CN103447887B - Based on sawing Weight detector and the method for band saw for metal Acoustic Emission Characteristic - Google Patents
Based on sawing Weight detector and the method for band saw for metal Acoustic Emission Characteristic Download PDFInfo
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- CN103447887B CN103447887B CN201310310325.5A CN201310310325A CN103447887B CN 103447887 B CN103447887 B CN 103447887B CN 201310310325 A CN201310310325 A CN 201310310325A CN 103447887 B CN103447887 B CN 103447887B
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Abstract
The invention discloses a kind of sawing Weight detector based on band saw for metal Acoustic Emission Characteristic and method, this device is a kind of sonic sensor by being arranged near sawing working region, to band saw in the sawing process propagated in air and the interactional acoustic signals of workpiece, carry out high frequency sampling, the band saw for metal load detecting of analysis and feature extraction puts; By being arranged on the sonic sensor on the guide wheel shaft of close contact bands for band, to band saw in the sawing process propagated in metal and the interactional acoustic signals of workpiece, carry out the band saw for metal load of high frequency sampling, analysis and feature extraction; By the contrast that sawing acoustic wave energy characteristic set and sawing load three axis force are directly measured, carry out the parameters revision of sawing remained capacity model, the band saw for metal Weight detector of final identification bands for band sawing load, apparatus of the present invention hardware configuration is simple, be suitable for site environment requirement, reliability is high, is convenient to safeguard and upgrading.
Description
Technical field
The present invention relates to a kind of contactless bands for band sawing Weight detector, particularly a kind of sawing Weight detector based on band saw for metal Acoustic Emission Characteristic and method.
Background technology
Sound emission refers to that material is under external force or endogenetic process, localized source quick release of energy and produce a kind of phenomenon of Elastic wave.This elastic wave can reflect the certain physical characteristics in band saw for metal sawing process, and the sensor that can be placed in band saw for metal surface receives, therefore adopts the method detecting acoustic emission signal, can judge the sawing load condition of band saw for metal.In actual Cutting indexes is produced, band saw sawing load can change along with the carrying out of processing, and this machining accuracy on subsequent technique, working (machining) efficiency have larger impact.Therefore, need the sawing Weight detector developing a kind of band sawing machine, high precision int, the high efficiency studying band saw machine is had very important significance.
At present, in association area not based on the sawing Weight detector of band saw for metal Acoustic Emission Characteristic, if the patent No. is CN201110177969.2 (Authorization Notice No. CN102275131A, authorized announcement date on December 14th, 2011) disclose a kind of method for supervising and the monitoring system that detect machining states of grinding machine, this system adopts sonar sensor to receive numerically control grinder for the sonar signal sent during work pieces process, and convert its sonar signal to the signal of telecommunication, the signal of telecommunication converts RS232 communication protocol signal and PROFIBUS communication protocol signal respectively to, RS232 communication protocol signal is passed in the digital control system on grinding machine, the RS232 protocol signal received is presented on screen in the mode of dynamic waveform by digital control system, PROFIBUS protocol signal is imported in PLC system and carries out PLC programming in logic, and PLC is exported the collision alarm of grinding machine and workpiece and activation signal imports in the parts program of digital control system, achieve anticollision and the idle running that disappears.This system can monitor the overall process of numerically control grinder processing, can improve grinding quality and efficiency to a certain extent.But this system is directly used for programming and display by gathering the acoustic emission signal of returning, and lack the analyzing and processing to interfering signal, precision is not high, therefore, and is not suitable for the requirement of band saw for metal sawing load detecting.The present invention is directed to the deficiency of above technology, provide a kind of sawing Weight detector based on band saw for metal Acoustic Emission Characteristic.
Summary of the invention
The present invention is directed to prior art deficiency, provide a kind of sawing Weight detector based on band saw for metal Acoustic Emission Characteristic and method.This device is a kind of sonic sensor by being arranged near sawing working region, to band saw in the sawing process propagated in air and the interactional acoustic signals of workpiece, carries out the band saw for metal Weight detector of high frequency sampling, analysis and feature extraction; This device is the sonic sensor on a kind of guide wheel shaft by being arranged on close contact bands for band, to band saw in the sawing process propagated in metal and the interactional acoustic signals of workpiece, carry out the band saw for metal Weight detector of high frequency sampling, analysis and feature extraction; This device is a kind of repeatedly to test in lower different medium unequally loaded sawing acoustic wave energy characteristic set for priori, by the contrast of directly measuring with sawing load three axis force, carry out the parameters revision of sawing remained capacity model, the band saw for metal Weight detector of final identification bands for band sawing load.
The technical scheme that technical solution problem of the present invention adopts is:
A kind of sawing Weight detector based on band saw for metal Acoustic Emission Characteristic comprises first sound wave sensor, rising tone wave sensor, mounting bracket, preamplifier, change-over panel, data collecting card, industrial computer.Wherein calibrate AE sensor module comprises first sound wave sensor and rising tone wave sensor and mounting bracket, and wherein rising tone wave sensor is SMD sonic sensor; Signal acquisition module comprises preamplifier and change-over panel and data collecting card; Signal analyse block and human-computer interface module are all integrated in industrial computer.
Described first sound wave sensor is fixed on band saw machine guider by mounting bracket, the axle of described directive wheel is fixed on band saw machine guider, directive wheel and bands for band fit tightly, and the wheel direction of principal axis of directive wheel is vertical with the direction of motion of bands for band, described sonic sensor is fixed on the axle of directive wheel;
First sound wave sensor is connected with the analog input port of preamplifier with the signal output port of rising tone wave sensor, preamplifier is connected to the analog input port of change-over panel with the mode changeover signal of single-ended connection, change-over panel transfers to data collecting card by external cable, and data collecting card transfers to industrial computer by pci interface;
When band saw sawing, bands for band and workpiece interact and produce acoustic emission signal, gathered the acoustic emission signal propagated in air by first sound wave sensor, gathered the acoustic emission signal propagated in metal by rising tone wave sensor, acoustic emission signal is converted to the signal of telecommunication through sonic sensor.The signal of telecommunication that sonic sensor exports is sent to the analog input port of preamplifier, the analog input port of the signal of telecommunication change-over panel after preamplifier amplifies.The analog signal of input change-over panel transfers to capture card by external cable, and the process of capture card advanced row of channels scanner uni gain operation, comes Optimized Simulated signal conversion efficiency and precision, then carry out high-speed a/d conversion to analog signal.
After signal acquisition module collection, conversion and computing, data signal, by pci interface, transfers in industrial computer and waits for based on digital signal filter, sampling, the Digital Signal Processing such as wavelet analysis.Characteristic signal after repeatedly digital processing, after based on potential-energy function disaggregated model analysis identification, shows band saw sawing load characteristic by man-machine interface (HMI) and load changes.
Based on the sawing load detection method of band saw for metal Acoustic Emission Characteristic, comprise the following steps:
When band saw work runs, described sonic sensor detection zone sawing acoustic emission signal, exports analog signals value
ai(
t).Described analog signals value
ai(
t) carry out signal amplification, AD conversion via described signal acquisition module, wherein said signal acquisition module is furnished with automatic channel/gain scan circuit and filter circuit of pressure-stabilizing, to described signal value
ai(
t) carry out preposition pretreatment.
After described signal acquisition module, output digit signals value
x(
t) to signal analyse block.Described signal analyse block is integrated with wavelet analysis method, and corresponding discrete wavelet transformer is changed to
, wherein
ψ j,k (
t) be wavelet function,
, a
0>0 is constant,
j,
k∈ Z, warp
ψ j,k (
t) wavelet transformation that acts on is in fact signal
x(
t) frequency band range
be limited in [
ω *-Δ
ω,
ω *+ Δ
ω] in sub-band, wavelet transform result is the time domain component in this frequency band, wherein
ω *----centered by frequency, Δ
ωbe 1/2 bandwidth, by wavelet transform, can make Computer Analysis signal when specific, energy feature frequently within the scope of window.
Acoustic emission signal after the wavelet analysis in signal analyse block, can extract acoustic emission energy characteristic vector, and the potential-energy function disaggregated model of recycling signal analyse block, can carry out Intelligent Recognition to sawing load.The sawing remained capacity of acoustic emission signal adopts potential-energy function disaggregated model, energy feature vector table is shown as [
x k], wherein
k=1,2 ... N, the potential-energy function of any one energy feature vector is expressed as
, wherein
for or not the real number of 0,
(
x) be normalized orthogonal functions, utilize Hull Mi Te (Hermite) orthogonal function to construct
(
x),
(
x) recurrence formula be
h k+1(
x)-2
xH k(
x)+2
kH k-1(
x)=0.To repeatedly test in lower different medium unequally loaded sawing acoustic wave energy characteristic set as potential-energy function disaggregated model training sample set (
x 1,
x 2...,
x k), each sawing acoustic characteristic vector produces corresponding potential-energy function
k i(
x), obtain
k k+1(
x) with
k k(
x) iterative relation
k k+1(
x)=
k k(
x)+γ
k+1 k(
x,
x k-1), the potential-energy function determined through training is exactly discriminant function d
k+1(
x)=d
k(
x)+γ
k+1 k(
x,
x k-1), all discriminant functions form sawing remained capacity model.After the training of potential-energy function disaggregated model completes, compare the accuracy of testing model with potential-energy function disaggregated model test for identification sample and actual sawing load three axis force.
Described acoustic emission signal is through potential-energy function disaggregated model Intelligent Recognition, be sent to human-computer interface module eventually through man-machine interface, human-computer interface module converts the acoustic emission signal after potential-energy function disaggregated model Intelligent Recognition to actual sawing load value and is presented in man-machine interface.
Advantage of the present invention is:
1, device hardware configuration is simple, easy to assembly with sawing machine, is suitable for site environment requirement.
2, device analysis identification mainly realizes by software programming exploitation, and reliability is high, is convenient to safeguard and upgrading.
3, checkout gear response frequency is high, and information storage is large, and precision is high, and adaptive ability is strong.
Accompanying drawing explanation
Fig. 1 is band saw sawing Weight detector schematic diagram;
Fig. 2 is band saw acoustic emission signal acquisition module schematic diagram;
Fig. 3 is sawing load detection method fundamental diagram.
Detailed description of the invention
Below in conjunction with accompanying drawing, the invention will be further described.
As shown in Figure 1 and Figure 2, a kind of sawing Weight detector based on band saw for metal Acoustic Emission Characteristic comprises first sound wave sensor 2, rising tone wave sensor 6, mounting bracket 1, preamplifier 8, change-over panel 9, data collecting card 10, industrial computer 11.Wherein calibrate AE sensor module comprises two sonic sensors 2 and 6 and mounting bracket 1, and wherein 6 is SMD sonic sensor; Signal acquisition module comprises preamplifier 8 and change-over panel 9 and data collecting card 10; Signal analyse block and human-computer interface module are all integrated in industrial computer 11.
Described first sound wave sensor 2 is fixed on band saw machine guider 5 by mounting bracket 1, the axle of described directive wheel is fixed on band saw machine guider 5, directive wheel 7 and bands for band 4 fit tightly, and the wheel direction of principal axis of directive wheel is vertical with the direction of motion of bands for band 4, described sonic sensor 6 is fixed on the axle of directive wheel 7;
First sound wave sensor 2 is connected with the analog input port of preamplifier 8 with the signal output port of rising tone wave sensor 6, preamplifier 8 is connected to the analog input port of change-over panel 9 with the mode changeover signal of single-ended connection, change-over panel 9 transfers to data collecting card 10 by external cable, and data collecting card 10 transfers to industrial computer 11 by pci interface;
When band saw sawing, bands for band 4 and workpiece 3 interact and produce acoustic emission signal, gathered the acoustic emission signal propagated in air by first sound wave sensor 2, gathered the acoustic emission signal propagated in metal by rising tone wave sensor 6, acoustic emission signal is converted to the signal of telecommunication through sonic sensor.The signal of telecommunication that sonic sensor exports is sent to the analog input port of preamplifier 8, the analog input port of the signal of telecommunication change-over panel 9 after preamplifier 8 amplifies.The analog signal of input change-over panel 9 transfers to capture card 10 by external cable, and the process of capture card 10 advanced row of channels scanner uni gain operation, comes Optimized Simulated signal conversion efficiency and precision, then carry out high-speed a/d conversion to analog signal.
After signal acquisition module collection, conversion and computing, data signal, by pci interface, transfers in industrial computer 11 and waits for based on digital signal filter, sampling, the Digital Signal Processing such as wavelet analysis.Characteristic signal after repeatedly digital processing, after based on potential-energy function disaggregated model analysis identification, shows band saw sawing load characteristic by man-machine interface (HMI) and load changes.
As shown in Figure 3, based on the sawing load detection method of band saw for metal Acoustic Emission Characteristic, comprise the following steps:
When band saw work runs, described sonic sensor detection zone sawing acoustic emission signal, exports analog signals value
ai(
t).Described signal value
ai(
t) carry out signal amplification, AD conversion via described signal acquisition module, wherein said signal acquisition module is furnished with automatic channel/gain scan circuit and filter circuit of pressure-stabilizing, can to described signal value
ai(
t) carry out preposition pretreatment.
After described signal acquisition module, output digit signals value
x(
t) to signal analyse block.Described signal analyse block is integrated with wavelet analysis method, and corresponding discrete wavelet transformer is changed to
, wherein
ψ j,k (
t) be wavelet function,
, a
0>0 is constant,
j,
k∈ Z, warp
ψ j,k (
t) wavelet transformation that acts on is in fact signal
x(
t) frequency band range
be limited in [
ω *-Δ
ω,
ω *+ Δ
ω] in sub-band, wavelet transform result is the time domain component in this frequency band.By wavelet transform, can make Computer Analysis signal when specific, energy feature frequently within the scope of window.
Acoustic emission signal after the wavelet analysis in signal analyse block, can extract acoustic emission energy characteristic vector, and the potential-energy function disaggregated model of recycling signal analyse block, can carry out Intelligent Recognition to sawing load.The sawing remained capacity of acoustic emission signal adopts potential-energy function disaggregated model, energy feature vector table is shown as [
x k], wherein
k=1,2 ... N, the potential-energy function of any one energy feature vector is expressed as
, wherein
for or not the real number of 0,
(
x) be normalized orthogonal functions, utilize Hull Mi Te (Hermite) orthogonal function to construct
(
x),
(
x) recurrence formula be
h k+1(
x)-2
xH k(
x)+2
kH k-1(
x)=0.To repeatedly test in lower different medium unequally loaded sawing acoustic wave energy characteristic set as potential-energy function disaggregated model training sample set (
x 1,
x 2...,
x k), each sawing acoustic characteristic vector produces corresponding potential-energy function
k i(
x), obtain
k k+1(
x) with
k k(
x) iterative relation
k k+1(
x)=
k k(
x)+γ
k+1 k(
x,
x k-1), the potential-energy function determined through training is exactly discriminant function d
k+1(
x)=d
k(
x)+γ
k+1 k(
x,
x k-1), all discriminant functions form sawing remained capacity model.After the training of potential-energy function disaggregated model completes, compare the accuracy of testing model with potential-energy function disaggregated model test for identification sample and actual sawing load three axis force.
Described acoustic emission signal is through potential-energy function disaggregated model Intelligent Recognition, be sent to human-computer interface module eventually through man-machine interface, human-computer interface module converts the acoustic emission signal after potential-energy function disaggregated model Intelligent Recognition to actual sawing load value and is presented in man-machine interface.
Claims (1)
1., based on the sawing load detection method of band saw for metal Acoustic Emission Characteristic, it is characterized in that, the method comprises the following steps:
When band saw work runs, sonic sensor detection zone sawing acoustic emission signal, exports analog signals value ai (t); Described analog signals value ai (t) carries out signal amplification, AD conversion via signal acquisition module, wherein said signal acquisition module is furnished with automatic channel/gain scan circuit and filter circuit of pressure-stabilizing, carries out preposition pretreatment to described signal value ai (t);
After described signal acquisition module, output digit signals value x (t) is to signal analyse block; Described signal analyse block is integrated with wavelet analysis method, and corresponding discrete wavelet transformer is changed to
Wherein ψ
j,kt () is wavelet function,
a
0>0 is constant, and j, k ∈ Z, through ψ
j,kt wavelet transformation that () acts on is in fact the frequency band range of signal x (t)
be limited in [ω
*-Δ ω, ω
*+ Δ ω] in sub-band, wavelet transform result is the time domain component in this frequency band, wherein ω
*centered by frequency, Δ ω is 1/2 bandwidth, by wavelet transform, can make Computer Analysis signal when specific, energy feature frequently within the scope of window;
Acoustic emission signal after the wavelet analysis in signal analyse block, can extract acoustic emission energy characteristic vector, and the potential-energy function disaggregated model of recycling signal analyse block, can carry out Intelligent Recognition to sawing load; The sawing remained capacity of acoustic emission signal adopts potential-energy function disaggregated model, and energy feature vector table is shown as [X
k], wherein k=1,2 ... N, the potential-energy function of any one energy feature vector is expressed as
wherein λ
ifor or not the real number of 0,
for normalized orthogonal functions, the close special orthogonal function in Hull is utilized to construct
recurrence formula be H
k+1(X)-2XH
k(X)+2KH
k-1(X)=0; Repeatedly will to test in lower different medium unequally loaded sawing acoustic wave energy characteristic set as potential-energy function disaggregated model training sample set (X
1, X
2..., X
k), each sawing acoustic characteristic vector produces corresponding potential-energy function K
i(X), K is obtained
k+1and K (X)
k(X) iterative relation K
k+1(X)=K
k(X)+γ
k+1k (X, X
k-1), the potential-energy function determined through training is exactly discriminant function d
k+1(X)=d
k(X)+γ
k+1k (X, X
k-1), all discriminant functions form sawing remained capacity model; After the training of potential-energy function disaggregated model completes, compare the accuracy of testing model with potential-energy function disaggregated model test for identification sample and actual sawing load three axis force;
Described sonic sensor detection zone sawing acoustic emission signal is through potential-energy function disaggregated model Intelligent Recognition, be sent to human-computer interface module eventually through man-machine interface, human-computer interface module converts the acoustic emission signal after potential-energy function disaggregated model Intelligent Recognition to actual sawing load value and is presented in man-machine interface.
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Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103949727B (en) * | 2014-04-23 | 2016-11-23 | 杭州电子科技大学 | A kind of band saw for metal sawing method based on dipulse characteristic |
| CN104889819B (en) * | 2015-03-19 | 2017-03-08 | 杭州飘哲电子科技有限公司 | Sawing face perpendicularity and saw blade draw detecting system and the method for tooth |
| US9989501B2 (en) * | 2016-05-10 | 2018-06-05 | The Boeing Company | Method and apparatus for acoustic emissions testing |
| CN111113151B (en) * | 2020-01-03 | 2021-05-07 | 湖南泰嘉新材料科技股份有限公司 | Online fault diagnosis method and system in sawing process of bimetal saw band |
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2013
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