | Main shaft rotary error accuracy testing device |
| CN105204436A (en) * | 2015-08-26 | 2015-12-30 | 山东省计算中心（国家超级计算济南中心） | Graded-early-warning-based fault diagnosis method of numerically-controlled machine tool |
| CN105269404A (en) * | 2014-11-20 | 2016-01-27 | 电子科技大学 | Detection device for knife point dynamic characteristics of numerical control machine tool and method of detection device |
| CN106695747A (en) * | 2015-11-13 | 2017-05-24 | 国网辽宁省电力有限公司检修分公司 | Valve hall inspection method and inspection robot based on laser radar |
| CN107199506A (en) * | 2017-06-05 | 2017-09-26 | 苏州微著设备诊断技术有限公司 | A kind of grinding trembling detection method based on stack self-encoding encoder and SVMs |
| CN108760813A (en) * | 2018-06-05 | 2018-11-06 | 哈尔滨工程大学 | A kind of turbine blade of gas turbine health monitoring systems and method based on temperature signal |
| CN109396953A (en) * | 2018-12-05 | 2019-03-01 | 上海交通大学 | Lathe work condition intelligent identification system based on signal fused |
| CN109746753A (en) * | 2017-11-06 | 2019-05-14 | 斗山机床株式会社 | The column of lathe and the curvature correction method of lathe bed |
| CN110320866A (en) * | 2019-07-24 | 2019-10-11 | 珠海格力智能装备有限公司 | The control method and device of machine spindle speed |
| CN110347114A (en) * | 2019-07-23 | 2019-10-18 | 清华大学 | A kind of Cnc ReliabilityintelligeNetwork Network data acquisition and analysis system |
| CN111007799A (en) * | 2019-12-18 | 2020-04-14 | 宁波财经学院 | Numerical control equipment remote diagnosis system based on neural network |
| CN112192250A (en) * | 2020-10-20 | 2021-01-08 | 衢州学院 | Information acquisition and fusion device and method based on thermal design of spindle system |
| CN112783138A (en) * | 2020-12-30 | 2021-05-11 | 上海交通大学 | Intelligent monitoring and abnormity diagnosis method and device for processing stability of production line equipment |
| CN113204212A (en) * | 2021-04-26 | 2021-08-03 | 江苏博尚工业装备有限公司 | Numerical control machine tool fault diagnosis method based on double-expert system |
| CN113377564A (en) * | 2021-06-08 | 2021-09-10 | 珠海格力电器股份有限公司 | Fault diagnosis method and device, computer equipment and storage medium |
| CN114273974A (en) * | 2021-12-14 | 2022-04-05 | 中国科学院合肥物质科学研究院 | Vibration signal-based tool runout parameter online estimation method in high-speed milling |
| US11341395B2 (en) | 2017-07-04 | 2022-05-24 | Siemens Aktiengesellschaft | Device and method for determining the status of a spindle of a machine tool |
| CN117884955A (en) * | 2024-03-14 | 2024-04-16 | 天津市漠金博实机电科技股份有限公司 | Numerical control lathe processing auxiliary system based on machine vision |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0349850A (en) * | 1989-07-13 | 1991-03-04 | Omron Corp | Tool damage detecting device |
| US6948381B1 (en) * | 2002-04-09 | 2005-09-27 | Rockwell Automation Technologies, Inc. | System and method for sensing torque on a rotating shaft |
| CN101722438A (en) * | 2008-10-28 | 2010-06-09 | 大隈株式会社 | Vibration suppressing method and vibration suppressing device for machine tool |
| CN102452018A (en) * | 2010-10-27 | 2012-05-16 | 株式会社捷太格特 | Spindle condition detection device for machine tool |
| CN102490086A (en) * | 2011-10-28 | 2012-06-13 | 浙江大学 | System for monitoring working state of boring rod in real time |
-
2012
- 2012-09-18 CN CN201210346785.9A patent/CN102825504B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0349850A (en) * | 1989-07-13 | 1991-03-04 | Omron Corp | Tool damage detecting device |
| US6948381B1 (en) * | 2002-04-09 | 2005-09-27 | Rockwell Automation Technologies, Inc. | System and method for sensing torque on a rotating shaft |
| CN101722438A (en) * | 2008-10-28 | 2010-06-09 | 大隈株式会社 | Vibration suppressing method and vibration suppressing device for machine tool |
| CN102452018A (en) * | 2010-10-27 | 2012-05-16 | 株式会社捷太格特 | Spindle condition detection device for machine tool |
| CN102490086A (en) * | 2011-10-28 | 2012-06-13 | 浙江大学 | System for monitoring working state of boring rod in real time |
Non-Patent Citations (2)
| Title |
|---|
| 张建民等: "《面向机电系统状态监测与故障诊断的现代技术》", 《北京理工大学学报》 * |
| 许志军: "《基于粒子群算法优化支持向量机的数控机床状态预测》", 《现代制造工程》 * |
Cited By (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103624633A (en) * | 2013-12-09 | 2014-03-12 | 大连理工大学 | Micro-milling vibration precision measurement system taking laser micro-displacement sensor as measuring element |
| CN105269404A (en) * | 2014-11-20 | 2016-01-27 | 电子科技大学 | Detection device for knife point dynamic characteristics of numerical control machine tool and method of detection device |
| CN104742153A (en) * | 2015-04-01 | 2015-07-01 | 中国计量学院 | Fault predication device of six-axis multi-joint industrial robot |
| CN104942655A (en) * | 2015-07-20 | 2015-09-30 | 徐文欢 | Main shaft rotary error accuracy testing device |
| CN105204436A (en) * | 2015-08-26 | 2015-12-30 | 山东省计算中心（国家超级计算济南中心） | Graded-early-warning-based fault diagnosis method of numerically-controlled machine tool |
| CN105204436B (en) * | 2015-08-26 | 2016-10-26 | 山东省计算中心（国家超级计算济南中心） | Diagnosing faults of numerical control machine method based on grading forewarning system |
| CN106695747A (en) * | 2015-11-13 | 2017-05-24 | 国网辽宁省电力有限公司检修分公司 | Valve hall inspection method and inspection robot based on laser radar |
| CN107199506B (en) * | 2017-06-05 | 2019-05-21 | 苏州微著设备诊断技术有限公司 | A kind of grinding trembling detection method based on stack self-encoding encoder and support vector machines |
| CN107199506A (en) * | 2017-06-05 | 2017-09-26 | 苏州微著设备诊断技术有限公司 | A kind of grinding trembling detection method based on stack self-encoding encoder and SVMs |
| US11341395B2 (en) | 2017-07-04 | 2022-05-24 | Siemens Aktiengesellschaft | Device and method for determining the status of a spindle of a machine tool |
| CN109746753A (en) * | 2017-11-06 | 2019-05-14 | 斗山机床株式会社 | The column of lathe and the curvature correction method of lathe bed |
| CN109746753B (en) * | 2017-11-06 | 2020-11-20 | 斗山机床株式会社 | Bending correction method for column and bed of machine tool |
| CN108760813A (en) * | 2018-06-05 | 2018-11-06 | 哈尔滨工程大学 | A kind of turbine blade of gas turbine health monitoring systems and method based on temperature signal |
| CN108760813B (en) * | 2018-06-05 | 2020-09-25 | 哈尔滨工程大学 | Gas turbine blade health monitoring system and method based on temperature signals |
| CN109396953A (en) * | 2018-12-05 | 2019-03-01 | 上海交通大学 | Lathe work condition intelligent identification system based on signal fused |
| CN110347114A (en) * | 2019-07-23 | 2019-10-18 | 清华大学 | A kind of Cnc ReliabilityintelligeNetwork Network data acquisition and analysis system |
| CN110320866A (en) * | 2019-07-24 | 2019-10-11 | 珠海格力智能装备有限公司 | The control method and device of machine spindle speed |
| CN111007799B (en) * | 2019-12-18 | 2021-04-27 | 宁波财经学院 | Numerical control equipment remote diagnosis system based on neural network |
| CN111007799A (en) * | 2019-12-18 | 2020-04-14 | 宁波财经学院 | Numerical control equipment remote diagnosis system based on neural network |
| CN112192250A (en) * | 2020-10-20 | 2021-01-08 | 衢州学院 | Information acquisition and fusion device and method based on thermal design of spindle system |
| CN112783138A (en) * | 2020-12-30 | 2021-05-11 | 上海交通大学 | Intelligent monitoring and abnormity diagnosis method and device for processing stability of production line equipment |
| CN113204212A (en) * | 2021-04-26 | 2021-08-03 | 江苏博尚工业装备有限公司 | Numerical control machine tool fault diagnosis method based on double-expert system |
| CN113377564A (en) * | 2021-06-08 | 2021-09-10 | 珠海格力电器股份有限公司 | Fault diagnosis method and device, computer equipment and storage medium |
| CN114273974A (en) * | 2021-12-14 | 2022-04-05 | 中国科学院合肥物质科学研究院 | Vibration signal-based tool runout parameter online estimation method in high-speed milling |
| CN117884955A (en) * | 2024-03-14 | 2024-04-16 | 天津市漠金博实机电科技股份有限公司 | Numerical control lathe processing auxiliary system based on machine vision |
| CN117884955B (en) * | 2024-03-14 | 2024-06-07 | 天津市漠金博实机电科技股份有限公司 | Numerical control lathe processing auxiliary system based on machine vision |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102825504B (en) | 2014-12-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102825504B (en) | State detection method for main shaft of numerically-controlled machine tool | |
| Zhou et al. | Review of tool condition monitoring methods in milling processes | |
| CN203894596U (en) | Multi-parameter online active monitoring system for machining states of numerical control machine bed | |
| CN107186547A (en) | Numerical control turning batch machining tool wear on-line monitoring method based on cutting power | |
| Zhou et al. | Tool wear condition monitoring in milling process based on current sensors | |
| CN102431043B (en) | Reducer abnormality determination method, abnormality determination device, and robot system | |
| CN103823409B (en) | Digit Control Machine Tool machining state multiparameter online actively monitoring system and its implementation | |
| CN102091972B (en) | Numerical control machine tool wear monitoring method | |
| CN102637017B (en) | Real-time monitoring device and method for spindle performance and calibrating experimental device and method thereof | |
| EP1927830B1 (en) | Device for overall machine tool monitoring and corresponding method therefor | |
| CN111947928B (en) | Multi-source information fusion bearing fault prediction system and method | |
| Čuš et al. | Real-time cutting tool condition monitoring in milling | |
| CN114102260B (en) | Mechanism-data fusion driven variable working condition cutter wear state monitoring method | |
| JP4301278B2 (en) | Machine tool machining dimension prediction device | |
| CN109799784A (en) | Cutter Abrasion detecting device, its detection method and cutter wear compensation method | |
| CN102929210A (en) | Control and optimization system for feature-based numerical control machining process and control and optimization method therefor | |
| JP4501918B2 (en) | Tool mounting abnormality detection device | |
| CN116871978A (en) | Drilling tool state monitoring method based on multi-sensor fusion | |
| CN107414600A (en) | The process monitoring method of internal thread low frequency exciting cold extrusion lathe based on multiple sensor signals | |
| Cheng et al. | Tool wear intelligent monitoring techniques in cutting: a review | |
| CN114800040A (en) | Cutter wear monitoring method and system based on process-state data correlation | |
| Bai et al. | Real-time tool breakage monitoring based on dimensionless indicators under time-varying cutting conditions | |
| Massol et al. | An exTS based neuro-fuzzy algorithm for prognostics and tool condition monitoring | |
| CN202735778U (en) | Numerical control machine tool cutting performance and cutting power testing device | |
| Elbestawi et al. | Tool condition monitoring in machining |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20141224 Termination date: 20150918 |
|
| EXPY | Termination of patent right or utility model |