A Fault Detection Method for a Radiation CT Dosimeter

Abstract

The present invention discloses a method for fault detection of a ray CT dosimeter, which relates to the field of fault detection. This method combines an improved continuous wavelet transform function and principal component analysis to obtain the fault detection result of the ray CT dosimeter to be detected. This method optimizes the parameters in the continuous wavelet transform function to obtain optimized parameters, making the fault detection of this method more accurate; this method improves the accuracy of fault detection by establishing a local kernel principal component analysis model corresponding to each local area.

Description

Technical Field

[0001] The present invention relates to the field of fault detection, and specifically, to a method for detecting faults in a ray CT dosimeter. Background Art

[0002] A ray CT dosimeter is an instrument specifically used to measure and monitor the radiation dose generated by a computed tomography (CT) device. Ray CT dosimeters are increasingly widely used in fields such as medicine and industry. However, most of the existing methods for detecting faults in ray CT dosimeters are based on traditional statistical analysis or single signal processing techniques, and have poor adaptability to the complex working environment and diverse fault modes of ray CT dosimeters. Although wavelet analysis can effectively extract the local features of signals, it has limitations in dealing with high-dimensional data and complex non-linear relationships; principal component analysis has certain advantages in reducing the data dimension, but the excavation of local features is not sufficient, resulting in insufficient accuracy in detecting faults in ray CT dosimeters. Therefore, the existing methods for detecting faults in ray CT dosimeters have problems of limitations or insufficient accuracy. Summary of the Invention

[0003] The object of the present invention is to solve the problems of limitations or insufficient accuracy in the existing methods for detecting faults in ray CT dosimeters.

[0004] To achieve the above object of the invention, the present invention provides a method for detecting faults in a ray CT dosimeter, the method comprising:

[0005] Step 1: Under the normal working state of the ray CT dosimeter, collect dose data of the ray CT dosimeter to obtain a number of training samples;

[0006] Step 2: Associate all the training samples with each wavelet basis function in the wavelet basis function library to obtain a first wavelet basis function with the highest degree of association with the dose data of the ray CT dosimeter;

[0007] Step 3: Optimize the parameters in the continuous wavelet transform function to obtain optimized parameters;

[0008] Step 4: Obtain an improved continuous wavelet transform function based on the optimized parameters and the first wavelet basis function;

[0009] Step 5: Use the improved continuous wavelet transform function to perform wavelet transform on each training sample. Each training sample correspondingly obtains several layers of wavelet coefficients. For each training sample, extract the mean, variance, and energy features of each layer of its corresponding wavelet coefficients, and obtain the feature vector of each training sample based on the mean, variance, and energy of each layer of wavelet coefficients;

[0010] Step 6: Divide each training sample into several local regions. For each local region, perform principal component analysis on the feature vector corresponding to the local region to obtain an analysis result, and establish a local kernel principal component analysis model corresponding to each local region based on the analysis result;

[0011] Step 7: Collect detection samples of the ray CT dosimeter to be detected;

[0012] Step 8: Perform wavelet transform on the detection samples using the improved continuous wavelet transform function to obtain several layers of wavelet coefficients. Extract the mean, variance, and energy features of each layer of wavelet coefficients of the detection samples, and obtain the feature vector of the detection samples based on the mean, variance, and energy of each layer of wavelet coefficients of the detection samples;

[0013] Step 9: Obtain the local region to which the feature vector of the detection sample belongs;

[0014] Step 10: Obtain the corresponding first local kernel principal component analysis model based on the local region to which the feature vector of the detection sample belongs. Based on the first local kernel principal component analysis model and the feature vector of the detection sample, calculate the first projection score of the feature vector of the detection sample in the principal component space;

[0015] Step 11: Calculate the difference between the first projection score and the standard projection score, and obtain the fault detection result of the ray CT dosimeter to be detected based on the difference.

[0016] Among them, in this method, by optimizing the parameters in the continuous wavelet transform function to obtain the optimized parameters, dynamic optimization of the parameters is achieved, the local features of the signal are captured more accurately, the efficiency and adaptability of feature extraction are significantly enhanced, and thus the improved continuous wavelet transform function can obtain more accurate features, and further the fault detection of this method is more accurate.

[0017] In this method, by establishing a local kernel principal component analysis model corresponding to each local region, flexible regulation of the kernel function parameters according to the local region data distribution is achieved, breaking the global rigid processing mode of traditional principal component analysis. This method can deeply explore the local non-linear characteristics of the data, enabling fault detection to focus on subtle abnormal points, and improving the accuracy of fault detection through this refined processing.

[0018] This method starts from the initial data preprocessing, extracts features through the improved wavelet transform, then smoothly connects to the local kernel principal component analysis, and finally completes the fault detection. Each link is interrelated and affects each other, promoting in-depth and multi-dimensional analysis of the complex dosimeter data. Overall, the collaborative mode can efficiently detect faults.

[0019] Preferably, step 3 specifically includes: obtaining the initial scaling factor a 1 and the initial translation factorb 1. Based on the initial scaling factor a 1 and the initial translation factor b 1, perform wavelet transform using the first wavelet basis function to obtain a first processing result. Analyze the chaos and stability of the data in the first processing result to obtain an analysis result, and optimize the initial scaling factor a 1 and the initial translation factor b 1 to obtain an optimized scaling factor a 2 and an optimized translation factor b 2; The specific steps of step 4 include: Based on the first wavelet basis function, the optimized scaling factor a 2 and the optimized translation factor b 2, obtain an improved continuous wavelet transform function.

[0020] Preferably, the improved continuous wavelet transform function is:

[0021] ;

[0022] Wherein, x ( t ) is the dose data of the t-th ray CT dosimeter, is the first wavelet basis function, W ( a2 , b 2) is the improved continuous wavelet transform function, t is the sample number.

[0023] Preferably, the method for obtaining the wavelet basis function library is: construct a set of wavelet basis functions, and obtain the wavelet basis function library based on the set of wavelet basis functions.

[0024] Preferably, the calculation method for the correlation degree between the dose data of the ray CT dosimeter and the wavelet basis function is:

[0025] ;

[0026] Wherein, C i is the i-th wavelet basis function and the dose data of the ray CT dosimeter x ( t ) The correlation degree between them, t is the sample number, is the i-th wavelet basis function of the complex conjugate.

[0027] Preferably, the specific steps of analyzing the chaos and stability of the data in the first processing result include:

[0028] Extract local data from the first processing result;

[0029] Calculate the variance and entropy value of the local data;

[0030] Perform clutter and stability analysis based on the variance and entropy value of the local data.

[0031] Preferably, step 8 specifically includes:

[0032] Perform wavelet transform on the detection sample using the improved continuous wavelet transform function to obtain several layers of wavelet coefficients, where d j ( k ) is x ( t )'s k-th wavelet coefficient of the j-th layer;

[0033] Extract x ( t )'s mean value of the wavelet coefficients of the j-th layer μ tj , variance and energy feature E tj , and the calculation methods are respectively:

[0034] ; ; ;

[0035] Among them, N j is the number of wavelet coefficients of the j-th layer, and k is the number of the wavelet coefficient;

[0036] Obtain the feature vector F t of the detection sample based on the mean value, variance and energy of the wavelet coefficients of each layer of the detection sample;

[0037] Among them, , m is the number of layers of wavelet transform, μ tm is x ( t )'s mean value of the wavelet coefficients of the m-th layer, is x ( t )'s variance of the wavelet coefficients of the m-th layer, E tm is x ( t )'s energy feature of the wavelet coefficients of the m-th layer.

[0038] Preferably, step 6 specifically includes:

[0039] Based on the feature vector of the detection sampleF t Perform clustering processing to obtain a clustering result, and divide the training samples into p local regions based on the clustering result: R 1, R 2,..., R P ;

[0040] Within each local region R s Based on the similarity between the sample points within the local region R s Construct a kernel matrix K s , 1 ≤ s ≤ p;

[0041] Perform centering processing on the kernel matrix K s to obtain a centered kernel matrix ;

[0042] Solve for the eigenvalues of the centered kernel matrix λ lk and the corresponding eigenvectors v lk , k = 1, 2,..., q, where q is the dimension of the eigenvectors;

[0043] Sort the eigenvectors λ lk in descending order according to the eigenvalues v lk and select the top several eigenvectors based on the sorting result as the principal components;

[0044] Establish a local kernel principal component analysis model corresponding to each local region based on the selected principal components.

[0045] Preferably, step 7 further includes: preprocessing the detection samples of the ray CT dosimeter to be detected.

[0046] Preferably, step 11 specifically includes:

[0047] Calculate the absolute value of the difference between the first projection score and the standard projection score d ;

[0048] Set a threshold T, when d > T, determine that the ray CT dosimeter to be detected is faulty; when d≤ ≤ T, determine that the ray CT dosimeter to be detected is normal;

[0049] where, ; y newis the first projection score, y normal is the standard projection score.

[0050] One or more technical solutions provided by the present invention have at least the following technical effects or advantages:

[0051] Compared with traditional detection methods, the detection accuracy of this method has been correspondingly improved: the improved wavelet transform captures more subtle and key features, and the subsequent local kernel principal component analysis further explores the potential relationships in the data. The combination of the two can accurately locate the fault features, reduce misjudgment and missed judgment situations, and can also keenly detect early minor faults.

[0052] This method has wide adaptability: the adaptive wavelet transform and the adjustable local kernel principal component analysis make the entire solution not limited to specific model dosimeters and fixed working scenarios. Whether it is equipment with different precisions or changing industrial and medical use environments, it can be flexibly adapted to stably detect faults.

[0053] This method has high intelligence and automation characteristics: the entire solution process is clear, without the need for manual adjustment of parameters based on experience. The system independently completes data processing, analysis, and diagnosis, reducing the dependence on professional operators, improving efficiency, and reducing the uncertainty introduced by human errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of the present invention, and do not limit the embodiments of the present invention;

[0055] Figure 1 is a schematic flow diagram of a method for detecting faults in a radiation CT dosimeter. DETAILED DESCRIPTION OF THE INVENTION

[0056] In order to more clearly understand the above objects, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.

[0057] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0058] Embodiment 1;

[0059] Please refer to Figure 1 , Figure 1It is a schematic flow diagram of a method for detecting faults in a ray CT dosimeter. The present invention provides a method for detecting faults in a ray CT dosimeter, and the method includes:

[0060] Step 1: Under the normal working state of the ray CT dosimeter, collect the dose data of the ray CT dosimeter to obtain a number of training samples; for example, under the normal working state of the ray CT dosimeter, collect the corresponding dose data as training samples, denoted as , where x ( t ) is the t-th sample, and N is the number of samples.

[0061] Step 2: Associate all the training samples with each wavelet basis function in the wavelet basis function library to obtain the first wavelet basis function with the highest correlation degree with the dose data of the ray CT dosimeter; in the improved wavelet transform feature extraction stage, by quantifying the correlation degree between the original dose data and different wavelet basis functions (for example, calculating the matching degree using the integral form), select the first wavelet basis function with the highest correlation degree. The purpose is to adapt to the characteristics of the current data. Different wavelet basis functions have different time-frequency characteristics. Selecting the wavelet basis function that best fits the data can capture the local features of the signal (such as mutations, trends, etc.) more accurately. This adaptability avoids the feature extraction deviation caused by the fixed wavelet basis in the traditional method, thus significantly improving the accuracy of subsequent fault detection.

[0062] Among them, the way to obtain the wavelet basis function library is: construct a wavelet basis function set, and obtain the wavelet basis function library based on the wavelet basis function set. The specific implementation method can be: form a wavelet basis function set, incorporate Coiflet, Meyer wavelets, etc. together to form a wavelet basis function library, where is the i-th wavelet basis function, i i = 1, 2, …, n, n is the total number of wavelet basis functions in the wavelet basis function library. For the original dose data x ( t ) collected by the ray CT dosimeter, use the integral form to quantify x ( t ) and the correlation degree of each wavelet basis function. Then pick out C i the largest one in it, and the corresponding is the first wavelet basis function that best fits the data at present.

[0063] Step 3: Optimize the parameters in the continuous wavelet transform function to obtain the optimized parameters; the core purpose of parameter optimization (dynamically adjusting the scale factor a and the translation factor b) is to enhance the flexibility of the wavelet transform so that it can adapt to the local characteristics of the data; specifically: give the initial scale factor a1 and the initial translation factor b Assign an empirical initial value to 1. During the wavelet transform of the training samples using the first wavelet basis function, continuously track the characteristics of the local data. In addition to calculating the variance of the local data, it is also necessary to calculate the entropy value of the local data. During the wavelet transform, the variance and entropy value of the local data need to be tracked simultaneously. The variance reflects the degree of data dispersion, and the entropy value characterizes the complexity of the data (such as randomness). The combination of the two can more comprehensively describe the characteristics of the local signal and provide a multi-dimensional basis for parameter optimization.

[0064] Among them, assume that the local data is , the window length is L , and the mean value of the data within the window is μ Ju , then the calculation formula for the local variance is:[[]]

[0065] ;

[0066] Among them, ju ( i ) is the i th data point within the window.

[0067] Among them, the calculation method of the entropy value H is:[[]] , where M is the local window length selected when calculating the entropy value of the local data, p ( m 1) is the probability of the data value appearing within the local window. The larger the entropy value, the more chaotic the data. j 1 is the starting number of the current window, m 1 is the value number. If the entropy value is high and the variance is large, it means that the data fluctuates and is chaotic. In this case, according to experience, reduce a 1 to make the wavelet more focused on high-frequency details, and at the same time adjust b 1; if the entropy value is low and the variance is small, it means that the data is stable and orderly. In this case, according to experience, increase a 1, broaden the time horizon, and adjust b 1 accordingly, so as to obtain the optimized scale factor a 2 and the translation factor b 2.

[0068] Step 4: Obtain an improved continuous wavelet transform function based on the optimized parameters and the first wavelet basis function; based on the optimized scale factor a 2 and the optimized translation factor b 2, substitute them into the improved continuous wavelet transform formula for calculation.

[0069] Step 5: Perform wavelet transform on each training sample using the improved continuous wavelet transform function. Each training sample obtains several layers of wavelet coefficients correspondingly. For each training sample, extract the mean, variance, and energy features of each layer of its corresponding wavelet coefficients, and obtain the feature vector of each training sample based on the mean, variance, and energy of each layer of wavelet coefficients. Specifically:

[0070] With the wavelet transform after optimizing the parameters, decompose the original dose data into wavelet coefficients of different scales and frequencies, where d j ( k ) is the wavelet coefficient of the j-th layer. Each layer of wavelet coefficients has its own function. The high layers capture subtle changes, and the low layers show the overall outline. Among them, the low layers correspond to the decomposition levels: relatively low decomposition levels (such as the 1st layer, the 2nd layer). The high layers correspond to the decomposition levels: relatively high decomposition levels (such as the 5th layer, the 6th layer). The low-layer wavelet coefficients extract low-frequency information through a wide time window, showing the overall structure of the signal. The high-layer wavelet coefficients extract high-frequency information through a narrow time window, focusing on the local details of the signal.

[0071] For the wavelet coefficient of the j-th layer, calculate the mean , which reveals the average level of the signal in this frequency band; calculate the variance , which characterizes the discrete situation of the data; calculate the energy , in order to characterize the total intensity or power of the signal on a specific frequency component. Energy reflects the cumulative intensity of this frequency component in the whole signal. The larger the energy value, the more significant the contribution of the signal component in this frequency band to the overall signal. For each training sample x ( t ) perform the improved wavelet transform, extract the mean μ tj , variance and energy feature E tj , and form the feature vector F t ; among them, , m is the number of layers of the wavelet transform, μ tm is the mean of the m-th layer wavelet coefficient of x ( t ), is the variance of the m-th layer wavelet coefficient of x ( t ), E tm is the energy feature of the m-th layer wavelet coefficient of x ( t ).

[0072] Step 6: Divide each training sample into several local regions. For each local region, perform principal component analysis on the feature vector corresponding to the local region to obtain the analysis result, and establish a local kernel principal component analysis model corresponding to each local region; specifically:

[0073] Input the extracted feature vectors F t into the kmean clustering algorithm to divide the training samples into p local regions R 1, R 2,... R P .

[0074] Within each local region R s , construct a kernel matrix according to the similarity between sample points within the local region R s , 1 ≤ s ≤ p; such as the Gaussian kernel function. Then, in order to eliminate the mean shift of the data in the feature space and ensure that the principal component analysis can accurately capture the variance structure of the data distribution, perform centering processing on the kernel matrix K s to obtain the centered kernel matrix K s . Solve the eigenvalues of the centered kernel matrix and the corresponding eigenvectors λ lk , k = 1, 2,..., q, where q is the dimension of the eigenvector; sort the eigenvectors v lk in descending order of the eigenvalues λ lk , and select the first r eigenvectors and the corresponding eigenvectors as the principal components based on the sorting result, that is, select the eigenvectors that have a greater impact on the result; establish a local kernel principal component analysis model corresponding to each local region based on the selected principal components v lk . Model p .

[0075] .

[0076] Among them, Model p is the p-th model, is the r-th eigenvector of the p-th model, is the r-th eigenvalue of the p-th model, μ p is the mean vector in the principal component space.

[0077] Step 7: Collect the detection samples of the ray CT dosimeter to be detected; specifically:

[0078] Collect the data of the ray CT dosimeter to be detected x ( new ) and perform preprocessing on it, such as removing noise (eliminating irrelevant interference in the signal), normalization (adjusting the signal to a unified scale, eliminating dimensional differences, and improving the consistency and efficiency of processing), etc., to obtain the preprocessed samples x ( pre ).

[0079] Step 8: Perform wavelet transform on the detection samples using the improved continuous wavelet transform function to obtain several layers of wavelet coefficients, extract the mean, variance, and energy characteristics of each layer of wavelet coefficients of the detection samples, and obtain the feature vector of the detection samples based on the mean, variance, and energy of each layer of wavelet coefficients of the detection samples; for example: perform improved wavelet transform on x ( pre ) to extract features such as the mean, variance, and energy of the wavelet coefficients, and form a feature vector F new .

[0080] Step 9: Obtain the local region to which the feature vector of the detection sample belongs; specifically: according to the clustering result in the training stage, use the Euclidean distance as the similarity measurement formula, and determine the F new belonging local region R new .

[0081] Step 10: Obtain the corresponding first local kernel principal component analysis model based on the local region to which the feature vector of the detection sample belongs, and calculate the first projection score of the feature vector of the detection sample in the principal component space based on the first local kernel principal component analysis model and the feature vector of the detection sample; specifically: substitute F new into the R new corresponding local kernel principal component analysis model to calculate its projection score in the principal component space y new .

[0082] Step 11: Calculate the difference between the first projection score and the standard projection score, and obtain the fault detection result of the ray CT dosimeter to be detected based on the difference. Specifically: calculate the absolute value of the difference between the first projection score and the standard projection score d ; set a threshold T, when d > T, it is determined that the ray CT dosimeter to be detected is faulty; when d≤ ≤ T, it is determined that the ray CT dosimeter to be detected is normal; where, ; y new is the first projection score, y normal is the standard projection score, and the size of the threshold T can be adjusted according to actual needs.

[0083] The present invention performs intelligent matching on demand, dynamically optimizes parameters, captures the local features of signals more precisely, and significantly enhances the efficiency and adaptability of feature extraction.

[0084] The present invention flexibly regulates the kernel function parameters according to the local data distribution, breaking the global and rigid processing mode of traditional principal component analysis. It can deeply explore the local non-linear characteristics of data, enabling fault detection to focus on extremely subtle abnormal points, reflecting refined processing.

[0085] The present invention starts from the initial data preprocessing, extracts features through improved wavelet transform, then smoothly connects to local kernel principal component analysis until fault detection is completed. Each link is interrelated and affects each other, promoting in-depth and multi-dimensional analysis of complex dosimeter data. This collaborative mode is crucial for efficiently screening out faults.

[0086] The present invention has the following advantages:

[0087] Improved detection accuracy: The improved wavelet transform captures more subtle and key features, and subsequent local kernel principal component analysis further explores the potential relationships in the data. The combination of the two can accurately locate fault features, reduce misjudgment and missed judgment situations, and can also sensitively detect early minor faults.

[0088] Wide adaptability: The adaptive wavelet transform and adjustable local kernel principal component analysis make the entire solution not limited to specific models of dosimeters and fixed working scenarios. Whether it is equipment with different precisions or changing industrial and medical usage environments, it can be flexibly adapted to stably detect faults.

[0089] Intelligence and automation: The entire solution process is clear, without the need for manual adjustment of parameters based on experience. The system autonomously completes data processing, analysis, and diagnosis, reducing the dependence on professional operators, improving efficiency, and reducing the uncertainty introduced by human errors.

[0090] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0091] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for detecting faults in a radiation CT dosimeter, characterized in that, The method includes: Step 1: Under the normal working state of the X-ray CT dosimeter, collect the dose data of the X-ray CT dosimeter to obtain a number of training samples; Step 2: Associate all the training samples with each wavelet basis function in the wavelet basis function library to obtain the first wavelet basis function with the highest correlation degree with the dose data of the X-ray CT dosimeter; Step 3: Optimize the parameters in the continuous wavelet transform function to obtain the optimized parameters; Step 4: Obtain an improved continuous wavelet transform function based on the optimized parameters and the first wavelet basis function; Step 5: Use the improved continuous wavelet transform function to perform wavelet transform on each training sample. Each training sample correspondingly obtains several layers of wavelet coefficients. For each training sample, extract the mean, variance, and energy features of each corresponding layer of wavelet coefficients, and obtain the feature vector of each training sample based on the mean, variance, and energy of each layer of wavelet coefficients; Step 6: Divide each training sample into several local regions. For each local region, perform principal component analysis on the feature vector corresponding to the local region to obtain an analysis result, and establish a local kernel principal component analysis model corresponding to each local region based on the analysis result; Step 7: Collect the detection samples of the X-ray CT dosimeter to be detected; Step 8: Use the improved continuous wavelet transform function to perform wavelet transform on the detection samples to obtain several layers of wavelet coefficients, extract the mean, variance, and energy features of each layer of wavelet coefficients of the detection samples, and obtain the feature vector of the detection samples based on the mean, variance, and energy of each layer of wavelet coefficients of the detection samples; Step 9: Obtain the local region to which the feature vector of the detection sample belongs; Step 10: Obtain the corresponding first local kernel principal component analysis model based on the local region to which the feature vector of the detection sample belongs. Based on the first local kernel principal component analysis model and the feature vector of the detection sample, calculate the first projection score of the feature vector of the detection sample in the principal component space; Step 11: Calculate the difference between the first projection score and the standard projection score, and obtain the fault detection result of the X-ray CT dosimeter to be detected based on the difference.

2. The fault detection method of a ray CT dosimeter according to claim 1, wherein, Step 3 specifically includes: obtaining an initial scaling factor a 1 and an initial translation factor b 1, and performing wavelet transform using a first wavelet basis function based on the initial scaling factor a 1 and the initial translation factor b 1 to obtain a first processing result, analyzing the randomness and stability of the data in the first processing result to obtain an analysis result, and optimizing the initial scaling factor a 1 and the initial translation factor b 1 to obtain an optimized scaling factor a 2 and an optimized translation factor b 2; Step 4 specifically includes: obtaining an improved continuous wavelet transform function based on the first wavelet basis function, the optimized scaling factor a 2 and the optimized translation factor b 2.

3. The method for detecting the failure of a ray CT dosimeter according to claim 2, wherein The improved continuous wavelet transform function is: ； Among them, x ( t ) is the dose data of the t-th radiographic CT dosimeter, is the first wavelet basis function, W ( a2 , b 2) is the improved continuous wavelet transform function, t is the sample number.

4. A method for detecting faults of a ray CT dosimeter according to claim 1, characterized in that, The obtaining method of the wavelet basis function library is: construct a wavelet basis function set, and obtain the wavelet basis function library based on the wavelet basis function set.

5. A method for detecting faults in a ray CT dosimeter according to claim 1, characterized in that, The calculation method of the correlation degree between the dose data of the X-ray CT dosimeter and the wavelet basis function is: ； Among them, C i is the i-th wavelet basis function and the dose data of the ray CT dosimeter x ( t ) the correlation degree between, t is the sample number, is the i-th wavelet basis function [[ID=IS]]the complex conjugate of. It should be noted that there may be some inaccuracies in the original text structure and expression. The above translation is done as accurately as possible based on the given rules.

6. A method for detecting faults of a ray CT dosimeter according to claim 2, characterized in that, The specific analysis of the clutter and stability of the data in the first processing result includes: Extract the local data from the first processing result; Calculate the variance and entropy value of the local data; Perform clutter and stability analysis through the variance and entropy value of the local data.

7. A method for detecting faults of a ray CT dosimeter according to claim 1, characterized in that, The specific content of Step 8 includes: Performing wavelet transform on the detection sample by using the improved continuous wavelet transform function to obtain several layers of wavelet coefficients, where, d j ( k ) is x ( t ) the k-th wavelet coefficient of the j-th layer; Extraction x ( t ) Mean of the j-th layer wavelet coefficients μ tj , Variance And energy feature E tj , The calculation methods are as follows: ； ； ； Among them, N j is the number of wavelet coefficients in the j-th layer, and k is the number of the wavelet coefficient; Obtain the feature vector of the detection sample based on the mean, variance, and energy of the wavelet coefficients of each layer of the detection sample F t ; Among them, , m is the number of layers of wavelet transform, μ tm is x ( t ) the mean of the m-th layer wavelet coefficients of is x ( t ) the variance of the m-th layer wavelet coefficients of E tm is x ( t ) the energy feature of the m-th layer wavelet coefficients of 8. A method for detecting faults of a ray CT dosimeter according to claim 1, characterized in that, The specific content of Step 6 includes: Feature vector based on detection samples F t Perform clustering processing to obtain clustering results, and divide the training samples into p local regions based on the clustering results: R 1, R 2,..., R P ; In each local region R s within, according to the local region R s construct a kernel matrix based on the similarity between sample points within K s , 1 ≤ s ≤ p; Center the kernel matrix K s to obtain the centered kernel matrix ; Solve the centralized kernel matrix for its eigenvalues λ lk and the corresponding eigenvectors v lk , where k = 1, 2,..., q and q is the dimension of the eigenvectors; According to the eigenvalue λ lk sort the eigenvectors v lk in descending order, and select the eigenvectors of the first several positions before sorting as the principal components based on the sorting result; Establish a local kernel principal component analysis model corresponding to each local region based on the selected principal components.

9. A method for detecting faults of a ray CT dosimeter according to claim 1, characterized in that, Step 7 further includes: preprocessing the detection samples of the X-ray CT dosimeter to be detected.

10. A method for detecting faults of a ray CT dosimeter according to claim 1, characterized in that, The specific content of Step 11 includes: Calculate the absolute value of the difference between the first projection score and the standard projection score d ; Set a threshold value T. When d > T, it is determined that the ray CT dosimeter to be detected is faulty; when d≤ ≤ T, it is determined that the ray CT dosimeter to be detected is normal; Among them, ; y new is the first projection score, y normal is the standard projection score.

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