A quality detection method and system for optical machine cast iron accessories

Abstract

The present invention relates to the technical field of accessory quality detection, and in particular to a method and system for detecting the quality of optical machine cast iron accessories. The method comprises: obtaining a preliminary complexity factor based on the frequency spectrum of a signal to be detected and a standard detection signal; performing EMD processing on the signal to be detected to obtain an IMF component, taking the normalized value of the standard deviation of each IMF component in a continuous segment as the energy proportion, and then obtaining the energy distribution deviation factor of the current continuous segment; obtaining the period integrity factor of the IMF component according to the matching similarity and the maximum period of the IMF component; obtaining a wavelet threshold according to the preliminary complexity factor, the energy distribution deviation factor and the period integrity factor; filtering the signal to be detected according to the wavelet threshold, reconstructing the filtered signal to be detected through Hilbert transform, identifying the attenuation index of the bottom peak of the signal to be detected to judge the quality of the optical machine cast iron accessories to be detected, and improving the accuracy of quality detection of the optical machine cast iron accessories.

Description

Technical Field

[0001] The present invention relates to the technical field of accessory quality detection, and in particular to a method and system for detecting the quality of optical machine cast iron accessories. Background Art

[0002] With the rapid advancement of industrial technology, the application scope of optomechanical cast iron accessories in many key fields such as machinery manufacturing, aerospace and automobile manufacturing continues to expand. Optomechanical cast iron accessories play a vital role in structural support and function realization, and their quality level is directly related to the reliability and safety of the final product. Therefore, it has become an industry consensus and urgent need to implement more stringent quality inspections on optomechanical cast iron accessories. Today, with the rapid development of non-destructive testing technology, ultrasonic flaw detection technology has become the preferred method for detecting internal defects of optomechanical cast iron accessories due to its unique advantages of high sensitivity, high resolution and non-destructiveness. Among them, the bottom surface multiple echo method, as an advanced ultrasonic flaw detection technology, can accurately identify various defects such as cracks, pores, inclusions, etc. inside optomechanical cast iron accessories.

[0003] However, in practical applications, when using the bottom surface multiple echo method to perform ultrasonic detection on optical machine cast iron accessories, the traditional manual observation method has many challenges. When there are complex defects inside the casting, these defects will reflect and scatter part of the sound energy, resulting in deviations in the bottom wave time interval, increased attenuation, and the appearance of defect echoes. In this case, the deviation of the bottom wave peak time interval may lead to misjudgment of the actual wave path of the entire echo band. Although in theory, the frequency of the echo caused by defects or the bottom surface is determined by the probe and the ultrasonic wave velocity, in the presence of complex defects, multiple echo signals will be superimposed in time and space. Especially under the influence of noise signals such as electromagnetic interference, this superposition will cause changes in the signal frequency characteristics, especially when there are differences in signal strength or arrival time at the probe, the problem is particularly prominent. Traditional processing methods, such as fixed threshold filtering of the default high-frequency component by wavelet threshold filtering after empirical mode decomposition (EMD), may lead to the loss of defect information or bottom surface echo information in the face of the above complex situations. Summary of the invention

[0004] In order to solve the technical problem that the prior art has poor effect in detecting optical machine cast iron accessories, the purpose of the present invention is to provide a quality detection method for optical machine cast iron accessories. The technical solution adopted is as follows:

[0005] Acquire a signal to be detected of the optical-mechanical cast-iron fitting to be detected and a standard detection signal of a standard optical-mechanical cast-iron fitting, and acquire a preliminary complexity factor based on a frequency spectrum of the signal to be detected and a standard frequency spectrum of the standard detection signal;

[0006] Performing EMD processing on the signal to be detected to obtain at least two IMF components, and after performing Hilbert transform on the IMF components, determining whether the amplitude of an adjacent wave path of a wave path with a non-zero amplitude in the signal to be detected is 0, if not, they are the same continuous wave path segment, extracting the corresponding continuous segments in the IMF components based on each of the continuous wave path segments, taking the normalized value of the standard deviation of each of the IMF components in the continuous segment as the energy proportion, and obtaining the energy distribution deviation factor of the current continuous segment according to the energy proportion;

[0007] The maximum period of the IMF component within a preset lag number is obtained through the ACF model, and the IMF component is segmented according to the maximum period, and any two segments are matched using the DTW algorithm to obtain matching similarity, and the period integrity factor of the IMF component is obtained according to the matching similarity and the maximum period of the IMF component;

[0008] Obtaining a wavelet threshold according to the preliminary complexity factor, the energy distribution deviation factor and the cycle integrity factor;

[0009] The signal to be detected is filtered according to the wavelet threshold, and the filtered signal to be detected is reconstructed through the Hilbert transform, and the attenuation index of the bottom peak of the signal to be detected is identified to judge the quality of the optical machine cast iron accessory to be detected.

[0010] Further, obtaining a preliminary complexity factor based on the to-be-detected spectrum of the to-be-detected signal and the standard spectrum of the standard detection signal includes:

[0011] Obtaining the amplitude corresponding to each frequency in the spectrum diagram to be detected and normalizing it, and constructing a frequency proportion sequence to be detected in order of frequency from small to large;

[0012] Obtaining the amplitude corresponding to each frequency in the standard spectrum diagram and normalizing it, and constructing a standard frequency proportion sequence in order of frequency from small to large;

[0013] Calculating the amplitude difference of the corresponding frequencies in the standard frequency proportion sequence and the frequency proportion sequence to be detected and obtaining the average value of the absolute value of the amplitude difference as the frequency component difference value;

[0014] Acquire standard information entropy based on the standard spectrum diagram, acquire information entropy to be detected based on the spectrum diagram to be detected, and subtract a normalized value of the information entropy to be detected from the standard information entropy as an information entropy difference;

[0015] The frequency component difference value and the information entropy difference value are added to obtain the preliminary complexity factor.

[0016] Further, obtaining the energy distribution deviation factor of the current continuous segment according to the energy proportion includes:

[0017] Obtaining an average value of the energy proportion;

[0018] After subtracting the average value from any of the energy proportions, an absolute value is taken to obtain an energy deviation value;

[0019] The energy deviation value acquisition process is repeated to obtain the energy deviation value corresponding to each of the IMF components, and an average value of the energy deviation values ​​is obtained as the energy distribution deviation factor.

[0020] Further, acquiring the period integrity factor of the IMF component according to the matching similarity and the maximum period of the IMF component includes:

[0021] Obtaining an average value of the matching similarities between the nth IMF component and the remaining N-1 IMF components as a matching average value, wherein the value of n is 1 to N, and N is the number of the IMF components;

[0022] Subtract the maximum period of any of the remaining N-1 IMF components from the maximum period of the nth IMF component, and then take the absolute value to obtain a period difference value;

[0023] Repeat the process of obtaining the cycle difference value to obtain N-1 cycle difference values, add the cycle difference values, and then perform normalization processing to obtain a normalized cycle difference value;

[0024] The cycle difference normalized value is multiplied by the matching average value to obtain a first product, and the matching average value is subtracted from the first product to obtain the cycle integrity factor.

[0025] Further, obtaining a wavelet threshold according to the preliminary complexity factor, the energy distribution deviation factor and the period integrity factor includes:

[0026] Normalizing the preliminary complexity factor, the energy distribution deviation factor, and the cycle integrity factor;

[0027] Multiplying the normalized energy distribution deviation factor and the negatively correlated normalized cycle integrity factor to obtain a second product, and performing normalization processing on the second product;

[0028] The normalized second product is multiplied by the normalized preliminary complexity factor to obtain a third product, and the third product is normalized to obtain the wavelet threshold.

[0029] The embodiment of the present invention further provides a quality inspection system for optical mechanical cast iron accessories, the system comprising:

[0030] A preliminary complexity factor acquisition module is used to acquire a signal to be detected of an optical-mechanical cast-iron accessory to be detected and a standard detection signal of a standard optical-mechanical cast-iron accessory, and to acquire a preliminary complexity factor based on a frequency spectrum of the signal to be detected and a standard frequency spectrum of the standard detection signal;

[0031] An energy distribution deviation factor acquisition module is used to perform EMD processing on the signal to be detected to obtain at least two IMF components, and after performing Hilbert transform on the IMF components, determine whether the amplitude of the adjacent wave path of the wave path with non-zero amplitude in the signal to be detected is 0, if not, they are the same continuous wave path segment, extract the continuous segment in the corresponding IMF component based on each continuous wave path segment, take the normalized value of the standard deviation of each IMF component in the continuous segment as the energy proportion, and obtain the energy distribution deviation factor of the current continuous segment according to the energy proportion;

[0032] A cycle integrity factor acquisition module, used to obtain the maximum cycle of the IMF component within a preset lag number through an ACF model, and segment the IMF component according to the maximum cycle, use the DTW algorithm to match any two segments to obtain matching similarity, and obtain the cycle integrity factor of the IMF component according to the matching similarity and the maximum cycle of the IMF component;

[0033] A calculation module, used for obtaining a wavelet threshold according to the preliminary complexity factor, the energy distribution deviation factor and the cycle integrity factor;

[0034] The quality detection module is used to filter the signal to be detected according to the wavelet threshold, reconstruct the filtered signal to be detected through the Hilbert transform, and identify the attenuation index of the bottom peak of the signal to be detected to judge the quality of the optical machine cast iron accessories to be detected.

[0035] Further, obtaining a preliminary complexity factor based on the to-be-detected spectrum of the to-be-detected signal and the standard spectrum of the standard detection signal includes:

[0036] Obtaining the amplitude corresponding to each frequency in the spectrum diagram to be detected and normalizing it, and constructing a frequency proportion sequence to be detected in order of frequency from small to large;

[0037] Obtaining the amplitude corresponding to each frequency in the standard spectrum diagram and normalizing it, and constructing a standard frequency proportion sequence in order of frequency from small to large;

[0038] Calculating the amplitude difference of the corresponding frequencies in the standard frequency proportion sequence and the frequency proportion sequence to be detected and obtaining the average value of the absolute value of the amplitude difference as the frequency component difference value;

[0039] Acquire standard information entropy based on the standard spectrum diagram, acquire information entropy to be detected based on the spectrum diagram to be detected, and subtract a normalized value of the information entropy to be detected from the standard information entropy as an information entropy difference;

[0040] The frequency component difference value and the information entropy difference value are added to obtain the preliminary complexity factor.

[0041] Further, obtaining the energy distribution deviation factor of the current continuous segment according to the energy proportion includes:

[0042] Obtaining an average value of the energy proportion;

[0043] After subtracting the average value from any of the energy proportions, an absolute value is taken to obtain an energy deviation value;

[0044] The energy deviation value acquisition process is repeated to obtain the energy deviation value corresponding to each of the IMF components, and an average value of the energy deviation values ​​is obtained as the energy distribution deviation factor.

[0045] Further, acquiring the period integrity factor of the IMF component according to the matching similarity and the maximum period of the IMF component includes:

[0046] Obtaining an average value of the matching similarities between the nth IMF component and the remaining N-1 IMF components as a matching average value, wherein the value of n is 1 to N, and N is the number of the IMF components;

[0047] Subtract the maximum period of any of the remaining N-1 IMF components from the maximum period of the nth IMF component, and then take the absolute value to obtain a period difference value;

[0048] Repeat the process of obtaining the cycle difference value to obtain N-1 cycle difference values, add the cycle difference values, and then perform normalization processing to obtain a normalized cycle difference value;

[0049] The cycle difference normalized value is multiplied by the matching average value to obtain a first product, and the matching average value is subtracted from the first product to obtain the cycle integrity factor.

[0050] Further, obtaining a wavelet threshold according to the preliminary complexity factor, the energy distribution deviation factor and the period integrity factor includes:

[0051] Normalizing the preliminary complexity factor, the energy distribution deviation factor, and the cycle integrity factor;

[0052] Multiplying the normalized energy distribution deviation factor and the negatively correlated normalized cycle integrity factor to obtain a second product, and performing normalization processing on the second product;

[0053] The normalized second product is multiplied by the normalized preliminary complexity factor to obtain a third product, and the third product is normalized to obtain the wavelet threshold.

[0054] The present invention has the following beneficial effects:

[0055] First, the signal to be detected of the optical machine cast iron accessories to be detected and the standard detection signal of the standard optical machine cast iron accessories are obtained, and the preliminary complexity factor is obtained based on the spectrum of the signal to be detected and the standard spectrum of the standard detection signal. The purpose of obtaining the preliminary complexity factor is to determine the complexity of the signal to be detected. The signal to be detected is subjected to EMD processing to obtain at least two IMF components, and after the IMF components are subjected to Hilbert transform, it is determined whether the amplitude of the adjacent wave path of the wave path with non-zero amplitude in the signal to be detected is 0. If not, it is the same continuous wave path segment. Based on each of the continuous wave path segments, the continuous segments in the corresponding IMF components are extracted, and the normalized value of the standard deviation of each IMF component in the continuous segment is used as the energy proportion. According to the energy proportion, the energy distribution deviation factor of the current continuous segment is obtained. The larger the energy distribution deviation factor, the greater the energy difference of each IMF component in the same continuous wave path segment. The maximum period of the IMF component within a preset lag number is obtained through the ACF model, and the IMF component is segmented according to the maximum period, and the DTW algorithm is used to match any two segments to obtain the matching similarity, and the period integrity factor of the IMF component is obtained according to the matching similarity and the maximum period of the IMF component. The period integrity factor is an overall evaluation of the actual detection signal in each IMF component. The wavelet threshold is obtained according to the preliminary complexity factor, the energy distribution deviation factor and the period integrity factor. The setting of the wavelet threshold is for denoising. The signal to be detected is filtered according to the wavelet threshold, and the filtered signal to be detected is reconstructed by the Hilbert transform, and the attenuation index of the bottom peak of the signal to be detected is identified to judge the quality of the optical machine cast iron accessories to be detected. After filtering and Hilbert transform reconstruction of the signal to be detected, judging the quality of the optical machine cast iron accessories to be detected according to the processed signal to be detected is a prior art. The present invention not only significantly improves the filtering efficiency of harmonic interference, but also greatly reduces the loss of defect response or bottom echo information. This improvement directly promotes the accuracy of subsequent quality inspection and defect location operations, making the entire inspection process more efficient and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1A flow chart of a method for quality inspection of optical machine cast iron accessories provided by the first embodiment of the present invention;

[0058] Figure 2 A flowchart of a process for obtaining a preliminary complexity factor provided by a second embodiment of the present invention;

[0059] Figure 3 A flowchart of a process for obtaining an energy distribution deviation factor provided in a third embodiment of the present invention;

[0060] Figure 4 A flowchart of a process for obtaining a cycle integrity factor provided in a fourth embodiment of the present invention;

[0061] Figure 5 A flowchart of a process for obtaining a wavelet threshold value provided by a fifth embodiment of the present invention;

[0062] Figure 6 This is a schematic diagram of a quality inspection system for optical mechanical cast iron accessories provided by a sixth embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, characteristics and effects of a quality inspection method for optical machine cast iron accessories proposed according to the present invention, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0064] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0065] The specific scheme of the quality inspection method for optical machine cast iron accessories provided by the present invention is described in detail below with reference to the accompanying drawings.

[0066] See also Figure 1 , which shows a flow chart of a method for quality inspection of optical machine cast iron accessories provided by the first embodiment of the present invention, the method comprising:

[0067] S101. Obtain a signal to be detected of an optical mechanical cast iron accessory to be detected and a standard detection signal of a standard optical mechanical cast iron accessory, and obtain a preliminary complexity factor based on a frequency spectrum of the signal to be detected and a standard frequency spectrum of the standard detection signal.

[0068] In view of the fact that optical and mechanical cast iron accessories are generally thin, have a single shape, and the detection surface is parallel to the ground, the bottom surface multiple reflection echo method is used for detection. The ultrasonic testing (UT) method is selected, and the corresponding ultrasonic probe and other equipment are equipped. Before testing the casting, the first step is to thoroughly clean the surface of the casting to ensure that all impurities that may affect the accuracy of the detection, such as oil stains, paint layers, etc., are removed. In addition, according to the requirements of industry standards EN12680-3 and GB / T 34904-2017, when conducting ultrasonic testing, the casting needs to be fully inspected from multiple directions. Therefore, before testing, the casting needs to be accurately marked, and the area to be tested and the corresponding detection direction are clearly marked. Subsequently, the results of each ultrasonic detection signal are labeled according to the detection area and detection direction, and recorded in detail to ensure the traceability and accuracy of the detection data.

[0069] The signal to be detected can be transformed into the frequency spectrum to be detected by short-time Fourier transform, and the standard detection signal can be transformed into the standard frequency spectrum by short-time Fourier transform.

[0070] The process of obtaining the preliminary complexity factor will be described in detail in the second embodiment and will not be repeated here.

[0071] S102. Perform EMD processing on the signal to be detected to obtain at least two IMF components, and after performing Hilbert transform on the IMF components, determine whether the amplitude of the adjacent wave path of the wave path with non-zero amplitude in the signal to be detected is 0, if not, they are the same continuous wave path segment, and based on each of the continuous wave path segments, extract the corresponding continuous segments in the IMF components, take the normalized value of the standard deviation of each of the IMF components in the continuous segment as the energy proportion, and obtain the energy distribution deviation factor of the current continuous segment according to the energy proportion.

[0072] The EMD (Empirical Mode Decomposition) is a prior art and will not be described in detail here.

[0073] The IMF component (Intrinsic Mode Function) is a result of performing EMD processing on the signal to be detected.

[0074] The process of obtaining the energy distribution deviation factor will be described in detail in the third embodiment and will not be repeated here.

[0075] S103. Obtain the maximum period of the IMF component within a preset lag number through the ACF model, segment the IMF component according to the maximum period, use the DTW algorithm to match any two segments to obtain matching similarity, and obtain the period integrity factor of the IMF component according to the matching similarity and the maximum period of the IMF component.

[0076] Whether it is a defect echo or a bottom echo, its essence is the multiple reflections of the ultrasonic signal inside the optical mechanical casting accessories, and as the number of reflections increases, the signal energy gradually weakens. Therefore, both of them show a relatively obvious periodicity, and the period is almost the same. However, harmonic interference does not have such characteristics. Based on this logic, in each IMF component, the part with poor periodicity and inconsistent with the overall periodicity of the component has a relatively higher proportion of harmonics. For such components, a higher wavelet threshold should be assigned in subsequent processing to improve the harmonic removal effect.

[0077] Taking the nth IMF component as an example, the wave path of the component signal is used as the time dimension, and the signal amplitude corresponding to each wave path is used as the observation value. Subsequently, these observation values ​​are input into the autocorrelation function (ACF model), and the lag number is set to 20% of the wave path number. It should be noted here that the setting of the lag number must be greater than the internal maximum period. According to the standard detection signal obtained from the standard optomechanical cast iron accessories, the maximum number of bottom surface echoes is five. Therefore, inside the defective optomechanical cast iron accessories, the number of bottom surface echoes can only be less than or equal to five. Therefore, the 20% here is a reasonable value based on the maximum number of reflections of five.

[0078] The IMF component is segmented according to the maximum period. Specifically, the IMF component is divided into a plurality of segments of equal length, and the length of each segment is equal to the maximum period.

[0079] It is an existing technology to use the DTW (Dynamic Time Warping) algorithm to match any two segments to obtain the matching similarity, which will not be described in detail here.

[0080] The process of obtaining the cycle integrity factor will be described in detail in the fourth embodiment and will not be repeated here.

[0081] S104. Obtain a wavelet threshold according to the preliminary complexity factor, the energy distribution deviation factor and the period integrity factor.

[0082] The process of obtaining the wavelet threshold will be described in detail in the fifth embodiment and will not be repeated here.

[0083] S105. Filter the signal to be detected according to the wavelet threshold, reconstruct the filtered signal to be detected through the Hilbert transform, and identify the attenuation index of the bottom peak of the signal to be detected to judge the quality of the optical machine cast iron accessories to be detected.

[0084] The purpose of filtering the signal to be detected according to the wavelet threshold is to remove harmonic interference.

[0085] Reconstructing the filtered signal to be detected by the Hilbert transform means that after filtering, the instantaneous phase and envelope of the signal to be detected are obtained by using the Hilbert transform. The Hilbert transform is a linear transform that can convert a real signal into an analytical signal, thereby obtaining the instantaneous frequency and amplitude information of the signal.

[0086] Identifying the attenuation index of the bottom peak of the signal to be detected refers to finding the bottom peak in the reconstructed signal to be detected and measuring its attenuation rate or degree. The attenuation index is a parameter that describes how fast a peak decreases over time.

[0087] The obtained attenuation index is compared with a certain standard or threshold value to evaluate the quality of the optical and mechanical cast iron accessories. If the attenuation index meets the predetermined standard, the accessories are considered qualified, and if not, they are considered unqualified.

[0088] Identifying the attenuation index of the bottom peak of the signal to be detected to judge the quality of the optical machine cast iron fittings to be detected is a well-known conventional technology and will not be described in detail here.

[0089] In addition, the present invention can also perform operations such as defect location by identifying wave peaks other than the bottom wave peak. Defect location by identifying wave peaks is an existing conventional technology for quality inspection of optical machine casting accessories, which will not be repeated here.

[0090] Figure 2 The flowchart of the process of obtaining the preliminary complexity factor provided in the second embodiment of the present invention, wherein obtaining the preliminary complexity factor based on the to-be-detected spectrum of the to-be-detected signal and the standard spectrum of the standard detection signal comprises:

[0091] S201. Obtain the amplitude corresponding to each frequency in the spectrum diagram to be detected and normalize it, and construct a frequency proportion sequence to be detected in the order of frequency from small to large.

[0092] The horizontal axis of the spectrum to be detected represents the frequency, and the vertical axis represents the amplitude. The frequency proportion sequence to be detected is the amplitude corresponding to the frequency order. When the amplitude corresponding to the frequency in the spectrum to be detected is 0, 0 can be used to fill the frequency proportion sequence to be detected.

[0093] S202. Obtain the amplitude corresponding to each frequency in the standard spectrum diagram and normalize it, and construct a standard frequency proportion sequence in order of frequency from small to large.

[0094] The horizontal axis of the standard spectrum graph represents the frequency, and the vertical axis represents the amplitude. The standard frequency proportion sequence is the corresponding amplitude arranged in the order of frequency. When the amplitude corresponding to a frequency in the standard spectrum graph is 0, 0 can be used to fill in the standard frequency proportion sequence.

[0095] The number of amplitudes in the to-be-detected frequency proportion sequence is equal to the number of amplitudes in the standard frequency proportion sequence.

[0096] S203. Calculate the amplitude difference of the corresponding frequencies in the standard frequency proportion sequence and the frequency proportion sequence to be detected, and obtain the average value of the absolute value of the amplitude difference as the frequency component difference value.

[0097] The frequency component difference value can be expressed as:

[0098] ;

[0099] Wherein, A represents the frequency component difference value, r is an integer from 1 to Q, Q is the number of amplitudes in the to-be-detected frequency proportion sequence and / or the standard frequency proportion sequence, is the amplitude corresponding to the rth frequency in the standard frequency proportion sequence, is the amplitude corresponding to the rth frequency in the frequency proportion sequence to be detected.

[0100] The larger the frequency component difference value is, the greater the difference between the frequency component in the current signal to be detected and the frequency component in the standard detection signal is, that is, the current signal to be detected may have a relatively higher number of defects or harmonic interference, resulting in serious superposition. Therefore, the frequency component difference in the frequency spectrum diagram to be detected obtained by Fourier transform of the signal to be detected is relatively large.

[0101] S204. Obtain standard information entropy based on the standard spectrum diagram, obtain information entropy to be detected based on the spectrum diagram to be detected, and subtract a normalized value of the information entropy to be detected from the standard information entropy as an information entropy difference.

[0102] The information entropy difference can be expressed as:

[0103] ;

[0104] Wherein, B is the information entropy difference, G1 represents the standard information entropy, G2 represents the information entropy to be detected, Represents the normalization function.

[0105] S205. Add the frequency component difference value and the information entropy difference value to obtain the preliminary complexity factor.

[0106] The preliminary complexity factor can be expressed as:

[0107] ;

[0108] Among them, Z represents the preliminary complexity factor, A represents the frequency component difference value, and B represents the information entropy difference value.

[0109] It should be clear that the larger the value of the preliminary complexity factor, the more serious the echo superposition phenomenon caused by internal defects, which in turn shows lower sensitivity in the subsequent noise differentiation process. As for information entropy, since the difference in frequency components needs to be reflected by cumulative averaging, this may lead to the fact that although the frequency components inside some optical machine cast iron accessories to be tested are not complex, the amplitude differences between a few frequencies are significant, which may also produce large frequency component differences. However, in the signal obtained by the bottom echo method, in order to comprehensively evaluate the signal characteristics, it is necessary to consider the complexity and proportion of the frequency components at the same time. Therefore, it is also necessary to calculate the information entropy difference here to make up for the shortcomings of the frequency component difference evaluation.

[0110] Figure 3 The flowchart of the process of obtaining the energy distribution deviation factor provided by the third embodiment of the present invention, wherein obtaining the energy distribution deviation factor of the current continuous segment according to the energy proportion includes:

[0111] S301. Obtain an average value of the energy proportion.

[0112] The average value of the energy proportion can be expressed as , wherein M is the number of energy proportions.

[0113] S302. Subtract the average value from any of the energy proportions, and then take the absolute value to obtain an energy deviation value.

[0114] The energy deviation value can be expressed as: , wherein the represents the mth energy proportion, where m is an integer ranging from 1 to M.

[0115] S303. Repeat the energy deviation value acquisition process to obtain the energy deviation value corresponding to each of the IMF components, and obtain the average value of the energy deviation value as the energy distribution deviation factor.

[0116] The energy distribution deviation factor can be expressed as:

[0117] ;

[0118] Wherein, S is the energy distribution deviation factor.

[0119] When the energy distribution deviation factor increases, it means that the energy difference of each IMF component in the continuous segment is significant, which usually indicates that the continuous segment may contain actual defects or bottom echo signals, rather than random and weak electromagnetic interference signals. Therefore, in subsequent processing, a more stringent wavelet threshold constraint needs to be applied to the continuous segment to prevent important internal information from being missed due to the threshold being set too high.

[0120] Figure 4 The flowchart of the process of obtaining the cycle integrity factor provided by the fourth embodiment of the present invention, wherein obtaining the cycle integrity factor of the IMF component according to the matching similarity and the maximum cycle of the IMF component includes:

[0121] S401. Obtain an average value of the matching similarities between the nth IMF component and the remaining N-1 IMF components as a matching average value, wherein the value of n is 1 to N, and N is the number of the IMF components.

[0122] It is easy to know that the number of matching similarities between the nth IMF component and the remaining N-1 IMF components is N-1, and the matching average value can be obtained by adding the N-1 matching similarities in sequence and then dividing by N-1.

[0123] S402. Subtract the maximum period of any of the remaining N-1 IMF components from the maximum period of the nth IMF component, and then take the absolute value to obtain a period difference value.

[0124] The period difference value can be expressed as: , wherein the represents the maximum period of the nth IMF component, represents the maximum period of the j-th IMF component, where j is not equal to n, and the value range of j is 1 to N-1 and is an integer.

[0125] S403. Repeat the process of obtaining the cycle difference value to obtain N-1 cycle difference values, add the cycle difference values, and then perform normalization processing to obtain a normalized cycle difference value.

[0126] The normalized value of the period difference can be expressed as:

[0127] ;

[0128] Among them, the is the normalization function.

[0129] S404: Multiply the normalized value of the cycle difference by the matching average value to obtain a first product, and subtract the first product from the matching average value to obtain the cycle integrity factor.

[0130] The cycle integrity factor can be expressed as:

[0131] ;

[0132] Among them, the represents the matching average value corresponding to the nth IMF component, represents the cycle integrity factor corresponding to the nth IMF component.

[0133] As the matching similarity increases, the proportion of the signal to be detected in the IMF component also increases. In addition, if the maximum period value obtained by the autocorrelation function (ACF) of the nth IMF component is closer to that of other IMF components, it further indicates that the maximum period of the nth IMF component is closer to the complete echo path.

[0134] Figure 5 The flowchart of the process of obtaining the wavelet threshold provided in the fifth embodiment of the present invention, wherein obtaining the wavelet threshold according to the preliminary complexity factor, the energy distribution deviation factor and the period integrity factor comprises:

[0135] S501. Normalize the preliminary complexity factor, the energy distribution deviation factor, and the cycle integrity factor.

[0136] The initial complexity factor can be normalized using To express, the energy distribution deviation factor can be expressed as s after negative correlation normalization, and the cycle integrity factor can be expressed as To express.

[0137] S502. Multiply the normalized energy distribution deviation factor and the negatively correlated normalized cycle integrity factor to obtain a second product, and perform normalization processing on the second product.

[0138] The second product can be expressed as follows after normalization:

[0139] ;

[0140] Among them, the is the normalization function.

[0141] S503. Multiply the normalized second product and the normalized preliminary complexity factor to obtain a third product, and normalize the third product to obtain the wavelet threshold.

[0142] The wavelet threshold can be expressed as:

[0143] ;

[0144] Wherein, K is the wavelet threshold.

[0145] After the period integrity factor is normalized by negative correlation, the larger its value is, the fewer actual defects or bottom echo signals may be contained in the nth IMF component, and the degree of harmonic noise existence increases accordingly. The energy distribution deviation factor reflects the performance difference between multiple IMF components within the range corresponding to the continuous segment, revealing the presence of large harmonic noise interference. These two features are used together to determine the wavelet threshold of the nth IMF component in the continuous segment. The larger the eigenvalue, the larger the corresponding wavelet threshold, thereby enhancing the strength of denoising. Indicates the wavelet basic threshold obtained as a whole for the signal to be detected.

[0146] Figure 6 This is a schematic diagram of a quality inspection system for optical mechanical cast iron accessories provided by a sixth embodiment of the present invention, the system comprising:

[0147] A preliminary complexity factor acquisition module 601 is used to acquire a signal to be detected of an optical-mechanical cast iron accessory to be detected and a standard detection signal of a standard optical-mechanical cast iron accessory, and acquire a preliminary complexity factor based on a frequency spectrum of the signal to be detected and a standard frequency spectrum of the standard detection signal;

[0148] The energy distribution deviation factor acquisition module 602 is used to perform EMD processing on the signal to be detected to obtain at least two IMF components, and after performing Hilbert transform on the IMF components, determine whether the amplitude of the adjacent wave path of the wave path with non-zero amplitude in the signal to be detected is 0, if not, they are the same continuous wave path segment, extract the continuous segment in the corresponding IMF component based on each continuous wave path segment, take the normalized value of the standard deviation of each IMF component in the continuous segment as the energy proportion, and obtain the energy distribution deviation factor of the current continuous segment according to the energy proportion;

[0149] A cycle integrity factor acquisition module 603 is used to obtain the maximum cycle of the IMF component within a preset lag number through an ACF model, and segment the IMF component according to the maximum cycle, use a DTW algorithm to match any two segments to obtain matching similarity, and obtain the cycle integrity factor of the IMF component according to the matching similarity and the maximum cycle of the IMF component;

[0150] A calculation module 604, configured to obtain a wavelet threshold according to the preliminary complexity factor, the energy distribution deviation factor and the period integrity factor;

[0151] The quality detection module 605 is used to filter the signal to be detected according to the wavelet threshold, reconstruct the filtered signal to be detected through the Hilbert transform, and identify the attenuation index of the bottom peak of the signal to be detected to judge the quality of the optical machine cast iron accessories to be detected.

[0152] Further, obtaining a preliminary complexity factor based on the to-be-detected spectrum of the to-be-detected signal and the standard spectrum of the standard detection signal includes:

[0153] Obtaining the amplitude corresponding to each frequency in the spectrum diagram to be detected and normalizing it, and constructing a frequency proportion sequence to be detected in order of frequency from small to large;

[0154] Obtaining the amplitude corresponding to each frequency in the standard spectrum diagram and normalizing it, and constructing a standard frequency proportion sequence in order of frequency from small to large;

[0155] Calculating the amplitude difference of the corresponding frequencies in the standard frequency proportion sequence and the frequency proportion sequence to be detected and obtaining the average value of the absolute value of the amplitude difference as the frequency component difference value;

[0156] Acquire standard information entropy based on the standard spectrum diagram, acquire information entropy to be detected based on the spectrum diagram to be detected, and subtract a normalized value of the information entropy to be detected from the standard information entropy as an information entropy difference;

[0157] The frequency component difference value and the information entropy difference value are added to obtain the preliminary complexity factor.

[0158] Further, obtaining the energy distribution deviation factor of the current continuous segment according to the energy proportion includes:

[0159] Obtaining an average value of the energy proportion;

[0160] After subtracting the average value from any of the energy proportions, an absolute value is taken to obtain an energy deviation value;

[0161] The energy deviation value acquisition process is repeated to obtain the energy deviation value corresponding to each of the IMF components, and an average value of the energy deviation values ​​is obtained as the energy distribution deviation factor.

[0162] Further, acquiring the period integrity factor of the IMF component according to the matching similarity and the maximum period of the IMF component includes:

[0163] Obtaining an average value of the matching similarities between the nth IMF component and the remaining N-1 IMF components as a matching average value, wherein the value of n is 1 to N, and N is the number of the IMF components;

[0164] Subtract the maximum period of any of the remaining N-1 IMF components from the maximum period of the nth IMF component, and then take the absolute value to obtain a period difference value;

[0165] Repeat the process of obtaining the cycle difference value to obtain N-1 cycle difference values, add the cycle difference values, and then perform normalization processing to obtain a normalized cycle difference value;

[0166] The cycle difference normalized value is multiplied by the matching average value to obtain a first product, and the matching average value is subtracted from the first product to obtain the cycle integrity factor.

[0167] Further, obtaining a wavelet threshold according to the preliminary complexity factor, the energy distribution deviation factor and the period integrity factor includes:

[0168] Normalizing the preliminary complexity factor, the energy distribution deviation factor, and the cycle integrity factor;

[0169] Multiplying the normalized energy distribution deviation factor and the negatively correlated normalized cycle integrity factor to obtain a second product, and performing normalization processing on the second product;

[0170] The normalized second product is multiplied by the normalized preliminary complexity factor to obtain a third product, and the third product is normalized to obtain the wavelet threshold.

[0171] The technical features and technical effects of the optical mechanical cast iron accessories quality inspection system proposed in the embodiment of the present invention are the same as those of the method proposed in the embodiment of the present invention, and will not be elaborated here.

[0172] The present invention has the following beneficial effects:

[0173] First, the signal to be detected of the optical machine cast iron accessories to be detected and the standard detection signal of the standard optical machine cast iron accessories are obtained, and the preliminary complexity factor is obtained based on the spectrum of the signal to be detected and the standard spectrum of the standard detection signal. The purpose of obtaining the preliminary complexity factor is to determine the complexity of the signal to be detected. The signal to be detected is subjected to EMD processing to obtain at least two IMF components, and after the IMF components are subjected to Hilbert transform, it is determined whether the amplitude of the adjacent wave path of the wave path with non-zero amplitude in the signal to be detected is 0. If not, it is the same continuous wave path segment. Based on each of the continuous wave path segments, the continuous segments in the corresponding IMF components are extracted, and the normalized value of the standard deviation of each IMF component in the continuous segment is used as the energy proportion. According to the energy proportion, the energy distribution deviation factor of the current continuous segment is obtained. The larger the energy distribution deviation factor, the greater the energy difference of each IMF component in the same continuous wave path segment. The maximum period of the IMF component within a preset lag number is obtained through the ACF model, and the IMF component is segmented according to the maximum period, and the DTW algorithm is used to match any two segments to obtain the matching similarity, and the period integrity factor of the IMF component is obtained according to the matching similarity and the maximum period of the IMF component. The period integrity factor is an overall evaluation of the actual detection signal in each IMF component. The wavelet threshold is obtained according to the preliminary complexity factor, the energy distribution deviation factor and the period integrity factor. The setting of the wavelet threshold is for denoising. The signal to be detected is filtered according to the wavelet threshold, and the filtered signal to be detected is reconstructed by the Hilbert transform, and the attenuation index of the bottom peak of the signal to be detected is identified to judge the quality of the optical machine cast iron accessories to be detected. After filtering and Hilbert transform reconstruction of the signal to be detected, judging the quality of the optical machine cast iron accessories to be detected according to the processed signal to be detected is a prior art. The present invention not only significantly improves the filtering efficiency of harmonic interference, but also greatly reduces the loss of defect response or bottom echo information. This improvement directly promotes the accuracy of subsequent quality inspection and defect location operations, making the entire inspection process more efficient and reliable.

[0174] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying 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.

[0175] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for quality inspection of optical machine cast iron accessories, characterized in that: The method comprises: Acquire a signal to be detected of the optical-mechanical cast-iron fitting to be detected and a standard detection signal of a standard optical-mechanical cast-iron fitting, and acquire a preliminary complexity factor based on a frequency spectrum of the signal to be detected and a standard frequency spectrum of the standard detection signal; Among them, the method for obtaining the preliminary complexity factor is: obtain the amplitude corresponding to each frequency in the spectrum diagram to be detected and normalize it, and construct a frequency proportion sequence to be detected in the order of frequency from small to large; obtain the amplitude corresponding to each frequency in the standard spectrum diagram and normalize it, and construct a standard frequency proportion sequence in the order of frequency from small to large; calculate the amplitude difference of the corresponding frequency in the standard frequency proportion sequence and the frequency proportion sequence to be detected, and obtain the average value of the absolute value of the amplitude difference as the frequency component difference value; obtain the standard information entropy based on the standard spectrum diagram, obtain the information entropy to be detected based on the spectrum diagram to be detected, and subtract the normalized value of the information entropy to be detected from the standard information entropy as the information entropy difference; add the frequency component difference value and the information entropy difference value to obtain the preliminary complexity factor; Performing EMD processing on the signal to be detected to obtain at least two IMF components, and after performing Hilbert transform on the IMF components, determining whether the amplitude of an adjacent wave path of a wave path with a non-zero amplitude in the signal to be detected is 0, if not, they are the same continuous wave path segment, extracting the corresponding continuous segments in the IMF components based on each of the continuous wave path segments, taking the normalized value of the standard deviation of each of the IMF components in the continuous segment as the energy proportion, and obtaining the energy distribution deviation factor of the current continuous segment according to the energy proportion; The maximum period of the IMF component within a preset lag number is obtained through the ACF model, and the IMF component is segmented according to the maximum period, and any two segments are matched using the DTW algorithm to obtain matching similarity, and the period integrity factor of the IMF component is obtained according to the matching similarity and the maximum period of the IMF component; The method for obtaining the cycle integrity factor is as follows: obtaining the average value of the matching similarity between the nth IMF component and the remaining N-1 IMF components as the matching average value, wherein the value of n is 1 to N, and N is the number of the IMF components; subtracting the maximum period of any of the remaining N-1 IMF components from the maximum period of the nth IMF component, and then taking the absolute value to obtain the cycle difference value; repeating the cycle difference value acquisition process to obtain N-1 cycle difference values, adding the cycle difference values, and then normalizing them to obtain a cycle difference normalized value; multiplying the cycle difference normalized value by the matching average value to obtain a first product, and subtracting the first product from the matching average value to obtain the cycle integrity factor; Obtaining a wavelet threshold according to the preliminary complexity factor, the energy distribution deviation factor and the cycle integrity factor; wherein the preliminary complexity factor, the energy distribution deviation factor and the cycle integrity factor are all normalized; the normalized energy distribution deviation factor is multiplied with the negatively correlated normalized cycle integrity factor to obtain a second product, and the second product is normalized; the normalized second product is multiplied with the normalized preliminary complexity factor to obtain a third product, and the third product is normalized to obtain the wavelet threshold; The signal to be detected is filtered according to the wavelet threshold, and the filtered signal to be detected is reconstructed through the Hilbert transform, and the attenuation index of the bottom peak of the signal to be detected is identified to judge the quality of the optical machine cast iron accessory to be detected.

2. The optical machine cast iron fittings quality inspection method according to claim 1, characterized in that: Obtaining the energy distribution deviation factor of the current continuous segment according to the energy proportion includes: Obtaining an average value of the energy proportion; After subtracting the average value from any of the energy proportions, an absolute value is taken to obtain an energy deviation value; The energy deviation value acquisition process is repeated to obtain the energy deviation value corresponding to each of the IMF components, and an average value of the energy deviation values ​​is obtained as the energy distribution deviation factor.

3. A quality inspection system for optical mechanical cast iron accessories, characterized in that: include: A preliminary complex factor acquisition module is used to obtain the signal to be detected of the optical machine cast iron accessories to be detected and the standard detection signal of the standard optical machine cast iron accessories, and obtain the preliminary complex factor based on the to-be-detected spectrum of the signal to be detected and the standard spectrum of the standard detection signal; wherein, the method for obtaining the preliminary complex factor is: obtaining the amplitude corresponding to each frequency in the spectrum to be detected and normalizing it, and constructing a frequency proportion sequence to be detected in the order of frequency from small to large; obtaining the amplitude corresponding to each frequency in the standard spectrum and normalizing it, and constructing a standard frequency proportion sequence in the order of frequency from small to large; calculating the amplitude difference of the corresponding frequencies in the standard frequency proportion sequence and the frequency proportion sequence to be detected, and obtaining the average value of the absolute value of the amplitude difference as the frequency component difference value; obtaining the standard information entropy based on the standard spectrum, obtaining the information entropy to be detected based on the spectrum to be detected, and subtracting the normalized value of the information entropy to be detected from the standard information entropy as the information entropy difference; adding the frequency component difference value to the information entropy difference to obtain the preliminary complex factor; An energy distribution deviation factor acquisition module is used to perform EMD processing on the signal to be detected to obtain at least two IMF components, and after performing Hilbert transform on the IMF components, determine whether the amplitude of the adjacent wave path of the wave path with non-zero amplitude in the signal to be detected is 0, if not, they are the same continuous wave path segment, extract the continuous segment in the corresponding IMF component based on each continuous wave path segment, take the normalized value of the standard deviation of each IMF component in the continuous segment as the energy proportion, and obtain the energy distribution deviation factor of the current continuous segment according to the energy proportion; A cycle integrity factor acquisition module is used to obtain the maximum cycle of the IMF component within a preset lag number through the ACF model, and segment the IMF component according to the maximum cycle, use the DTW algorithm to match any two segments to obtain matching similarity, and obtain the cycle integrity factor of the IMF component according to the matching similarity and the maximum cycle of the IMF component; wherein the cycle integrity factor is obtained by: obtaining the average value of the matching similarities between the nth IMF component and the remaining N-1 IMF components as the matching average value, wherein the value of n is The value is 1 to N, where N is the number of the IMF components; the maximum period of the nth IMF component is subtracted from the maximum period of any of the remaining N-1 IMF components, and then the absolute value is taken to obtain a period difference value; the process of obtaining the period difference value is repeated to obtain N-1 period difference values, and the period difference values ​​are added and then normalized to obtain a period difference normalized value; the period difference normalized value is multiplied by the matching average value to obtain a first product, and the matching average value is subtracted from the first product to obtain the period integrity factor; A calculation module, used for obtaining a wavelet threshold according to the preliminary complexity factor, the energy distribution deviation factor and the cycle integrity factor; wherein the preliminary complexity factor, the energy distribution deviation factor and the cycle integrity factor are all normalized; the normalized energy distribution deviation factor is multiplied with the negatively correlated normalized cycle integrity factor to obtain a second product, and the second product is normalized; the normalized second product is multiplied with the normalized preliminary complexity factor to obtain a third product, and the third product is normalized to obtain the wavelet threshold; The quality detection module is used to filter the signal to be detected according to the wavelet threshold, reconstruct the filtered signal to be detected through the Hilbert transform, and identify the attenuation index of the bottom peak of the signal to be detected to judge the quality of the optical machine cast iron accessories to be detected.

4. The optical mechanical cast iron fittings quality inspection system as claimed in claim 3, characterized in that: Obtaining the energy distribution deviation factor of the current continuous segment according to the energy proportion includes: Obtaining an average value of the energy proportion; After subtracting the average value from any of the energy proportions, an absolute value is taken to obtain an energy deviation value; The energy deviation value acquisition process is repeated to obtain the energy deviation value corresponding to each of the IMF components, and an average value of the energy deviation values ​​is obtained as the energy distribution deviation factor.

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