CN103264317B - A kind of appraisal procedure of Milling Process cutter operational reliability - Google Patents
A kind of appraisal procedure of Milling Process cutter operational reliability Download PDFInfo
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Abstract
The invention discloses a kind of appraisal procedure of Milling Process cutter operational reliability.The present invention first gathers machining tool main shaft and feed shaft three-phase current signal by Hall element, then extracts time domain characteristics of mean and tooth frequency energy feature by wavelet analysis.For these characteristic vectors, pass through
mean algorithm, obtains observation vector output function: Gaussian mixtures density function.Then adopt Bao Mu-Wei Erqi (Baum-Welch) Algorithm for Training " hidden-semi-Markov " model continuously, obtain parameter estimation result, adopt Qie Puman-Andrei Kolmogorov differential equation, calculate Milling Process cutter operational reliability level.The present invention provides decision support for the preventative maintenance of Milling Process cutter.
Description
Technical field
The present invention relates to a kind of appraisal procedure of Milling Process cutter operational reliability.
Technical background
In modern manufacturing, cutting tool state is to guarantee crudy and boost productivity most important.But tool wear work in-process is inevitable again, it directly can affect machining accuracy and the surface roughness of workpiece, not only reduces crudy, also can affect the safety of system of processing and normal operation time serious.Therefore, good cutting tool state has become the necessary condition in modern mechanical manufacturing and automation processing, and it is also the key technology ensureing processing work quality.And artificial cognition cutting tool state has become the important bottleneck of restriction process industry development, therefore, in the urgent need to a kind of new method of carrying out intellectual monitoring and automatic evaluation for cutter running status of design and development, it does not need to wait for that Milling Process cutter breaks down through long-play, just can analyze exactly, assess Milling Process Tool Reliability change conditions, thus instruct user to take rational preventive measure in advance, prevent from affecting processing work quality and system of processing safe operation because of cutter problem.
Summary of the invention
For solving in prior art the above-mentioned technical problem existed, the invention provides a kind ofly can provide the Milling Process cutter Analysis of Running Reliability method of decision support for preventative maintenance.
The technical scheme solved the problems of the technologies described above comprise the following steps into:
1) according to design data and the use history of Milling Process cutter, its running status sum M is determined; Milling Process cutter running status set representations is S={s
1, s
2... s
m... s
m, s
mfor Milling Process cutter is entirely ineffective.
2) for milling cutter processing operating mode, record cutting parameter, utilizes Hall element to gather machine tool chief axis and feed shaft three-phase current signal, and the average therefrom in extraction time domain and the energy value of tooth frequency signal are as characteristic quantity; Specifically comprise: 1. frequency analysis, carry out wavelet decomposition to spindle motor of machine tool current signal, the signal extracting tooth frequency content is reconstructed, and obtains tooth frequency curve, carries out the extraction of energy eigenvalue; 2. time-domain analysis: get feed shaft current signal three-phase current root-mean-square value and carry out wavelet analysis, extracts the characteristic quantity in signal time domain.
3) according to Milling Process cutter running status sum M, the gauss of distribution function number k corresponding to each Milling Process cutter running status is determined
m; For a certain running status of Milling Process cutter, the characteristic signal of above-mentioned collection is averaged segmentation, and the parameter belonging to a section in characteristic signal is formed a large matrix, K mean cluster is carried out to the vector of each section, obtains the continuous mixed Gaussian probability density function corresponding to certain state of Milling Process cutter
wherein:
for t observation vector, k
mfor state s
mthe number of corresponding Gaussian component,
for Gaussian Profile Mean Parameters, Σ
mkfor Gaussian Profile covariance matrix, c
mkstate s
mthe weight of corresponding each Gaussian component; Repeat step 3), obtain the continuous mixed Gaussian probability density function of all running statuses of Milling Process cutter.
4) continuous hidden Semi-Markov Process is adopted to carry out reliability level estimation to cutter running status; First to the given initial value of continuous hidden Semi-Markov Process CHSMM, then adopt Baum-Welch algorithm to model training, complete parameter Estimation, carry out Milling Process cutter operational reliability level calculation;
Wherein, state duration model adopts Gamma distribution, namely
v
mfor form parameter, w
mfor scale parameter; Observation model adopts Gaussian mixtures, namely
for t observation vector, k
mfor state s
mthe number of corresponding Gaussian component,
for Gaussian Profile Mean Parameters, Σ
mkfor Gaussian Profile covariance matrix, c
mkstate s
mthe weight of corresponding each Gaussian component.
Above-mentioned steps 3) detailed process as follows:
1. model definition: being expressed as of continuous hidden-semi-Markov model
wherein, M is cutter running status sum, and m is Milling Process cutter running status, initial state distribution π
m=(π
1..., π
m), state transition probability matrix a
mnrepresent that cutter is from motion state s
mjump to motion state s
nprobability.
2. model training: adopt Bao Mu-Wei Erqi (Baum-Welch) algorithm to model training, namely solve the Parameter Estimation Problem of model, obtain model parameter
estimate
successively all running statuses of cutter are trained, obtain often kind of running status hidden-semi-Markov model.
3. operation reliability evaluation: after model training completes, according to the result of model parameter estimation, adopts Qie Puman-Andrei Kolmogorov differential equation, calculates Milling Process cutter operational reliability level.
Beneficial effect of the present invention has: the present invention is by gathering machining tool spindle motor current and feed shaft current signal, adopt wavelet analysis method to extract time domain and the frequency domain character information of above-mentioned signal, recycling continuous hidden Semi-Markov Process and Qie Puman-Andrei Kolmogorov differential equation calculate Milling Process cutter operational reliability level.This maintenance decision analysis being Milling Machining Center cutter provides important reference information.
Accompanying drawing explanation
Fig. 1 is flow chart of the present invention;
Fig. 2 is machine tool chief axis current signal time domain average time domain waveform in the present invention;
Fig. 3 is machine tool chief axis current signal frequency-domain waveform in the present invention;
Fig. 4 is the reliable linearity curve of Milling Process cutter of the present invention.
Detailed description of the invention
Below in conjunction with drawings and Examples, the present invention is described in more detail.First the inventive method gathers machining tool main shaft and feed shaft current signal by Hall element, then wavelet analysis method is adopted to carry out feature extraction, then parameter Estimation is carried out by continuous hidden Semi-Markov Process, and adopt Qie Puman-Andrei Kolmogorov differential equation to calculate Milling Process cutter operational reliability level, thus provide decision support for preventative maintenance.
As shown in Figure 1, the present invention includes following steps:
First step determination Milling Process cutter running status.
According to design data and the use history of Milling Process cutter, determine its running status sum M.Milling Process cutter running status set representations is S={s
1, s
2... s
m... s
m, s
mfor Milling Process cutter is entirely ineffective.
Second step Milling Process cutter running status Characteristic Extraction.
Utilize Hall element to gather machining tool main shaft and feed shaft three-phase current signal, the group number of sampling interval and each collection signal can be determined according to the actual conditions of enterprise.Then the energy value of average in time domain and tooth frequency signal is therefrom extracted as characteristic quantity.Detailed process is: (1) frequency analysis, carries out wavelet decomposition to spindle motor of machine tool current signal, and the signal extracting tooth frequency content is reconstructed, and obtains tooth frequency curve, carries out the extraction of energy eigenvalue.(2) time-domain analysis: get feed shaft current signal three-phase current root-mean-square value and carry out wavelet analysis, extracts the characteristic quantity in signal time domain.
3rd step observation sample sequence probability density function extracts.
According to Milling Process cutter running status sum M, determine the gauss of distribution function number k corresponding to each Milling Process cutter running status
m.For a certain running status of Milling Process cutter, the characteristic signal of above-mentioned collection is averaged segmentation, and the parameter belonging to a section in characteristic signal is formed a large matrix, K mean cluster is carried out to the vector of each section, calculates three key parameter c
mk,
and Σ
mk, obtain the continuous mixed Gaussian probability density function corresponding to certain state of Milling Process cutter
the like, obtain the continuous mixed Gaussian probability density function of all running statuses of Milling Process cutter.
4th step Milling Process estimating operation reliability of tool.
HSMM (HiddenSemi-MarkovModel, hidden-semi-Markov model) is the expansion of HMM (HiddenMarkovModel, hidden Markov model).Be the limitation of exponential distribution to improve state duration in hidden Markov model, on the basis of hidden Markov model, hidden-semi-Markov model allows according to the self-defined residence time distribution of practical problem.From the use history of Milling Process cutter, adopt Gamma distribution as state duration probability-distribution function.Meanwhile, observation sequence output function adopts Gaussian mixtures to carry out matching.
Step (a1): model training.First, initialize continuous hidden Semi-Markov Process parameter, wherein initial state distribution adopts and is uniformly distributed, and sets iterations and convergence error.Then adopt Baum-Welch algorithm to model training, obtain model parameter
estimate
successively all running statuses of cutter are trained, obtain the continuous hidden-semi-Markov model of often kind of running status.
Step (a2): Milling Process estimating operation reliability of tool.Make P
j(t)=P (q
t=s
j) represent that being equipped in t is in s
jshape probability of state.P'(t is had according to Qie Puman-Andrei Kolmogorov differential equation)=P (t) A, wherein P (t)=(p
0(t), P
1(t) ..., P
k(t), P
k+1(t)) be state vector, P'(t) be the first differential state vector of P (t),
for state-transition matrix.Laplace transformation is carried out to the differential equation, can obtain:
Wherein Milling Process cutter is in normal condition when primary condition, can obtain P (0)=(p
0(0), P
1(0) ..., P
k(0), P
k+1(0))=(1,0,0,0).
There is P (s)=(p
0(s), P
1(s) ..., P
k(s), P
k+1(s)), Laplace Transform is carried out to P (s) and obtains being equipped in probability P (t)=(p that t is in different conditions
0(t), P
1(t) ..., P
k(t), P
k+1(t)), thus reliability R (the t)=1-P of Milling Process cutter t can be calculated
k+1(t).
The present invention below in conjunction with object lesson makes further elaboration:
1: according to certain Milling Process Tool Design data and use history, determine that its running status adds up to 4, s
1for normal condition, s
2for minor degradation state, s
3for severe deterioration state, s
4for total failure mode.
2: this cutter is actual adds man-hour, cutting parameter is speed of mainshaft 130r/min, feed speed 140mm/min, cutting depth 1mm, and slotting cutter is processed, 6 teeth, sample frequency 1000Hz.Sampling interval is 48 hours, sampling total time T=960 hour.Wavelet analysis is adopted to carry out time domain and the extraction of frequency domain character amount.Fig. 2 is machine tool chief axis current signal time domain average time domain waveform; Fig. 3 is machine tool chief axis current signal frequency-domain waveform;
3: be equally divided into four sections to above-mentioned characteristic signal, corresponding 4 gauss of distribution function of each state, adopt K mean algorithm, obtain observation vector probability density function: mixed Gaussian density function.For saving space, here only list the some time and carve t, cutter is in the Gaussian mixtures function parameter corresponding to normal condition: weight parameter matrix is weight=[0.3450.2870.1290.239], Mean Matrix mean=[0.00990.00790.55680.3234], covariance matrix cov=[3.94483.86486.39457.4736].
4: Milling Process estimating operation reliability of tool, first to the given initial value of continuous hidden Semi-Markov Process CHSMM, if initial state distribution is for being uniformly distributed, cutter running status sum M=4.Then adopt Baum-Welch algorithm to model training, complete parameter Estimation, carry out Milling Process cutter operational reliability level calculation, Fig. 4 is milling cutter operational reliability horizontal line graph for this reason.
The present invention is not only confined to above-mentioned detailed description of the invention; persons skilled in the art are according to content disclosed by the invention; other multiple detailed description of the invention can be adopted to implement the present invention; therefore; every employing project organization of the present invention and thinking; do the design that some simply change or change, all fall into the scope of protection of the invention.
Claims (4)
1. an appraisal procedure for Milling Process cutter operational reliability, the steps include:
1) according to design data and the use history of Milling Process cutter, its running status sum M is determined; Milling Process cutter running status set representations is S={s
1, s
2... s
m... s
m, s
mfor Milling Process cutter is entirely ineffective;
2) for milling cutter processing operating mode, record cutting parameter, utilizes sensor to gather machine tool chief axis and feed shaft three-phase current signal, and the average therefrom in extraction time domain and the energy value of tooth frequency signal are as characteristic quantity;
3) according to Milling Process cutter running status sum M, the gauss of distribution function number k corresponding to each Milling Process cutter running status is determined
m, m is Milling Process cutter running status s
msubscript, be used for representing s
m; For a certain running status of Milling Process cutter, the characteristic signal of above-mentioned collection is averaged segmentation, and the parameter belonging to a section in characteristic signal is formed a large matrix, K mean cluster is carried out to the vector of each section, obtains the continuous mixed Gaussian probability density function corresponding to certain state of Milling Process cutter
wherein:
for t observation vector, t is the moment, k
mfor state s
mcorresponding gauss of distribution function number,
for Gaussian Profile Mean Parameters, Σ
mkfor Gaussian Profile covariance matrix, c
mkstate s
mthe weight of corresponding each Gaussian component; Repeat step 3), obtain the continuous mixed Gaussian probability density function of all running statuses of Milling Process cutter;
4) continuous hidden Semi-Markov Process is adopted to carry out reliability level estimation to cutter running status; First to the given initial value of continuous hidden Semi-Markov Process CHSMM, then adopt Baum-Welch algorithm to model training, complete parameter Estimation, carry out Milling Process cutter operational reliability level calculation;
Wherein, state duration model adopts Gamma distribution, namely
v
mfor form parameter, w
mfor scale parameter; Observation model adopts Gaussian mixtures, namely
2. the appraisal procedure of a kind of Milling Process cutter operational reliability as claimed in claim 1, is characterized in that: step 2) comprising:
1) frequency analysis, carries out wavelet decomposition to spindle motor of machine tool current signal, and the signal extracting tooth frequency content is reconstructed, and obtains tooth frequency curve, carries out the extraction of energy eigenvalue;
2) time-domain analysis: get feed shaft current signal three-phase current root-mean-square value and carry out wavelet analysis, extracts the characteristic quantity in signal time domain.
3. the appraisal procedure of a kind of Milling Process cutter operational reliability as claimed in claim 1, is characterized in that: step 2) described in sensor be Hall element.
4. the appraisal procedure of a kind of Milling Process cutter operational reliability as described in claim arbitrary in claim 1-3, is characterized in that: step 4) detailed process as follows:
1) model definition: being expressed as of continuous hidden-semi-Markov model
wherein, the quantity of hidden state and cutter running status is M, initial state distribution π
m=(π
1..., π
m); State transition probability matrix a
mnrepresent that cutter is from motion state s
mjump to motion state s
nprobability, c
mkstate s
mthe weight of corresponding each Gaussian component,
for Gaussian Profile Mean Parameters, Σ
mkfor Gaussian Profile covariance matrix, v
mfor form parameter, w
mfor scale parameter;
2) model training: adopt Bao Mu-Wei Erqi (Baum-Welch) algorithm to model training, namely solve the Parameter Estimation Problem of model, obtain model parameter
estimate
successively all running statuses of cutter are trained, obtain often kind of running status hidden-semi-Markov model;
3) operation reliability evaluation: after model training completes, according to the result of model parameter estimation, adopts Qie Puman-Andrei Kolmogorov differential equation, calculates Milling Process cutter operational reliability level.
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