CN106841403A - A kind of acoustics glass defect detection method based on neutral net - Google Patents

A kind of acoustics glass defect detection method based on neutral net Download PDF

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CN106841403A
CN106841403A CN201710053519.XA CN201710053519A CN106841403A CN 106841403 A CN106841403 A CN 106841403A CN 201710053519 A CN201710053519 A CN 201710053519A CN 106841403 A CN106841403 A CN 106841403A
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neural network
signal
neutral net
knocking
detection method
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张涛
唐伟
丁碧云
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Tianjin University
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Tianjin University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/04—Analysing solids
    • G01N29/045—Analysing solids by imparting shocks to the workpiece and detecting the vibrations or the acoustic waves caused by the shocks
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/04—Analysing solids
    • G01N29/048—Marking the faulty objects
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
    • G01N29/4481—Neural networks
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2291/00—Indexing codes associated with group G01N29/00
    • G01N2291/02—Indexing codes associated with the analysed material
    • G01N2291/023—Solids
    • G01N2291/0232—Glass, ceramics, concrete or stone
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2291/00—Indexing codes associated with group G01N29/00
    • G01N2291/02—Indexing codes associated with the analysed material
    • G01N2291/028—Material parameters
    • G01N2291/0289—Internal structure, e.g. defects, grain size, texture

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Immunology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • Health & Medical Sciences (AREA)
  • Pathology (AREA)
  • General Health & Medical Sciences (AREA)
  • Acoustics & Sound (AREA)
  • Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Signal Processing (AREA)
  • Investigating Or Analyzing Materials By The Use Of Ultrasonic Waves (AREA)

Abstract

A kind of acoustics glass defect detection method based on neutral net：The knocking of glass sample is gathered using sound pick-up in actual production environment；Knocking is pre-processed；Feature extraction is carried out to pure knocking；Set the initial parameter of BP neural network：Input of the feature that will be extracted as neutral net, the input layer number for setting BP neural network is 7, the node in hidden layer for setting BP neural network is 15, the output layer nodes for setting BP neural network are 2, set output result as (0,1) represent that glass sample is defective, (1,0) represents glass sample zero defect；BP neural network is trained, the learning rate for setting BP neural network is 0.1, and target square error value is 0.1, and BP neural network is trained using LeVenberg Marquardt algorithms, when the error of neutral net is less than the target square error value for setting, deconditioning.The signal characteristic that the present invention is extracted has good discrimination, can more accurately and efficiently complete glass defect detection task.

Description

A kind of acoustics glass defect detection method based on neutral net
Technical field
The present invention relates to a kind of glass defect detection method.More particularly to a kind of acoustics glass based on neutral net lacks Fall into detection method.
Background technology
Glass is one of most common material, is widely used in each industrial circle, while being also most fragile, most easily damaged One of material.In the generation and transportation of glassware, the defects such as hole, crackle, trachoma can be caused.If can not be Before sales rejects defective product, then will reduce the qualification rate of product, brings certain economic loss.
At present, mainly there are artificial process, Computer Vision Detection method, ultrasound for the lossless detection method of glassware defect Detection method, vibration detection method etc..Artificial process is exactly that product is checked by experienced workman.There is following lacking in the method Point：(1) False Rate is higher.Because the labour intensity of workman is larger, observed for a long time, human eye is easy to fatigue, meeting occur Directly affect the accuracy of classification.(2) whether qualified judgement standard disunity is be during for small defect during hand inspection Examiner's subjective judgement, different people's even criterions of the same people in the state of difference are difficult to accomplish unification.Calculate Machine visual detection method is the image information for using camera to obtain body surface, then detects surface by Digital Image Processing Crackle.The method has a wide range of application, but can only detect surface defect, and is influenceed greatly by ambient lighting.Ultrasonic Detection Method is Using ultrasonic wave defect is detected by being detected the decay of reflected signal and received wave that object is produced.Vibration detection method is analysis The Vibrating modal parameters (vibration shape, amplitude, damping etc.) of object detect defect.
Sound is produced by mechanical oscillation.Gas molecule around vibrating body disturbance is so that air pressure produces the cycle The change of property.This pressure change forms close or thin air wave, and is radiate to the surrounding of object, is formed sound Ripple.Object structures are damaged research and are shown, the damage of object structures must cause the change of rigidity, and this change and damage Type, degree have close relationship.The situation that object is damaged is achieved with by the monitoring of the change to rigidity.However, just Degree is to be difficult to measured directly, so the knocking for passing through object analysis, can reflect the change of rigidity, and then determine to damage Situation.After Crack Damage occurs in vial, the distribution of the time domain energy of knocking, frequency peak and peak are all sent out Change is given birth to, it is possible to judge that vial whether there is defect by analyzing these features of knocking.
Current existing method correlation collection signal mainly using signal in passage on a timeline, but due to sound The unstability of message number, correlation and bad, causes the effect after sparse treatment undesirable on a timeline.
The content of the invention
The technical problems to be solved by the invention are to provide one kind and can more accurately and efficiently complete glass defect detection times The acoustics glass defect detection method based on neutral net of business.
The technical solution adopted in the present invention is：A kind of acoustics glass defect detection method based on neutral net, including Following steps：
1) knocking of glass sample is gathered using sound pick-up in actual production environment；
2) knocking for collecting is pre-processed, including noise reduction, end-point detection and rejecting abnormalities data, obtain pure Net knocking；
3) feature extraction is carried out to pure knocking, including temporal signatures are extracted, frequency domain character is extracted and wavelet field Feature extraction；
4) initial parameter of BP neural network is set：Using step 3) extract feature as neutral net input, set The input layer number of BP neural network is 7, and the node in hidden layer for setting BP neural network is 15, sets BP neural network Output layer nodes be 2, set output result for (0,1) expression glass sample it is defective, (1,0) expression glass sample it is intact Fall into；
5) BP neural network is trained, the learning rate for setting BP neural network is 0.1, target square error value is 0.1, BP neural network is trained using LeVenberg-Marquardt algorithms, when the error of neutral net is less than setting Target square error value when, deconditioning.
Step 2) in, noise reduction is carried out using high-pass filter, using short-time energy and the double threshold of short-time average zero-crossing rate Algorithm carries out end-point detection to tapping voice signal.
Step 3) described in temporal signatures extract include：
(1) average of signal is extracted, using equation below：
Wherein, N is the length of complete knocking, xiIt is i-th amplitude of signaling point,It is the signal average extracted；
(2) the root-mean-square value RMS of signal is extracted, using equation below：
(3) the peak value peak of signal is extracted, using equation below：
Step 3) described in frequency domain character extract：Fourier transformation first is carried out to signal, signal spectrum is obtained, then ask respectively The area and signal spectrum basic frequency of signal spectrum are taken, wherein：
The area of described signal spectrum refers to the area that spectrum signal is surrounded with reference axis；Described signal spectrum dominant frequency Rate refers to the corresponding abscissa value of signal spectrum maximum.
Step 3) described in small echo characteristic of field extraction, be that db4 wavelet package transforms are carried out to signal, using the He of node 3.1 3.3 used as characteristic node, and uses the energy of the signal after reconstruct as small echo characteristic of field.
Step 4) described in setting BP neural network node in hidden layer be 15, be according to Kolomogorov determine Reason is chosen：
nh=2nr+1
In formula, nhIt is hidden layer node number, nrIt is input layer number.
A kind of acoustics glass defect detection method based on neutral net of the invention, the present invention is by analyzing knocking Time domain, frequency domain and small echo characteristic of field change, and sample is learnt and is trained using neutral net, so as to judge glass Glass product whether there is defect, be a kind of more effective spatial audio coding method, using less transmission channel Accurate Reconstruction Go out primary signal.The signal characteristic that the present invention is extracted has good discrimination, and using the non-linear correspondence pass of neutral net Disaggregated model is built by system, can efficiently solve that existing detection method at present is present influenceed by objective environment greatly, detection ties Really undesirable, excessively sensitive to noise the problems such as, more accurately and efficiently complete glass defect detection task.
Brief description of the drawings
Fig. 1 is the flow chart of acoustics glass defect detection method of the present invention based on neutral net.
Specific embodiment
With reference to embodiment and accompanying drawing to a kind of acoustics glass defect detection method based on neutral net of the invention It is described in detail.
As shown in figure 1, a kind of acoustics glass defect detection method based on neutral net of the invention, including following step Suddenly：
1) knocking of glass sample is gathered using sound pick-up in actual production environment；
2) due to there are noise and other inevitable factors in environment, there is noise and different in the data for causing collection Regular signal.In order to obtain pure knocking, it is necessary to the knocking to collecting is pre-processed, including noise reduction, end points Detection and rejecting abnormalities data, obtain pure knocking., wherein, noise reduction is carried out using high-pass filter, using in short-term The double-threshold algorithm of amount and short-time average zero-crossing rate carries out end-point detection to tapping voice signal；
3) feature extraction is carried out to pure knocking, including temporal signatures are extracted, frequency domain character is extracted and wavelet field Feature extraction；Wherein：
Described temporal signatures are extracted to be included：
(1) average of signal is extracted, using equation below：
Wherein, N is the length of complete knocking, xiIt is i-th amplitude of signaling point,It is the signal average extracted；
(2) the root-mean-square value RMS of signal is extracted, using equation below：
(3) the peak value peak of signal is extracted, using equation below：
Described frequency domain character is extracted：Fourier transformation first is carried out to signal, signal spectrum is obtained, then asks for letter respectively The area and signal spectrum basic frequency of number frequency spectrum, wherein：The area of described signal spectrum refers to that spectrum signal encloses with reference axis Into area；Described signal spectrum basic frequency refers to the corresponding abscissa value of signal spectrum maximum.
The extraction of described small echo characteristic of field, is to carry out db4 wavelet package transforms to signal, using the conduct of node 3.1 and 3.3 Characteristic node, and the energy of the signal after reconstruct is used as small echo characteristic of field.
4) initial parameter of BP neural network is set：Using step 3) extract feature as neutral net input because The dimension of eigenmatrix is 7, so the input layer number for setting BP neural network is 7, sets the implicit of BP neural network Node layer number is 15, and system is divided into the defective and class of zero defect two for the identification of sample, therefore sets the defeated of BP neural network It is 2 to go out node layer number, sets output result as (0,1) represents that glass sample is defective, and (1,0) represents glass sample zero defect；
The node in hidden layer of described setting BP neural network is 15, is selected according to Kolomogorov theorems Take：
nh=2nr+1
In formula, nhIt is hidden layer node number, nrIt is input layer number.
5) BP neural network is trained, the learning rate for setting BP neural network is 0.1, target square error value is 0.1, BP neural network is trained using LeVenberg-Marquardt algorithms, when the error of neutral net is less than setting Target square error value when, deconditioning.
In a kind of acoustics glass defect detection method based on neutral net of the invention, duration of knocking compared with It is short, while the real-time in order to meet detecting system, so intercepting 1024 sampled points for each signal.Spy is carried out to signal Levy after extraction, the eigenmatrix dimension of acquisition is 7.According to Kolomogorov theorems and many experiments, selected according to following formula Take the number of hidden layer node：
nh=2nr+1
In formula, nhIt is hidden layer node number, nrIt is input layer number.So, the BP network hidden layer nodes of use Number is 15.BP networks are trained using LeVenberg-Marquardt algorithms, learning rate is 0.1.
The training time of neutral net can be had a huge impact from different algorithms, the present invention attempted it is various not Same convergence algorithm, in these algorithms, LeVenberg-Marquardt convergences of algorithm speed and neural network accuracy are all preferable. Believe that also having more preferable acoustic feature and convergence algorithm can more accurately detect using method used in the present invention Defect sample.

Claims (6)

1. a kind of acoustics glass defect detection method based on neutral net, it is characterised in that comprise the following steps：
1) knocking of glass sample is gathered using sound pick-up in actual production environment；
2) knocking for collecting is pre-processed, including noise reduction, end-point detection and rejecting abnormalities data, obtain pure Knocking；
3) feature extraction is carried out to pure knocking, including temporal signatures are extracted, frequency domain character is extracted and small echo characteristic of field Extract；
4) initial parameter of BP neural network is set：Using step 3) extract feature as neutral net input, set BP god It is 7 through the input layer number of network, the node in hidden layer for setting BP neural network is 15, sets the defeated of BP neural network It is 2 to go out node layer number, sets output result as (0,1) represents that glass sample is defective, and (1,0) represents glass sample zero defect；
5) BP neural network is trained, the learning rate for setting BP neural network is 0.1, target square error value is 0.1, BP neural network is trained using LeVenberg-Marquardt algorithms, when the mesh of the error less than setting of neutral net During mark square error value, deconditioning.
2. a kind of acoustics glass defect detection method based on neutral net according to claim 1, it is characterised in that step It is rapid 2) in, noise reduction is carried out using high-pass filter, using short-time energy and short-time average zero-crossing rate double-threshold algorithm to tap Voice signal carries out end-point detection.
3. a kind of acoustics glass defect detection method based on neutral net according to claim 1, it is characterised in that step It is rapid 3) described in temporal signatures extract include：
(1) average of signal is extracted, using equation below：
x ‾ = 1 N Σ i = 1 N ( x i )
Wherein, N is the length of complete knocking, xiIt is i-th amplitude of signaling point,It is the signal average extracted；
(2) the root-mean-square value RMS of signal is extracted, using equation below：
R M S = 1 N Σ i = 1 N ( x i - x ‾ ) 2
(3) the peak value peak of signal is extracted, using equation below：
p e a k = 1 2 ( m a x ( x i ) - m i n ( x i ) ) .
4. a kind of acoustics glass defect detection method based on neutral net according to claim 1, it is characterised in that step It is rapid 3) described in frequency domain character extract：Fourier transformation first is carried out to signal, signal spectrum is obtained, then asks for signal spectrum respectively Area and signal spectrum basic frequency, wherein：
The area of described signal spectrum refers to the area that spectrum signal is surrounded with reference axis；Described signal spectrum basic frequency is Refer to the corresponding abscissa value of signal spectrum maximum.
5. a kind of acoustics glass defect detection method based on neutral net according to claim 1, it is characterised in that step It is rapid 3) described in small echo characteristic of field extraction, be that db4 wavelet package transforms are carried out to signal, using node 3.1 and 3.3 as feature Node, and the energy of the signal after reconstruct is used as small echo characteristic of field.
6. a kind of acoustics glass defect detection method based on neutral net according to claim 1, it is characterised in that step It is rapid 4) described in the node in hidden layer of setting BP neural network be 15, be to be chosen according to Kolomogorov theorems：
nh=2nr+1
In formula, nhIt is hidden layer node number, nrIt is input layer number.
CN201710053519.XA 2017-01-23 2017-01-23 A kind of acoustics glass defect detection method based on neutral net Pending CN106841403A (en)

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Cited By (15)

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CN108269249A (en) * 2017-12-11 2018-07-10 深圳市智能机器人研究院 A kind of bolt detecting system and its implementation
CN108519149A (en) * 2018-03-28 2018-09-11 长安大学 A kind of tunnel accident monitor and alarm system and method based on sound Time-Frequency Analysis
CN108899048A (en) * 2018-05-10 2018-11-27 广东省智能制造研究所 A kind of voice data classification method based on signal Time-frequency Decomposition
CN109142547A (en) * 2018-08-08 2019-01-04 广东省智能制造研究所 A kind of online lossless detection method of acoustics based on convolutional neural networks
CN110208377A (en) * 2019-06-19 2019-09-06 南京邮电大学 A kind of more characteristic parameters damage degree assessment method based on Lamb wave
CN111044621A (en) * 2018-10-11 2020-04-21 苏州奥科姆自动化科技有限公司 Nondestructive testing system and method based on sound quality and acoustic characteristics
CN111443131A (en) * 2020-04-26 2020-07-24 广州市市政工程试验检测有限公司 Method for detecting grouting compactness of steel bar sleeve
CN111507418A (en) * 2020-04-21 2020-08-07 中国科学技术大学 Encaustic tile quality detection method
CN111783616A (en) * 2020-06-28 2020-10-16 北京瓦特曼科技有限公司 Data-driven self-learning-based nondestructive testing method
CN112185419A (en) * 2020-09-30 2021-01-05 天津大学 Glass bottle crack detection method based on machine learning
CN112255308A (en) * 2020-09-09 2021-01-22 中国大唐集团科学技术研究院有限公司火力发电技术研究院 Bolt knocking detection method based on K-means clustering algorithm
CN112683926A (en) * 2021-01-09 2021-04-20 杭州晶硝子玻璃科技有限公司 Glass defect detection device
CN113671031A (en) * 2021-08-20 2021-11-19 北京房江湖科技有限公司 Wall hollowing detection method and device
CN113706468A (en) * 2021-07-27 2021-11-26 河北光兴半导体技术有限公司 Glass defect detection method based on BP neural network
CN113671031B (en) * 2021-08-20 2024-06-21 贝壳找房(北京)科技有限公司 Wall hollowing detection method and device

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CN108269249A (en) * 2017-12-11 2018-07-10 深圳市智能机器人研究院 A kind of bolt detecting system and its implementation
CN108519149A (en) * 2018-03-28 2018-09-11 长安大学 A kind of tunnel accident monitor and alarm system and method based on sound Time-Frequency Analysis
CN108899048A (en) * 2018-05-10 2018-11-27 广东省智能制造研究所 A kind of voice data classification method based on signal Time-frequency Decomposition
CN109142547B (en) * 2018-08-08 2021-02-23 广东省智能制造研究所 Acoustic online nondestructive testing method based on convolutional neural network
CN109142547A (en) * 2018-08-08 2019-01-04 广东省智能制造研究所 A kind of online lossless detection method of acoustics based on convolutional neural networks
CN111044621B (en) * 2018-10-11 2022-04-26 苏州奥科姆自动化科技有限公司 Nondestructive testing system and method based on sound quality and acoustic characteristics
CN111044621A (en) * 2018-10-11 2020-04-21 苏州奥科姆自动化科技有限公司 Nondestructive testing system and method based on sound quality and acoustic characteristics
CN110208377A (en) * 2019-06-19 2019-09-06 南京邮电大学 A kind of more characteristic parameters damage degree assessment method based on Lamb wave
CN111507418A (en) * 2020-04-21 2020-08-07 中国科学技术大学 Encaustic tile quality detection method
CN111507418B (en) * 2020-04-21 2022-09-06 中国科学技术大学 Encaustic tile quality detection method
CN111443131A (en) * 2020-04-26 2020-07-24 广州市市政工程试验检测有限公司 Method for detecting grouting compactness of steel bar sleeve
CN111443131B (en) * 2020-04-26 2022-11-18 广州市市政工程试验检测有限公司 Method for detecting grouting compactness of steel bar sleeve
CN111783616A (en) * 2020-06-28 2020-10-16 北京瓦特曼科技有限公司 Data-driven self-learning-based nondestructive testing method
CN111783616B (en) * 2020-06-28 2024-03-26 北京瓦特曼科技有限公司 Nondestructive testing method based on data-driven self-learning
CN112255308A (en) * 2020-09-09 2021-01-22 中国大唐集团科学技术研究院有限公司火力发电技术研究院 Bolt knocking detection method based on K-means clustering algorithm
CN112185419A (en) * 2020-09-30 2021-01-05 天津大学 Glass bottle crack detection method based on machine learning
CN112683926B (en) * 2021-01-09 2023-08-22 临汾常兴玻璃有限公司 Glass defect detection device
CN112683926A (en) * 2021-01-09 2021-04-20 杭州晶硝子玻璃科技有限公司 Glass defect detection device
CN113706468A (en) * 2021-07-27 2021-11-26 河北光兴半导体技术有限公司 Glass defect detection method based on BP neural network
CN113671031A (en) * 2021-08-20 2021-11-19 北京房江湖科技有限公司 Wall hollowing detection method and device
CN113671031B (en) * 2021-08-20 2024-06-21 贝壳找房(北京)科技有限公司 Wall hollowing detection method and device

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