479 results about "Failure data" patented technology

The invention relates to a power supply switching control optimization method and system, and the method comprises the steps: constructing a multi-dimensional data fusion monitoring system, integrating a real-time voltage ripple coefficient detection module of a main power supply, a load transient response analysis module and an environment temperature and humidity sensor array, and generating a dynamic health degree index; the method comprises the following steps: establishing a fault prediction model based on transfer learning, pre-training through multi-scene failure data in a historical database, receiving a health degree index in real time, outputting a reliability attenuation curve, and activating a dual verification mechanism when the slope of the attenuation curve exceeds a preset threshold; extracting characteristic energy distribution of a frequency band of 0.1-10 kHz; executing a transient load impact test, and measuring dynamic response parameters of voltage recovery time and overshoot amplitude; and generating a switching confidence score based on cross processing of the dual verification result and the attenuation curve, and triggering a hierarchical switching instruction. The accuracy of the switching time of the main power supply and the standby power supply can be improved.

The invention relates to the technical field of strength detection, and discloses radiator strength detection equipment and a detection method. The method comprises the following steps: carrying out adaptive wavelet denoising and structural association mapping on collected multi-scale structural feature data and multi-environment coupling material feature data of a radiator to be detected, and carrying out multi-dimensional feature extraction, space-time alignment and interactive mapping on an obtained fusion data set to obtain a multi-physical field coupling data matrix; carrying out physical field control equation set separation iteration solution and heat dissipation cyclic load and environmental action calculation on the coupling data matrix to obtain a damage evolution state; and performing multi-dimensional damage sensitive area identification based on the damage evolution state, generating a risk distribution map, performing failure comparison and life calculation based on historical failure data of the same type of radiators, and generating an intensity comprehensive detection result. Through multi-physical field coupling analysis and multi-dimensional damage evaluation, accurate detection of the strength state of the radiator is realized, and the accuracy of structural reliability evaluation is improved.

The invention belongs to the technical field of bearing fault diagnosis, and particularly relates to a transmission casing typical small bearing fault weak vibration signal extraction method, which comprises the following steps: reconstructing a bearing vibration signal to obtain a modulation impact signal, and calculating a weighted kurtosis value based on the modulation impact signal to improve a Protugram algorithm; noise interference is added to a bearing fault signal, and extraction of a small bearing fault weak impact signal is realized by optimizing a filtering frequency band based on a weighted kurtosis value improved Protugram algorithm; bearing impact characteristics can be enhanced based on a simulation sensor resonance enhancement algorithm (SPM); the impact quantification of the bearing can eliminate the influence of different rotating speeds and load working conditions, and realizes the quantitative diagnosis of bearing faults. And finally, the fault data of the rolling body, the inner ring and the outer ring of the flight attachment case small bearing part test bed are verified, and effective extraction and enhancement of bearing fault weak signals can be realized.

The invention discloses a property maintenance prediction system based on big data, and the system comprises the steps: monitoring equipment and environment in real time through an intelligent sensor, transmitting the data to a cloud or local storage through a wireless network, and carrying out the historical data processing and cleaning through a distributed architecture; predicting the equipment fault risk through machine learning, establishing a customized prediction model for different equipment, analyzing the life cycle and predicting the service life of the equipment; for old equipment, constructing a life extension model by using historical fault data; the system generates intelligent decision suggestions, pushes equipment health states to property management personnel, automatically formulates maintenance plans and priorities, and dispatches maintenance personnel; during fault prediction, early warning is given out in real time, related personnel are reminded through a short message or an APP, meanwhile, maintenance operation is recorded, historical data are analyzed, and future maintenance plans and budget are optimized; the system dynamically adjusts the life prediction model of the old equipment by accumulating historical data, and ensures the accuracy and efficiency of equipment health management.

The invention relates to the technical field of equipment management, and discloses an industrial park equipment operation state management method and system. The method comprises the following steps: acquiring equipment basic information, historical fault data and real-time operation data of equipment; according to the basic information of the equipment, performing category analysis on the equipment to obtain an equipment category; according to the historical fault data and the equipment category, performing fault period prediction on the equipment based on a pre-trained support vector regression model to obtain an equipment fault period; according to the real-time operation data and the equipment category, performing health index calculation on the equipment to obtain an equipment health index; and according to the equipment category, the equipment fault period and the equipment health index, performing fault prediction to obtain a predicted fault probability, and sending the predicted fault probability to an operation and maintenance end, so that the operation and maintenance end performs equipment maintenance according to the predicted fault probability. According to the method, the equipment management efficiency of the industrial park can be improved, and the equipment state of the industrial park can be effectively analyzed and managed.

The invention discloses a simulation optimization method for a digital twin factory. The simulation optimization method comprises the steps of obtaining workshop equipment data and performing classified storage; generating a task priority and resource configuration rule based on the basic operation data of the equipment; constructing a three-dimensional space model by using a 3D modeling tool, and creating a digital twinborn simulation factory model in combination with real-time production data; constructing a dynamic planning state space and a decision variable, and determining the resource use time, the resource allocation position and the specific quantity of each task; according to the resource allocation scheme and the task chain dependency relationship, evaluating a risk value of a task node in combination with historical equipment fault data; and based on the influence range report of the production fault chain, evaluating a task execution sequence and adjusting resource allocation and task time arrangement. According to the method, the influence of faults on a production plan and resource waste are reduced, the stability and adaptability of a factory system are remarkably enhanced, and the reliability and fine management level of overall production are improved.

The invention relates to a hydrogen fuel cell system reliability evaluation method based on a Weibull distribution model, and the method comprises the steps: collecting the state detection quantity of a hydrogen fuel cell system in real time, and carrying out the calculation to obtain a wear index and a functional age; historical failure data of the hydrogen fuel cell system are obtained, and shape parameters and scale parameters are obtained through calculation; based on the wear index, the functional age, the shape parameter and the scale parameter, a Weibull distribution reliability model is constructed and used for calculating the cumulative failure probability and the aging failure rate; and determining the failure risk and the residual life of each component in the hydrogen fuel cell system based on the accumulated failure probability and the aging failure rate in combination with the improved risk priority. Compared with the prior art, the method has the advantages that the problems of single parameter evaluation, static analysis and inadequate individuation in the prior art are solved, dynamic, intelligent and systematic reliability evaluation is realized, and scientific support can be provided for full-life-cycle management and maintenance optimization of the hydrogen fuel cell system.

The invention discloses an automatic driving test scene set optimization method, apparatus and device, and a storage medium. The method comprises the steps of generating a simulation scene file in an OpenSCENARIO format through preprocessing data; analyzing the simulation scene file through a teacher model, outputting a risk description text, receiving a scene feature vector and the risk description text through a student model, and outputting a failure probability; determining a comprehensive value index according to the failure probability, dynamically updating a scene library of automatic driving test scenes, collecting failure data in an AUT test, performing incremental fine tuning on the student model, and obtaining an optimized target test scene set; the problem of gradient estimation variance explosion caused by sparseness disasters can be effectively solved, the stability of model training is improved, the recognition capability of a rare long-tail failure scene is enhanced, the accuracy of failure probability prediction is improved, the judgment accuracy of the model to a boundary scene is improved, and the accuracy and comprehensiveness of scene value quantification are improved.

The invention relates to the technical field of PVD coating, in particular to a PVD coating design quality control method based on machine learning. The method comprises the following steps: obtaining a coating substrate object, carrying out PVD coating design, and carrying out substrate PVD coating simulation based on the design to obtain related simulation data; analyzing mechanical property parameters by using the PVD coating simulation data of the base material, evaluating the mechanical property of the coating material in combination with the mechanical data, and generating dynamic stress response data of the coating layer through a stability test; evaluating based on the chemical stability of the base material coating film, and predicting the failure condition of the coating film layer by combining the dynamic stress response data of the coating film layer; and through comprehensive analysis of dynamic stress response data and failure data of the coating layer, the coating design is further optimized. According to the invention, through the quality control of PVD coating, the quality of the PVD coating is more perfect.

The invention discloses a chip life detection method and a life prolonging method, and the method comprises the following steps: 1, extracting a chip sample, constructing a chip life detection system, and obtaining a simulation data set of the chip sample through the chip life detection system, the simulation data set at least comprises a chip performance data set, a chip operation data set, a chip working environment data set and a chip failure data set, and 2, constructing an improved neural network model to train and learn detected chip data information parameters so as to improve the chip life detection fitting capability. The service life of the chip is detected by adopting the technical scheme of constructing the improved neural network model, so that the reliability and the performance stability of the chip under the conditions of long-time use and load can be evaluated conveniently, and the detection capability of the service life of the chip is improved.

The invention provides a product storage life evaluation method based on multi-stage success and failure data, which evaluates the storage life of a product by adopting a discrete parameter algorithm according to multi-stage success and failure natural storage test data of the product, and comprises the following steps: S1, performing zero-moment failure probability distribution fitting and mean value estimation, and according to prior information and factory data, calculating a failure probability distribution value; performing fitting on zero-moment failure probability distribution by adopting beta distribution, and calculating expected mean estimation of the zero-moment failure probability distribution; s2, abnormal value screening and order-preserving processing of fusion data are carried out, multi-stage data are combined, an error threshold value is set, abnormal points are eliminated, and order-preserving processing is carried out; s3, discretizing position parameters, and carrying out Weibull parameter estimation; the interval range of the position parameters is determined, equal interval division is carried out, and corresponding translation processing is carried out on the data; and S4, life parameter selection and abnormal point consistency judgment. According to the method, multi-stage success and failure data can be fully utilized, and the product evaluation precision is improved.

The invention relates to a visual analysis method and device for multiple internal corrosion failure types of a pipeline. The method comprises the following steps: inputting internal corrosion failure picture data to be recognized into an internal corrosion failure type recognition model to obtain a predicted internal corrosion failure type and a feature map of the last convolutional layer; analyzing the predicted internal corrosion failure type and the feature map of the last convolutional layer by using Grad-CAM to obtain a feature region thermodynamic diagram; based on the predicted internal corrosion failure type and the characteristic region thermodynamic diagram, obtaining the contribution degree of the characteristic of each characteristic region to an identification result; wherein the internal corrosion failure type identification model is obtained by the following steps: obtaining a pipeline internal corrosion failure data set; inputting the internal corrosion failure data set into an initial internal corrosion failure type identification model for training to obtain a trained internal corrosion failure type identification model; wherein the initial internal corrosion failure type identification model is constructed by using a Resnet18 convolutional neural network.

The invention discloses a life prediction method and device for an accelerated test product and electronic equipment, and the method carries out typical failure mechanism analysis based on fault data, so as to determine a key device and sensitive stress of a target product. And carrying out clustering analysis on the fault data related to the key device, and determining the failure contribution rate of each sensitive stress to the target product. And according to the failure contribution rate corresponding to each sensitive stress, carrying out a multi-stress accelerated life test of the target product. And training the neural network model by adopting test data of the multi-stress accelerated life test. The accelerated test life of a target product under the target stress condition is predicted by using the trained neural network model, life prediction of the accelerated test product is realized, the neural network model is trained by using a multi-stress accelerated life test based on fault physics, the influence of the coupling effect between environmental stresses on the product is emphasized, and the service life prediction of the accelerated test product is realized. Therefore, the model better conforms to the actual application process of the product, and the life prediction precision is improved.

A hotspot data elimination and maintenance method in a distributed cache system comprises the steps that the access frequency, the time locality score, the service weight and the real-time data popularity value of data are acquired, and the dynamic priority score of the data is generated; determining failure data based on the dynamic priority score; dividing the data into hot spot area data, temperature spot area data and cold spot area data, when the cache space is insufficient, preferentially eliminating the data with the lowest dynamic priority score and the retention benefit value lower than a preset threshold value in the cold spot area data, and if the cache space is still insufficient, performing data elimination on the temperature spot area data according to the latest access time of the temperature spot area data; packaging the cold point area elimination data, the warm point area elimination data and the failure data into a final transaction message; and broadcasting the final transaction message through a preset protocol, and performing distributed consistency maintenance operation on the final transaction message through the receiving node. According to the invention, intelligent and efficient cache data elimination and consistency maintenance management can be realized.

The invention is suitable for the field of equipment fault prediction, and provides an underground equipment multi-source fault prediction method and system based on a large model, and the method comprises the following steps: synchronously collecting an acoustic signal, a visual image, a vibration signal, environment data and working condition parameters of underground equipment in real time, and integrating the acoustic signal, the visual image, the vibration signal, the environment data and the working condition parameters into a multi-modal time sequence data stream; performing time alignment and cleaning on the multi-modal time sequence data stream, extracting deep features of each modal, and generating a multi-source fusion feature vector; inputting the multi-source fusion feature vector into a preset prediction model to obtain prediction information of the underground equipment, and generating fault traceability information; continuously optimizing parameters of the prediction model based on fault data collected in real time; and dynamically displaying prediction and traceability results, and automatically generating a maintenance strategy and an equipment scheduling scheme based on the results. According to the invention, panoramic perception of the operation state of the equipment and accurate capture of early weak fault features are realized, and the comprehensiveness and accuracy of fault detection are greatly improved.

The invention belongs to the technical field of photovoltaic power station equipment diagnosis, and discloses a distributed photovoltaic power station equipment diagnosis method based on big data analysis, and the method comprises the steps: S1, data collection: collecting data transmitted by a sensor on each piece of equipment of a distributed photovoltaic power station, and receiving the operation data of the equipment in real time; s2, data preprocessing: preprocessing the collected data; s3, feature extraction: extracting feature parameters capable of reflecting the running state of the equipment from the preprocessed data through data analysis and data mining; s4, model establishment: constructing an equipment fault diagnosis model based on historical fault data and corresponding characteristic parameters; and S5, fault diagnosis: inputting data acquired in real time into the fault diagnosis model, completing state judgment of each device of the distributed photovoltaic power station, and identifying potential faults. According to the invention, the operation data of the photovoltaic power station is collected, analyzed and processed, so that intelligent diagnosis and maintenance of equipment are realized.

The invention discloses an intelligent monitoring method and device for a video monitoring system based on cloud edge collaboration, and relates to a video monitoring technology. An edge computing platform is deployed on one side of the video monitoring system, so that the computing load of a central cloud platform is reduced; through combination of video fault data, historical operation data and real-time operation data, a correlation index between a picture parameter and a video fault type is calculated so as to accurately determine a video picture fault reason. Constructing a panoramic canvas through equipment parameters, and constructing a panoramic background model based on historical operation data; the real-time operation data and the panoramic background model are combined to continuously monitor faults of the holder and the zoom lens, and monitoring blind areas or picture loss caused by faults of the video monitoring system are avoided; the fault evaluation platform receives the detected suspected fault data and suspected fault indexes and feeds back the suspected fault data and suspected fault indexes to the fault alarm system in time, corresponding processing measures are taken, and the reliability of the monitoring camera system can be ensured.

The invention discloses a dynamic correction method and system for equipment fault mode occurrence degree, and belongs to the technical field of power plant electrical overhaul and maintenance, and the method comprises the steps: collecting operation parameters, maintenance records and historical fault data of target electrical equipment, and recognizing a potential fault mode set of the target electrical equipment; failure mode and influence analysis is carried out on each failure mode, and the initial occurrence degree, severity and detectability of each failure mode are determined; calculating an initial risk priority number of each fault mode, and determining an initial risk according to a preset risk grade division rule; dynamically adjusting the initial occurrence degree, recalculating the updated risk priority number, and generating a corrected risk level; and constructing a dynamic maintenance decision model, and outputting a maintenance priority and a maintenance time window for each fault mode. According to the method, the defects of real-time performance, comprehensiveness and flexibility of electrical operation and maintenance of the power plant are overcome, and efficient and accurate data support is provided for operation and maintenance of electrical equipment.

The invention discloses a nondestructive testing device and a testing method for heterogeneous parts of engineering machinery. The method comprises the following steps: acquiring material attributes and historical failure data of a heterogeneous component, extracting defect form and position feature parameters, constructing a statistical distribution model and generating a typical feature set; according to the statistical distribution model and the material attributes, generating a virtual defect data set containing form and position features by adopting numerical simulation; performing finite element analysis on the virtual defect data set, calculating a stress concentration effect and an expansion trend, and outputting a defect harmfulness quantitative index; and if the harmfulness index exceeds a preset threshold value, marking the defect as a high-risk defect, and generating a preferential simulation target set and the like. According to the method, all links of nondestructive testing are covered from data acquisition to standard construction, a set of complete working process is formed, and the nondestructive testing problem of the heterogeneous parts of the engineering machinery can be systematically solved.

A system is described that combines outputs of network simulations, which are used for capacity planning, with empirical data of network element failure probabilities, to produce what traffic utilization is needed for each network device interface to achieve a specified availability target. Utilization of each interface can be captured for each failure case simulated. From this failure data, the utilization distribution for each link can be produced, across all failure cases. This utilization-by-failure data from simulation is combined with the empirical mean-time between failure (MTBF) and mean-time to recovery (MTTR) of each failure from the span reliability data set. This allows for a prediction of what percentage of time the interface is utilized. Thus, a relationship is established between the capacity provisioned and the percentage of time that the capacity will be uncongested. Network devices can then be added to the network to establish the availability target.

The invention discloses a data transmission method and computer equipment, and relates to the technical field of data transmission. The method comprises the following steps: acquiring a sending work request containing a local sending queue pair number and a work queue element; querying target sending information by taking a local sending queue number as an index; storing the working queue elements and the target sending information as operation contexts into an internal memory of a network card of a request end, and recording corresponding indexes; assembling a target data packet based on the target sending information and the memory index; receiving a feedback data packet of the target data packet and extracting a memory index and a serial number carried by the feedback data packet; if the feedback data packet is a correct confirmation data packet, judging that the sending of the work request is completed and releasing the context; and if the feedback data packet is a confirmation failure data packet, calculating a retransmission data initial address and organizing retransmission. The technical problem that the effective connection number of network cards is limited by hardware resources in the prior art is solved, and the technical effect of supporting large-scale network parallel communication is achieved.

The invention discloses a high-speed rail vehicle body aluminum alloy material corrosion fatigue performance simulation method and system, and belongs to the technical field of material corrosion fatigue testing. The method comprises the following steps: acquiring multi-source data of a high-speed rail body aluminum alloy material on an actual service route; establishing a material corrosion conversion coefficient database; obtaining an equivalent mapping relation based on the service environment and the load input set; generating a laboratory equivalent accelerated environment spectrum; and executing the laboratory equivalent accelerated environment spectrum, obtaining material failure data, and if the comparison difference is not less than a preset threshold value, correcting the calculation parameters of the equivalent mapping relation and regenerating the spectrum system. Through synchronous acquisition and fusion analysis of the multi-source environmental data and the mechanical load and in combination with the corrosion conversion coefficient database construction and closed-loop verification optimization method, the problem of test result distortion caused by separation of a laboratory simulation environment and an actual service condition in the prior art is solved.

The invention relates to the technical field of building construction, in particular to a prefabricated building construction progress automatic monitoring method. The method comprises the following steps: obtaining a building construction design drawing; based on the building construction design, prefabricated building component design data is extracted; building a building construction three-dimensional model based on the prefabricated building component design data; building component transportation simulation is conducted according to the building construction three-dimensional model, and building component transportation data are obtained; identifying a component cracking degree based on the building component transportation data; performing connection failure analysis based on the component cracking degree to obtain component connection failure data; predicting a reinforcement corrosion risk based on the component connection failure data; evaluating assembly durability based on the reinforcement corrosion risk; and extracting hoisting design data of the building construction design drawing. Based on the building construction technology, the efficiency and accuracy of prefabricated building construction progress monitoring are improved, and the accuracy of construction plan execution and the resource scheduling efficiency are improved.

The invention discloses an insulation residual life prediction algorithm and device based on real-time temperature acquisition and a medium, and relates to the technical field of new energy automobiles, and the method mainly comprises the steps: constructing an offline insulation failure database through an accelerated thermal aging experiment of a twisted pair sample, and recording the PDIV of a target motor at a brand new state room temperature as an initial PDIV0; based on the off-line insulation failure database, obtaining the aging slope of the PDIV decaying along with the aging time at each motor winding temperature and a corresponding aging curve; according to the mapping relation between the PDIV and the motor winding temperature, obtaining an insulation threshold voltage corresponding to the current motor winding temperature under the correction of a temperature correction coefficient; and based on the current PDIV0 and the insulation threshold voltage, substituting the aging curve corresponding to the current motor winding temperature to obtain and output the difference value of the corresponding insulation failure time of the two as the insulation residual life. The method can accurately capture the initial characteristics of insulation aging, the early warning time is advanced, and the timeliness is improved.

The invention provides a low-power-consumption embedded data recording system and algorithm for on-line monitoring of SF density. The system is provided with an MCU main control unit, an external independent watchdog circuit, a LoRa wireless communication module, an RS485 interface, an SF density and temperature measurement sensor, a battery power supply and voltage acquisition module and a Flash data storage and real-time clock module. Algorithms of the system comprise an intelligent data recording strategy, a dynamic sensor activation and sleep mechanism, a power failure data protection and breakpoint continuation mechanism, a multi-parameter compression coding algorithm, a dynamic sampling frequency adjustment strategy, a real-time clock synchronization and timestamp calibration mechanism, a multi-sensor fusion sensing mechanism and an ultra-low power consumption system architecture mechanism. The low-power-consumption embedded data recording system and algorithm for on-line monitoring of the SF density have the functions of multi-data change trend intelligent judgment, multi-data fusion perception judgment, ultra-low power consumption and flexible communication, and are suitable for long-term field deployment.

The invention relates to the technical field of intelligent locks, and discloses a bill of material generation method and device of an intelligent lock, computer equipment and a medium, the method comprises the following steps: obtaining service information of the intelligent lock in a preset range of a service system, the service information comprising repair data and installation data of a plurality of lock models; determining an initial bill of materials according to the repair data, and querying historical fault data of each lock model; on the basis of the installation data and the historical fault data, predicting accessory information required by each lock model in a future time period to obtain a predicted bill of materials; and constructing a target bill of material of the service system by using the initial bill of material and the predicted bill of material. According to the invention, the problems that the intelligent lock accessories are violently fluctuated due to the interference of multiple factors, the coordination of reasonable resource allocation and user experience guarantee is difficult to realize, and the requirements of maintenance accessory inventory management on accuracy and high-efficiency technical indexes cannot be met are solved.

The invention provides a product storage life evaluation method based on composite sampling inspection success and failure data. An evaluation object is a complex mechanical and electrical product. Comprising the following steps: carrying out point estimation and confidence limit estimation of failure probability on special test data, and carrying out randomization processing on user use data containing fuzzy samples by adopting uniform distribution or truncated normal distribution; carrying out compatibility test on the special test data and the user use data; fusing the special test data meeting the compatibility test and the user use data by adopting a Bayesian method, and obtaining special stage failure probability estimation after updating of different storage periods; and further screening out degradation failure sample data based on the updated special failure probability estimation, and performing storage life evaluation on the degradation failure data by adopting a weighted least square method. The method combines the characteristics of two kinds of sampling data, and can solve the evaluation problems of small evaluation data volume, fuzzy samples and the like.

The invention discloses an intelligent switching method and system based on multi-platform overseas payment, and the method comprises the following steps: accessing a plurality of overseas third-party payment platforms, and building a unified order processing flow through a transaction service; a monitoring point is embedded in the payment interaction process of the application program and the transaction service, payment success data and payment failure data are collected, and corresponding counters are maintained respectively; regularly pulling the counter in a parallel task mode, and counting the failure rate of each payment mode in the first time window T1; when it is detected that the failure rates of a certain payment mode in continuous N first time windows T1 all exceed a threshold theta, triggering exception handling, including marking the payment mode as abnormal, and automatically switching to a standby payment mode; and when it is detected that the failure rate of the off-shelf payment mode in the second time window T2 is recovered to be below the threshold value, automatically executing recovery operation, including re-shelving the payment mode.

The invention discloses an intelligent prediction method and system for the life cycle of a building material, and the method comprises the steps: obtaining the failure data and state data of the building material, fitting the failure data to obtain the basic predicted life of the building material, calculating the comprehensive damage degree according to the state data of the building material, calculating the environmental impact factor according to the environmental parameter, and predicting the life cycle of the building material. And calculating a first predicted life, a life deviation coefficient, an environmental damage degree, a loss life and a second predicted life, constructing a building material life cycle intelligent prediction model according to a calculation result, and inputting failure data and state data of a to-be-predicted building material into the building material life cycle intelligent prediction model to obtain a predicted life and a risk factor. The method not only can improve the efficiency and accuracy of building material life cycle intelligent prediction, but also has good interpretability, and can be directly applied to a building material life cycle intelligent prediction system.

The invention discloses a safety valve service life evaluation method based on pressure history statistics and dynamic damage accumulation. According to the method, only safety valve inlet pressure time sequence data is needed, a load cycle is extracted through an improved rain flow counting method, and the nonlinear damage increment is calculated according to an S-N curve model dynamically degraded along with the accumulated damage degree. According to the method, a self-adaptive failure probability threshold is established based on historical failure data, and probabilistic prediction of the residual life is realized. The model is clear in physical significance, only needs a single data source, significantly reduces the implementation complexity and cost, greatly improves the precision of life prediction by considering the degradation of material performance, and is especially suitable for predicting the accelerated failure risk of the safety valve in the later period of long-term service.