阅读说明：本技术 一种基于不平等距离优化聚类算法的伤损裂纹声发射信号检测方法 (Damage crack acoustic emission signal detection method based on unequal distance optimization clustering algorithm ) 是由 章欣 常永祺 郝秋实 宋树帜 沈毅 王艳 于 2021-02-04 设计创作，主要内容包括：一种基于不平等距离优化聚类算法的伤损裂纹声发射信号检测方法,涉及伤损裂纹信号处理与检测领域的方法,解决了传统裂纹信号检测方法速度慢、效率低的问题。本发明的步骤为：一、加载原始声发射信号,获得噪声声发射信号；二、对噪声声发射信号提取γ倒谱系数特征,依据自适应鲁棒系数对其进行筛选；三、将噪声信号特征聚类,计算各特征到各聚类质心的距离,计算各聚类的不平等优化距离值；提取待测声发射信号的γ倒谱系数特征,依照步骤二选择待测信号特征,计算各特征到各聚类质心的距离,依据不平距离优化判别算法,判别待测信号。本发明运算速率快,检测精度高。在高铁钢轨与车轮伤损裂纹检测领域,具有很高的社会意义和经济价值。(A damage crack acoustic emission signal detection method based on an unequal distance optimization clustering algorithm relates to a method in the field of damage crack signal processing and detection, and solves the problems of low speed and low efficiency of the traditional crack signal detection method. The method comprises the following steps: loading an original acoustic emission signal to obtain a noise acoustic emission signal; secondly, extracting gamma cepstrum coefficient characteristics of the noise acoustic emission signals, and screening the gamma cepstrum coefficient characteristics according to the adaptive robust coefficient; clustering the noise signal features, calculating the distance from each feature to each clustering centroid, and calculating unequal optimization distance values of each cluster; and (4) extracting the gamma cepstrum coefficient characteristics of the acoustic emission signals to be detected, selecting the characteristics of the signals to be detected according to the second step, calculating the distance from each characteristic to each clustering centroid, and judging the signals to be detected according to an uneven distance optimization judgment algorithm. The invention has fast operation speed and high detection precision. The method has high social significance and economic value in the field of high-speed rail and wheel damage crack detection.)

1. A damage crack acoustic emission signal detection method based on an unequal distance optimization clustering algorithm is characterized by comprising the following steps:

the method comprises the following steps: loading acoustic emission signals containing damage cracks and noiseSelecting multiple noise sound emission signal samples from the data, and connecting them into a longer noise sound emission signal in time dimensionNumber, mark as

Step two: for noise acoustic emission signalsExtracting gamma cepstrum coefficient characteristics to obtain multidimensional characteristics Representing the signal after the calculation of the characteristicThe number of the feature points is selected according to the adaptive robust coefficient, and the features with higher stability are selected

Step three: characterizing noise signalsDividing clusters into K clusters, calculating the distance from each feature to each cluster centroid in the K clusters, and calculating unequal optimization distance values of each cluster centroid; extracting a section of acoustic emission signal to be detectedThe gamma cepstrum coefficient characteristics are selected according to the characteristic serial numbers obtained by screening in the step twoIs characterized by Representing the signal after the calculation of the characteristicThe number of feature points of (a); and calculating the distance from each feature to each clustering centroid in the K clusters, and judging whether each feature belongs to a damage signal according to an unequal distance optimization judgment algorithm to finish detection of the damage acoustic emission signal.

2. The method for detecting acoustic emission signals of flaw cracks based on unequal distance optimization clustering algorithm according to claim 1, wherein the first step specifically comprises:

1) loading acoustic emission signals containing damage cracks and noiseWherein N is₀Representing the signal length, namely the number of sampling points;

2) fromSelecting multiple sections of noise acoustic emission signal samples, connecting the multiple sections of noise acoustic emission signal samples in the time dimension to form a long section of noise acoustic emission signal, and recording the long section of noise acoustic emission signal as a long section of noise acoustic emission signalWherein N is₁Representing the signal length, i.e. the number of samples.

3. The method for detecting acoustic emission signals of flaw cracks based on unequal distance optimization clustering algorithm according to claim 1, wherein the second step specifically comprises:

1) to acoustic emission signalUsing moving time windows inMoving on the signal, and calculating gamma cepstrum coefficient characteristics of each signal in a moving window, the window length being set to N_wTo obtain a characteristicH₁Representing a feature dimension, wherein the features of the gamma cepstrum coefficients comprise GTCC coefficients, first-order difference GTCC coefficients and second-order difference GTCC coefficientsMaintaining;

2) for H obtained₁The invention provides a self-adaptive robust coefficient as a screening condition for screening the characteristics of the dimensional gamma cepstrum coefficient, and provides a gamma cepstrum coefficient characteristic sequence of any one dimensionCorresponding to a time sequence of Representing timeA characteristic value of (d);

firstly, a characteristic parameter sequence is divided into a stationary trend item and a random margin item by adopting a moving average method:

wherein the content of the first and second substances,represents a smooth trend portion of the feature, andthen the random residue part of the feature is represented and then the robust coefficient of the feature is calculated:

wherein

3) Respectively calculating to obtain H according to the formula₁Robust coefficient of each dimension feature in the dimension, denoted asRaising adaptive robust coefficient threshold and comparing with H₁Comparing robust coefficients of the dimensional features, selecting features of which the robust coefficients are larger than a robust coefficient threshold, wherein the dimension of the selected features is H₂The characteristics obtained after screening are as followsThe discrimination formula is as follows:

wherein, theta_RobThe adaptive robust coefficient threshold is usually selected according to experimental experience.

4. The method for detecting acoustic emission signals of flaw cracks based on unequal distance optimization clustering algorithm according to claim 1, wherein the third step specifically comprises:

1) from the characteristicsRandomly selecting K points as initial clustering centers { c₁，c₂，...，c_KI.e. characteristicsIs clustered into K clusters, and is marked as B ═ B₁，B₂，...，B_K}；

2) Calculating the distance from each feature point to a specified centroid:

wherein the content of the first and second substances,

3) updating the centroid of each cluster by using the following formula, and repeating the steps 2) to 3) until the centroid position is not changed any more:

wherein j is₂K, the resulting centroid is recorded as 1, 2The features included in each cluster are respectively notedWherein

4) Calculating the distance from the characteristic point of each cluster to the cluster centroid thereof, and determining the maximum value of the distances:

wherein i₃＝1，2，...，K，

5) Setting unequal optimized distance values of the centroids of each cluster, wherein for K clusters, the judgment optimized distance values of the signal characteristics to be detected belonging to the cluster are as follows:

wherein p is 1, 2., K,α_prepresenting the noise suppression coefficient, and adjusting in real time according to the noise condition;

6) extracting acoustic emission signals to be detected under moving time window processingThe gamma cepstrum coefficient characteristics are selected according to the characteristic serial numbers obtained by screening in the step twoIs characterized byCalculating the distance between each feature and the mass center of each cluster in the K clusters, and acquiring the cluster number of each feature of the signal to be detected according to the following formula:

wherein i₄＝1，2，...，K，

7) Further, under the condition of obtaining the serial number of each feature cluster of the signal to be detected, carrying out uneven distance optimization judgment according to the uneven optimization distance value in the step 5), wherein the judgment method is as follows:

if the calculation result R is 1, the characteristic belongs to a damage crack signal; otherwise, the signal belongs to a noise signal; and judging whether each characteristic belongs to a damage signal or not, and finishing detection of the damage acoustic emission signal.

Technical Field

The invention relates to a method in the field of steel rail crack signal processing and detection, in particular to a method for detecting a damage crack acoustic emission signal based on an unequal distance optimization clustering algorithm.

Background

Since the first Qinsheng passenger transport special line of the high-speed railway in China was opened in 2003, the high-speed rail industry in China began to fly across, and in 17 years, the high-speed rail territory was expanded again and again, was spread over the big and good river mountain in China, communicated north and south, and transversely spanned things. In recent years, high-speed rails are going out of China and become a beautiful business card manufactured in China. In the face of the current situations of complicated lines, large mileage and the like of high-speed rail crossing, ensuring safe, stable and reliable operation of a high-speed railway is the central importance of later railway safety work, and the method has important significance in exploring a new rail damage detection method and improving the detection speed.

At present, the main damage and crack detection methods include ultrasonic detection, magnetic flux leakage detection, eddy current detection, visual detection and the like. The detection method has different principles and characteristics, is widely applied to the field of damage and crack detection, and has a plurality of defects. In the ultrasonic detection method, a coupling agent needs to be added between the detection probe and the detected object, so that the detection speed is reduced, and the detection efficiency is seriously influenced. The magnetic flux leakage detection technology has high damage crack detection speed, but is often limited by a physical mechanism, only can detect cracks on the surface or shallow surface layer, cannot detect internal damage cracks, and has low detection precision; the eddy current detection technology can detect internal damage cracks, has high detection efficiency and simple detection process, but the eddy current detection method has large application limitation and is only suitable for detecting the damage of an object with larger cracks on the cross section; visual inspection is widely applied, but the detection precision is far lower than that of other methods. In recent years, an acoustic emission detection method is more and more emphasized by people, the method is a passive monitoring method, the damage state of a steel rail can be monitored in real time, but due to the fact that huge noise interference exists in the running process of a train, the detection accuracy of the method is low, misjudgment on noise is easy to form, and therefore the method for detecting the damage, which can overcome the noise interference and has high detection accuracy, has important significance.

The method is based on the unequal distance optimization clustering algorithm, combines the characteristic information of gamma cepstrum Coefficients (GTCC), performs clustering analysis on the characteristic information, calculates the distance between each sample point and each clustering mass center, sets each clustering mass center judgment optimization distance value, and achieves the purpose of distinguishing noise interference and crack damage signals according to the unequal distance optimization discrimination algorithm, thereby completing the detection of the damage acoustic emission signals and improving the detection precision of the crack damage of the steel rail.

Disclosure of Invention

The invention aims to provide a damage crack acoustic emission signal detection method based on an unequal distance optimization clustering algorithm. The detection precision of the rail crack acoustic emission signal is improved.

The purpose of the invention is realized by the following technical scheme: firstly, extracting gamma cepstrum coefficient characteristics from a noise acoustic emission signal by using the characteristic that the gamma cepstrum coefficient characteristics are sensitive to an acoustic signal, and screening the characteristics according to a self-adaptive robust coefficient to obtain low-dimensional gamma cepstrum coefficient characteristics with strong robustness; then, clustering the screened features into K clusters, calculating the distance from each feature to each clustering centroid in the K clusters, and calculating the unequal optimization distance value of each clustering centroid; and finally, extracting the gamma cepstrum coefficient characteristics of the acoustic emission signals to be detected, screening characteristic serial numbers according to the noise acoustic emission signals, selecting the screening characteristics of the acoustic emission signals to be detected, calculating the distance from each characteristic to each clustering centroid in the K cluster, judging whether each characteristic belongs to the damage signals according to an uneven distance optimization judgment algorithm, and finishing the detection of the damage acoustic emission signals.

The flow chart of the invention is shown in figure 1, and the specific steps are as follows:

the method comprises the following steps: loading acoustic emission signals containing damage cracks and noiseSelecting multiple sections of noise sound emission signal samples from the sound emission signals, and connecting the noise sound emission signal samples into a long section of noise sound emission signal in the time dimensionThe method comprises the following specific steps:

1) loading acoustic emission signals containing damage cracks and noiseWherein N is₀Representing the signal length, namely the number of sampling points;

2) fromSelecting multiple sections of noise acoustic emission signal samples, connecting the multiple sections of noise acoustic emission signal samples in the time dimension to form a long section of noise acoustic emission signal, and recording the long section of noise acoustic emission signal as a long section of noise acoustic emission signalWherein N is₁Representing the signal length, i.e. the number of samples.

Step two: for noise acoustic emission signalsExtracting gamma cepstrum coefficient characteristics to obtain multidimensional characteristicsRepresenting the signal after the calculation of the characteristicThe number of the feature points is selected according to the adaptive robust coefficient, and the features with higher stability are selectedThe method comprises the following specific steps:

1) to acoustic emission signalMoving on the signal by adopting a moving time window, calculating the characteristics of gamma cepstrum coefficient of each section of signal in the moving window, and setting the window length to be N^wTo obtain a characteristicH₁Representing the feature dimension. Wherein, the gamma cepstrum coefficient characteristics comprise GTCC coefficients, first-order difference GTCC coefficients and second-order difference GTCC coefficientsMaintaining;

2) for H obtained₁The feature of the dimension gamma cepstrum coefficient is screened, and the invention provides a self-adaptive robust coefficient as a screening condition. Characteristic sequence of gamma cepstrum coefficient for arbitrary one dimensionCorresponding to a time sequence ofRepresenting timeA characteristic value of (d);

firstly, a characteristic parameter sequence is divided into a stationary trend item and a random margin item by adopting a moving average method:

wherein the content of the first and second substances,represents a smooth trend portion of the feature, andthe random margin portion of the feature is represented. The robust coefficients of the features are then calculated:

wherein

3) Respectively calculating to obtain H according to the formula₁Robust coefficient of each dimension feature in the dimension, denoted asi₀＝1，2，...，H₁. Raising adaptive robust coefficient threshold and comparing with H₁Comparing robust coefficients of the dimensional features, selecting features of which the robust coefficients are larger than a robust coefficient threshold, wherein the dimension of the selected features is H₂The characteristics obtained after screening are as followsThe discrimination formula is as follows:

i₀＝1，2，...，H₁；

wherein, theta_RobThe adaptive robust coefficient threshold is usually selected according to experimental experience.

Step three: characterizing noise signalsClustering into K clustersCalculating the distance from each feature to each clustering centroid in the K clusters, and calculating the unequal optimization distance value of each clustering centroid; extracting a section of acoustic emission signal to be detectedThe gamma cepstrum coefficient characteristics are selected according to the characteristic serial numbers obtained by screening in the step twoIs characterized byRepresenting the signal after the calculation of the characteristicThe number of feature points of (a); calculating the distance from each feature to each clustering centroid in the K clusters, judging whether each feature belongs to a damage signal according to an unequal distance optimization judgment algorithm, and completing detection of a damage acoustic emission signal, wherein the method comprises the following specific steps:

1) from the characteristicsRandomly selecting K points as initial clustering centers { c₁，c₂，...，c_KI.e. characteristicsIs clustered into K clusters, and is marked as B ═ B₁，B₂，...，B_K}；

2) Calculating the distance from each feature point to a specified centroid:

wherein the content of the first and second substances,j₁＝1，2，...，K；

3) updating the centroid of each cluster by using the following formula, and repeating the steps 2) to 3) until the centroid position is not changed any more:

wherein j is₂K, the resulting centroid is recorded as 1, 2The features included in each cluster are respectively notedWherein

4) Calculating the distance from the characteristic point of each cluster to the cluster centroid thereof, and determining the maximum value of the distances:

wherein i₃＝1，2，...，K，

5) Setting unequal optimized distance values of the centroids of each cluster, wherein for K clusters, the judgment optimized distance values of the signal characteristics to be detected belonging to the cluster are as follows:

wherein p is 1, 2., K,α_prepresenting the noise suppression coefficient, and adjusting in real time according to the noise condition;

6) extracting acoustic emission signals to be detected under moving time window processingThe gamma cepstrum coefficient characteristics are selected according to the characteristic serial numbers obtained by screening in the step twoIs characterized byCalculating the distance between each feature and the mass center of each cluster in the K clusters, and acquiring the cluster number of each feature of the signal to be detected according to the following formula:

wherein i₄＝1，2，...，K，

7) Further, under the condition of obtaining the serial number of each feature cluster of the signal to be detected, carrying out uneven distance optimization judgment according to the uneven optimization distance value in the step 5), wherein the judgment method is as follows:

if the calculation result R is 1, the characteristic belongs to a damage crack signal; otherwise, the signal belongs to a noise signal. And judging whether each characteristic belongs to a damage signal or not, and finishing detection of the damage acoustic emission signal.

Compared with the prior art, the invention has the following advantages:

the invention adopts the gamma cepstrum coefficient characteristic to represent the identifiability information of the acoustic emission signal containing the damage crack, thereby achieving the purpose of eliminating noise interference. In addition, based on the characteristic that the gamma cepstrum coefficient features have multi-dimensional information, a self-adaptive robust coefficient method is provided, low-dimensional features with high robustness are obtained through screening, feature dimensions are reduced, data operation amount is reduced, time cost is saved, and signal detection rate is increased.

Meanwhile, the invention provides an optimal clustering algorithm based on unequal distances, and the characteristic parameters of the noise acoustic emission signals are divided into a plurality of clusters on the basis of similarity; calculating an unequal optimization distance value of the clustering centroid of each cluster according to the distance from each cluster characteristic parameter to each cluster centroid; and according to the difference between the noise signal and the damage signal, and according to an uneven distance optimization discrimination algorithm, carrying out damage detection on the acoustic emission signal to be detected.

The existing damage crack detection methods are only suitable for laboratory operating data, and the data only add a few known noise signals. However, in practice, the flaw signal contains a large amount of noise interference, and useful information is buried therein. Meanwhile, the existing damage crack detection method is low in operation efficiency and consumes a lot of time. The clustering algorithm based on unequal distances can separate various known noises from damage crack signals, thereby improving the detection precision of the damage signals and meeting the requirements of high detection rate and precision in actual working production.

Drawings

FIG. 1 is a flow chart of the method of the present invention.

Fig. 2 is a time-frequency diagram of a noise emission signal.

FIG. 3 is a time-frequency diagram of an acoustic emission signal to be measured.

FIG. 4 is a graph of signal detection inequality optimization distance

FIG. 5 is a diagram illustrating signal detection

Detailed Description

The following describes the specific embodiment of the present invention with reference to the measured and simulated signal data of the rail crack:

executing the step one: loading acoustic emission signals containing damage cracksSignal sampling rate 5MHz then N₀2867200. Obtaining multiple sections of noise signals, and connecting the noise signals into longer acoustic emission signals in time dimensionAt this time N₁503937, the signal is shown in fig. 2.

And (5) executing the step two: under the process of moving time window, acoustic emission signal is processedExtracting the gamma cepstrum coefficient characteristic, marking as M_11855×39Namely, after 39-dimensional features are obtained, 13 dimensions of the GTCC coefficient, the first-order difference GTCC coefficient and the second-order difference GTCC coefficient are obtained respectively. Robust coefficients for the 39-dimensional gamma cepstral coefficient features were calculated and the results are shown in table 1.

TABLE 1 clustering gamma cepstrum coefficients for each dimension robust coefficient

Dimension number	1	2	3	4	5	6	7	8
									Robust coefficient	0.9861	0.9820	0.7854	0.7804	0.6498	0.6551	0.5348	0.6062
Dimension number	9	10	11	12	13	14	15	16
									Robust coefficient	0.5531	0.4962	0.4963	0.5034	0.5013	0.6261	0.6240	0.6310
Dimension number	17	18	19	20	21	22	23	24
									Robust coefficient	0.6245	0.6282	0.6208	0.6252	0.6301	0.6246	0.6083	0.6189
Dimension number	25	26	27	28	29	30	31	32
									Robust coefficient	0.6241	0.6169	0.7094	0.7102	0.7145	0.7110	0.7095	0.7116
Dimension number	33	34	35	36	37	38	39
									Robust coefficient	0.7094	0.7075	0.7118	0.7011	0.7051	0.7101	0.7027

In this case, the adaptive robust coefficient is set to 0.9, and it can be found from table 1 that the robust coefficient of the 1 st and 2 nd co-2 d features exceeds 0.9, so that the gamma cepstrum coefficient feature M with better 2 d stability is selected_11855×2Subsequent experiments were performed.

And step three is executed: from the features M of the gamma cepstral coefficients_11855×2And randomly selecting 5 characteristic points as an initial clustering mass center. And calculating the distance from each characteristic point to each initial clustering centroid, and repeatedly calculating the centroid position until the centroid does not change any more. At this time, the gamma cepstrum coefficient characteristic M_11855×2Is clustered into 5 classes, respectively M_1506×2、M_2575×2、M_2988×2、M_1443×2And M_3343×2. Then, each clustering feature point is calculated toDistance of each clustering centroid based onDetermining the maximum value of the distance from each clustering characteristic point to each clustering mass center, and setting the judgment optimization distance value tau of each mass center of 5 clusters_pThe noise suppression coefficient at this time is α_pAre all set to 0.35.

Next, a section of acoustic emission signal to be detected is loadedN₂2047745, the signal is as shown in fig. 3. Under moving time window processing, extractingCharacteristic M of gamma cepstrum coefficients_8000×39Selecting the 1 st and 2 nd-dimensional gamma cepstrum coefficient characteristics M according to the noise signal characteristic screening serial number in the step two_8000×2。

Calculating M_8000×2And (3) obtaining the class number of each feature point and the distance value under the corresponding class number according to the distance from each feature point to each cluster centroid in the 5 clusters, and carrying out unequal distance optimization judgment according to the unequal distance optimization values, wherein the unequal distance optimization values are shown in fig. 4, so that the judgment of each feature point of the signal to be detected is completed, for example, the judgment result of the signal to be detected is given in fig. 5, and the marked position in the graph is the position with the judgment result of 1, namely, the damage crack signal.