929 results about "Failure analysis" patented technology

The invention relates to the field of production fault analysis, in particular to a fault monitoring analysis method and system for lubricating oil production. The method comprises the following steps: acquiring a multi-dimensional working monitoring parameter and an omnibearing working image; performing abnormal outlier filtering processing on the multi-dimensional working monitoring parameters, and performing time sequence evolution simulation to generate time sequence state evolution data; performing continuous frame optical flow motion analysis on the omnibearing working image, performing dynamic parameter mapping based on time sequence state evolution data, and constructing a dynamic production twinborn model; obtaining a preset production standard demand log; carrying out dynamic process deviation detection based on a preset production standard demand log dynamic production twinborn model, and carrying out production fault analysis so as to obtain stage production fault data; performing potential fault traceability speculation on the staged production fault data to obtain an abnormal fault potential node; and constructing a potential fault mark twinborn model based on the abnormal fault potential nodes. According to the invention, efficient and accurate fault analysis of lubricating oil production is realized.

A data processing system implements obtaining build logs that include information associated with a software build problem; analyzing the logs to generate a knowledge graph identifying the relationship between various entities in the logs; extracting a signature of a candidate root cause of the build problem from the knowledge graph representing a subset of nodes and edges of the knowledge graph; providing the signature of the candidate root cause to a graphical language model to obtain a prediction of a category of root cause failure selected from among a plurality of root cause failures; constructing a prompt for a language model to generate a root cause failure analysis that describes the root cause of the build problem, the prompt including the category of root cause; receiving the root cause failure analysis from the language model; and performing one or more actions in response to receiving the root cause failure analysis.

The invention relates to a pier damage early warning analysis method under the action of flood, and solves the technical problems of high disaster recognition cost, low bridge damage recognition precision, untimely early warning and alarming and the like based on monitoring equipment in the existing concrete bridge flood-disaster-resistant technology. According to the method, by means of an established plastic damage simulation model, expensive sensors do not need to be arranged on a bridge structure, only basic information of the bridge structure and flood information such as the flow speed, the water level and the scouring depth are obtained, and damage and failure analysis of the bridge pier structure under the action of different influence factors by the flood action can be studied; structural damage state qualitative analysis and modal integrity damage index quantitative analysis are combined to determine bridge pier structure damage grades and damage failure judgment criteria, and early warning values corresponding to different damage grades of a bridge pier are inverted, so that accurate damage identification and rapid grading early warning of the bridge pier structure under the flood effect are realized. The flood disaster resistance of the bridge structure is improved, safe operation of the bridge is guaranteed, and the service life of the bridge is prolonged.

This application relates to a test device applied to a coupled environment of stress, wear, and corrosion, and a quantitative evaluation method, and belongs to the field of failure analysis of materials. The test device includes a stress-loading mechanism configured to load a stress on a sample, a corrosion mechanism configured to conduct an electrochemical corrosion test on the sample, and a wear mechanism configured to wear a sample. The test device applied to a coupled environment of stress, wear, and corrosion of this application can establish a wear-stress-corrosion coupled action test process, and can investigate service behaviors of various metallic materials and metal matrix composites under conditions close to actual service conditions to obtain key behavior data under a multi-factor coupled action, quantitatively analyze a wear, a corrosion, a wear-corrosion interaction, a stress-wear interaction.

The invention discloses a digital chip fault detection method and system based on big data, and the method comprises the steps: collecting the historical hierarchy data of chip failure fault analysis, extracting the feature data of each stage, and building the multi-dimensional feature association of the failure fault analysis data based on the feature data; synchronizing a single fault positioning model of the chip failure fault established based on the single failure root cause and a multi-fault positioning model of the chip failure fault established based on the associated interaction failure multiple root causes; failure fault positioning is carried out through a single fault positioning model and a multi-fault positioning model, a failure fault positioning result is output, and finally, the fault positioning output results of the single fault positioning model and the multi-fault positioning model are verified and reproduced. According to the method, the corresponding fault positioning model is constructed by using the judgment result of the failure root cause, and the problem of low positioning precision caused by various influence factors and interaction of an existing single fault positioning method is solved.

The invention proposes a semiconductor device test optimization method, and the method comprises the steps: initializing a test environment, obtaining the test data of a semiconductor, and automatically configuring the test environment; constructing a signal adjustment model to dynamically generate a preliminary test signal; designing a sensor network to monitor a preliminary test signal and response of the semiconductor device in real time, dynamically adjusting sensor data, and designing a data optimization acquisition strategy; dividing the parameters into a high priority and a low priority, dynamically compressing the parameters, implementing fast Fourier transform, extracting spectrum components as features, and analyzing the features; decompressing and preprocessing the compressed data, and performing deep fault analysis and positioning; and optimizing test data of the conductor, carrying out secondary test, and verifying a parameter adjustment result. According to the invention, the test quality and efficiency of the semiconductor device can be obviously improved, the product failure rate caused by insufficient test can be reduced, and the method plays an important role in semiconductor manufacturing industry, especially high-end chip production.

The invention relates to the technical field of semiconductor power device testing, in particular to a dynamic reverse bias testing method and system for a semiconductor power device, which can accurately evaluate the performance of the device under a dynamic reverse bias condition and improve the testing efficiency and accuracy. The method comprises the following steps: acquiring a plurality of electrical characteristic data of a to-be-detected semiconductor power device to obtain an electrical characteristic data set; inputting the electrical characteristic data set into a dynamic reverse bias test model to obtain a first dynamic reverse bias test result; transmitting a fast changing backward voltage pulse signal to the to-be-detected semiconductor power device to obtain an output signal after the backward voltage pulse signal passes through the to-be-detected semiconductor power device; judging whether the output signal exceeds a preset abnormal threshold value or not; if not, outputting the first dynamic reverse bias test result as a dynamic reverse bias test result of the semiconductor power device; and if yes, extracting the obtained abnormal output signal, and inputting the abnormal output signal into the dynamic reverse bias failure analysis model.

The invention discloses a process quality control method combining FMEA analysis and a multivariate control chart. The method comprises the following steps: planning and preparing PFMEA; identifying special quality indexes of the process parameter diagram; a control limit and an abnormal mode are determined through failure analysis and risk analysis in the PFMEA, so that abnormal fluctuation in the product machining process is accurately identified; performing multivariate quality index decoupling and correlation analysis; constructing a multivariate SPC control chart; performing abnormal mode identification and source tracing on the multivariate SPC control chart; pFMEA optimization is combined with an SPC control chart to adjust the sampling frequency and the control limit. According to the method, the technical problems that the product quality is difficult to control through frequent detection during large part machining, a single-variable SPC control chart is inaccurate in abnormal mode recognition, the SPC control chart cannot be formulated according to the actual production process, and the production process cannot be effectively improved by the SPC technology are solved.

The invention discloses a composite material microcosmic failure analysis method based on a micromechanical failure theory. The method comprises the following steps: obtaining performance parameters of each component of a composite material according to a composite material component performance test; establishing a finite element mesoscopic RVE model; the rigidity of the interface between the fiber and the matrix is reversely deduced through fitting of experimental data of a macroscopic experiment; applying a periodic boundary condition to the finite element mesoscopic RVE model; a load is applied to the finite element mesoscopic RVE model through ABAQUS finite element software, the failure behavior of the composite material in a complex stress state is simulated, and the stress-strain relation and the mesoscopic failure rule of the composite material in different loading modes are predicted. The method can be applied to composite material mesoscopic failure simulation analysis in axial, transverse and off-axis load, thermal load and composite load forms, can more accurately predict the mesoscopic failure rule of the composite material, and has important significance on research and application of the composite material.

The invention discloses an intelligent fault diagnosis method and system for an automatic numerical control machine tool, and relates to the technical field of data processing, and the method comprises the steps: interacting fault event records of a target numerical control machine tool; based on the fault event records, fault characteristic mining is carried out to construct a fault network, a fault characteristic network is constructed through graph theory analysis based on fault propagation, and a fault probability network is constructed through fault failure analysis; according to the fault network, diagnosis large model training and lightweight processing are carried out, a fault diagnosis module is built, and module building is carried out through two-step training based on a lightweight loss function; time-staggered sampling frequency is determined, monitoring management and control of a front-end sensor are carried out based on the time-staggered sampling frequency, and machine tool operation data are determined; and the machine tool operation data is transmitted back to the fault diagnosis module, fault occurrence judgment and failure level measurement are carried out, and a fault diagnosis result is determined. Therefore, the technical effects of improving the fault diagnosis accuracy and real-time performance and improving the deployment adaptability are achieved.

The invention relates to the technical field of deformation monitoring, in particular to a reservoir dam deformation monitoring method and system based on a sensor network, and the method comprises the steps: carrying out the calibration analysis of the failure degree of a corresponding position based on the basic engineering parameters of each position of a reservoir dam and a failure degree prediction result; and performing failure analysis on each position according to the predicted future water flow impact condition and the failure degree calibration analysis result of each position of the dam, and performing maintenance analysis resource allocation on the corresponding position based on the failure analysis result of each position. Failure analysis and maintenance resource allocation are carried out based on water flow conditions, deformation conditions, basic engineering parameters and failure degree prediction and calibration analysis of all positions of a reservoir dam, potential risks are found in time by collecting water flow and deformation data in real time, the maintenance priority of all areas is determined, it is ensured that high-risk areas are processed in time, and the maintenance efficiency is improved. And according to the real-time monitoring and prediction result, the maintenance plan is dynamically adjusted, and the flexibility and adaptability of resource allocation are improved.

A maintenance and monitoring infrastructure can collect real machine samples and train failure analysis models to identify operational anomalies, indicative of a machine failure. Failure analysis models can be trained and more robustly tested and developed by using artificial samples that can simulate what a real monitor would have measured had the real monitor been deployed in a set of selected circumstances (a scenario). A synthetic sample generator (SSG) can include a seed development cycle stage (SDCS) and a synthesis execution stage (SES). The SDCS can generate a signal, from which the SES can generate a multi-phenomena synthetic signal, simulating a scenario, where a monitor would have likely yielded the generated synthetic signal, or a similar signal. The synthetic signal data can be used to train failure analysis models.

The embodiment of the invention provides a vehicle fault auxiliary analysis method and system, electronic equipment and a storage medium, and belongs to the field of vehicles. The method comprises the following steps: acquiring bus data of a current vehicle, storing the bus data in a first storage area, and determining whether an abnormal value or a fault value exists in the acquired bus data based on a normal value or a normal range value of each preset bus data; if the abnormal value or the fault value exists, intercepting bus data in a preset time period before and after the abnormal value or the fault value occurs in the first storage area, and storing the bus data in a second storage area; and after the vehicle prompts a fault or reads a fault code after sale, analyzing a fault reason and reproducing the fault according to the bus data of the second storage area. By intercepting the bus data of the specific time before and after the abnormal data, after-sales fault analysis is assisted, for some faults difficult to check, the possibility of reproduction is effectively improved by means of the bus data before the faults occur, and the analysis and processing efficiency is improved.

The invention discloses a permanent magnet synchronous motor failure monitoring method, system and equipment for a space environment, and relates to the technical field of motor fault monitoring, and the method comprises the steps: constructing a simulation space environment experiment module, carrying out the accelerated aging experiment of a permanent magnet synchronous motor, and obtaining multi-dimensional degradation data; performing material performance evaluation on the permanent magnet synchronous motor, determining a material performance degradation result, performing failure analysis, and constructing a space environment failure feature library; and monitoring a motor operation parameter set of the permanent magnet synchronous motor in real time, determining an abnormal mode, and making a failure maintenance suggestion. The technical problems that in the prior art, the failure condition of the permanent magnet synchronous motor in the space environment cannot be accurately monitored, the reliability of the motor in the space environment is poor, and the operation stability is insufficient are solved, the failure condition of the permanent magnet synchronous motor is accurately monitored, and the reliability of the motor is improved. And the reliability and the operation stability of the permanent magnet synchronous motor in a space environment are improved.

The invention relates to the technical field of metal strip degreasing production line on-line control, and particularly discloses a metal strip degreasing production line on-line control system and method, and the system comprises a degreasing state judgment module, a degreasing information collection module, a degreasing incomplete analysis module, a degreasing excessive analysis module, a database, and a production line degreasing effect feedback module. According to the method, the reason for incomplete degreasing is determined by judging the degreasing state corresponding to the target metal strip, the corresponding control scheme is matched according to the reason for incomplete degreasing, meanwhile, when the degreasing state is excessive degreasing, the PH value corresponding to the degreasing fluid in the degreasing tank in next batch of degreasing is adjusted, and the degreasing efficiency is improved. And finally, whether the degreasing effect corresponding to the target production line has a problem or not is judged, the overall performance of the product is improved, the service life of the product is prolonged, meanwhile, the problem can be accurately positioned, a clear direction is provided for subsequent control measures, and production stability and product quality consistency are improved.

The invention relates to the field of intelligent diagnosis of oil and gas field development, in particular to a fracturing wellhead device failure analysis method and system based on a large model and a knowledge graph. The system is helpful for solving the problem that the accuracy and timeliness of failure analysis and fault diagnosis of the fracturing wellhead device in the prior art are limited. The method comprises the steps of building a knowledge graph, setting a knowledge graph dynamic updating mechanism oriented to the fault evolution process, building an open source large model and introducing a closed loop feedback mechanism. According to the method, the large model, the knowledge graph and the RAG technology are applied to fracturing wellhead failure analysis, and the intelligent level of the oil and gas industry is remarkably improved; the problem that a large model is high in generalization but insufficient in specialty is optimized, and the accuracy of answer generation is improved.

The invention belongs to the technical field of tire performance simulation, and particularly relates to a tire durability simulation analysis system and method.The tire durability simulation analysis method comprises the steps that a tire three-dimensional finite element model containing cord-rubber interface details is used for dynamically recognizing a first-time debonding event by focusing a cord-rubber interface area in simulation; a debonding extension path and a failure evolution judgment mechanism are constructed through time-space accompanying monitoring of a newly added debonding area, so that tire durability evaluation starting from a microcosmic interface scale is realized, and the accuracy of fatigue life prediction is improved; meanwhile, road conditions are introduced under a microscopic failure analysis framework based on a tire three-dimensional finite element model, tire responses at different speeds are simulated by adopting a stepped speed-increasing loading sequence, and durability simulation results under different road condition and speed combinations are obtained and visually presented in an equivalent map form. And the fatigue performance distribution characteristics of the tire under a complex operation condition can be intuitively reflected.

The invention belongs to the field of material mechanical property testing and strain measurement, and particularly relates to a local strain measurement method and system based on a four-point bending and digital image correlation (DIC) technology, and the method specifically comprises the following steps: cutting to prepare a sample, carrying out surface electrolytic polishing, preparing speckles on the surface of the sample to be tested, and carrying out a four-point bending experiment; speckle images of the lower surface of the sample are collected, displacement correction is conducted in combination with bending deflection calculation, the bending load and the strain of the upper surface of the sample are obtained in real time, the stress-strain relation and local strain distribution of the lower surface of the sample are obtained through calculation, and finally deformation and failure mechanism analysis is conducted in combination with surface deformation and crack morphology. The method provided by the invention combines four-point bending and DIC plane strain measurement technologies, can realize stretching, compression and dynamic fatigue loading on the surface of the sample, is suitable for performance test and failure analysis of tension, compression and fatigue deformation of brittle metal materials such as intermetallic compounds and amorphous materials, and has the characteristics of convenience, rapidness and low cost.

The invention discloses a SiC MOSFET maximum surge current test and failure analysis method and device based on three-terminal resistance monitoring. The performance change of the device under the extreme stress is quickly evaluated by applying the stepping surge current and combining the online monitoring of the three-terminal resistor, so that the maximum single surge current bearing capacity and the failure mode of the device are judged. The method comprises the following specific steps: applying proper grid bias voltage to a SiC MOSFET to be tested, and setting parameters of a surge current generating circuit; and after the stepping single surge current is applied, measuring and recording three-terminal static resistance values of the gate-source terminal, the drain-source terminal and the gate-drain terminal. By monitoring the change of the resistance value, the maximum single surge current is determined, the failure mode of the device is judged, and the modes of open circuit, short circuit, electric leakage, burning and the like are determined. The method is easy and convenient to operate and low in cost, and reliability information of the SiC MOSFET can be rapidly provided under the condition that a high-precision instrument is not used.

The invention relates to an aero-engine stator part crack critical dimension comprehensive determination method and system, and belongs to the technical field of aviation, the method comprises the following steps: obtaining an aero-engine typical hot end stator part geometric model and material parameters, and carrying out static strength analysis according to fluid-thermal-solid coupling load characteristics in actual working conditions; inserting an initial crack into a first principal stress plane of a dangerous part of the real component, carrying out crack propagation simulation to determine a crack propagation path, and calculating a critical crack size of a dangerous point; analyzing critical crack sizes under different failure modes, such as cooling failure and buckling failure; and integrating analysis results under the multi-failure mode, and determining a comprehensive critical dimension as a final judgment standard. According to the method, through combination of fracture failure and function failure analysis, accurate judgment of the critical dimension of the crack is realized, limitation of a traditional single fracture criterion is broken through, comprehensive function failure analysis is realized, the accuracy of a maintenance decision is remarkably improved, and a scientific basis is provided for condition-based maintenance decision.

The techniques disclosed herein provide a system for condensing large log files to enable interoperability with transformer-based large language models in automated failure analysis. In various contexts such as enterprise cloud services, log files can be large oftentimes containing millions of lexical units that can incur significant processing time and resource consumption. As such, the disclosed techniques provide a system for producing a directional knowledge graph representing a condensed form of a log file for automated failure analysis. Firstly, the log file is preprocessed to filter information that is irrelevant to the failure that previously occurred. The filtered lexical units are then used to generate a directional knowledge graph comprising a plurality of vertices which correspond to individual lexical units, and which are connected by edges representing the semantic connection between the lexical units. In this way, the directional knowledge graph significantly reduces processing times and improves resource consumption.

The invention discloses a petroleum pipe microscopic fracture morphology identification method, system and device and a medium, and belongs to the technical field of failure analysis image process.The method comprises the steps that a deep learning classification network with a residual module is constructed and trained, and a deep learning classification network loaded with a network connection weight matrix is obtained; and finally, the deep learning classification network is utilized to efficiently and accurately identify the to-be-tested object region image. According to the method, massive data sets can be quickly processed and analyzed by using the high-speed processing capability of a modern computer, so that the efficiency is greatly improved. Compared with a traditional method, the automatic fracture classification method can reduce subjective interference of people and enhance reliability and consistency of results. And meanwhile, the identification method is simple, engineering application integration is facilitated, and good practicability and expansibility are achieved.

The invention discloses a failure analysis method and system for the electrical property of a gallium nitride integrated circuit, and the method comprises the following steps: carrying out the preliminary electrical property test of the gallium nitride integrated circuit, and collecting a first group of electrical property test data; secondly, preprocessing the data to improve the accuracy of subsequent analysis; analyzing the preprocessed first group of electrical test data by using a trained artificial intelligence algorithm, and identifying potential failure modes or abnormal features; performing infrared imaging on the gallium nitride integrated circuit to obtain a heat distribution image of the gallium nitride integrated circuit, and combining layout and circuit design information of the integrated circuit by analyzing hot spots or abnormal temperature areas in the heat distribution image; the method has the beneficial effects that artificial intelligence and an infrared imaging technology are applied to failure analysis of the gallium nitride integrated circuit for the first time, and technical fusion and innovation are realized; through intelligent analysis of an artificial intelligence algorithm and visual display of an infrared imaging technology, the efficiency and accuracy of failure analysis are remarkably improved.

The invention discloses a multi-axis multi-field coupling fatigue test device, and belongs to the technical field of material testing. The device comprises a rack, a multi-axis loading unit, an environment simulation unit, a dynamic sealing interface and a cooperative control system. The core of the device is that the test sample can simultaneously bear the coupling effect of multi-axis mechanical stress (pulling, pressing and twisting) and various environmental fields (such as high and low temperatures, corrosive media, illumination and electric fields) through a highly integrated design; and the dynamic sealing problem of the actuator rod in the environment box is solved through an innovative dynamic sealing interface (such as labyrinth-magnetofluid composite sealing). According to the invention, accurate linkage control of multiple channels is realized through the cooperative control system, the service environment of key parts under actual complex working conditions can be truly simulated, and a reliable test platform is provided for failure analysis and life prediction of products under multi-field coupling.

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 a method for predicting high-temperature degradation of a micro-copper pillar bump interconnection resistor. The method comprises the following steps: establishing a relationship between the net flux of Ni atoms and the thickness change of a Ni-Cu-Sn compound layer based on the thermal diffusion flux of the Ni atoms; establishing a relationship between the net flux of the Cu atoms and the thickness change of the Cu6Sn5 layer and the Cu3Sn layer based on the thermal diffusion flux of the Cu atoms; based on the thickness change relationship, establishing a thickness high-temperature growth model of the Ni-Cu-Sn compound layer, the Cu6Sns layer and the Cu3Sn layer; based on the thickness high-temperature growth model, establishing a thickness high-temperature consumption model of the Cu bonding pad, the Sn solder and the Ni layer; and establishing a resistance high-temperature degradation model based on the thickness high-temperature growth model and the thickness high-temperature consumption model. According to the method, an effective solution can be provided for predicting the high-temperature degradation condition of the copper pillar bump interconnection resistor plated with the Ni layer, and a large amount of service life and time and economic cost generated by failure analysis tests are greatly reduced or avoided.

A lithium ion battery polycrystal / single crystal positive electrode material multi-physics field coupling modeling and failure analysis method comprises the steps that a three-dimensional positive electrode particle structure model is constructed, a polycrystal structure is generated by randomly filling spherical boundaries with non-overlapped spherical crystal grains, and single crystals are complete spheres; importing the model into a finite element simulation platform, and setting a multi-physics field coupling interface; establishing a diffusion, migration and electrochemical reaction kinetic model; establishing a lithium concentration-strain-stress coupling relation and solving the lithium concentration-strain-stress coupling relation; setting a failure criterion based on stress distribution to identify a damaged area; and outputting a simulation result and carrying out structure-performance correlation analysis. The method can reveal the evolution behavior difference of the polycrystalline positive electrode and the monocrystalline positive electrode in the electrochemical-mechanical process, provides reliable theoretical and simulation support for electrode material design, optimization and life evaluation, and provides powerful technical support for optimization design of the high-performance lithium ion battery positive electrode material.

The invention relates to the technical field of submarine cable engineering equipment, and discloses a submarine cable bending limiter failure analysis method and system, and the method comprises the steps: constructing a large-scale wave water tank experiment system; a wave flow test is carried out in a large-scale wave water tank experiment system, and three-dimensional terrain evolution data of a scouring pit formed at the single-pile foundation model and dynamic strain response and displacement of the submarine cable bending limiter model are obtained; constructing a hydrodynamic force-sediment transportation-submarine cable motion coupling numerical model according to the three-dimensional terrain evolution data, the dynamic strain response and the displacement; and according to the hydrodynamic force-sediment transportation-submarine cable motion coupling numerical model and different hydrodynamic force conditions, the quantitative relation between the scour range extension and the failure threshold value of the submarine cable bending limiter is determined. According to the scheme, the tensile stress and bending response change rule of the submarine cable and the bending limiter can be quantitatively analyzed, and reference is provided for optimal design and protection of the offshore wind power pile foundation submarine cable bending limiter.

The invention discloses a power battery fault detection method and system, and belongs to the technical field of batteries and detection, and the method comprises the steps that a data processing center receives power battery operation data collected by a collection device, sets a first threshold value and first delay time, and generates a diagnosis report; secondary diagnosis is carried out according to the diagnosis report, the fault degree is quantified, and at least two fault levels are generated; dynamically adjusting parameters according to the fault level; and performing fault analysis again according to the parameter dynamic adjusting quantity, and outputting an early warning signal. In the implementation process of the technical scheme of the invention, preliminary diagnosis and secondary diagnosis are performed on the power battery, the fault degree is quantized, a plurality of fault levels are generated according to the quantized result, and then parameter dynamic adjustment is performed under different fault levels, so that the accuracy of the fault detection result is improved, and the fault detection efficiency is improved. And the fault misjudgment condition under the influence of the external environment is reduced, various early warning signals are generated after analysis is completed, and the driving safety is improved.

The invention provides a crack-containing pipeline failure analysis software system, and belongs to the field of crack-containing pipeline failure evaluation. The system comprises a parameter import module, a random sampling module, a penetration crack length module, a leakage rate module, a failure analysis module and a display module. The parameter import module imports pipeline material parameters, operation environment parameters and the like into the computer; the random sampling module is used for carrying out random sampling according to a common distribution function on the basis of importing parameters; the fatigue crack propagation module is used for calculating a stress intensity factor and fatigue crack propagation; the leakage rate module is used for calculating a detectable leakage length; and the result analysis module is used for integrating all calculation results and input parameters into a failure probability analysis chart. According to the method, the probability fracture mechanics method is adopted, the detection accuracy is improved, and the failure probability of equipment can be evaluated more quickly and visually.