CN102183951A - Device for monitoring state of rotary bearing and diagnosing fault based on laboratory virtual instrument engineering workbench (Lab VIEW) - Google Patents
Device for monitoring state of rotary bearing and diagnosing fault based on laboratory virtual instrument engineering workbench (Lab VIEW) Download PDFInfo
- Publication number
- CN102183951A CN102183951A CN 201110072759 CN201110072759A CN102183951A CN 102183951 A CN102183951 A CN 102183951A CN 201110072759 CN201110072759 CN 201110072759 CN 201110072759 A CN201110072759 A CN 201110072759A CN 102183951 A CN102183951 A CN 102183951A
- Authority
- CN
- China
- Prior art keywords
- pivoting support
- signal
- fault diagnosis
- module
- data acquisition
- 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.)
- Pending
Links
Images
Landscapes
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The invention relates to a device for monitoring the state of a rotary bearing and diagnosing a fault based on a laboratory virtual instrument engineering workbench (Lab VIEW). The device comprises an industrial personal computer, a detection device, a stabilized-voltage supply, a data acquisition module, a data processing module and a fault diagnosis module; the industrial personal computer is provided with a programmable communication interface (PCI) data acquisition card including a simulated input channel; the detection device is provided with an acceleration transducer and a signal conditioning circuit; the signal conditioning circuit is used for amplifying and filtering signals picked up by the acceleration transducer and the like; the data acquisition module is used for inputting acquired signals into the industrial personal computer by a program, editing a data acquisition (DAQmx) driving program in the Lab VIEW, controlling sampling frequencies of the signals, inputting and outputting and the like; the data processing module comprises denoising display, storage and characteristic extraction of the signals, and is used for removing the interference of noise, extracting time-domain and frequency-domain characteristic quantity of the signals and preparing for fault diagnosis; the fault diagnosis module is used for obtaining fault information according to extracted characteristic data of the rotary bearing, outputting a diagnosis result and processing suggestions. The device has the advantages of simple structure, high openness, good real-time performance, friendly human-computer interface, no need of diagnostician participation and higher reliability, and can be used for automatically identifying the fault of the rotary bearing.
Description
Technical field
The invention belongs to the mechanical fault diagnosis field, be specifically related to a kind of pivoting support monitoring and fault diagnosis device based on virtual instrument development platform (LabVIEW).
Background technology
Large-size pivoting support is all kinds of engineering mechanical devices, as the important composition parts of tower crane, high pedestal jib crane, excavator etc.Whether its fault directly influences the operation of entire machine, is related to the safety of workmen and production equipment again.Yet owing to it costs an arm and a leg, dismounting is relatively more difficult, maintenance cycle is long, therefore need carry out status monitoring and early stage fault diagnosis, can avoid catastrophic discontinuityfailure and unnecessary opening and inspecting, thereby increase economic efficiency large-size pivoting support.
In the operational process of pivoting support, owing to the phenomenon that exists fault to have an accident is of common occurrence.Particularly the be squeezed effect of power or parts wear of each parts of pivoting support in the course of work causes the continuous conversion of its duty to cause its fault to be difficult to identification.Traditional pivoting support fault diagnosis is normally judged or the off-lined signal analysis according to slip-stick artist's experience.The shortcoming of this method is that diagnosis efficiency is low, and too relies on diagnostician's professional knowledge.
In recent years, along with the development of computer technology, microelectric technique, sensor technology and artificial intelligence technology, the intelligent trouble diagnosis system has applied in a lot of large-scale engineering machinery equipment.This patent has proposed a kind of large-size pivoting support fault diagnosis system based on NI virtual instrument platform LabVIEW, can locate the fault of pivoting support rapidly and accurately, and provide failure message automatically.
Summary of the invention
The object of the present invention is to provide a kind of pivoting support condition monitoring and fault diagnosis device based on virtual instrument development platform LabVIEW.This device need not the fault diagnosis expert and participates in just inline diagnosis going out the pivoting support most common failure, and has higher accuracy rate.
The present invention utilizes the significant achievement of signal Processing field, artificial intelligence field and computer realm in recent years, in conjunction with America NI company be the virtual instrument technique of representative with LabVIEW, developed a kind of pivoting support condition monitoring and fault diagnosis device based on LabVIEW.
The pivoting support trouble-shooter that the present invention proposes based on virtual instrument development platform, form by industrial computer 1, pick-up unit 3, stabilized voltage supply 7, data acquisition module 11, data processing module 12 and fault diagnosis module 13, be provided with a pci data capture card 2 that comprises analog input channel in the industrial computer 1, be provided with acceleration transducer 5 and signal conditioning circuit 4 in the pick-up unit 3, signal conditioning circuit 4 be used for to the signal that acceleration transducer 5 picks up amplify, filtering etc.; Data acquisition module 11 is imported the signal that collects in the industrial computer 1 by programmed control, writes DAQmx data acquisition driver in the LabVIEW platform, the sample frequency of control signal, input and output etc.Data processing module 12 comprises denoising demonstration, storage, the feature extraction of signal, and all the function library research and development that carry based on LabVIEW form.The interference of data processing module 12 cancelling noises, the time domain and the frequency domain character amount of extraction signal are for fault diagnosis is prepared; Fault diagnosis module 13 draws failure message according to the characteristic of the pivoting support that extracts, output diagnostic result and handling suggestion.
Among the present invention, the PIC-6023E data acquisition plug-in card that described pci data capture card 2 adopts NI companies to release, by computer standard interface PCI slot with the signals collecting of picking up in industrial computer 1.
Among the present invention, described acceleration transducer 5 comprises radial acceleration sensor and axial acceleration sensor, evenly arranges along the circle of deciding of pivoting support respectively, picks up the radial and axial vibration signal of each measuring point of pivoting support.
Among the present invention, described fault diagnosis module 11 adopts the BP neural networks to carry out failure modes, and the mean square value by extracting signal is as the input of neural network.
Among the present invention, described fault diagnosis module 13 adopts the method for calling the MATLAB neural network procedure that designs in the LabVIEW development platform by the MatlabScript node in the function library, makes up the pivoting support online system failure diagnosis.
The invention has the beneficial effects as follows:
1, adopts modular structure, the reliability height.Man-machine interface close friend, easy and simple to handle.
2, fault diagnosis has real-time, and shows failure message automatically.
3, system adopts virtual instrument technique, has open characteristics, can integrated fault diagnosis to multiple parts.
Description of drawings
Fig. 1 is a structural representation of the present invention.
Fig. 2 is a pivoting support vibration measuring point arrangenent diagram of the present invention.
Fig. 3 is a program structure synoptic diagram of the present invention.
Number in the figure: 1 is industrial computer, and 2 is the pci data capture card, and 3 is pick-up unit, 4 is signal conditioning circuit, 5 is acceleration transducer, and 6 is the pivoting support test platform, and 7 is stabilized voltage supply, 8 is the outer ring, 9 is rolling body, and 10 is inner ring, and 11 is data acquisition module, 12 is data processing module, and 13 is fault diagnosis module.
Embodiment
Below in conjunction with accompanying drawing, will further narrate specific embodiments of the present invention.
Embodiment 1: the pivoting support condition monitoring and fault diagnosis device based on LabVIEW that the present invention proposes, the Virtual instrument LabVIEW development platform that it adopts are the graphic programming systems that is developed by NI company.It is a powerful Integrated Development Environment, has huge function library, the communication of intactly integrated and hardware such as GPIB, VXI, PCI and interpolation type data collecting card.
Accompanying drawing 1 is a hardware block diagram of the present invention, comprises industrial computer 1, pick-up unit 3 and stabilized voltage supply 7, is provided with a pci data capture card 2 that comprises analog input channel in the industrial computer 1, is provided with acceleration transducer 5 and signal conditioning circuit 4 in the pick-up unit 3.The present invention is a monitoring parameter with the vibration acceleration of pivoting support, so the layout of acceleration transducer 5 is a foundation with the vibration characteristics of pivoting support.Because pivoting support bears bigger axial force and upsetting moment, and the load that each rolling body bears is not quite similar, and therefore must measure pivoting support respectively axially and acceleration vibration signal radially, and evenly arranges along fixed circle.The layout of acceleration transducer is evenly arranged 4 measuring points along fixed circle as shown in Figure 2 among the present invention.
The curtage signal amplitude that obtains from acceleration transducer 5 is very low, be generally milliampere or millivolt level, be not suitable for gathering and transmitting, and owing to cause that the factor of pivoting support vibration is more, there is more interference, therefore must amplify and filtering output is fit to after the integer voltage or electric current by 4 pairs of acceleration transducers of signal conditioning circuit, 5 detected signals.
The pivoting support gyrofrequency is lower, and the Internal and external cycle failure-frequency is comparatively approaching, therefore needs higher signal acquisition precision and resolution in the monitoring, diagnosing process.The data collecting card PCI-6023E that pci data capture card 2 among the present invention adopts NI company to release based on the PCI slot, comprise 16 road analog input channels, 8 digital I/0 ports, the method for 8 road vibration signals utilization difference input of pivoting support arrives the LabVIEW interface with signals collecting.
The module of LabVIEW comprises as shown in Figure 3 among the present invention: data acquisition module 11, data processing module 12, fault diagnosis module 13.Data acquisition module 11 is to write with the function among the DAQmx to finish, and is arranged in the measurement I/O function module of LabVIEW, because the present invention is real-time fault diagnosis, therefore must adopt the method for continuous sampling.The function of data processing module 12 comprises denoising, demonstration, storage and the features extraction of signal, pivoting support each ingredient in operation process sends the characteristic signal of determining separately, and these signals are with damage or the degree of wear and the change in pressure of discrete component.Pivoting support is in the environment work down of low-speed heave-load usually, and its low frequency fault signature signal often is subjected to the complex environment interference of noise, influences final diagnosis effect.The method that the present invention uses wavelet threshold to remove to make an uproar is rejected the noise in the pivoting support vibration signal, selects the db4 small echo, carries out 7 grades of decomposition, and passing threshold function and wavelet reconstruction are removed noise.This program is write in Matlab and is finished, and calls this program by the MatlabScript node in LabVIEW.The effective filtering of wavelet transformation noise, by the wavelet coefficient of reconstruct being asked for the proper vector that root-mean-square value extracts fault.
The present invention is for dissimilar pivoting supports, need the different neural network of training, therefore before being used for fault diagnosis, the BP neural network must obtain the vibration acceleration signal of pivoting support fault, extract proper vector, and in Matlab the planned network training program, the roll-off network weights carry out fault diagnosis again.
Claims (5)
1. pivoting support trouble-shooter based on virtual instrument development platform, by industrial computer (1), pick-up unit (3), stabilized voltage supply (7), data acquisition module (11), data processing module (12) and fault diagnosis module (13) are formed, it is characterized in that being provided with in the industrial computer (1) a pci data capture card (2) that comprises analog input channel, be provided with acceleration transducer (5) and signal conditioning circuit (5) and signal conditioning circuit (4) in the pick-up unit (3), signal conditioning circuit (4) is used for the signal that acceleration transducer (5) picks up is amplified, filtering; Data acquisition module (11) is imported the signal that collects in the industrial computer (1) by programmed control, writes DAQmx data acquisition driver in the LabVIEW platform, the sample frequency of control signal, input and output; Data processing module (12) comprises denoising demonstration, storage, the feature extraction of signal, and all the function library research and development that carry based on LabVIEW form; The interference of data processing module (12) cancelling noise, the time domain and the frequency domain character amount of extraction signal are for fault diagnosis is prepared; Fault diagnosis module (13) draws failure message according to the characteristic of the pivoting support that extracts, output diagnostic result and handling suggestion.
2. the pivoting support trouble-shooter based on virtual instrument development platform according to claim 1, it is characterized in that described pci data capture card (2) adopts the PIC-6023E data acquisition plug-in card of NI company, the signals collecting of picking up is arrived in the industrial computer (1) by computer standard interface PCI slot.
3. the pivoting support trouble-shooter based on virtual instrument development platform according to claim 1, it is characterized in that described acceleration transducer (5) comprises radial acceleration sensor and axial acceleration sensor, evenly arrange along the circle of deciding of pivoting support respectively, pick up the radial and axial vibration signal of each measuring point of pivoting support.
4. the pivoting support trouble-shooter based on virtual instrument development platform according to claim 1, it is characterized in that described fault diagnosis module (11) adopts the BP neural network to carry out failure modes, the mean square value by extracting signal is as the input of neural network.
5. the pivoting support trouble-shooter based on virtual instrument development platform according to claim 1, it is characterized in that described fault diagnosis module (13) adopts the method for calling the MATLAB neural network procedure that designs in the LabVIEW development platform by the MatlabScript node in the function library, makes up the pivoting support online system failure diagnosis.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201110072759 CN102183951A (en) | 2011-03-25 | 2011-03-25 | Device for monitoring state of rotary bearing and diagnosing fault based on laboratory virtual instrument engineering workbench (Lab VIEW) |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201110072759 CN102183951A (en) | 2011-03-25 | 2011-03-25 | Device for monitoring state of rotary bearing and diagnosing fault based on laboratory virtual instrument engineering workbench (Lab VIEW) |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN102183951A true CN102183951A (en) | 2011-09-14 |
Family
ID=44570138
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 201110072759 Pending CN102183951A (en) | 2011-03-25 | 2011-03-25 | Device for monitoring state of rotary bearing and diagnosing fault based on laboratory virtual instrument engineering workbench (Lab VIEW) |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102183951A (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102819219A (en) * | 2012-06-13 | 2012-12-12 | 南京工业大学 | Intelligent movement control method for prolonging service life of slewing bearing |
| CN104007724A (en) * | 2014-05-15 | 2014-08-27 | 华侨大学 | Excavator remote fault diagnosis system and method based on LabVIEW |
| CN106762340A (en) * | 2016-12-02 | 2017-05-31 | 国家电网公司 | A kind of hydraulic turbine servomotor intelligence leak detection system and its method |
| CN107063666A (en) * | 2017-05-15 | 2017-08-18 | 江苏省特种设备安全监督检验研究院 | The detecting system and detection method of the pivoting support of high pedestal jib crane |
| CN107229271A (en) * | 2017-06-14 | 2017-10-03 | 王东红 | A kind of intelligent transportation development platform method for diagnosing faults and device |
| CN108500498A (en) * | 2018-03-26 | 2018-09-07 | 华中科技大学 | A kind of appearance of weld quality monitoring method |
| CN109949631A (en) * | 2019-02-19 | 2019-06-28 | 同济大学 | A kind of classification of convectional signals and observation experiment device and application method |
| CN110736506A (en) * | 2019-10-29 | 2020-01-31 | 青岛四方思锐智能技术有限公司 | Remote environment monitoring system and method |
| CN111897220A (en) * | 2020-09-01 | 2020-11-06 | 北京清立科技有限公司 | Engineering project control method and control system based on neural network operation mode |
| CN111982500A (en) * | 2020-08-30 | 2020-11-24 | 东科克诺尔商用车制动技术有限公司 | Data detection and analysis system of power steering gear safety unloading valve |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2005071620A2 (en) * | 2004-01-14 | 2005-08-04 | Abb Inc. | Method and apparatus to diagnose mechanical problems in machinery |
| CN1825082A (en) * | 2006-03-31 | 2006-08-30 | 洛阳轴研科技股份有限公司 | Automatic diagnosing system for rolling bearing fault |
| CN101334335A (en) * | 2008-07-10 | 2008-12-31 | 武汉科技大学 | Synchronous gear box initial failure on-line monitoring and diagnostic equipment |
| CN201740656U (en) * | 2010-05-27 | 2011-02-09 | 山东理工大学 | Bulldozer dynamic parameter testing device based on LabVIEW |
-
2011
- 2011-03-25 CN CN 201110072759 patent/CN102183951A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2005071620A2 (en) * | 2004-01-14 | 2005-08-04 | Abb Inc. | Method and apparatus to diagnose mechanical problems in machinery |
| CN1825082A (en) * | 2006-03-31 | 2006-08-30 | 洛阳轴研科技股份有限公司 | Automatic diagnosing system for rolling bearing fault |
| CN101334335A (en) * | 2008-07-10 | 2008-12-31 | 武汉科技大学 | Synchronous gear box initial failure on-line monitoring and diagnostic equipment |
| CN201740656U (en) * | 2010-05-27 | 2011-02-09 | 山东理工大学 | Bulldozer dynamic parameter testing device based on LabVIEW |
Non-Patent Citations (3)
| Title |
|---|
| 《噪声与振动控制》 20030831 龚群协 等 大型回转支承振动诊断技术应用研究 39-42 , 第4期 * |
| 《工程机械》 20090331 朱飞 等 基于LabVIEW的回转友承测试***设计 45-48 第40卷, 第3期 * |
| 《自动测量与控制》 20081130 刘洪昌 等 基于虚拟仪器的某型导弹发射台检测*** 67-69 第27卷, 第11期 * |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102819219B (en) * | 2012-06-13 | 2014-10-29 | 南京工业大学 | Intelligent movement control method for prolonging service life of slewing bearing |
| CN102819219A (en) * | 2012-06-13 | 2012-12-12 | 南京工业大学 | Intelligent movement control method for prolonging service life of slewing bearing |
| CN104007724A (en) * | 2014-05-15 | 2014-08-27 | 华侨大学 | Excavator remote fault diagnosis system and method based on LabVIEW |
| CN104007724B (en) * | 2014-05-15 | 2017-01-04 | 华侨大学 | A kind of excavator remote failure diagnosis system based on LabVIEW and method |
| CN106762340B (en) * | 2016-12-02 | 2019-02-26 | 国家电网公司 | A kind of hydraulic turbine servomotor intelligence leak detection system and its method |
| CN106762340A (en) * | 2016-12-02 | 2017-05-31 | 国家电网公司 | A kind of hydraulic turbine servomotor intelligence leak detection system and its method |
| CN107063666A (en) * | 2017-05-15 | 2017-08-18 | 江苏省特种设备安全监督检验研究院 | The detecting system and detection method of the pivoting support of high pedestal jib crane |
| CN107229271A (en) * | 2017-06-14 | 2017-10-03 | 王东红 | A kind of intelligent transportation development platform method for diagnosing faults and device |
| CN108500498A (en) * | 2018-03-26 | 2018-09-07 | 华中科技大学 | A kind of appearance of weld quality monitoring method |
| CN109949631A (en) * | 2019-02-19 | 2019-06-28 | 同济大学 | A kind of classification of convectional signals and observation experiment device and application method |
| CN110736506A (en) * | 2019-10-29 | 2020-01-31 | 青岛四方思锐智能技术有限公司 | Remote environment monitoring system and method |
| CN111982500A (en) * | 2020-08-30 | 2020-11-24 | 东科克诺尔商用车制动技术有限公司 | Data detection and analysis system of power steering gear safety unloading valve |
| CN111897220A (en) * | 2020-09-01 | 2020-11-06 | 北京清立科技有限公司 | Engineering project control method and control system based on neural network operation mode |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102183951A (en) | Device for monitoring state of rotary bearing and diagnosing fault based on laboratory virtual instrument engineering workbench (Lab VIEW) | |
| CN102053016B (en) | System for monitoring vibration of rotating machinery rolling bearing in wireless mode | |
| CN109186744B (en) | Turbine blade crack evaluation system and turbine blade crack early warning method | |
| CN103471841B (en) | A kind of rotating machinery vibrating failure diagnosis method | |
| CN102269655B (en) | Method for diagnosing bearing fault | |
| CN202083555U (en) | System for extracting and diagnosing fault feature of gasoline engine | |
| CN202065278U (en) | Hydraulic system fault analyzing system of whole automobile crane | |
| CN101798957B (en) | Failure diagnosis method of power equipment | |
| CN111238815B (en) | Bearing fault identification method based on data enhancement under sample imbalance | |
| CN104564542B (en) | A kind of fault diagnosis system and its diagnostic method based on big data technology | |
| CN106441896A (en) | Characteristic vector extraction method for rolling bearing fault mode identification and state monitoring | |
| CN111504645B (en) | Rolling bearing fault diagnosis method based on frequency domain multipoint kurtosis | |
| CN110632484A (en) | ELM-based GIS partial discharge defect diagnosis and classification system and method | |
| CN104215456A (en) | Plane clustering and frequency-domain compressed sensing reconstruction based mechanical fault diagnosis method | |
| CN103234746A (en) | Device and method for online diagnosing faults of wind turbine generator gear case | |
| CN203881539U (en) | Wind turbine generator state monitoring and fault diagnosis device | |
| CN103267652A (en) | Intelligent online diagnosis method for early failures of equipment | |
| CN203163988U (en) | Wind turbine generator gear case on-line fault diagnosis device | |
| CN107677958A (en) | Motor performance test system and method based on LabVIEW | |
| CN105784364A (en) | Bearing fault diagnosis method based on total experience mode decomposition and fractal box dimensions | |
| CN202947794U (en) | Acquisition testing device of vibration signal of body surface of transformer | |
| CN209783910U (en) | Intelligent fault diagnosis system for nuclear power pump bearing | |
| CN206114295U (en) | Portable bearing state monitoring system based on FPGA | |
| CN205426531U (en) | Single round drive mode test system of high accuracy and test device who constitutes thereof | |
| CN204630729U (en) | Bearing detection system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20110914 |