CN106021906B - Flutter on-line monitoring method based on cepstrum analysis - Google Patents
Flutter on-line monitoring method based on cepstrum analysis Download PDFInfo
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- CN106021906B CN106021906B CN201610323930.XA CN201610323930A CN106021906B CN 106021906 B CN106021906 B CN 106021906B CN 201610323930 A CN201610323930 A CN 201610323930A CN 106021906 B CN106021906 B CN 106021906B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
- B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
- B23Q17/12—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring vibration
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Z—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
- G16Z99/00—Subject matter not provided for in other main groups of this subclass
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
- B23Q2717/00—Arrangements for indicating or measuring
- B23Q2717/006—Arrangements for indicating or measuring in milling machines
Abstract
The present invention provides a kind of flutter on-line monitoring method based on cepstrum analysis, the acceleration signal in vertical milling cutting process, which is acquired, by the acceleration transducer being mounted on the main shaft of vertical knee-type milling machine is expressed as X=[x (1), x (2), ..., x (n)], n indicates signal length, the acceleration signal includes steady state signal, transition state signal and chatter state signal, this flutter on-line monitoring method based on cepstrum analysis is based on WAVELET PACKET DECOMPOSITION and cepstrum analysis method, first, discrete wavelet packet decomposition is carried out to the acceleration signal that cutting experiment obtains, each frequency band signals after reconstruct decomposition, calculate each frequency band signals energy, determine that frequency domain occurs for flutter；Then, frequency band signals are occurred to flutter and carry out cepstrum analysis, using cepstrum root mean square as flutter feature, it can find the sign of cutting-vibration earlier compared with conventional monitoring chatter method, accurate identification is realized at the eve that violent flutter occurs, and prevents violent vibration from causing irremediable damage to workpiece and machine tool component.
Description
Technical field
The present invention relates to machine tooling technical field more particularly to a kind of flutter on-line monitoring sides based on cepstrum analysis
Method.
Background technique
The mechanism study of cutting-vibration was can be traced earliest to nineteen forty-six, in research initial stage, it is believed that flutter is led
Being attributed to cutting system, there are negative dampings.With the continuous deepening of research, discovery cutting-vibration is mainly by regeneration and mode coupling
Group photo rings and causes, and wherein regeneration effect has become the main reason for flutter occurs, and usually said flutter refers to that regeneration is quivered more
Vibration.Flutter during End Milling Process can seriously affect workpiece surface quality and material removing rate, aggravate tool wear and deterioration
Working environment.Although most of monitoring chatter system can monitor flutter, to workpiece and knife when flutter occurs
Tool produces serious damage, and therefore, it is necessary to monitor flutter feature in advance.Non-linear due to process causes vibration to be believed
Number frequency content complexity, single Time-Frequency Analysis Method are difficult to obtain reliable flutter feature.
Summary of the invention
The technical problem to be solved by the present invention is being unable to get reliable flutter to solve existing monitoring chatter method
The problem of feature, the present invention provides a kind of flutter on-line monitoring methods based on cepstrum analysis to solve the above problems.
The technical solution adopted by the present invention to solve the technical problems is: a kind of flutter based on cepstrum analysis is supervised online
Survey method, comprising the following steps:
S1, the acceleration in vertical milling cutting process is acquired by the acceleration transducer that is mounted on the main shaft of vertical knee-type milling machine
Signal is expressed as X=[x (1), x (2) ..., x (n)], and n indicates signal length, and the acceleration signal includes stable state letter
Number, transition state signal and chatter state signal；
S2, WAVELET PACKET DECOMPOSITION is carried out to chatter state signal, obtains wavelet packet coefficient, then pass through wavelet packet inverse transformation weight
The chatter state signal of each frequency range of structure:
Wherein, cj0,kFor in scale j0On approximation wavelet coefficients；dj,kFor in j0And its specific small echo on following scale
Coefficient；
S3, cepstrum analysis is carried out to the chatter state signal after above-mentioned reconstruct, extracts the root-mean-square value of scramble spectral amplitude ratio
Minimum value rminWith maximum value rmax；
S4, WAVELET PACKET DECOMPOSITION is carried out to the collected acceleration signal, obtains wavelet packet coefficient, then passes through small echo
Packet inverse transformation reconstructs the acceleration signal of each frequency range, then carries out cepstrum analysis, extracts the root-mean-square value of scramble spectral amplitude ratio:
Wherein, piI-th of discrete scramble spectral amplitude ratio of frequency band signals occurs for flutter, n is the discrete points of cepstrum；
S5, generalThe on-line monitoring of flutter is carried out as flutter feature, ifIllustrate to enter flutter
State.
The invention has the advantages that this flutter on-line monitoring method based on cepstrum analysis is based on WAVELET PACKET DECOMPOSITION
It is reconstructed after decomposing with cepstrum analysis method firstly, carrying out discrete wavelet packet decomposition to the acceleration signal that cutting experiment obtains
Each frequency band signals, calculate each frequency band signals energy, determine flutter occur frequency domain；Then, frequency band signals are occurred to flutter
Cepstrum analysis is carried out, the cepstrum that frequency band signals occur for flutter is obtained, using cepstrum root mean square as flutter feature, with routine
Monitoring chatter method can find the sign of cutting-vibration earlier compared to it, realized at the eve that violent flutter occurs accurate
Identification, prevents violent vibration from causing irremediable damage to workpiece and machine tool component.
Detailed description of the invention
Present invention will be further explained below with reference to the attached drawings and examples.
Fig. 1 is that the present invention is based on the small echo tree structure diagrams that the flutter on-line monitoring method of cepstrum analysis uses.
Fig. 2 is the signal graph after the wavelet package reconstruction of chatter state signal.
Fig. 3 is the spectrogram of chatter state signal reconstruction signal.
Fig. 4 is the scramble spectrogram of each status signal in acceleration signal.
Fig. 5 is the trend chart of cepstrum accounting coefficient.
Fig. 6 is the schematic diagram that wavelet packet is successively decomposed according to wavelet tree.
Specific embodiment
The embodiment of the present invention is described below in detail, examples of the embodiments are shown in the accompanying drawings, wherein from beginning to end
Same or similar label indicates same or similar element or element with the same or similar functions.Below with reference to attached
The embodiment of figure description is exemplary, and for explaining only the invention, and is not considered as limiting the invention.On the contrary, this
The embodiment of invention includes all changes fallen within the scope of the spiritual and intension of attached claims, modification and is equal
Object.
In the description of the present invention, it is to be understood that, term " center ", " longitudinal direction ", " transverse direction ", " length ", " width ",
" thickness ", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom" "inner", "outside", " axial direction ",
The orientation or positional relationship of the instructions such as " radial direction ", " circumferential direction " is to be based on the orientation or positional relationship shown in the drawings, merely to just
In description the present invention and simplify description, rather than the device or element of indication or suggestion meaning must have a particular orientation, with
Specific orientation construction and operation, therefore be not considered as limiting the invention.
In addition, term " first ", " second " etc. are used for description purposes only, it is not understood to indicate or imply relatively important
Property.In the description of the present invention, it should be noted that unless otherwise clearly defined and limited, term " connected ", " connection " are answered
It is interpreted broadly, for example, it may be being fixedly connected, may be a detachable connection, or be integrally connected；It can be mechanical connect
It connects, is also possible to be electrically connected；It can be directly connected, it can also be indirectly connected through an intermediary.For the common of this field
For technical staff, the concrete meaning of above-mentioned term in the present invention can be understood with concrete condition.In addition, in description of the invention
In, unless otherwise indicated, the meaning of " plurality " is two or more.
The present invention provides a kind of flutter on-line monitoring method based on cepstrum analysis, comprising the following steps:
S1, the acceleration in vertical milling cutting process is acquired by the acceleration transducer that is mounted on the main shaft of vertical knee-type milling machine
Signal is expressed as X=[x (1), x (2) ..., x (n)], and n indicates signal length, and acceleration signal includes steady state signal, mistake
Cross status signal and chatter state signal；
S2, WAVELET PACKET DECOMPOSITION is carried out to chatter state signal, obtains wavelet packet coefficient,
Define the filter used:
G [n]: low-pass filter can filter the high frequency part of input signal and output low frequency part.
H [n]: high-pass filter, with low-pass filter on the contrary, filtering frequency component and exporting high frequency part.
↓ Q: desampling fir filter；
WAVELET PACKET DECOMPOSITION is successively decomposed according to Fig. 1 and wavelet tree shown in fig. 6, and WAVELET PACKET DECOMPOSITION not only connects low frequency signal
It is continuous to decompose, high frequency band signal is also similarly decomposed:
The 1st layer in framework:
The 2nd layer in framework:
Then pass through wavelet packetInverse transformation reconstructs quivering for each frequency range
Shake status signal:
Wherein, cj0,kFor in scale j0On approximation wavelet coefficients；dj,kFor in j0And its specific small echo on following scale
Coefficient；
Fig. 2 is the wavelet package reconstruction result of chatter state signal；
Fig. 3 is the frequency spectrum of chatter state signal reconstruction signal；
S3, cepstrum analysis is carried out to the chatter state signal after above-mentioned reconstruct, extracts the root-mean-square value of scramble spectral amplitude ratio
Minimum value rminWith maximum value rmax；
Cepstrum is defined as follows: setting the amplitude spectral density function of time-domain signal x (t) as Sx(f), the scramble of signal x (t)
Spectral function is C (τ):
C (τ)={ F-1[lgSx(f)]}2
τ is inverted frequency in formula, and the big person of τ value is high inverted frequency, indicates rapid fluctuations and intensive harmonics on cepstrum；Conversely, τ value
Small person is low inverted frequency, indicates slowly to fluctuate and sparse harmonics, F on cepstrum-1Represent inverse Fourier transform；
And amplitude cepstrum are as follows:
Due to SxIt (f) is even function, amplitude cepstrum can be write as Cx(τ)=F [lgSx(f)]；
The side frequency of vibration signal can be composed a single spectral line simplified in order to facilitate observation of and distinguished by cepstrum analysis to be vibrated
Signal source and path are conducive to the analysis of vibration signal of the complicated processing process of multisignal source；
S4, WAVELET PACKET DECOMPOSITION is carried out to collected acceleration signal, obtains wavelet packet coefficient, it is then inverse by wavelet packet
Transformation reconstructs the acceleration signal of each frequency range, then carries out cepstrum analysis, and analysis result is as shown in Figure 4, it can be seen that and
Stable state is compared, and the scramble spectral amplitude ratio of chatter state signal increases obvious, scramble spectral amplitude ratio Jie of transition state vibration signal
Between stabilization and chatter state, illustrates to be difficult to the flutter feature identified in time domain and frequency domain, only pass through amplitude in cepstrum
It can distinguish well；
Then the root-mean-square value of scramble spectral amplitude ratio is extracted:
Wherein, piI-th of discrete scramble spectral amplitude ratio of frequency band signals occurs for flutter, n is the discrete points of cepstrum；
S5, generalThe on-line monitoring of flutter is carried out as flutter feature, ifIllustrate to enter flutter
State.
In order to verify the validity of flutter feature in this monitoring method, the variation tendency of T is calculated, as shown in figure 5, steady
Surely it cuts the stage, flutter feature T overall variation amplitude is little, is maintained at 16% or so.Into transition stage, flutter feature T is opened
Beginning is increased rapidly to 60% or so, increases to flutter always and occurs completely.The cepstrum accounting coefficient in flutter stage about maintains
95% or so.The above analysis finds that, in the different phase of cutting process, cepstrum accounting index variation is obvious, is suitable as
Flutter feature is to realize the early prediction of flutter.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is included at least one embodiment or example of the invention.In the present specification, not to the schematic representation of the term
Centainly refer to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be any
One or more embodiment or examples in can be combined in any suitable manner.
Taking the above-mentioned ideal embodiment according to the present invention as inspiration, through the above description, relevant staff is complete
Various changes and amendments can be carried out without departing from the scope of the technological thought of the present invention' entirely.The technology of this invention
Property range is not limited to the contents of the specification, it is necessary to which the technical scope thereof is determined according to the scope of the claim.
Claims (1)
1. a kind of flutter on-line monitoring method based on cepstrum analysis, which comprises the following steps:
S1, the acceleration signal in vertical milling cutting process is acquired by the acceleration transducer that is mounted on the main shaft of vertical knee-type milling machine
It is expressed as X=[x (1), x (2) ..., x (n)], n indicates signal length, and the acceleration signal includes steady state signal, mistake
Cross status signal and chatter state signal；
S2, WAVELET PACKET DECOMPOSITION is carried out to chatter state signal, obtains wavelet packet coefficient, it is then each by wavelet packet inverse transformation reconstruct
The chatter state signal of a frequency range:
Wherein, t is time series；J is the continuous scale parameter of WAVELET PACKET DECOMPOSITION；K is the continuous location parameter of WAVELET PACKET DECOMPOSITION；
cj0,kFor in scale j0, the approximation wavelet coefficients of position k；dj,kFor in j0And its on following scale, the wavelet coefficient of position k；Letter
NumberFor in scale j0With the small echo on the k of position, ψj,k(t) in j0And its following scale, the small echo on the k of position；
S3, cepstrum analysis is carried out to the chatter state signal after above-mentioned reconstruct, extracts the root-mean-square value of scramble spectral amplitude ratio most
Small value rminWith maximum value rmax；
S4, WAVELET PACKET DECOMPOSITION is carried out to the collected acceleration signal, obtains wavelet packet coefficient, it is then inverse by wavelet packet
Transformation reconstructs the acceleration signal of each frequency range, then carries out cepstrum analysis, extracts the root-mean-square value of scramble spectral amplitude ratio:
Wherein, piI-th of discrete scramble spectral amplitude ratio of frequency band signals occurs for flutter, n is the discrete points of cepstrum；
S5, generalThe on-line monitoring of flutter is carried out as flutter feature, ifIllustrate to enter chatter state.
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Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106564012B (en) * | 2016-11-01 | 2018-08-21 | 苏州微著设备诊断技术有限公司 | A kind of detection method of grinding flutter |
| CN106553084B (en) * | 2016-11-29 | 2019-01-11 | 天津大学 | A kind of lathe flutter on-line monitoring method based on wavelet package transforms and approximate entropy feature |
| TWI629136B (en) * | 2017-07-31 | 2018-07-11 | 鍵和機械股份有限公司 | Method of touch detection |
| CN110010155A (en) * | 2019-04-11 | 2019-07-12 | 中国一拖集团有限公司 | Flutter recognition methods and system based on convolutional neural networks and MFCC |
| CN110555243B (en) * | 2019-08-13 | 2023-11-10 | 中国一拖集团有限公司 | Two-dimensional map construction method for vibration information of machine tool spindle in milling process |
| CN112405072B (en) * | 2020-11-11 | 2022-04-26 | 上海交通大学 | On-line monitoring method and device for cutting chatter of machine tool |
| CN113705421B (en) * | 2021-08-24 | 2022-12-09 | 西安交通大学 | Method and system for online monitoring of vibration marks on surface of grinding workpiece |
| CN115811062B (en) * | 2023-02-13 | 2023-05-02 | 西南交通大学 | Method for quantifying contribution degree of generator set to frequency oscillation of power system |
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