X-ray tube and filament performance degradation prediction and failure judgment method and system

Abstract

The present application relates to the technical field of X-ray tubes. The present application discloses a method and system for predicting X-ray tube and filament performance degradation and failure determination. The method comprises: obtaining filament data of an X-ray tube and constructing training sample data and test sample data; predicting the predicted filament current using a degradation prediction model and predicting the predicted filament current distribution using an uncertainty prediction model, wherein the uncertainty prediction model is constructed based on at least two degradation prediction models; calculating the uncertainty of the predicted current; calculating the failure range of the filament; in response to the filament degradation rate exceeding the failure range at a certain moment, determining the moment as the failure moment; and calculating the filament failure time based on the failure moment and the uncertainty. In an embodiment of the present application, the reliability of the predicted filament current can be determined based on the uncertainty, and the filament failure time is calculated based on the uncertainty, thereby further improving the accuracy of predicting the filament failure time.

Description

Technical Field

[0001] The present application relates to the technical field of X-ray tubes, and in particular to a method and system for predicting performance degradation and failure identification of X-ray tubes and filaments. Background Art

[0002] Computed tomography (CT) scanners are essential medical devices used in hospital diagnosis and treatment. X-ray tubes are a core component of CT scans, directly impacting imaging quality and diagnostic accuracy. Due to stringent clinical operating conditions, such as prolonged exposure to high voltage, rotation, and non-standard operation, X-ray tube components, such as the anode target, bearings, and cathode filament, are prone to malfunction, leading to tube failure and posing significant risks during critical surgical procedures.

[0003] In an X-ray tube, the cathode filament is heated to generate free electrons, which collide with a metal target to produce X-rays. Long-term use can cause the filament to evaporate and become thinner, leading to filament failure and, consequently, tube failure.

[0004] To address this issue, related technologies have proposed methods for determining filament failure by measuring parameters such as filament resistance and using models to predict filament failure time. However, these methods lack testing and evaluation of the model's output predictions, making it difficult to ensure the accuracy of the output data. Furthermore, X-ray tube failures can arise from a variety of factors, and different tube components, due to differences in their own structure and service environment, have varying degrees of influence on the filament's degradation process, causes, and rate. This leads to ambiguous filament degradation characteristics. Therefore, related technologies are unable to reasonably predict performance degradation and identify failures caused by filament degradation. Summary of the Invention

[0005] The present application provides a method and system for predicting the performance degradation and failure identification of an X-ray tube and a filament, which can at least be used to solve the problem of low accuracy in predicting the failure time of a filament in an X-ray tube.

[0006] In a first aspect, an embodiment of the present application provides a method for predicting performance degradation and failure identification of an X-ray tube filament, comprising the following steps: acquiring filament data of an X-ray tube and constructing training sample data and test sample data; predicting a predicted current of the filament using a degradation prediction model based on the training sample data, and predicting a predicted current distribution of the filament using an uncertainty prediction model, wherein the uncertainty prediction model is constructed based on at least two of the degradation prediction models; calculating the uncertainty of the predicted current based on the predicted current distribution and the predicted current; calculating a failure range of the filament; in response to the degradation rate of the filament exceeding the failure range at a certain moment, determining the moment as a failure moment; and calculating the failure time of the filament based on the failure moment and the uncertainty.

[0007] An embodiment of the present application provides a method for predicting X-ray tube performance degradation and determining failure, comprising the following steps: executing the aforementioned method for predicting X-ray tube filament performance degradation and determining failure; and, in response to the filament degradation rate at any time not exceeding the failure range, ruling out filament failure as the cause of the X-ray tube failure.

[0008] An embodiment of the present application provides an X-ray tube filament performance degradation prediction and failure identification system, comprising: an acquisition module configured to acquire filament data of an X-ray tube and construct training sample data and test sample data; a prediction module configured to predict the predicted current of the filament using a degradation prediction model based on the training sample data, and to predict the predicted current distribution of the filament using an uncertainty prediction model, wherein the uncertainty prediction model is constructed based on at least two of the degradation prediction models; a calculation module configured to calculate the uncertainty of the predicted current based on the predicted current distribution and the predicted current; calculate the failure range of the filament; a determination module configured to determine a moment as a failure moment in response to the degradation rate of the filament exceeding the failure range at a certain moment; and calculate the failure time of the filament based on the failure moment and the uncertainty.

[0009] An embodiment of the present application provides an X-ray tube performance degradation prediction and failure determination system, comprising: an X-ray tube filament performance degradation prediction and failure determination system as described above; and a second determination module configured to exclude filament failure as the cause of failure of the X-ray tube in response to the degradation rate of the filament at any time not exceeding the failure range.

[0010] An embodiment of the present application provides a computer-readable storage medium, which stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer runs a method for predicting and distinguishing the performance degradation and failure of an X-ray tube filament and a method for predicting and distinguishing the performance degradation and failure of an X-ray tube.

[0011] The technical solution disclosed in this application has the following beneficial effects:

[0012] In the X-ray tube filament performance degradation prediction and failure identification method disclosed in the embodiments of this application, a degradation prediction model is used to predict the current filament current based on training sample data. Because this predicted current is a single piece of data, it is impossible to use it to determine the accuracy of the degradation prediction model's prediction results. Therefore, the embodiments of this application, based on this, construct an uncertainty prediction model, perform multiple predictions based on different degradation prediction models, and form a predicted current distribution based on the predicted currents obtained from these multiple predictions. Therefore, the predicted current distribution can more comprehensively reflect the prediction results of the current filament current by the degradation prediction model in different states and at different training levels. Based on this predicted current distribution, the reliability of the predicted current obtained by the degradation prediction model can be determined, and the uncertainty of the current filament prediction can be obtained. Furthermore, the degradation prediction model can be trained based on the predicted current distribution, thereby continuously improving the prediction capability of the degradation prediction model and further improving the accuracy of the degradation prediction model's predictions. In addition, based on the above uncertainty, the failure moment is corrected, the failure time of the filament is calculated, and the final predicted failure time is adaptively adjusted according to the accuracy of the model prediction results. This can further optimize the failure time prediction process, improve the intelligence and automation of filament failure prediction, make the predicted filament failure time closer to the true value, and make the prediction result more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The drawings described herein are used to provide further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute improper limitations on the present application.

[0014] In the attached figure:

[0015] Figure 1 An exemplary flow chart of a method for predicting X-ray tube filament performance degradation and determining failure according to some embodiments of the present application;

[0016] Figure 2 A schematic diagram of the calculation uncertainty disclosed for some embodiments of the present application;

[0017] Figure 3 A schematic diagram of determining the failure moment disclosed in some embodiments of the present application;

[0018] Figure 4 This is a module diagram of an X-ray tube filament performance degradation prediction and failure judgment system disclosed in some embodiments of the present application;

[0019] FIG5( a ) is a schematic diagram of the original filament current disclosed in some embodiments of the present application;

[0020] FIG5( b ) is a schematic diagram of the filament current after pretreatment disclosed in some embodiments of the present application;

[0021] Figure 6 A schematic diagram comparing the predicted current and the actual current predicted by the degradation prediction model disclosed in some embodiments of the present application;

[0022] Figure 7 This is a schematic diagram of the change of first-order difference data over time disclosed in some embodiments of the present application. DETAILED DESCRIPTION

[0023] To make the purpose, technical solutions, and advantages of this application more clear, the following will provide a clear and complete description of the technical solutions of this application in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0024] To facilitate understanding of the X-ray tube and filament performance degradation prediction and failure determination method and system provided in the embodiments of the present application, the relevant technologies are first introduced in conjunction with application scenarios.

[0025] X-ray tubes are critical components of medical equipment, combining X-rays with computer technology to generate images for rapid and accurate diagnosis of various illnesses and injuries. However, due to rigorous clinical operating conditions, such as prolonged exposure to high voltage, rotation, and non-standard operation, X-ray tubes are prone to malfunction and failure, leading to interruptions in use.

[0026] X-ray tube failures can occur for a variety of reasons, such as filament failure. Existing methods for determining filament failure in X-ray tubes typically use filament resistance as a parameter and apply big data models to determine failure. However, due to the diverse operating environments in which X-ray tubes operate, operating parameters fluctuate constantly, and structural parameters vary from tube to tube, the data generated by big data models is disjointed, making it difficult to assess the reliability of the resulting determinations.

[0027] In view of this, the embodiments of the present application provide a method and system for predicting the performance degradation and failure judgment of an X-ray tube and a filament. Figure 1-Figure 7 Various embodiments disclosed in this application are described.

[0028] Figure 1 This is an exemplary flow chart of a method for predicting X-ray tube filament performance degradation and failure identification disclosed in some embodiments of the present application. Process 100 is executed by an X-ray tube filament performance degradation prediction and failure identification system 400.

[0029] Step 110: Acquire filament data of the X-ray tube and construct training sample data and test sample data.

[0030] Filament data refers to monitoring data related to the filament in the X-ray tube.

[0031] In some embodiments, filament data may include multiple parameters. Exemplarily, filament data includes filament current, scanning frequency, focal spot, and tube voltage. In some embodiments, filament data can be acquired in a variety of ways. For example, filament data corresponding to X-ray tubes replaced due to filament failure can be collected using existing medical information systems.

[0032] Training sample data refers to the sample data used for model training, and test sample data refers to the sample data used to test the model prediction results.

[0033] In some embodiments, based on the filament data, the training sample data and the test sample data can be divided in various ways. For example, according to a certain ratio, a portion of the sample data is divided into the training sample data and the remaining sample data is divided into the test sample data.

[0034] In some embodiments, constructing training sample data and test sample data by acquiring filament data of an X-ray tube further includes: acquiring filament data of N X-ray tubes; preprocessing the filament data; constructing training sample data based on the preprocessed filament data of N-1 X-ray tubes and the first sample data of the preprocessed filament data of the N-th X-ray tube; and constructing test sample data based on the other sample data in the preprocessed filament data of the N-th X-ray tube except the first sample data.

[0035] In some embodiments, pre-processing the filament data includes the following steps:

[0036] First, the filament data of the X-ray tube is obtained, and the mode of the scanning frequency, focus, and tube voltage of each X-ray tube during use is calculated in the filament data. The mode of the scanning frequency, focus, and tube voltage is used as a filtering condition to filter the filament data of each X-ray tube.

[0037] For example, the modes of the scan frequency, focus, and tube voltage for a particular X-ray tube are: scan frequency 120 Hz, focus is a small focus, and tube voltage 120 kV. The filtering conditions are: scan frequency 120 Hz, focus is a small focus, and tube voltage 120 kV. During the use of this X-ray tube, the filament data that meets these filtering conditions is retained, and the remaining filament data is discarded.

[0038] The filament current in the filtered filament data is interpolated by an interpolation method, and the interpolated filament current is normalized.

[0039] As shown in FIG5(a) and FIG5(b), FIG5(a) shows the original filament current data without preprocessing, and FIG5(b) shows the filament current data after interpolation and normalization.

[0040] Furthermore, the normalized filament current is constructed into sample data through sliding window slicing, and the sample data is divided into training sample data and test sample data.

[0041] Specifically, the interpolated and normalized filament current of each X-ray tube can be expressed as , express The filament current at time Indicates the life span of the filament.

[0042] In some embodiments, the sample data includes input samples and output samples, wherein the input samples refer to sample data input to the model, and the output samples refer to actual filament currents.

[0043] In some embodiments, the aforementioned model includes a degradation prediction model and an uncertainty prediction model. For more information about the degradation prediction model and the uncertainty prediction model, please refer to the following content.

[0044] Specifically, the input sample can be expressed as , the output sample can be expressed as . That is, the input sample is Time has come Between moments, The filament current at the moment, the output sample is The actual measured filament current at that moment.

[0045] In some embodiments, Moments can include various time units, such as hours, days, weeks, months, etc. Specifically, The smaller the unit of time (that is, the shorter the interval between adjacent moments), the closer the sample data that can be obtained are, and the smaller the degree of data change; conversely, the more separated the sample data obtained, the greater the degree of data change.

[0046] In the specific implementation process, if the sample data are too close or too far apart, the degradation characteristics of the filament in the data sample will be blurred. The unit can be days.

[0047] In some embodiments, when When used to represent a time period, it can be determined in a variety of ways For example, the filament current at a certain time in a day can be determined as the filament current of the day. For another example, the filament current at a certain time in a day can be determined as the filament current of the day.

[0048] In some embodiments, all data samples for each X-ray tube can be expressed as:

[0049] ;

[0050] in, Represents each X-ray tube, Indicates the number of X-ray tubes, Indicates the All data samples of an X-ray tube.

[0051] To ensure The time corresponding to each sample in is the time after the X-ray tube starts to be used, that is, The time must be greater than the initial time 1, so, , so we can Start taking sample data of the X-ray tube at time . is the life span of the X-ray tube filament. Therefore, the total data samples of each X-ray tube are Time to All input samples and output samples at time.

[0052] In some embodiments, in order to verify the effectiveness of the method for predicting the performance degradation and failure identification of X-ray tube filaments, N-1 of the N collected X-ray tubes are used as training X-ray tubes, and the Nth X-ray tube is used as the X-ray tube to be tested. The training sample data includes all sample data of the N-1 X-ray tubes and the first sample data of the Nth X-ray tube, which can be expressed as ; The test sample data includes the Nth X-ray tube, except the first sample data All other sample data except ,Right now .

[0053] Due to the diverse usage scenarios of X-ray tubes, the tube voltage scanning frequency and focus parameters used in X-ray tubes are constantly changing in different surgeries. Due to the differences in their service environments, they also have different effects on the loss of the X-ray tube filament. Therefore, when predicting the failure time of the filament, the data should be controlled under the same service conditions as much as possible to avoid the influence of interference data on the prediction results. The embodiment of the present application determines the filtering conditions based on the mode of the scanning frequency, focus and tube voltage, and filters the filament data. This can obtain as much data as possible, while removing the filament current corresponding to different usage parameters, improving the purity of the data, avoiding interference data that reduces the accuracy of the prediction results, and facilitating analysis and processing. At the same time, reducing the amount of data is also conducive to reducing the amount of calculation and improving calculation efficiency. At the same time, normalizing, interpolating and sliding window slicing the data can further summarize and filter the data, summarizing the huge and complex data into sample data that can be quantified and calculated, simplifying the calculation while ensuring the accuracy of the data. Furthermore, because different X-ray tubes have different structures and processing methods, each has its own unique characteristics. Using only the remaining X-ray tubes as sample data makes it difficult to accurately control the impact of the characteristics of the X-ray tube under test on the accuracy of the prediction results. Therefore, by incorporating the first sample data of the Nth X-ray tube (the X-ray tube under test) into the training sample data, we can further make the training sample data more realistic and reliable based on the X-ray tube's own characteristics and combine it with universal data, thereby improving the accuracy of the model's prediction results.

[0054] Step 120 : Based on the training sample data, predict the predicted current of the filament using the degradation prediction model, and predict the predicted current distribution of the filament using the uncertainty prediction model.

[0055] The predicted current is the filament current predicted by the degradation prediction model. The predicted current distribution is the collection of serialized predicted current data.

[0056] The degradation prediction model is used to predict the filament current of an X-ray tube. In some embodiments, the degradation prediction model is a machine learning model, such as a convolutional neural network, a recurrent neural network, a fully connected neural network, or an attention mechanism.

[0057] In some embodiments, the input of the degradation prediction model includes input samples in the training sample data , the output includes the predicted current at time t .

[0058] In some embodiments, the degradation prediction model is expressed as: ,in, Representation Model The parameters can be preset by the staff in this field.

[0059] In some embodiments, the degradation prediction model can be trained based on a large number of first training samples with first labels through various feasible training methods, such as a step-descent method.

[0060] The first training sample can be an input sample , the first label can be the output sample .

[0061] In some embodiments, the uncertainty prediction model is a model for predicting the filament current distribution of an X-ray tube. In some embodiments, the uncertainty prediction model is a machine learning model, such as a convolutional neural network, a recurrent neural network, a fully connected neural network, or an attention mechanism.

[0062] In some embodiments, the input to the uncertainty prediction model includes the input sample , the output includes the predicted current distribution at time t.

[0063] The training method of the uncertainty prediction model is the same as that of the degradation prediction model and will not be described here.

[0064] For more information on uncertainty prediction model construction, please refer to Figure 2 Detailed description.

[0065] Step 130 : Calculate the uncertainty of the predicted current based on the predicted current distribution and the predicted current.

[0066] For more information about step 130, see Figure 2 Related content.

[0067] Step 140: Calculate the failure range of the filament.

[0068] The failure range refers to the range within which the degradation rate of the filament exceeds the allowable range of the standard value. In some embodiments, the failure range of the filament can be determined based on training sample data in various ways. For example, it can be preset based on the experience of personnel in the field.

[0069] In some embodiments, the failure range can be obtained by calculation: First, based on the training sample data, first-order difference data of the predicted current distribution at two adjacent moments are calculated.

[0070] Specifically, each input sample in the training data samples is input into the uncertainty prediction model to obtain the predicted current distribution corresponding to each input sample, and the first-order difference of the predicted current distribution of the input samples at every two adjacent moments is calculated.

[0071] The first-order difference data refers to a data set of the first-order difference of the predicted current distribution of input samples at every two adjacent moments.

[0072] In some embodiments, the first-order difference data can be calculated based on formula (1):

[0073] (1)

[0074] in, for The predicted current distribution at time for Predicted current distribution at time. Time is The moment before the moment. and It can be predicted through the uncertainty prediction model.

[0075] Next, the first-order difference data is filtered Filtering is performed to obtain a reference degradation rate of the filament.

[0076] The reference degradation rate refers to a set of degradation rates of the filament obtained by filtering based on the training sample data.

[0077] In some embodiments, the degradation rate It can be calculated based on formula (2):

[0078] (2)

[0079] in, is the filtering coefficient, which can be determined based on experience by those skilled in the art.

[0080] The filter is a filter circuit composed of capacitors, inductors, and resistors. In some embodiments, the filter used in this embodiment can be of various types, such as an infinite impulse response filter.

[0081] Then, the degradation mean and degradation standard deviation of the reference degradation rate are calculated. The degradation mean refers to the average value of the reference degradation rate, and the degradation standard deviation refers to the standard deviation of the reference degradation rate.

[0082] The failure range is calculated based on the degradation mean, degradation standard deviation, and failure threshold coefficient.

[0083] In some embodiments, the failure range can be expressed as ,in, is the degradation mean value, is the fault threshold coefficient, is the degradation standard deviation. The fault threshold coefficient can be preset according to requirements.

[0084] like Figure 7 As shown in the figure, the first-order difference data that has not been filtered by the filter shows a scattered distribution, and it is difficult to capture its corresponding change pattern, while the data that has been filtered by the filter shows a linear distribution with obvious and clear change characteristics.

[0085] By using training sample data, the reference degradation rate of the filament can be calculated based on a large amount of data, thereby widely obtaining the changing characteristics of the filament degradation rate. This can then be used as a reference. Using a filter, the reference degradation rate data can be processed and filtered, making the changing characteristics of the reference degradation rate clearer and more intuitive. Furthermore, the failure range of the filament is determined based on the mean value, standard deviation, and failure threshold coefficient of the reference degradation rate of the filament. This method is supported by a large amount of actual data. At the same time, considering the possible differences in filaments in different X-ray tubes, the mean value, standard deviation, and failure threshold coefficient are introduced to calculate and correct the failure range of the filament, making the calculated failure range more accurate and universal.

[0086] Step 150 : In response to the degradation rate of the filament exceeding the failure range at a certain moment, determining the moment as a failure moment.

[0087] The failure moment refers to the point in time when the filament fails.

[0088] In some embodiments, in response to the degradation rate of the filament not exceeding the failure range at any time, the main failure cause of the X-ray tube is ruled out as filament failure.

[0089] In some embodiments, the degradation rate corresponding to each moment can be calculated based on the test sample data, and the failure moment can be determined based on the degradation rate. For more information about step 150, please refer to Figure 3 Detailed description.

[0090] Step 160 : Calculate the failure time of the filament based on the failure moment and the uncertainty.

[0091] The failure time refers to the time when the filament fails after correcting the calculation of the filament failure moment.

[0092] In some embodiments, the failure time can be calculated using formula (3):

[0093] (3)

[0094] in, is the expiration time, is the expiration time, is the safety margin, is the uncertainty.

[0095] In some embodiments, the safety margin is a parameter used to calibrate the failure time.

[0096] In some embodiments, the safety margin can be preset by workers in the field.

[0097] In a specific implementation, the expiration time can be used as the final maintenance time. Maintenance personnel can repair or replace the X-ray tube filament before the expiration time is reached to avoid filament failure and X-ray tube failure. For example, maintenance personnel can inspect the X-ray tube filament one week before the expiration time is reached.

[0098] like Figure 6 As shown in the figure, the actual value of the filament current after preprocessing is highly consistent with the predicted current data output by the degradation prediction model, which further illustrates that after the degradation prediction model is trained by the above method, it can obtain a prediction result close to the true value, and the prediction accuracy is greatly improved.

[0099] Obtaining filament data, processing it, and constructing the processed filament data into sample data for model training can improve data purity, simplify the calculation process, and further improve the accuracy and precision of model measurements. Furthermore, based on the filament data, the model predicts its current and current distribution, and the reliability of the predicted current is evaluated based on the predicted current distribution. This can further determine whether the prediction result is accurate. Based on the reliability of the model's prediction results, the model is then trained to improve its prediction capabilities, obtain better prediction results, and enhance the intelligence and automation of the filament failure prediction method. By calculating the filament degradation rate and determining filament failure based on the degradation rate, the changing characteristics of the degradation rate during the filament degradation process can be effectively obtained. Based on this changing characteristic, an intuitive expression of the filament failure process can be provided, thereby improving the accuracy of determining the filament failure time and avoiding X-ray tube failure due to filament failure, which could lead to medical accidents.

[0100] Figure 2 Schematic diagram of the calculation uncertainty disclosed for some embodiments of this application.

[0101] In some embodiments, the uncertainty prediction model 220 is constructed based on the degradation prediction model 230 .

[0102] Specifically, based on the training sample data 210, M degradation prediction models are constructed by using different random seeds, which are recorded as .in, The above M degradation prediction models are combined to form an uncertainty prediction model 220 .

[0103] like Figure 2 As shown, M degradation prediction models 230 - 1 , 230 - 2 . . . 230 -M are constructed respectively by using different random seeds 1 -M, and M predicted currents are obtained based on 230 - 1 , 230 - 2 . . . 230 -M to form a predicted current distribution 250 .

[0104] In some embodiments, M degradation prediction models are arranged in parallel.

[0105] In some embodiments, the training sample data 210 is input into the uncertainty prediction model 220 to obtain the predicted current distribution 250 .

[0106] In some embodiments, the predicted current distribution 250 includes the predicted currents 240 predicted by M degradation prediction models.

[0107] Uncertainty 260 is a parameter used to measure the reliability of the predicted current. In some embodiments, uncertainty 260 can be determined based on various methods. For example, uncertainty can be the difference between predicted current 240 and the average value of predicted current distribution 250.

[0108] In some embodiments, uncertainty 260 may also be determined based on a standard deviation of predicted current 240 and predicted current distribution 250 .

[0109] In some embodiments, the smaller the uncertainty 260 between the predicted current and the predicted current distribution, the smaller the difference between the predicted current 240 and the predicted current distribution 250, and the closer the predicted current 240 is to the true value. Conversely, the larger the uncertainty 260 between the predicted current 240 and the predicted current distribution 250, the more the predicted current 240 deviates from the predicted current distribution 250 in terms of value, indicating that the predicted result is further away from the true value and less reliable.

[0110] In some embodiments, the uncertainty It can be calculated by formula (4):

[0111] (4)

[0112] (5)

[0113] By constructing an uncertainty prediction model using multiple degradation prediction models trained with different random seeds, the output of the uncertainty model can cover as many prediction results as possible in the degradation prediction model. On this basis, the predicted current distribution is obtained. Through the distributed prediction results, the comprehensive conclusions presented by the prediction results of the degradation prediction model on a large amount of data can be intuitively reflected. Then, the standard deviation between the predicted current distribution and the predicted current predicted by the degradation prediction model is calculated to obtain the uncertainty, so that the uncertainty can quantitatively reflect the accuracy of the degradation prediction model prediction, help model training, and improve the computing power of the model.

[0114] Figure 3 A schematic diagram of determining the failure moment disclosed in some embodiments of the present application.

[0115] In some embodiments, the X-ray tube filament performance degradation prediction and failure determination system 400 can update the test sample data based on the queue algorithm; input the updated test sample data into the uncertainty prediction model 220 to obtain a predicted current distribution; calculate the degradation rate at the current moment based on the predicted current distribution of the test sample data corresponding to the current moment and the predicted current distribution of the test sample data corresponding to the previous moment; and determine the current moment as a failure moment in response to the degradation rate exceeding the failure range.

[0116] A queue algorithm refers to a linear data structure with a first-in, first-out order. In the embodiments of the present application, updating test sample data based on a queue algorithm means that within a sequence of test sample data, a single test sample data entry is made at the head of the queue and exits at the tail of the queue. For example, if the number set is {1, 2, 3} and the number 4 is added, 4 is added to the head of the queue and the number 3 is removed from the tail of the queue, thus changing the number set to {4, 1, 2}.

[0117] For example, Figure 3 As shown, the first sample data in the test sample data will be tested, that is, Input sample corresponding to time Input to the uncertainty prediction model 220 to obtain the predicted current distribution I1, and add the predicted current distribution I1 to the Input sample corresponding to time The first one, excluding the original last sample data , forming the updated Input sample corresponding to time .

[0118] Then update the Input sample corresponding to time The uncertainty prediction model 220 is input to obtain the predicted current distribution I2, and the degradation rate 3-1 of the predicted current distribution I1 and the predicted current distribution I2 is calculated according to formulas (1) and (2).

[0119] Determine whether the degradation rate 3-1 exceeds the failure range. If not, continue to repeat the above steps to calculate; if it exceeds the failure range, The time is determined as the expiration time 330 .

[0120] In some embodiments, in response to the degradation rate of the filament not exceeding the failure range at any time, the main failure cause of the X-ray tube is ruled out as filament failure.

[0121] By calculating the filament degradation rate, the changing trend of the filament degradation rate at each moment can be dynamically obtained. Filament failure can be determined based on whether the filament degradation rate exceeds the failure range. This can further improve the accuracy of filament failure determination and minimize the possibility of sudden filament failure or damage before maintenance and replacement, which could lead to medical equipment malfunction and interruption of treatment. Furthermore, if the degradation rate does not exceed the degradation rate, the impact of filament failure on the X-ray tube can be ruled out, further helping maintenance personnel identify the cause of failure, reducing the economic loss of unified part replacement, extending the service life of the X-ray tube, and reducing its maintenance costs.

[0122] Figure 4 This is a module diagram of an X-ray tube filament performance degradation prediction and failure judgment system disclosed in some embodiments of the present application.

[0123] In some embodiments, the X-ray tube filament performance degradation prediction and failure judgment system 400 includes an acquisition module 410, a prediction module 420, a calculation module 430, and a determination module 440. Detailed descriptions of the functions of the various modules of the X-ray tube filament performance degradation prediction and failure judgment system 400 can be found in Figures 1 to 3 Related content.

[0124] The acquisition module 410 is a module for acquiring filament data. In some embodiments, the acquisition module 410 is configured to acquire filament data of an X-ray tube and construct training sample data and test sample data.

[0125] Prediction module 420 is a module for predicting filament degradation characteristics. In some embodiments, prediction module 420 is configured to predict the predicted filament current using a degradation prediction model based on training sample data, and to predict the predicted filament current distribution using an uncertainty prediction model, where the uncertainty prediction model is constructed based on at least two degradation prediction models.

[0126] The calculation module 430 is a module for implementing a calculation function. In some embodiments, the calculation module 430 is configured to calculate the uncertainty of the predicted current based on the predicted current distribution and the predicted current.

[0127] In some embodiments, the calculation module 430 is configured to calculate the failure range of the filament.

[0128] The determination module 440 is a module for determining the filament failure time.

[0129] In some embodiments, the determination module 440 is configured to determine a moment as a failure moment in response to a degradation rate of the filament at a certain moment exceeding a failure range.

[0130] In some embodiments, the determination module 440 is configured to calculate the failure time of the filament based on the failure moment and the uncertainty.

[0131] In some embodiments, the X-ray tube performance degradation prediction and failure determination system includes all modules of the above-mentioned X-ray tube filament performance degradation prediction and failure determination system 400 .

[0132] The X-ray tube performance degradation prediction and failure judgment system also includes a second determination module.

[0133] In some embodiments, the second determination module is configured to determine a failure cause of the X-ray tube. In some embodiments, the second determination module is configured to exclude filament failure as the failure cause of the X-ray tube in response to a degradation rate of the filament at any time not exceeding a failure range.

[0134] It should be noted that the above description of the X-ray tube filament performance degradation prediction and failure determination system 400, the X-ray tube performance degradation prediction and failure determination system, and its modules is for convenience only and does not limit this specification to the scope of the illustrated embodiments. It is understood that those skilled in the art, after understanding the principles of the system, may arbitrarily combine the modules or form subsystems connected to other modules without departing from these principles.

[0135] In some embodiments, Figure 4 The acquisition module 410, prediction module 420, calculation module 430, and determination module 440 disclosed in the X-ray tube performance degradation prediction and failure identification system, as well as the second determination module in the system, can be different modules within a single system, or a single module can implement the functions of two or more of the aforementioned modules. For example, each module can share a storage module, or each module can have its own storage module. Such variations are within the scope of protection of this specification.

[0136] The above embodiments of this application focus on the differences between the various embodiments. As long as the different optimization features between the various embodiments are not contradictory, they can be combined to form a better embodiment. Considering the simplicity of the text, they will not be repeated here.

[0137] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for predicting X-ray tube filament performance degradation and failure determination, characterized in that: The following steps are involved: Obtain X-ray tube filament data and construct training sample data and test sample data; Based on the training sample data, predicting the predicted current of the filament using a degradation prediction model, and predicting the predicted current distribution of the filament using an uncertainty prediction model, wherein the uncertainty prediction model is constructed based on at least two of the degradation prediction models; Calculating uncertainty of the predicted current based on the predicted current distribution and the predicted current; Calculate the failure range of the filament; In response to a degradation rate of the filament exceeding the failure range at a certain moment, determining the moment as a failure moment; The failure time of the filament is calculated based on the failure moment and the uncertainty.

2. The method for predicting X-ray tube filament performance degradation and failure determination according to claim 1, characterized in that: The obtaining of X-ray tube filament data and constructing training sample data and test sample data includes: Acquiring the filament data of N X-ray tubes; Preprocessing the filament data; constructing training sample data based on the preprocessed filament data of N-1 X-ray tubes and first sample data of the preprocessed filament data of the Nth X-ray tube; Test sample data is constructed based on the sample data other than the first sample data in the preprocessed filament data of the Nth X-ray tube.

3. The method for predicting X-ray tube filament performance degradation and failure determination according to claim 1, wherein: The step of predicting the predicted current of the filament using a degradation prediction model based on the training sample data and predicting the predicted current distribution of the filament using an uncertainty prediction model, wherein the uncertainty prediction model is constructed based on at least two of the degradation prediction models and includes: Based on the training sample data, construct at least two degradation prediction models using different random seeds; merging the degradation prediction models to form the uncertainty prediction model; The training sample data is input into the uncertainty prediction model to obtain the predicted current distribution.

4. The method for predicting X-ray tube filament performance degradation and failure determination according to claim 1, wherein: The calculating the uncertainty of the predicted current based on the predicted current distribution and the predicted current includes: The uncertainty is determined based on a standard deviation of the predicted current and the predicted current distribution.

5. The method for predicting X-ray tube filament performance degradation and determining failure according to claim 1, wherein: Calculating the failure range of the filament includes: Based on the training sample data, calculating first-order difference data of predicted current distribution corresponding to two adjacent moments; filtering the first-order difference data through a filter to obtain a reference degradation rate of the filament; Calculating a degradation mean value and a degradation standard deviation of the reference degradation rate; The failure range is calculated based on the degradation mean value, the degradation standard deviation, and a failure threshold coefficient.

6. The method for predicting X-ray tube filament performance degradation and determining failure according to claim 1, wherein: In response to the degradation rate of the filament exceeding the failure range at a certain moment, determining the moment as a failure moment includes: Based on a queue algorithm, updating the test sample data; Inputting the updated test sample data into the uncertainty prediction model to obtain the predicted current distribution; Calculating the degradation rate at the current moment based on the predicted current distribution of the test sample data corresponding to the current moment and the predicted current distribution of the test sample data corresponding to the previous moment; In response to the degradation rate exceeding the failure range, the current time is determined as the failure time.

7. A method for predicting performance degradation and failure determination of an X-ray tube, characterized in that: The following steps are involved: Executing the method for predicting performance degradation and failure determination of an X-ray tube filament according to any one of claims 1 to 6; In response to the degradation rate of the filament at any time not exceeding the failure range, the failure cause of the X-ray tube is ruled out as filament failure.

8. An X-ray tube filament performance degradation prediction and failure judgment system, characterized in that: include: an acquisition module, configured to acquire filament data of an X-ray tube and construct training sample data and test sample data; a prediction module configured to predict the predicted current of the filament using a degradation prediction model based on the training sample data, and predict the predicted current distribution of the filament using an uncertainty prediction model, wherein the uncertainty prediction model is constructed based on at least two of the degradation prediction models; a calculation module configured to calculate an uncertainty of the predicted current based on the predicted current distribution and the predicted current; Calculate the failure range of the filament; a determination module configured to, in response to a degradation rate of the filament exceeding the failure range at a certain moment, determine the moment as a failure moment; The failure time of the filament is calculated based on the failure moment and the uncertainty.

9. An X-ray tube performance degradation prediction and failure judgment system, characterized in that: include: An X-ray tube filament performance degradation prediction and failure judgment system as claimed in claim 8; The second determining module is configured to exclude filament failure as the cause of failure of the X-ray tube in response to the degradation rate of the filament at any time not exceeding the failure range.

10. A computer-readable storage medium, characterized in that The storage medium stores computer instructions. When the computer reads the computer instructions in the storage medium, the computer runs the method for predicting and distinguishing the performance degradation and failure of an X-ray tube filament according to any one of claims 1 to 6, or the method for predicting and distinguishing the performance degradation and failure of an X-ray tube according to claim 7.

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