CN102172849A - Cutter damage adaptive alarm method based on wavelet packet and probability neural network - Google Patents
Cutter damage adaptive alarm method based on wavelet packet and probability neural network Download PDFInfo
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- CN102172849A CN102172849A CN 201010594595 CN201010594595A CN102172849A CN 102172849 A CN102172849 A CN 102172849A CN 201010594595 CN201010594595 CN 201010594595 CN 201010594595 A CN201010594595 A CN 201010594595A CN 102172849 A CN102172849 A CN 102172849A
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Abstract
The invention discloses a cutter damage adaptive alarm method based on a wavelet packet and a probability neural network. The method comprises the following steps of: fixing an acoustic emission sensor on a cutter bar, acquiring acoustic emission signals, performing three-layer wavelet packet analysis, selecting characteristic frequency bands and taking root mean square values thereof, normalizing the root mean square values to obtain smoothing factors and prior probability, establishing a cutter damage state probability model by using a probability neural network, determining an alarm value of the cutter abrasion state according to the model and the Pauta criterion, forming a dynamic alarm line, and performing adaptive alarm monitoring of the cutter operating state according to the dynamic alarm line. By the method, the probability distribution curve of the root mean square value related with the cutter abrasion can be found, the alarm value is determined by using a mathematical statistic method, the dynamic alarm line is formed together with the cutter abrasion state change, and missing alarm and error alarm are not caused.
Description
Technical field
The present invention relates to machine tool status monitoring field, be specifically related to tool failure adaptive alarm method based on wavelet packet analysis and probabilistic neural network modeling.
Background technology
The adaptive alarm technology index that refers to report to the police should be that actual conditions such as condition of work, working time, power, speed along with equipment change and change, its target is to set up the dynamic judge rule of warning index and equipment ruuning situation, forms the dynamic warning curve of a variation.
Because the complexity and the diversity of manufacture process, the life-span of cutter is in a discrete distribution, make many cutters by in advance or postpone with changing, cause the unnecessary waste of cutter because process quality issue, make necessity that cutting tool state detects.Current equipment state alarm technique still is based upon on the static basis of reporting to the police, in case find that parameter has surmounted set-point in advance, just reports to the police immediately or takes appropriate measures.Easy like this appearance fails to report and reports by mistake.
Summary of the invention
In order to overcome the shortcoming of above-mentioned prior art, the object of the present invention is to provide tool failure adaptive alarm method based on WAVELET PACKET DECOMPOSITION and probabilistic neural network, can find out the probability distribution curve of the root-mean-square value relevant with tool wear, determine alarming value with mathematical statistic method, form dynamic alarming line along with cutting-tool wear state changes, can not occur failing to report and reporting by mistake.
In order to achieve the above object, the technical solution used in the present invention is:
Based on wavelet packet and probabilistic neural network tool failure adaptive alarm method, may further comprise the steps:
The first step, with the knife bar position polishing of fixed sound emission sensor, coating butter is fixed to calibrate AE sensor on the knife bar then, adopts the acoustic emission signal data acquisition program based on Labview, gathers acoustic emission signal by pci card earlier;
Second step, the acoustic emission signal that collects is carried out three layers of wavelet packet analysis, each group signal decomposition is gone on eight frequency ranges, i.e. 0～124khz, 125～249khz, 250～499khz, 500～549khz, 550～599khz, 600～649khz, 650～699khz, 700～749khz, wherein two frequency band energy maximums of 125～249khz and 250～499khz are chosen it and are the feature band of signal, and get the root-mean-square value of feature band;
The 3rd step, root-mean-square value is carried out normalized earlier, carry out identical value again and handle, obtain smoothing factor with different value, obtain prior probability with identical value;
The 4th step, all equal based on the prior distribution of each sample of Bayes theory hypothesis, utilize probabilistic neural network to set up tool failure state probability model;
The 5th step, determine the alarming value of cutting-tool wear state according to tool failure state probability model and La Yida criterion, operation along with cutting, historical data increases, alarm threshold value constantly changes, form a dynamic alarming line,, carry out the adaptive alarm monitoring of cutter running status according to this dynamic alarming line.
Because the present invention has adopted calibrate AE sensor, by energy and the probabilistic neural network apparatus for establishing state probability model primary signal gathered, wavelet packet analysis extracts characteristic spectra, utilize historical data to form dynamic alarming line, set up the relation of alarming line and equipment actual motion state, so improved the precision of reporting to the police, had advantages such as real-time detection cutting tool state and the cutter of warning reminding replacing in time.
Description of drawings
Accompanying drawing is a framework flow chart of the present invention.
The specific embodiment
Below in conjunction with accompanying drawing the present invention is described in further detail.
With reference to accompanying drawing,, may further comprise the steps based on wavelet packet and probabilistic neural network tool failure adaptive alarm method:
The first step, with the knife bar position polishing of fixed sound emission sensor, coating butter is fixed to calibrate AE sensor on the knife bar then, adopts the acoustic emission signal data acquisition program based on Labview, gathers acoustic emission signal by pci card earlier;
Second step, the acoustic emission signal that collects is carried out three layers of wavelet packet analysis, each group signal decomposition is gone on eight frequency ranges, i.e. 0～124khz, 125～249khz, 250～499khz, 500～549khz, 550～599khz, 600～649khz, 650～699khz, 700～749khz, wherein two frequency band energy maximums of 125～249khz and 250～499khz are chosen it and are the feature band of signal, and get the root-mean-square value of feature band;
In the 3rd step, the root-mean-square value that calculates is carried out preliminary treatment earlier: normalized is handled with identical value, and the normalized process is:
Wherein: { x
iBe the equipment operation history data, x
iBe the data after the normalization, max (x
i) maximum in being, min (x
i) minimum of a value in being, in identical value was handled, different value was estimated smoothing factor, the method for Cain is adopted in the estimation of smoothing factor, wherein the sample point average minimum range of asking
d
Ij=| x
i-x
j|, in the formula: N is the data number of sample layer, and the smoothing factor in the probabilistic neural network can be expressed as by empirical equation
G=1.1～1.4;
The 4th step, all equal based on the prior distribution of each sample of Bayes theory hypothesis, utilize probabilistic neural network to set up tool failure state probability model, detailed process is: according to condition probability formula
N is the sum of training sample, x
iBe i sample value, δ is a smoothing factor, and x is certain index of state undetermined, calculates the conditional probability under each sample point, then the add up conditional probability of each sample point of summation layer, and the result according to summation constructs the equipment running status probabilistic model at last;
In the 5th step, determine the alarming value of cutting-tool wear state according to the La Yida criterion, along with the operation of cutting, historical data increases, and alarm threshold value constantly changes, and forms a dynamic alarming line, according to this dynamic alarming line, carry out the adaptive alarm monitoring of cutter running status.
Claims (1)
1. based on wavelet packet and probabilistic neural network tool failure adaptive alarm method, it is characterized in that: may further comprise the steps:
The first step, with the knife bar position polishing of fixed sound emission sensor, coating butter is fixed to calibrate AE sensor on the knife bar then, adopts the acoustic emission signal data acquisition program based on Labview, gathers acoustic emission signal by pci card earlier;
Second step, the acoustic emission signal that collects is carried out three layers of wavelet packet analysis, each group signal decomposition is gone on eight frequency ranges, i.e. 0～124khz, 125～249khz, 250～499khz, 500～549khz, 550～599khz, 600～649khz, 650～699khz, 700～749khz, wherein two frequency band energy maximums of 125～249khz and 250～499khz are chosen it and are the feature band of signal, and get the root-mean-square value of feature band;
The 3rd step, root-mean-square value is carried out normalized earlier, carry out identical value again and handle, obtain smoothing factor with different value, obtain prior probability with identical value;
The 4th step, all equal based on the prior distribution of each sample of Bayes theory hypothesis, utilize probabilistic neural network to set up tool failure state probability model;
The 5th step, determine the alarming value of cutting-tool wear state according to tool failure state probability model and La Yida criterion, operation along with cutting, historical data increases, alarm threshold value constantly changes, form a dynamic alarming line,, carry out the adaptive alarm monitoring of cutter running status according to this dynamic alarming line.
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Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102689230A (en) * | 2012-05-09 | 2012-09-26 | 天津大学 | Tool wear condition monitoring method based on conditional random field model |
| CN103439413A (en) * | 2013-08-07 | 2013-12-11 | 湘潭大学 | Acoustic emission signal analytical method for automatically identifying damage mode of thermal barrier coating |
| CN103465107A (en) * | 2013-09-24 | 2013-12-25 | 沈阳利笙电子科技有限公司 | Tool wear monitoring method |
| CN105033763A (en) * | 2015-09-02 | 2015-11-11 | 华中科技大学 | Method for predicting abrasion state of numerically-controlled machine tool ball screw |
| CN105196114A (en) * | 2015-11-05 | 2015-12-30 | 西安科技大学 | Real-time online tool wear monitoring method based on wavelet analysis and neural network |
| CN105225223A (en) * | 2015-08-27 | 2016-01-06 | 南京市计量监督检测院 | Based on the damage Detection of Smart Composite Structure method of wavelet analysis and BP neural network |
| CN106334969A (en) * | 2016-10-31 | 2017-01-18 | 南开大学 | Cutter life estimation method for cutting power tool |
| CN106446494A (en) * | 2016-05-11 | 2017-02-22 | 新疆大学 | Wavelet packet-neural network-based wind/photovoltaic power prediction method |
| CN107350900A (en) * | 2017-07-06 | 2017-11-17 | 西安交通大学 | A kind of tool condition monitoring method based on the extraction of chip breaking time |
| CN107690660A (en) * | 2016-12-21 | 2018-02-13 | 深圳前海达闼云端智能科技有限公司 | Image-recognizing method and device |
| CN108723895A (en) * | 2018-05-25 | 2018-11-02 | 湘潭大学 | A kind of signal dividing method monitored in real time for drilling machining state |
| TWI640390B (en) * | 2017-03-24 | 2018-11-11 | 國立成功大學 | Tool wear monitoring and predicting method |
| CN108942409A (en) * | 2018-08-26 | 2018-12-07 | 西北工业大学 | The modeling and monitoring method of tool abrasion based on residual error convolutional neural networks |
| CN111024820A (en) * | 2019-12-19 | 2020-04-17 | 南通大学 | Health monitoring system for offshore wind power blade and data processing method thereof |
| CN111331429A (en) * | 2020-03-12 | 2020-06-26 | 中国民航大学 | Cutter wear state monitoring method and device based on wavelet packet energy analysis |
| CN111590390A (en) * | 2020-04-27 | 2020-08-28 | 黄河水利职业技术学院 | Cutter wear state real-time assessment method and system, storage medium and terminal |
| CN114833636A (en) * | 2022-04-12 | 2022-08-02 | 安徽大学 | Cutter wear monitoring method based on multi-feature space convolution neural network |
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| CN102689230A (en) * | 2012-05-09 | 2012-09-26 | 天津大学 | Tool wear condition monitoring method based on conditional random field model |
| CN103439413A (en) * | 2013-08-07 | 2013-12-11 | 湘潭大学 | Acoustic emission signal analytical method for automatically identifying damage mode of thermal barrier coating |
| CN103439413B (en) * | 2013-08-07 | 2015-11-18 | 湘潭大学 | A kind of Analysis of Acoustic Emission Signal method that heat barrier coatings damage pattern identifies automatically |
| CN103465107A (en) * | 2013-09-24 | 2013-12-25 | 沈阳利笙电子科技有限公司 | Tool wear monitoring method |
| CN105225223A (en) * | 2015-08-27 | 2016-01-06 | 南京市计量监督检测院 | Based on the damage Detection of Smart Composite Structure method of wavelet analysis and BP neural network |
| CN105033763A (en) * | 2015-09-02 | 2015-11-11 | 华中科技大学 | Method for predicting abrasion state of numerically-controlled machine tool ball screw |
| CN105196114A (en) * | 2015-11-05 | 2015-12-30 | 西安科技大学 | Real-time online tool wear monitoring method based on wavelet analysis and neural network |
| CN106446494A (en) * | 2016-05-11 | 2017-02-22 | 新疆大学 | Wavelet packet-neural network-based wind/photovoltaic power prediction method |
| CN106446494B (en) * | 2016-05-11 | 2019-01-11 | 新疆大学 | Honourable power forecasting method based on wavelet packet-neural network |
| CN106334969A (en) * | 2016-10-31 | 2017-01-18 | 南开大学 | Cutter life estimation method for cutting power tool |
| CN106334969B (en) * | 2016-10-31 | 2018-08-10 | 南开大学 | A kind of cutter life method of estimation for cutting power tool |
| CN107690660A (en) * | 2016-12-21 | 2018-02-13 | 深圳前海达闼云端智能科技有限公司 | Image-recognizing method and device |
| CN107690660B (en) * | 2016-12-21 | 2021-03-23 | 达闼机器人有限公司 | Image recognition method and device |
| TWI640390B (en) * | 2017-03-24 | 2018-11-11 | 國立成功大學 | Tool wear monitoring and predicting method |
| CN107350900A (en) * | 2017-07-06 | 2017-11-17 | 西安交通大学 | A kind of tool condition monitoring method based on the extraction of chip breaking time |
| CN108723895A (en) * | 2018-05-25 | 2018-11-02 | 湘潭大学 | A kind of signal dividing method monitored in real time for drilling machining state |
| CN108942409A (en) * | 2018-08-26 | 2018-12-07 | 西北工业大学 | The modeling and monitoring method of tool abrasion based on residual error convolutional neural networks |
| CN111024820A (en) * | 2019-12-19 | 2020-04-17 | 南通大学 | Health monitoring system for offshore wind power blade and data processing method thereof |
| CN111024820B (en) * | 2019-12-19 | 2023-01-31 | 南通大学 | Health monitoring system for offshore wind power blade and data processing method thereof |
| CN111331429A (en) * | 2020-03-12 | 2020-06-26 | 中国民航大学 | Cutter wear state monitoring method and device based on wavelet packet energy analysis |
| CN111590390A (en) * | 2020-04-27 | 2020-08-28 | 黄河水利职业技术学院 | Cutter wear state real-time assessment method and system, storage medium and terminal |
| CN111590390B (en) * | 2020-04-27 | 2021-08-27 | 黄河水利职业技术学院 | Cutter wear state real-time assessment method and system, storage medium and terminal |
| CN114833636A (en) * | 2022-04-12 | 2022-08-02 | 安徽大学 | Cutter wear monitoring method based on multi-feature space convolution neural network |
| CN114833636B (en) * | 2022-04-12 | 2023-02-28 | 安徽大学 | Cutter wear monitoring method based on multi-feature space convolution neural network |
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Application publication date: 20110907 |