Failure predictive maintenance system for medical X-ray equipment

Abstract

The invention discloses a medical X-ray equipment fault predictive maintenance system, and relates to the technical field of medical equipment maintenance. Comprising a multi-source data acquisition module, a feature construction module, a coupling degradation modeling module, a risk judgment module and a maintenance strategy generation module. The method comprises the following steps: based on a voltage distortion rate, a current harmonic component and a temperature field gradient, extracting a voltage distortion rate standard deviation, a current harmonic total distortion coefficient and a temperature gradient change rate after de-noising through a wavelet threshold method, and fusing into a feature vector; calculating a multi-parameter coupled comprehensive degradation index in combination with a long short-term memory network and a Bayesian network; then constructing a multi-parameter feature space, and outputting a comprehensive risk level through an unsupervised learning method; and finally, a targeted maintenance strategy is generated based on the risk level and an optimization algorithm. According to the invention, accurate prediction and intelligent maintenance of faults of the medical X-ray equipment are realized, sudden faults are reduced, the maintenance cost is reduced, and the operation reliability and diagnosis and treatment safety of the equipment are improved.

Description

Technical Field

[0001] This invention relates to the field of medical equipment maintenance technology, and more specifically, to a predictive maintenance system for medical X-ray equipment. Background Technology

[0002] The maintenance of existing medical X-ray equipment largely relies on a passive approach of periodic inspections or post-failure repairs, which has the following drawbacks: First, data acquisition is limited, relying heavily on simple parameters from the equipment's built-in sensors (such as surface temperature and operating time), making it difficult to reflect the potential degradation status of core components (such as high-voltage generators and cooling systems). Second, it fails to consider the coupling relationships between parameters, such as the electromagnetic induction relationship between voltage distortion and current harmonics, and the thermal conduction relationship between abnormal current and temperature rise, leading to a one-sided degradation assessment. Third, risk assessment depends on human experience and lacks quantitative standards, making it prone to misjudgment or omission. Fourth, maintenance strategies are highly generalized and fail to be dynamically adjusted based on the real-time status of the equipment, potentially leading to over-maintenance or under-maintenance. These problems result in a high probability of sudden equipment failure, high maintenance costs, and even affect the continuity and safety of diagnosis and treatment. Summary of the Invention

[0003] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a predictive maintenance system for medical X-ray equipment failures, which addresses the problems of single data, one-sided assessment, subjective risk judgment, and rigid strategies mentioned in the background art through the following solutions.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a predictive maintenance system for medical X-ray equipment, comprising: Multi-source data acquisition module: used to acquire multi-source data during the operation of medical X-ray equipment and transmit it to the feature construction module; The multi-source data includes voltage distortion rate, current harmonic components, and temperature field gradient. Feature construction module: connected to the multi-source data acquisition module, used to perform noise suppression and feature extraction on the multi-source data to generate feature vectors; Coupled degradation modeling module: connected to the feature construction module, based on the feature vector, calculates the comprehensive degradation index by establishing a multi-parameter degradation model; Risk assessment module: connected to the coupled degradation modeling module, constructs a multi-parameter feature space based on comprehensive degradation index, and uses unsupervised learning method to classify and recognize the multi-parameter features. It classifies and outputs the comprehensive risk level by the distribution of patterns and the degree of abnormal deviation in the feature space. Maintenance strategy generation module: Connected to the risk assessment module, it generates corresponding maintenance strategies and decision instructions based on the comprehensive risk level and multi-parameter feature space through optimization algorithms.

[0005] The technical effects and advantages of this invention are as follows: This invention uses the Pockels effect, giant magnetoresistive sensing, and infrared-FBG fusion technology to accurately acquire three core parameters: voltage, current, and temperature, covering the electromagnetic and thermal states of equipment. It overcomes the shortcomings of traditional single data and provides a comprehensive basis for degradation assessment. This invention quantifies the coupling relationships between parameters, such as electromagnetic induction and thermal conduction, and combines the temporal fitting ability of long short-term memory networks with the probabilistic reasoning ability of Bayesian networks. The output comprehensive degradation index can truly reflect the overall degradation status of the equipment, avoiding the one-sidedness of single parameter evaluation. This invention constructs a multi-parameter feature space and classifies it through unsupervised learning. It combines pattern deviation measurement to quantify risk and refines the risk level into 5 levels, solving the problems of subjectivity and vague standards in traditional manual judgment and providing a quantitative basis for maintenance decisions. This invention generates targeted maintenance strategies based on risk levels and parameter anomalies. It uses a cost-benefit algorithm to select the optimal combination of operations, avoiding over-maintenance or under-maintenance, reducing the probability of sudden failures, and improving the reliability of equipment operation and the safety of diagnosis and treatment. Attached Figure Description

[0006] Figure 1 This is a system architecture diagram of the present invention; Figure 2 This is a system function flowchart of the present invention. Detailed Implementation

[0007] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0008] like Figure 1 and Figure 2 The illustrated medical X-ray equipment fault prediction maintenance system includes: Multi-source data acquisition module: used to acquire multi-source data during the operation of medical X-ray equipment and transmit it to the feature construction module; The multi-source data includes voltage distortion rate, current harmonic components, and temperature field gradient: Feature construction module: connected to the multi-source data acquisition module, used to perform noise suppression and feature extraction on the multi-source data to generate feature vectors; Coupled degradation modeling module: connected to the feature construction module, based on the feature vector, calculates the comprehensive degradation index by establishing a multi-parameter degradation model; Risk assessment module: connected to the coupled degradation modeling module, constructs a multi-parameter feature space based on comprehensive degradation index, and uses unsupervised learning method to classify and recognize the multi-parameter features. It classifies and outputs the comprehensive risk level by the distribution of patterns and the degree of abnormal deviation in the feature space. Maintenance strategy generation module: Connected to the risk assessment module, it generates corresponding maintenance strategies and decision instructions based on the comprehensive risk level and multi-parameter feature space through optimization algorithms.

[0009] It should be specifically noted that the function of the multi-source acquisition module is to acquire multi-source data during the operation of medical X-ray equipment and transmit it to the feature construction module.

[0010] It should be further explained that the multi-source data includes voltage distortion rate, current harmonic components, and temperature field gradient. The reason for selecting these three sets of data is that voltage distortion rate reflects the transient stability of the output voltage of the high-voltage generator and is an important parameter for assessing whether the equipment has voltage fluctuations or abnormalities; current harmonic components can reflect the degree of distortion of the current waveform and are an important basis for judging whether the equipment has current abnormalities; temperature field gradient can reflect the heat dissipation efficiency of the cooling system and is an important parameter for assessing whether the equipment has overheating risks.

[0011] It should be further explained that the voltage distortion rate is obtained based on the Pockels effect, a physical phenomenon where the refractive index of certain crystals changes linearly with the electric field strength under the influence of an electric field. The distortion degree of the high-voltage generator's output voltage is indirectly obtained through optical measurement. The tools used include an optical fiber probe (containing a Pockels effect crystal, such as lithium niobate (LiNbO3), used to sense the electric field and convert it into an optical signal), a laser source (providing stable incident light), a photodetector (converting the optical signal into an electrical signal), and a signal processor (analyzing and calculating the electrical signal). First, the Pockels crystal in the optical fiber probe is placed in the electric field at the output of the high-voltage generator. The crystal's refractive index *n* changes with the electric field strength *E*, with the following relationship: (n0 is the refractive index without an electric field, (This refers to the Pockels coefficient of the crystal, which is related to the crystal material). Then comes optical signal modulation. When the laser passes through the crystal, the change in refractive index causes a phase change in the light, which is transmitted through an optical fiber to a photodetector and converted into a voltage-related electrical signal. The relationship between voltage U and electric field strength E is: d represents the distance between the two electrodes of the crystal; finally, the electrical signal is analyzed by a signal processor to reconstruct the real-time voltage waveform output by the high-voltage generator. Voltage distortion rate is also known as total harmonic distortion (THD). nThe effective value of each harmonic voltage is defined as the ratio of the effective value of the fundamental voltage to the effective value of each harmonic voltage. The formula is: Where U1 is the effective value of the fundamental voltage (50Hz, the voltage component of the device's operating frequency), U 2， U 3，. ..U m The effective values ​​of the 2nd, 3rd...mth harmonic voltages (voltage components whose frequency is an integer multiple of the fundamental frequency).

[0012] It should be further explained that the method for obtaining the current harmonic components utilizes the giant magnetoresistive effect, i.e., the phenomenon that changes in magnetic field cause significant changes in material resistance. The current value is deduced by measuring the magnetic field generated by the current, and then each harmonic component is separated. Specifically, a giant magnetoresistive sensor array, a magnetic field shield, a signal conditioning circuit (converting the resistance change into a measurable voltage signal), and a spectrum analyzer (performing frequency domain analysis of the current signal) are used. First, the magnetic field is measured. The current-carrying conductors of high-voltage equipment generate a surrounding magnetic field. The relationship between the magnetic field strength B and the current I follows Ampere's circuital law: ,in Let R be the vacuum permeability, N be the number of turns of the wire, and r be the distance between the sensor and the wire; then comes the resistance-to-current conversion, where the resistance R of the giant magnetoresistive sensor changes with the magnetic field B, i.e. Where R0 is the zero magnetic field resistance, and s is the sensitivity coefficient (s is an inherent characteristic parameter of the giant magnetoresistive sensor, determined through calibration experiments during sensor production: the sensor is placed in a standard magnetic field of known strength, and its resistance change is measured, based on the relationship between resistance change and magnetic field strength). The value of s is calculated, and the resistance change is converted into a voltage signal through a signal conditioning circuit to deduce the real-time current waveform i(t). Finally, harmonic separation is performed by using a spectrum analyzer to perform a Fourier transform on the current waveform i(t) to decompose the fundamental frequency and each harmonic component. The calculation of the current harmonic components is carried out through Fourier series expansion, and the current waveform i(t) can be expressed as: Where I1, I2...I m The current amplitudes are the fundamental frequency, the 2nd harmonic, ..., the mth harmonic. The fundamental angular frequency ( (where f is the fundamental frequency) This represents the phase angle of each harmonic; in practical applications, it is necessary to extract the effective value of each harmonic. (j=2,3...m) are used as characteristic values ​​of the current harmonic components.

[0013] It should be further explained that the temperature field gradient is obtained by fusing infrared thermal imaging technology (large-area temperature distribution measurement) with fiber Bragg grating (FBG) sensing technology (high-precision point temperature measurement). The gradient distribution of the spatial temperature field is obtained through a data fusion algorithm. Specifically, the intensity of infrared radiation emitted from the surface of the device is detected by a high-precision infrared thermal imager, converted into temperature values, and a two-dimensional temperature matrix is ​​output. (x, y are planar coordinates); Fiber Bragg grating measurement center reflection wavelength

[0014] It changes linearly with temperature T, that is... ,in K is the center wavelength at the reference temperature T0. T The temperature sensitivity coefficient is obtained by demodulating the wavelength shift to obtain the point temperature T. FBG (x i ,y i ,z i (3D coordinates); then, using the high-precision point temperature of the fiber Bragg grating as a reference, the measurement error of infrared thermal imaging is corrected, and a 3D temperature field model T(x,y,z) is constructed through an interpolation algorithm; the temperature field gradient is the rate of change of temperature in three-dimensional spatial directions, and is a vector, with the formula: Temperature field gradient , in the formula The unit is ℃ / m, which is degrees Celsius per meter and represents the rate of temperature change per unit distance along the x-axis. These are the partial derivatives of temperature along the x, y, and z axes, respectively. In actual calculations, the ratio of the temperature difference to the distance between adjacent points is used as an approximation.

[0015] It should be specifically explained that the function of the feature construction module is to use the wavelet thresholding method to suppress noise in the voltage distortion rate, current harmonic components, and temperature field gradient. Then, based on the denoised data, the standard deviation of voltage distortion rate, the total distortion coefficient of current harmonics, and the rate of change of temperature gradient are calculated respectively. Finally, through feature fusion, they are integrated into a feature vector.

[0016] It should be further explained that the noise suppression process is as follows: For voltage distortion rate, a db4 wavelet basis suitable for non-stationary signal processing is selected, decomposed into 5 layers, and then a soft thresholding function is applied to the high-frequency coefficients of each layer to filter out the pulse noise introduced by the high-voltage electric field interference. Specifically, for current harmonic components, a sym8 wavelet basis is selected and decomposed into 4 layers, and a hard thresholding function is applied to the high-frequency coefficients to eliminate electromagnetic interference noise in magnetic field measurement. For temperature field gradient, wavelet thresholding is applied along the three spatial dimensions respectively. After decomposition using the coif5 wavelet basis, a hybrid thresholding strategy is adopted to suppress spatial noise during the fusion of infrared thermal imaging and FBG sensing. After processing, the three denoised data sequences are obtained through wavelet reconstruction.

[0017] It should be further explained that the threshold calculation method is based on the high-frequency coefficients of the j-th layer obtained from the decomposition, where the threshold is... ,in The noise standard value, with units consistent with the high-frequency coefficient, is estimated by the median absolute deviation of the highest-level high-frequency coefficient after decomposition. N j The number of high-frequency coefficients in the j-th layer.

[0018] It should be further explained that the soft threshold function processing method refers to the high-frequency coefficient w after the voltage distortion rate is decomposed, and the formula is as follows: Where sign(w) is the sign function, which, by shrinking coefficients exceeding a threshold, can filter out pulse noise introduced by the high-voltage electric field while retaining the subtle fluctuation characteristics of the voltage distortion rate; the hard threshold function processing method is the high-frequency coefficient w after the decomposition of the current harmonic components, and the formula is: Directly removing coefficients below a threshold can effectively eliminate sharp noise caused by electromagnetic interference and avoid noise interference with harmonic component extraction. The hybrid threshold strategy processes the high-frequency coefficients w after temperature field gradient decomposition along the three-dimensional space (x, y, z), and the formula is as follows: hour, ,when hour ,when hour For strong noise ( ) uses soft threshold shrinkage for weak noise ( This method directly preserves the temperature gradient, which can suppress spatial noise during the fusion of infrared and FBG while retaining the detailed changes in temperature gradient.

[0019] It should be further explained that the high-frequency coefficients are the key components obtained after wavelet decomposition. In wavelet decomposition, the low-frequency coefficients mainly reflect the overall trend of the signal, while the high-frequency coefficients contain the details of signal changes and noise. By analyzing the high-frequency coefficients, noise and signal details can be separated.

[0020] It should be further explained that, based on the denoised data, the standard deviation of the voltage distortion rate is calculated by assuming the denoised voltage distortion rate time series is {u1, u2, ... u...}. n (n is the number of sampling points within the time window), standard deviation The formula is: ,in The average value within the time window reflects the degree of fluctuation and dispersion of the voltage distortion rate; the calculation method for the total current harmonic distortion coefficient is as follows: Let the effective value of the fundamental current be I1, and the effective value of the j-th (j=2,3,...m) harmonic current be... ,in Let be the amplitude of the j-th harmonic current, and then calculate the square root of the sum of the squares of the effective values ​​of each harmonic current, using the formula: Finally, the total current harmonic distortion coefficient is calculated. A higher CHDC value indicates a more severe current distortion; the temperature gradient change rate is calculated by setting the magnitude of the temperature gradient vector at time t1 to be... If G2 is at time t2, then the rate of change is... .

[0021] It should be further explained that the feature vector is obtained by taking the feature parameter x (i.e. The three normalized features are normalized and mapped to the interval [0, 1]. Then, the three normalized features are fused to form a feature vector. ,in These are the normalized standard deviation of voltage distortion rate, the total distortion coefficient of current harmonics, and the rate of change of temperature gradient, respectively, with a vector dimension of 1×3.

[0022] It should be specifically explained that the function of the coupled degradation modeling module is to perform correlation analysis on each feature parameter based on feature vectors, quantify the coupling correlation strength between parameters, then preset the basic correlation coefficient of each parameter in degradation assessment based on the physical characteristics and fault mechanism of the equipment, and finally input the multi-parameter features after correlation analysis into the model based on the fusion model of long short-term memory network and Bayesian network, and output a comprehensive degradation index through time-series degradation trend fitting and multi-parameter probability coupling calculation.

[0023] It should be further explained that the method for quantifying the coupling strength is based on the feature vector V, using a dual-index approach to quantify the coupling strength between parameters. Specifically, it first calculates the degree of linear correlation between any two parameters. For parameters a and b, The formula is: in, The covariance (N is the sample size) is... For the i-th sample value, (sample mean) , are the standard deviations of the parameters, The larger the absolute value, the stronger the linear coupling; then the degree of nonlinear correlation between parameters is calculated, using the following formula: Where p(a,b) is the joint probability distribution of the two parameters, p(a) and p(b) are the marginal probability distributions (obtained through interval statistics of historical sample data), and I(a,b) is greater than or equal to 0, with a larger value indicating more significant nonlinear coupling; finally, the two correlations are weighted and fused to form a 3×3 coupling coefficient matrix C: Among them, I max The maximum value of the mutual information entropy (used to normalize to [0,1]), and the diagonal element c aa=1 (parameter self-association), off-diagonal element It reflects the overall coupling strength between the two parameters.

[0024] It should be further explained that the method of presetting the basic correlation coefficients of each parameter in the degradation assessment is based on the electromagnetic coupling characteristics and heat conduction laws of medical X-ray equipment. A basic correlation coefficient matrix B (3×3) is preset to quantify the inherent correlation between parameters: voltage and current ( :b 12 =0.6 (Voltage distortion and current harmonics in the high-voltage generator are strongly coupled by electromagnetic induction). Voltage and temperature ):b 13 =0.4 (The increase in power consumption caused by voltage anomalies has a relatively weak effect on temperature). Current and temperature ):b 23 =0.5 (The Joule heating generated by the current harmonics has a significant impact on the temperature field). diagonal element b aa =1 (self-associative); By fusing data-driven and mechanism-driven correlation information through matrix dot product, a dynamic correlation coefficient matrix is ​​obtained: Element-wise multiplication, i.e. ).

[0025] It should be further explained that the comprehensive degradation index is obtained by first sorting the feature vectors according to a time series. (t is the time step) Input the Long Short-Term Memory network, learn the long-term dependencies of the parameters, and output the time-series predicted values ​​of each parameter: , , in The network parameters are (optimized through training with historical time-series data); then, a Bayesian network is constructed using the dynamic correlation coefficient matrix K as the conditional probability table, and the time-series predicted values ​​are used as input nodes to calculate the joint degradation probability of multiple parameters: Where F represents a device failure event, P t This represents the probability of device degradation due to parameter coupling at time t (calculated via Bayesian conditional probability propagation); finally, the time-series predicted value and the joint degradation probability are fused to output a comprehensive degradation index. in, The mean of the time-series predicted values ​​(reflecting the trend of time-series degradation), 0.7 and 0.3 are weights calibrated based on historical fault data, determined by minimizing the prediction error; finally The closer the value is to 1, the more severe the equipment degradation.

[0026] It should be specifically explained that the function of the risk assessment module is to construct a multi-parameter feature space by analyzing the interrelationships and combination patterns among the feature parameters in the comprehensive degradation index; then, to classify and recognize the multi-parameter features using unsupervised learning methods, and output the comprehensive risk level through the distribution of patterns and the degree of abnormal deviation in the feature space.

[0027] It should be further explained that the construction method of the multi-parameter feature space is based on the interrelationship of each feature parameter in the comprehensive degradation index and the comprehensive degradation index. Construct a 4-dimensional feature space: The first three dimensions are the normalized original feature parameters (preserving the independent characteristics of each parameter), and the fourth dimension is the comprehensive degradation index (reflecting the overall degradation state after parameter coupling). This space includes both the combination patterns between parameters and the overall degradation level.

[0028] It should be further explained that the K-means algorithm in unsupervised learning is used to classify the samples in the feature space into patterns. The specific steps are as follows: first, initial cluster centers are set; then, based on the historical normal operation data of the equipment (fault-free state), the mean vector of normal samples in the feature space is calculated. Based on the typical characteristics of "slight degradation," "moderate degradation," and "severe degradation" in historical fault data, three abnormal cluster centers are preset (a total of four cluster centers, corresponding to four patterns); then, sample clustering iteration is performed, and pattern division is achieved by minimizing the K-means objective function. in, This represents the k-th cluster (corresponding to a pattern). Let be the center vector of this cluster. Using Euclidean distance, the cluster centers are iteratively updated until convergence, ultimately dividing the samples in the feature space into 4 modes: normal mode C1, low-risk mode C2, medium-risk mode C3, and high-risk mode C4.

[0029] It should be further explained that the comprehensive risk level is obtained by first statistically analyzing the current sample S. t Cluster C to which it belongs k And calculate the failure probability P of this cluster in historical data. fault (C k (e.g., C4 corresponds to a historical failure probability ≥80%, C3 to 30%–80%, C2 to 5%–30%, etc.), then calculate the Euclidean distance between the current sample and the center of its respective pattern to quantify the degree of deviation: in, Let d be the center vector of the k-th pattern. tThe larger the value, the more significant the deviation of the sample from the typical characteristics of its pattern, and the higher the potential risk. Finally, combining the pattern category and the degree of abnormal deviation, a five-level comprehensive risk level is determined: Level 1 (No risk): ( Level 2 (minor risk): (deviation threshold from normal mode) ,or Level 3 (Medium Risk): ,or Level 4 (High Risk): ,or Level 5 (Urgent Risk): ;in, Deviation threshold set based on historical data ( The 95th percentile of the normal sample deviating from the center. The 80th percentile of the normal sample deviating from the center The 70th percentile of the normal sample deviating from the center. (60th percentile of normal sample deviating from the center).

[0030] It should be specifically noted that the function of the maintenance strategy generation module is to receive the comprehensive risk level output by the risk assessment module, and based on the assessment information reflected in the risk assessment by each characteristic parameter, evaluate and combine the executable maintenance operations through a cost-benefit optimization algorithm, and generate a structured maintenance strategy and decision instructions that match the comprehensive risk level and multi-parameter feature space based on the equipment status and risk changes.

[0031] It should be further explained that the maintenance strategy is generated by first establishing a maintenance operation library and feature relationship mapping, including: based on the core components of medical X-ray equipment (such as high-voltage generators, cooling systems, and sensors), a standardized maintenance operation library is preset, covering three types of basic operations and combined operations: voltage-related operations: M1 (high-voltage generator insulation detection), M2 (voltage regulation module calibration); current-related operations: M3 (giant magnetoresistive sensor array calibration), M4 (filter capacitor replacement); temperature-related operations: M5 (cooling fan cleaning), M6 (heat sink dust removal); combined operations: such as M1+3 (simultaneously executing M1 and M3, etc., for multi-parameter abnormal scenarios); based on the improvement effect of operations on parameters in historical maintenance data, a correlation coefficient matrix A (6×3 matrix) is established to quantify the targeted impact of operations on the standard deviation of voltage distortion rate, the total distortion coefficient of current harmonics, and the rate of change of temperature gradient.

[0032] It should be further explained that subsequent priority assessments are performed based on risk level and parameter anomalies, including: determining the maintenance urgency and operational intensity according to the comprehensive risk level (levels 1-5): Level 1 (no risk): outputting a "continuous monitoring" command, recording the feature vector every 24 hours. Level 2 (Minor Risk): Prioritize low-intrusion operations (such as M2, M5) to avoid interrupting normal equipment operation; Level 3 (Medium Risk): Combine 1-2 targeted operations (such as... In case of an anomaly, execute M1+M2); Level 4 (High Risk): Initiate high-intensity operations (such as M4, M6), and simultaneously arrange for a shutdown inspection; Level 5 (Emergency Risk): Immediately execute emergency operations (such as M1+M4+M6) to trigger equipment safety shutdown protection; simultaneously, combine the abnormal deviation d of parameters in the feature space. t (Refer to the risk assessment module) to dynamically adjust operation priorities: if deviation If the voltage parameters are significantly abnormal, the priority of voltage-related operations will be increased by 20%.

[0033] It needs further explanation that the final optimization algorithm-based combination of operations and generation strategy includes: using a cost-benefit optimization algorithm to evaluate and combine operations, with the goal of "achieving the maximum reduction in risk level with the lowest maintenance cost". Specifically, the operation cost C(Mk) is defined (including time cost and consumable cost), such as C(M1) = 60 maintenance units, C(M5) = 20. The combined operation cost is the sum of the individual operation costs, and an improvement function E(Mk) for the risk level is fitted based on historical data. For example, when executing... The risk level subsequently decreased by an average of 1.3 levels, and then the objective function was optimized. To maximize the benefit-cost ratio, the constraint is that the number of combined operations ≤ 3 (to avoid over-maintenance) and all abnormal parameters must be covered (e.g., In case of an anomaly, at least one current-related operation must be selected. After the algorithm outputs the optimal operation combination, it generates a strategy containing three elements: Operation steps: such as "1. Perform M3 to calibrate the sensor; 2. Perform M5 to clean the air duct; 3. Remeasure S after 2 hours." t Time window: For example, Level 5 requires "completion within 1 hour", Level 2 can be "completion within 3 days"; Expected goal: For example, "risk level ≤ Level 2 after execution", ".

[0034] It should be further explained that the decision instructions are generated by converting structured strategies into machine-executable instructions, such as: Level 5 risk: "[Emergency Instruction] The equipment shall be shut down immediately, execute M1+M4+M6, and can only be restarted after passing the high-voltage insulation test and harmonic distortion rate test after maintenance"; Level 2 risk: "[Planned Instruction] Execute M2 to calibrate the voltage module within this week, and retest..." And upload it to the system.

[0035] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other. In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A predictive maintenance system for medical X-ray equipment, characterized in that, include: Multi-source data acquisition module: used to acquire multi-source data during the operation of medical X-ray equipment and transmit it to the feature construction module; The multi-source data includes voltage distortion rate, current harmonic components, and temperature field gradient. Feature construction module: connected to the multi-source data acquisition module, used to perform noise suppression and feature extraction on the multi-source data to generate feature vectors; Coupled degradation modeling module: connected to the feature construction module, based on the feature vector, calculates the comprehensive degradation index by establishing a multi-parameter degradation model; Risk assessment module: connected to the coupled degradation modeling module, constructs a multi-parameter feature space based on comprehensive degradation index, and uses unsupervised learning method to classify and recognize the multi-parameter features. It classifies and outputs the comprehensive risk level by the distribution of patterns and the degree of abnormal deviation in the feature space. Maintenance strategy generation module: Connected to the risk assessment module, it generates corresponding maintenance strategies and decision instructions based on the comprehensive risk level and multi-parameter feature space through optimization algorithms.

2. The predictive maintenance system for medical X-ray equipment according to claim 1, characterized in that: The voltage distortion rate is obtained based on the Paulcile effect. The change in crystal refractive index caused by the output voltage of the high voltage generator is measured by an optical fiber probe, and the voltage distortion rate is calculated. The current harmonic components are obtained by using a giant magnetoresistive sensor array, which uses changes in the magnetic field to infer the current, and then separates and calculates the current harmonic components. The temperature field gradient is obtained by combining a high-precision infrared thermal imager with a fiber Bragg grating sensor and calculating the temperature field gradient through a data fusion algorithm.

3. The predictive maintenance system for medical X-ray equipment according to claim 1, characterized in that: The function of the feature construction module is to use wavelet thresholding to suppress noise in voltage distortion rate, current harmonic components, and temperature field gradient. Then, based on the denoised data, the standard deviation of voltage distortion rate, the total distortion coefficient of current harmonics, and the rate of change of temperature gradient are calculated respectively. Finally, through feature fusion, they are integrated into a feature vector.

4. The predictive maintenance system for medical X-ray equipment according to claim 3, characterized in that: The wavelet thresholding method is adapted to different data sources with differentiated processing strategies. For voltage distortion rate, the db4 wavelet basis is decomposed to 5 layers and a soft thresholding function is applied. For current harmonic components, the sym8 wavelet basis is decomposed to 4 layers and a hard thresholding function is applied. For temperature field gradient, the coif5 wavelet basis is decomposed and a hybrid thresholding strategy is applied.

5. The predictive maintenance system for medical X-ray equipment according to claim 1, characterized in that: The coupled degradation modeling module is designed to perform correlation analysis on each feature parameter based on feature vectors, quantify the coupling strength between parameters, and then, based on the physical characteristics and fault mechanisms of the equipment, preset the basic correlation coefficients of each parameter in degradation assessment. Finally, based on the fusion model of long short-term memory network and Bayesian network, the multi-parameter features after correlation analysis are input into the model, and the comprehensive degradation index is output through time-series degradation trend fitting and multi-parameter probability coupling calculation.

6. The predictive maintenance system for medical X-ray equipment according to claim 1, characterized in that: The function of the risk assessment module is to construct a multi-parameter feature space by analyzing the interrelationships and combination patterns among the feature parameters in the comprehensive degradation index; then, to classify and recognize the multi-parameter features using unsupervised learning methods, and output the comprehensive risk level by the distribution of patterns and the degree of abnormal deviation in the feature space.

7. A predictive maintenance system for medical X-ray equipment according to claim 6, characterized in that: The unsupervised learning method is the K-means clustering algorithm, which classifies samples in a multi-parameter feature space using the K-means clustering algorithm. In the clustering initialization stage, the selection of initial cluster centers is optimized based on the distribution characteristics of the data. During the clustering process, Euclidean distance is used as the similarity metric between samples, and the distance between samples and cluster centers is iteratively calculated until convergence.

8. The predictive maintenance system for medical X-ray equipment according to claim 1, characterized in that: The function of the maintenance strategy generation module is to receive the comprehensive risk level output by the risk assessment module, and based on the assessment information reflected in the risk assessment by each characteristic parameter, evaluate and combine the executable maintenance operations through a cost-benefit optimization algorithm, and generate a structured maintenance strategy and decision instructions that match the comprehensive risk level and multi-parameter feature space based on the equipment status and risk changes.

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