A method for detecting steel belt faults for spindle transportation

Abstract

This application relates to the technical field of fault detection, and specifically relates to a steel belt fault detection method for spindle transportation. The method includes: collecting the echo signals received after an ultrasonic probe emits ultrasonic waves to the steel belt during operation; obtaining each echo band; calculating the echo time difference of each echo band; obtaining the peak-to-peak delay coefficient of each echo band; calculating the echo consistency coefficient of each echo band; and further obtaining all the first quarter echo bands; obtaining the steel belt echo attenuation value of the echo signal; after performing wavelet decomposition on the echo signal, obtaining the defect noise estimation of each wavelet coefficient; and further obtaining the adaptive wavelet threshold of each wavelet coefficient to perform fault detection on the spindle transportation steel belt. This application improves the fault detection accuracy of the ultrasonic transmission method.

Description

Technical Field

[0001] This application relates to the technical field of fault detection, and particularly to a method for detecting faults in steel belts for spindle transportation. Background Art

[0002] In the textile industry, spindles are key components on spinning frames, responsible for evenly stretching and twisting roving into fine yarn. The transportation efficiency and stability of spindles directly affect the operating efficiency of the entire textile production line and the product quality. Therefore, the steel belts responsible for transporting spindles need to have the characteristics of wear resistance, high temperature resistance, and strong tensile strength. The steel belts may experience faults such as wear, fracture, and corrosion. If these faults are not detected and processed in a timely manner, it will not only lead to a decrease in production efficiency but may also cause safety accidents, resulting in equipment damage and personal injuries.

[0003] The ultrasonic transmission method can penetrate deep into the internal of the steel belt material. Therefore, using ultrasonic waves to detect faults in the steel belt can detect microscopic defects that cannot be directly observed by the naked eye and can be carried out without shutting down the machine, which is crucial for maintaining the stability of the steel belt and extending its service life. However, the noise generated during the operation of the spindle will mix with the echoes generated when detecting the steel belt by ultrasonic waves, affecting the accuracy of the fault detection results. Summary of the Invention

[0004] In order to solve the above technical problems, this application provides a method for detecting faults in steel belts for spindle transportation to solve the existing problems.

[0005] A method for detecting faults in steel belts for spindle transportation in this application adopts the following technical solutions:

[0006] An embodiment of this application provides a method for detecting faults in steel belts for spindle transportation, and this method includes the following steps:

[0007] Collect the echo signals received after the ultrasonic probe emits ultrasonic waves to the steel belt during operation; obtain each echo band in the echo signals;

[0008] Obtain the echo time differences of each echo band according to the time differences between the peaks of the echo bands; based on the differences between the echo time differences of each echo band and the average situation of the echo time differences, obtain the peak-to-peak delay coefficients of each echo band; based on the distance differences between the echo bands and the peak-to-peak delay coefficients, obtain the echo consistency coefficients of each echo band;

[0009] Obtain all the first quartile echo bands based on the first quartile of all the echo consistency coefficients; based on the comparison between the peak values of each first quartile echo band and the peak value of the first echo band in the echo signal, obtain the steel belt echo attenuation value of the echo signal;

[0010] After wavelet decomposition of the echo signal, the amplitude distribution differences of the decomposed signals corresponding to the wavelet coefficients are obtained, and the defect noise estimates of the wavelet coefficients are acquired.

[0011] Based on the steel strip echo attenuation value, the average value of the amplitudes of the wavelet coefficients, and the defect noise estimate, the adaptive wavelet threshold of each wavelet coefficient is obtained; the denoised echo signal is acquired according to the adaptive wavelet threshold, and fault detection is performed on the spindle transportation steel strip.

[0012] Furthermore, the obtaining of each echo band in the echo signal includes:

[0013] Extract the envelope of the echo signal, and take the signal between the envelope passing through the preset amplitude threshold twice continuously and with the average amplitude greater than the preset amplitude threshold as an echo band, and obtain each echo band in the echo signal.

[0014] Furthermore, the method for obtaining the echo time difference is:

[0015] Use the peak detection algorithm to obtain the peaks of each echo band, and calculate the time difference between the peaks of each echo band and the peaks of the adjacent echo immediately following it as the echo time difference of each echo band.

[0016] Furthermore, the method for obtaining the inter-peak delay coefficient is: ; where represents the inter-peak delay coefficient of the i-th echo band, N is the total number of echo bands in the echo signal, represents the echo time difference of the i-th echo band, is the average value of the echo time differences of all echoes.

[0017] Furthermore, the method for obtaining the echo consistency coefficient is:

[0018] Calculate the mean value of the distances between each echo band and all other echoes, and take the ratio of the inter-peak delay coefficient of each echo band to the mean value as the echo consistency coefficient of each echo band.

[0019] Furthermore, the method for obtaining the first quartile echo band is:

[0020] Sort the echo consistency coefficients of all echoes from smallest to largest, obtain the first quartile of the echo consistency coefficients, and take the echoes with echo consistency coefficients greater than the first quartile as the first quartile echo bands.

[0021] Furthermore, the formula for the steel strip echo attenuation value is: ; where J is the steel strip echo attenuation value of the echo signal, W represents the total number of the first quartile echo bands, ln() represents the logarithmic function with the natural constant e as the base, represents the amplitude peak of the j-th first quartile echo band, Represents the amplitude peak of the first echo in the echo signal.

[0022] Further, the method for obtaining the defect noise estimate is as follows:

[0023] Perform wavelet decomposition on the echo signal to obtain each wavelet coefficient;

[0024] Calculate the difference between the median and the mean absolute deviation of the amplitude of the decomposed signal corresponding to each wavelet coefficient as the defect noise estimate of each wavelet coefficient.

[0025] Further, the method for obtaining the adaptive wavelet threshold is as follows: ; where is the adaptive wavelet threshold of the m-th wavelet coefficient, is the amplitude sequence of the decomposed signal corresponding to the m-th wavelet coefficient, J is the echo attenuation value of the steel strip, represents the defect noise estimate of the m-th wavelet coefficient, avg() is the mean value function, and norm() is the normalization function.

[0026] Further, the method for obtaining the denoised echo signal and performing fault detection on the spindle transportation steel strip includes:

[0027] Denoise the wavelet coefficients according to the adaptive wavelet threshold of each wavelet coefficient, and reconstruct all the denoised wavelet coefficients to obtain an echo signal with spindle noise removed;

[0028] If there is an echo with an echo amplitude greater than the preset amplitude threshold in the echo signal of the steel strip to be detected in addition to the front echo and the rear echo, or there is an echo with a width exceeding one-fourth of the front echo, then the steel strip to be detected has a fault; otherwise, the steel strip has no fault.

[0029] This application has at least the following beneficial effects:

[0030] This application collects the echo signal of the steel strip during the operation of the spindle transportation, divides the echo signal into each echo band, can analyze the noise of each part in the echo signal more accurately, and obtains the peak-to-peak delay coefficient according to the difference between the peaks of the echo bands and the average situation of the differences, which reflects the consistency of the intervals between the echo bands, and then reflects the internal geometric characteristics of the steel strip. According to the peak-to-peak delay coefficient, the echo consistency coefficient is obtained, which can analyze more accurately whether there are internal defects in the steel strip, and then construct the echo attenuation value of the steel strip, which better reflects the attenuation of sound waves in the steel strip. Furthermore, the defect noise estimate is calculated to estimate the noise of the defect echo, the relationship between the internal defects and the noise of the steel strip is analyzed according to the estimation situation, and the adaptive wavelet threshold of each wavelet coefficient is obtained; finally, denoising is completed to obtain the reconstructed echo signal, and the result of the steel strip fault detection is obtained according to the echo signal with spindle noise removed.

[0031] Its beneficial effect lies in that, according to the attenuation characteristics of the echo signal when there are internal defects in the steel strip, combined with the characteristics of the spindle noise, a threshold value is obtained. Thus, the internal faults of the steel strip are detected by using the echo signal, and while filtering out noise interference, the faults of the steel strip are accurately detected, improving the fault detection accuracy of the ultrasonic transmission method. Description of the Drawings

[0032] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0033] Figure 1 It is a flowchart of a method for detecting steel strip faults for spindle conveying provided by the present application;

[0034] Figure 2 It is a flowchart for obtaining an adaptive wavelet threshold provided by an embodiment of the present application. Detailed Embodiments

[0035] In order to further elaborate on the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following, in combination with the drawings and preferred embodiments, details the specific embodiments, structures, features and effects of a method for detecting steel strip faults for spindle conveying proposed according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs.

[0037] The following specifically describes the specific solution of a method for detecting steel strip faults for spindle conveying provided by the present application in combination with the drawings.

[0038] A method for detecting steel strip faults for spindle conveying provided by an embodiment of the present application, specifically, provides the following method for detecting steel strip faults for spindle conveying. Please refer to Figure 1 , and this method includes the following steps:

[0039] Step S1, collect the echo signal received after the ultrasonic probe emits ultrasonic waves to the steel strip during operation; obtain each echo band in the echo signal.

[0040] During the operation of the steel belt for spindle conveying, an ultrasonic probe is used to emit ultrasonic waves. The ultrasonic frequency for detection is 2.5 MHz, the size of the ultrasonic probe wafer is 20 mm, the probe type is a single-crystal straight probe, and the probe couplant is alcohol.

[0041] A receiver is used to receive the echo signal, and the echo signal received by the receiver is amplified to improve the detection sensitivity so that weak signals can also be detected. Signal amplification and ultrasonic flaw detection are well-known technologies, and the specific process will not be elaborated here.

[0042] Step S2: Obtain the echo time difference of each echo band according to the time difference between the peaks of the echo bands; based on the difference between the echo time difference of each echo band and the average situation of the echo time difference, obtain the peak-to-peak delay coefficient of each echo band; based on the distance difference between the echo bands and the peak-to-peak delay coefficient, obtain the echo consistency coefficient of each echo band.

[0043] The steel belt runs for a long time in the task of conveying spindles, which may cause fatigue, wear or other forms of failures. To ensure production efficiency and safety, ultrasonic waves can be used to detect the positions prone to internal defects to evaluate the structural integrity.

[0044] During the actual detection process, the ultrasonic probe emits high-frequency sound waves that penetrate the steel belt. When encountering internal defects, the sound waves will be reflected and return to the probe, and the receiver receives the echo signal. However, these returned signals are often mixed with the noise generated by the spindle operation. Without proper processing, these noises will seriously affect the accuracy of the detection results.

[0045] Furthermore, in this embodiment, wavelet transform is used to denoise the echo signal. The threshold in wavelet transform is a parameter for denoising, which determines which coefficients in the wavelet domain are considered noise and need to be suppressed or removed, and which coefficients are considered part of the signal and need to be retained.

[0046] During the wavelet transform denoising process, the setting of the threshold is crucial for the denoising effect. To obtain a better denoising effect, in this embodiment, the threshold is determined according to the characteristics of the noise generated by the spindle operation and the coefficient characteristics after wavelet transform.

[0047] Specifically, when the ultrasonic probe emits an ultrasonic pulse into the steel belt, the sound waves will propagate inside the material. If the sound waves encounter changes in the internal structure of the material during propagation, such as grain boundaries, inclusions, cracks or other defects, they will be reflected at these interfaces. These reflected sound waves then return to the probe, forming an echo signal that can be captured by the detection system.

[0048] Echo signals are usually displayed in the form of waveforms on an oscilloscope or detection software. The horizontal axis represents time, while the vertical axis represents amplitude. A healthy steel strip only shows simple echoes from the two side surfaces of the material. If there are internal defects, additional echoes will be shown at the corresponding time or distance positions.

[0049] Specifically, when an ultrasonic pulse is emitted from the probe and first encounters the surface of the steel strip, an initial echo, called the front-side echo, is generated. The amplitude of the front-side echo is usually high because it is the direct reflection when the ultrasonic wave first contacts the material. The ultrasonic wave penetrates the steel strip and generates a second main echo, called the back-side echo, when it reaches the other side surface.

[0050] When the ultrasonic wave propagates inside the steel strip and encounters defects, reflections will occur at these defect interfaces, forming defect echoes. The time and amplitude of the defect echoes depend on the type, size, shape, and position relative to the ultrasonic beam of the defects. The defect echoes are located between the front-side and back-side echoes in terms of time, and the amplitude may vary depending on the nature of the defects. Usually, it is less than the surface echo but is sufficient to be recognized.

[0051] Based on the above analysis, the amplitude of the ultrasonic wave gradually decreases during its propagation in the steel strip, that is, attenuation occurs. And there is a certain relationship between the attenuation degree of the ultrasonic wave and the wavelet threshold. Therefore, the initial threshold can be set by calculating the echo attenuation value of the steel strip according to the attenuation degree of the ultrasonic wave in the steel strip.

[0052] When using ultrasonic waves to detect the steel strip of the conveying spindle, in order to ensure production efficiency, non-stop detection is often carried out. At this time, the noise generated by the spindle operation will be mixed with the echoes, resulting in the attenuation characteristics of the echoes being masked or distorted, which is not conducive to signal interpretation.

[0053] The attenuation between different echoes may be damaged. It is necessary to exclude the echoes with different attenuation from other echoes to avoid large errors in the obtained echo attenuation coefficient, which affects the calculation of the wavelet threshold. Therefore, it is first necessary to calculate the consistency between different echoes and exclude the echoes with low consistency.

[0054] To determine the difference between the echoes generated by the reflection of the steel strip surface and the echoes generated by internal defects in the echo signal from other parts, it can be determined according to the envelope of the echo signal.

[0055] Specifically, in this embodiment, the moving average method is used to obtain the envelope of the echo signal. Among them, the methods for extracting the envelope include the Hilbert transform, the moving average method, etc. Implementers can select other methods for extracting the envelope according to the actual situation. It should be noted that the process of extracting the envelope is a well-known technology and will not be elaborated here. The signal between the envelope passing through the amplitude threshold twice continuously and the average amplitude being greater than the amplitude threshold is taken as an echo band. In this embodiment, the value of the amplitude threshold is 0.1.

[0056] The peak detection algorithm is used to obtain the peaks of each echo band, and the time difference between the peaks of each echo band and the peak of the adjacent echo band behind it is calculated as the echo time difference of each echo band. In this embodiment, the AMPD algorithm is selected to implement peak detection. Implementers can select other peak detection algorithms according to the actual situation. It should be noted that the last echo band is no longer calculated.

[0057] Furthermore, in order to reflect the geometric characteristics inside the steel strip, this application calculates the inter-peak delay coefficient according to the difference between the echo time difference of each echo band and the average situation of the echo time differences. The formula is: ; In the formula, represents the inter-peak delay coefficient of the i-th echo band; N represents the total number of echo bands in the echo signal, represents the echo time difference of the i-th echo band, is the average value of the echo time differences of all echoes.

[0058] It should be noted that the inter-peak delay coefficient represents the time interval between two echo bands. If the inside of the steel strip is a layered structure or has repeated geometric characteristics, or there are no internal defects or the defects are small, then the time intervals of the echo bands will be relatively consistent. If it is affected by spindle noise, even if there are no defects, the time interval will decrease, affecting the judgment of internal defects in the steel strip.

[0059] Furthermore, based on the inter-peak delay coefficient, the echo consistency coefficient of each echo band is obtained. The formula is: ; In the formula, represents the echo consistency coefficient of the i-th echo band; the i-th echo band represents the inter-peak delay coefficient of the i-th echo band, represents the average value of the distances between the i-th echo band and all other echo bands. In this embodiment, the DTW distance is selected as the method for measuring the distance. Implementers can select other methods for measuring the distance according to the actual situation.

[0060] The DTW distance represents the correlation between echo bands. Since the correlation between the echoes generated by the collision of ultrasonic waves with the surface or inside of the steel strip is relatively high, the correlation between the echo bands affected by spindle noise and other echo bands will decrease and is easily recognized as an internal defect.

[0061] So far, the greater the echo consistency coefficient of each echo band, the higher the consistency between this echo band and other echo bands, the smaller the influence of spindle noise, and the greater the possibility of internal defects.

[0062] Step S3: Obtain all first-quarter echo bands based on the first quartile of all echo consistency coefficients; based on the comparison between the peak value of each first-quarter echo band and the peak value of the first echo band in the echo signal, obtain the steel strip echo attenuation value of the echo signal.

[0063] Sort the echo consistency coefficients of all echo bands from smallest to largest, obtain the first quartile of the echo consistency coefficients, and use the echo bands with echo consistency coefficients greater than the first quartile as the first-quarter echo bands.

[0064] Furthermore, in order to reflect the attenuation of sound waves in the steel strip, this application calculates the steel strip echo attenuation value J based on the comparison between the peak value of each first-quarter echo band and the peak value of the first echo band in the echo signal. The formula is: ; where J represents the steel strip echo attenuation value of the echo signal, W represents the total number of first-quarter echo bands, ln() represents the logarithmic function with the natural constant e as the base, represents the amplitude peak value of the j-th first-quarter echo band, represents the amplitude peak value of the first echo band in the echo signal.

[0065] The numerical value of the steel strip echo attenuation value represents the attenuation degree of ultrasonic waves in the steel strip. The larger its value, the more severe the attenuation of ultrasonic waves during propagation in the steel strip. Therefore, the amplitude of the rear echo may become very small due to attenuation. If the wavelet threshold is set too high, the true signal with a small amplitude due to attenuation may be wrongly regarded as noise and removed, resulting in missed detection.

[0066] Therefore, the greater the steel strip echo attenuation value, the smaller the wavelet threshold should be set. On the contrary, the wavelet threshold should be set larger. The steel strip echo attenuation value first excludes the echoes in the original echo signal that are greatly interfered by spindle noise, considering the characteristics of spindle noise and ultrasonic attenuation characteristics. Therefore, the obtained wavelet threshold can better remove spindle noise.

[0067] Step S4: After performing wavelet decomposition on the echo signal, obtain the amplitude distribution difference of the decomposed signals corresponding to each wavelet coefficient, and obtain the defect noise estimation value of each wavelet coefficient.

[0068] In order to better identify the echo signals generated by defects, wavelet transform is used to process the echo signals. First, the echo signals should be wavelet decomposed, and then adaptive wavelet thresholds are set for the wavelet coefficients of each decomposed signal obtained by the decomposition. The noise signals are separated from the details of the source signals through the wavelet thresholds. The wavelet coefficients smaller than the wavelet threshold are generated by noise, and the wavelet coefficients greater than or equal to the wavelet threshold are generated by the source signals. Then, the denoised wavelet coefficients are reconstructed to obtain the denoised echo signals.

[0069] In this embodiment, the wavelet function selected for wavelet transform of the echo signals is the Daubechies wavelet function, the value of N is selected as 3, and the echo signals are decomposed into 4 layers to obtain each wavelet coefficient.

[0070] Since there is spindle noise in the echo signals, and after wavelet transform, the noise is decomposed into different wavelet coefficients, and the spindle noise will affect the echo signals generated by defects. In order to consider the different characteristics among the wavelet coefficients obtained after wavelet transform, the noise level of the steel strip defects is estimated according to different wavelet coefficients to obtain the adaptive wavelet threshold. The wavelet coefficients include the real part and the imaginary part, where the real part represents the amplitude of the signal at different scales and positions, that is, the magnitude, and the imaginary part represents the phase information.

[0071] The noise level of the steel strip defects can be obtained through the median and the mean absolute deviation of the amplitude of the decomposed signal corresponding to the wavelet coefficient. Because the median, as a robust statistic, is not sensitive to outliers. In the actual industrial environment, there may be grease, rust or other surface irregularities on the steel strip, and these factors may cause outliers in the signals, but these outliers have nothing to do with the internal defects of the steel strip and should be excluded. Using the median as the basis for noise estimation can reduce the influence of these outliers on noise estimation.

[0072] The mean absolute deviation (MAD), as a measure of the deviation of data points from the central tendency, can adapt to the characteristics of different noise sources and provide a more comprehensive description for noise estimation. The mean absolute deviation is a well-known technology and will not be elaborated in this embodiment.

[0073] Therefore, the combined use of the median and the mean absolute deviation helps to distinguish the normal signal changes caused by these microstructures from the abnormal changes caused by noise, and calculate the defect noise estimation value of each wavelet coefficient. The formula is: ; In the formula, represents the defect noise estimation value of the m-th wavelet coefficient; represents the amplitude sequence of the decomposed signal corresponding to the m-th wavelet coefficient, median() represents calculating the median, and mad() represents calculating the mean absolute deviation.

[0074] The numerical value of the defect noise estimation represents the level of defect noise estimation in the current wavelet coefficient. The larger its value, the more spindle noise there is in the current wavelet coefficient, and the smaller the proportion of echoes caused by defects. Therefore, a larger wavelet threshold should be set. On the contrary, a smaller wavelet threshold should be selected to more effectively remove spindle noise.

[0075] Step S5: Based on the steel strip echo attenuation value, the average value of the amplitudes of each wavelet coefficient, and the defect noise estimation, obtain the adaptive wavelet threshold of each wavelet coefficient; according to the adaptive wavelet threshold, obtain the denoised echo signal, and perform fault detection on the spindle transportation steel strip.

[0076] The initial wavelet threshold of each wavelet coefficient is set to the average value of the amplitudes of each wavelet coefficient, and the adaptive wavelet threshold of each wavelet coefficient is obtained by adjusting according to the steel strip echo attenuation value and spindle noise estimation. The formula is: ; In the formula, represents the adaptive wavelet threshold of the m-th wavelet coefficient, represents the amplitude sequence of the decomposed signal corresponding to the m-th wavelet coefficient, J represents the steel strip echo attenuation value, represents the defect noise estimation of the m-th wavelet coefficient, avg() represents the average value function, and norm() represents the normalization function. The flowchart for obtaining the adaptive wavelet threshold is as shown in Figure 2 shown.

[0077] In the formula, the adaptive wavelet coefficient is adjusted according to the steel strip echo attenuation value and the defect noise estimation. When the steel strip echo attenuation value of the echo signal is larger, the ultrasonic wave attenuates more severely during the propagation in the steel strip, so the amplitude of the rear echo may become very small due to attenuation. At this time, the wavelet threshold should be reduced. When the defect noise estimation is small, there is less noise in the wavelet coefficient, and the proportion of echoes caused is less. At this time, the wavelet threshold should be reduced; on the contrary, the wavelet threshold should be increased.

[0078] Thus, the adaptive wavelet threshold of each wavelet coefficient is obtained.

[0079] Denoise the wavelet coefficient according to the adaptive wavelet threshold of each wavelet coefficient, and reconstruct all the denoised wavelet coefficients to obtain the echo signal with spindle noise removed. Wavelet transform denoising and signal reconstruction are well-known technologies, and the specific process will not be elaborated here.

[0080] Use the ultrasonic probe to move uniformly on the steel strip to be detected, and process the signals of each echo band according to the above steps to obtain the reconstructed signals of each echo band.

[0081] Analyze the internal defect faults of the steel strip for spindle transportation based on the processed echo signal. The first echo that appears in the echo signal usually has a larger amplitude, which is the echo from the front surface of the steel strip. Similarly, the last echo has a smaller amplitude, which is the back echo generated by the ultrasonic wave colliding with the back side of the steel strip after attenuation during propagation inside the steel strip.

[0082] The amplitude of the echo is usually proportional to the size of the defect. A larger defect generally generates a higher echo signal. The shape of the echo signal provides clues about the type of defect. For example, a sharp echo may indicate a small hole or crack, while a wide echo may suggest the presence of a defect with a larger area.

[0083] In addition, if there are multiple defects in the material, then multiple echoes may be observed in the echo signal, and each multiple echo corresponds to a different defect or interface.

[0084] If there is a defect echo with an echo amplitude greater than the preset amplitude threshold in the echo signal of the steel strip to be detected, in addition to the front echo and the back echo, or if there is an echo with a width exceeding one-fourth of the front echo, then the steel strip to be detected has a fault; otherwise, the steel strip has no fault.

[0085] So far, a method for detecting faults in the steel strip for spindle transportation has been completed.

[0086] It should be noted that: the above sequence of the embodiments of the present application is only for description and does not represent the advantages or disadvantages of the embodiments. And the above specific embodiments of the present application have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0087] The embodiments in the present application are all described in a progressive manner. The same or similar parts between the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

[0088] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; modifying the technical solutions recorded in the foregoing embodiments, or equivalently replacing some of the technical features, does not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included within the protection scope of the present application.

Claims

1. A method for detecting steel belt faults for spindle conveying, characterized in that: The method comprises the following steps: Collect the echo signal received after the ultrasonic probe emits ultrasonic waves to the steel strip in the running process; extract the envelope of the echo signal, take the signal between the envelope passing through the preset amplitude threshold twice in a row, and the signal whose amplitude average value is greater than the preset amplitude threshold as an echo segment, and obtain each echo segment in the echo signal; Obtain the echo time difference of each echo band according to the time difference between the peak of the echo band and its subsequent adjacent echo band; obtain the peak-to-peak delay coefficient of each echo band based on the difference between the echo time differences of all echo bands and the average value of the echo time differences; obtain the echo consistency coefficient of each echo band based on the ratio of the peak-to-peak delay coefficient to the average value of the distance between the echo band and the remaining echo bands; Sort all echo consistency coefficients from small to large, obtain the first quartile, and use the echo segments greater than the first quartile as the first quartile echo segments; based on the comparison between the peak values ​​of each first quartile echo segment and the peak value of the first echo segment in the echo signal, obtain the steel strip echo attenuation value of the echo signal; the expression of the steel strip echo attenuation value is: Wherein, J is the steel strip echo attenuation value of the echo signal, W represents the total number of the first quarter echo band, ln() represents the logarithmic function with the natural constant e as the base, represents the peak amplitude of the jth first quarter echo band, Indicates the peak amplitude of the first echo in the echo signal; Perform wavelet decomposition on the echo signal to obtain each wavelet coefficient, and use the difference between the median of the amplitude of the decomposed signal corresponding to each wavelet coefficient and the mean absolute deviation as the defect noise estimation of each wavelet coefficient; Based on the steel strip echo attenuation value, the average value of the amplitude of each wavelet coefficient and the defect noise estimation, the adaptive wavelet threshold of each wavelet coefficient is obtained; the method for obtaining the adaptive wavelet threshold is: ; In the formula, is the adaptive wavelet threshold of the mth wavelet coefficient, is the amplitude sequence of the decomposed signal corresponding to the mth wavelet coefficient, J is the attenuation value of the steel strip echo, It represents the defect noise estimation of the mth wavelet coefficient, avg() is the average function, and norm() is the normalization function; the denoised echo signal is obtained according to the adaptive wavelet threshold, and the fault detection of the ingot transport steel belt is performed; The method for obtaining the peak-to-peak delay coefficient is: ; In the formula, represents the peak-to-peak delay coefficient of the i-th echo segment, N is the total number of echo segments in the echo signal, represents the echo time difference of the i-th echo segment, is the average of the echo time differences of all echoes.

2. A method for detecting steel belt faults for spindle conveying according to claim 1, characterized in that: The method for obtaining the echo time difference is: The peak value of each echo band is obtained by using a peak detection algorithm, and the time difference between each echo band and the peak of its next adjacent echo is calculated as the echo time difference of each echo band.

3. A method for detecting steel belt faults for spindle conveying according to claim 1, characterized in that: The method of obtaining the denoised echo signal and performing fault detection on the ingot transport steel belt comprises: De-noising the wavelet coefficients according to the adaptive wavelet threshold of each wavelet coefficient, reconstructing all the de-noised wavelet coefficients to obtain the echo signal with the spindle noise removed; If, in addition to the front echo and the rear echo, the echo signal of the steel strip to be detected contains an echo whose amplitude is greater than the preset amplitude threshold, or an echo whose width exceeds one-fourth of the front echo, then the steel strip to be detected is faulty; otherwise, the steel strip is not faulty.

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