492 results about "Fault modeling" patented technology

The invention discloses a power plant intelligent maintenance method and system based on multi-modal dynamic graph learning. The method comprises the following steps: acquiring structured sensor data, unstructured data and equipment physical topology data of equipment operation in real time through a multi-source sensor cluster and an industrial terminal; the method comprises the following steps: preprocessing multi-modal data, and fusing multi-modal features by using a double-flow Transform architecture and a gated attention mechanism to generate a joint embedded representation; constructing a dynamic causal graph based on equipment physical topology data and sensor time sequence characteristics, updating an edge weight through a GraphSAGE algorithm, fusing domain rule constraints, and outputting equipment state information; generating a maintenance strategy through an improved near-end strategy optimization algorithm according to the state and the equipment health index; and finally, the maintenance strategy triggers third-level early warning of the DCS through an OPC UA protocol, and a maintenance instruction is accurately issued. According to the method, the defects of a traditional method in the aspects of data fusion, fault modeling and decision making are overcome, and the safety, the economical efficiency and the operation and maintenance intelligent level of power plant equipment are remarkably improved.

The invention discloses an industrial equipment health monitoring and fault early warning system based on edge computing, belongs to the technical field of edge computing, and aims to solve the technical problem of how to efficiently realize health monitoring and fault early warning of equipment in real time and identify different types of faults. Each edge computing node is used for taking local data as input, predicting a fault category and a probability value corresponding to the fault category through a local model, outputting the fault category and the probability value as a prediction result, encrypting the local data and the prediction result through the cloud edge collaboration module and then sending the local data and the prediction result to the central server layer, the fusion model comprises three fault diagnosis models, namely a first fault model constructed based on LSTM, a second fault analysis model constructed based on a rule expert model and a third fault analysis model constructed based on a physical model; and the central server layer is used for fusing prediction results based on a self-adaptive weight mechanism.

An optical cable multi-dimensional state monitoring and fault prediction method and system are provided, and the method comprises the steps: obtaining optical cable environment characteristic data, historical fault distribution probability and operation risk indexes, selecting key nodes, deploying state sensors, and collecting multi-dimensional state data to generate an initial state data set; establishing a fault judgment model based on the initial data set, and calculating multi-dimensional state feature data and determining an initial distribution range in combination with historical fault probability and environment features; arranging the physical state value and the communication state value according to a time sequence, and generating a dynamic threshold range of the key node by combining dynamic correction of environment characteristic data; and when the feature data exceed the threshold range, marking abnormity, calculating a judgment parameter through a fault model, and comparing the judgment parameter with a preset value to confirm a fault.

The invention belongs to the technical field of thermal circuit model construction, and particularly relates to a high-voltage power cable intermediate connection thermal circuit fault modeling analysis method and device, and the method comprises the steps: building a high-voltage power cable intermediate connection thermal circuit fault circuit model through analyzing a cable intermediate connection temperature inversion principle; the method comprises the following steps: establishing a geometric model for multi-physics field analysis of the intermediate connection of the high-voltage power cable by utilizing Comsol finite element simulation software, setting material parameters of each component, and setting field domain parameters; an electromagnetic field module is added, boundary conditions of electromagnetic field analysis are set, and a cable intermediate connection electromagnetic field simulation model is obtained; adding a solid heat transfer module to obtain a solid temperature field simulation model; obtaining an electromagnetic-thermal coupling model, and solving to obtain the temperature, current and loss of the cable middle connection wire core and the outer surface in different insulation states. According to the method, the cable intermediate connection fault problem can be found and predicted, and the reliability and the safety of the power cable are improved.

The invention discloses a real-model experiment distribution line fault positioning method and system. The method comprises the following steps: acquiring a topological structure diagram of a target power distribution network in a real-model experiment; based on the topological structure diagram of the target power distribution network, combining a preset diagram input feature selection logic, and obtaining target power distribution network node and edge features as input of a power distribution network fault model to be established; according to the input of the to-be-established power distribution network fault model and the output of a preset power distribution network fault model, training the power distribution network fault model based on the improved graph neural network, the output of the preset power distribution network fault model being the probability of the line fault in the power distribution network; and according to the trained power distribution network fault model based on the improved graph neural network, real-type experiment power distribution line fault positioning is completed. The system and the method have good expansibility and adaptability, can be suitable for power distribution networks of different scales and structures, and have wide application prospects.

The invention discloses a PLC controller fault detection system, and relates to the technical field of industrial control equipment fault detection.The system is characterized in that PLC operation environment data is acquired through a multi-physical-quantity holographic acquisition module, and after the PLC operation environment data is cleaned and subjected to feature fusion through a data preprocessing module, a fault model is constructed through a multi-physical-quantity fusion model module; the self-adaptive threshold value judgment module dynamically calculates and judges a threshold value and evaluates a state; the fault traceability analysis module constructs a propagation path diagram based on a model and historical cases, and realizes accurate traceability of a fault source and a propagation process; according to the invention, multi-physical-quantity holographic acquisition and feature fusion algorithms are integrated, multi-dimensional parameters are monitored synchronously, a comprehensive feature model is constructed, the fault identification precision is improved, and misjudgment is avoided; dynamic optimization is achieved through self-adaptive threshold judgment, the early warning accuracy is improved, meanwhile, accurate traceability is achieved through an element fault propagation algorithm, a path is optimized in combination with historical cases, the downtime is shortened through full-process intelligent support, and the maintenance cost is reduced.

The invention discloses a fault model construction method for adaptive testing of abnormal values of a system-on-chip. The method relates to the technical field of fault construction of chip testing, and comprises the following steps: testing chips on a large scale under the conditions of different temperatures, voltages, frequencies, loads and the like, acquiring performance parameters such as signal delay and power consumption, screening abnormal values by applying a Z-score or box plot method, and processing by using a sliding window; extracting features through CNN or RNN, and normalizing the features; fault features are classified through K-Means or hierarchical clustering, and the accuracy rate is larger than or equal to 80%; a model is built through an SVM or a decision tree, faults can be predicted, self-updating can be achieved, self-adaptive adjustment can be achieved according to chip parameter changes, independent data verification optimization, one-out-of-leave evaluation and gradient descent optimization are achieved. According to the method, multi-condition testing can be carried out on the system-level chip, abnormal values are accurately screened, features are extracted, faults are classified, a fault model capable of being self-updated is constructed, self-adaptive adjustment and verification optimization are achieved, the accuracy is improved, and the chip testing and fault diagnosis efficiency and accuracy are improved.

The invention discloses a discrete element-based three-dimensional fault / fracture network modeling method and device under a backlash pushing and covering system, and relates to the technical field of three-dimensional fault modeling, and the method comprises the steps: building an initialized fault plane model based on the seismic logging information parameters of a target region; extracting stratum parameters of the target area, and determining a deformation process in combination with an equilibrium profile recovery method; the method comprises the following steps: calibrating mesoscopic parameters of discrete elements according to stratum parameters, depositing a first discrete element model, optimizing and generating a second discrete element model, setting boundary conditions to carry out back-flushing pushing-covering simulation, comparing an output result of the second discrete element model with an actual geological section, adjusting model parameters until the output result and the actual geological section are matched, and establishing a three-dimensional fault and fracture network. And analyzing and establishing spatial distribution characteristics of the target area, and carrying out dynamic evaluation and trend prediction on the activity of the three-dimensional fault. According to the method, the geomechanical model is fused, the subjectivity of manual interpretation is overcome, and the problems of fault tip extension and the like in grid reconstruction are effectively avoided.

The invention relates to the technical field of industrial automation, in particular to an industrial equipment self-diagnosis system based on algorithm model prediction, and the system comprises a model building module which comprises a historical fault model; the data acquisition module divides a plurality of to-be-detected point locations and acquires and converts vibration signals; the period judgment module performs periodic judgment on the real-time vibration graph, determines that the vibration period is abnormal according to the judgment result and gives an alarm, or analyzes the abnormal condition of the point location; the data analysis module adjusts a preset frequency based on the point location abnormal conditions, or determines an overall abnormal condition based on a plurality of point location abnormal conditions so as to determine a real-time fault degree, and performs fault early warning on a comparison result of the real-time fault degree and a preset fault degree, or adjusts a preset interval; the model comparison module inputs a plurality of pieces of real-time vibration data into a historical fault model and calculates a fault degree difference value; the model calibration module calibrates the historical fault model based on the fault degree difference. According to the invention, the accuracy of the model fault prediction result is improved.

The invention provides a system kernel reliability evaluation method and system based on fault sequence generation, and relates to the technical field of linux system kernel reliability evaluation, and the method comprises the steps: collecting performance indexes and log information of a target system through a data collector, various fault types and attribute definitions possibly encountered in the system are put into a fault model library, and a fault sequence is provided for a fault injection manager according to a specific fault injection scene; the fault injection manager sends instructions to a target system through the system adapter, and the fault injection executor executes the instructions to simulate a fault scene; and the result analyzer receives data generated after the fault injection actuator executes the fault, carries out deep analysis and records an error log. Wherein in the fault model library, an LSTM algorithm is adopted to extract the rule of fault signals in fault information to establish a fault sequence generation model, and the fault sequence is predicted.

The invention discloses a spring operating mechanism fault detection and optimization method and a related device, and the method comprises the steps: carrying out the real-time collection of key data of a spring operating mechanism through a stress sensor, a speed sensor and a vibration sensor; based on the collected key data, constructing a multi-dimensional data framework to obtain a multi-dimensional data set; fault feature extraction and data analysis are carried out on the obtained multi-dimensional data set, an adaptive fault model is constructed based on the extracted fault features, and then a real-time fault detection result is obtained through multi-modal data fusion; and optimizing a stress sensor, a speed sensor and a vibration sensor by utilizing automatic calibration and optimization of reinforcement learning according to the real-time fault detection result. Compared with a traditional fault detection method, high-precision real-time monitoring and fault prediction of the spring operating mechanism are achieved through the multi-modal data fusion and deep learning technology, and comprehensive and reliable technical support is provided for safe operation and maintenance optimization of equipment.

The invention provides a rotating machine fault diagnosis method and a related system. The rotating machine fault diagnosis method comprises the following steps: S1, constructing a rotor vibration equation according to a vibration behavior of a rotor system under an operation condition; s2, acquiring real-time information of a to-be-diagnosed rotating machine, performing modal analysis based on a rotor vibration equation according to the real-time information of the to-be-diagnosed rotating machine to determine a sensitive frequency band and key features, and forming multi-dimensional feature data; s3, carrying out denoising processing on the multi-dimensional feature data and carrying out signal reconstruction to obtain a reconstructed signal; s4, inputting the reconstruction signal into a pre-trained rotating machine fault model to obtain a fault diagnosis result; and the rotating machine fault model is fused by adopting a neural network ordinary differential equation and a convolutional neural network-gating cycle unit. The method can accurately simulate and predict the continuous dynamic change of the system, improves the diagnosis precision, and enhances the real-time performance and robustness of the model.

The invention discloses a cable accessory fault accurate positioning method and system based on multi-physics field cooperation, and belongs to the technical field of cable fault detection, and the method comprises the steps: installing and deploying a plurality of signal monitoring sensors in a key region of a cable accessory, and forming a multi-physics field sensing matrix; the method comprises the following steps: monitoring a cable accessory in real time by adopting heterogeneous sampling frequency, collecting a multi-physics signal, extracting a multi-physics signal feature in the multi-physics signal based on a time-frequency domain joint feature algorithm, performing signal analysis on the multi-physics signal feature, and obtaining a multi-physics signal analysis result; a multi-physical field fault model is constructed based on a multi-field coupling positioning algorithm, and a fault point in the cable accessory is positioned according to a multi-physical field signal analysis result, so that the fault of the cable accessory can be quickly and accurately identified and positioned, and the reliability and stability of a power system are improved.

The invention relates to an intelligent server fault detection method based on a BMC, and the method comprises the steps: adjusting a to-be-detected server to a debug mode of the BMC to obtain original data, judging whether the data received by the BMC is sensor fault data or not, and when the BMC receives an alarm that a sensor is abnormal; constructing a fault model and carrying out training and optimization; acquiring specified data of a to-be-detected server in real time, preprocessing the specified data, inputting the preprocessed specified data into the optimized fault model, automatically classifying the preprocessed specified data by the fault model, judging whether the to-be-detected server has a fault or a fault tendency, if so, starting an automatic repair strategy, and if not, starting an automatic repair strategy. According to the automatic decision, which repair measure is taken is determined according to the severity and the influence range of the fault, and the repair operation is executed through the automatic script or tool of the automatic operation. Through deep mining of BMC underlying data value and fusion of an intelligent model and an automatic repair mechanism, full-process optimization of server fault detection and processing is realized.

The invention, which relates to the processing and manufacturing field, discloses an intelligent processing and manufacturing management system comprising a data acquisition module, a data transmission and processing module, a production planning and scheduling module, an equipment management module, a quality management module, an inventory management module and a personnel management module. And the equipment management module is used for carrying out full-life-cycle management on the processed and manufactured equipment, monitoring the running state of the equipment in real time, and predicting the fault occurrence probability of the equipment through analysis of the running data of the equipment. According to the management system for intelligent processing and manufacturing, refined and intelligent management of the whole life cycle of equipment from access to scrapping can be realized, the reliability, the available duration and the operation efficiency of the equipment can be effectively improved, common fault models can be identified through the constructed diagnosis model, and the reliability of the equipment can be improved. And faults caused by multiple factors can be accurately positioned and analyzed.

Systems and methods for detecting and diagnosing machine faults are discussed. An exemplary system includes at least one sensor node to sense a signal indicative of an operation status of a machine part, and a machine health analyzer circuit to generate a computational machine fault model comprising an autoencoder (AE) network and an associative module. The AE network encodes the sensed signal into signal features in a latent feature space, and decodes the signal features to produce a reconstructed signal. The associative module transforms the encoded signal features into an associative output using a dynamically updatable codebook. The machine health analyzer circuit detects a presence or absence of fault in the machine part based on reconstruction losses determined respectively from the reconstructed signal and the associative output. The detected fault can be presented to a user or to a process such as fault diagnosis or fault correction.

Systems and methods for integrating system information for communication and / or electrical systems to provide a mapping for diagnostic trouble codes generated by both systems. A unified mapping is generated using design outputs for both systems. When a set of diagnostic trouble codes is generated by a system, the unified mapping is applied by a troubleshooting tool to direct maintenance personnel to a failure mode and the source of malfunction. The result may be applicable to a troubleshooting tool for a vehicle system.

The invention discloses a power distribution network grounding fault research and judgment analysis method, relates to the technical field of power distribution network fault analysis, and solves the technical problem that fault analysis is not accurate and deep enough due to the fact that fault features in a neutral point grounding complex fault scene cannot be comprehensively considered. According to the method, single-phase grounding fault models of a neutral point ungrounded system, an arc suppression coil grounded system and a small resistor grounded system are respectively constructed, grounding current, phase voltage and neutral point voltage expressions under the models are detailedly deduced, and characteristics of voltage and current before and after a fault point in the steady-state and transient-state processes are deeply analyzed; the method comprehensively considers various complex fault scenes, can more accurately grasp essential characteristics of faults in different grounding modes, and provides a more comprehensive and accurate basis for fault diagnosis and processing.

The invention relates to the technical field of motor faults, and discloses a motor fault detection method and system. Motor operation data are collected in real time through a sensor, and data preprocessing is carried out to eliminate noise and missing values. A deep learning algorithm based on an attention mechanism is adopted, historical motor operation data and fault data are combined, and a motor fault model is established. And inputting the preprocessed real-time data into the model, and outputting the probability of various faults of the motor. And when the probability of a certain fault exceeds a preset threshold value, triggering an early warning strategy. Meanwhile, the invention further provides a motor fault detection system which comprises a data acquisition module, a data preprocessing module, a model construction module, a fault detection module and an early warning strategy module, and intelligent and automatic detection and early warning of motor faults are achieved. According to the method and the system, the accuracy and the robustness of fault identification are improved, the safe and stable operation of the motor and the system is ensured, and the method and the system have wide application prospects.

The invention discloses an offshore wind turbine generator fault prediction method and device considering the influence of environmental factors, and the method comprises the steps: dividing an offshore wind turbine generator into a sensor system, a mechanical system and a tower structure system according to the correlation between the fault rate and the environmental factors, and building fault detection models respectively; time convolutional network prediction models are respectively established in combination with unit operation data and environment data to predict the operation state of each system, and fault occurrence is predicted in combination with a fault prediction model; coupling each system fault prediction model, constructing a coupling fault prediction model based on unit operation and an offshore wind turbine generator fault tree, carrying out label classification on predicted faults of each system fault model, carrying out verification through the fault tree, carrying out analysis and classification on fault causes, and supplementing the fault tree when judging that a new fault type appears; according to the method, the correlation between the offshore environment and the fault generation increments of different parts in the wind turbine generator is considered, so that the fault generation prediction accuracy of the offshore wind turbine generator is improved.

The invention relates to a hydroelectric generating set flat pressing pipe working condition monitoring method based on MFCC and a fault model library, and the method comprises the steps: collecting a voiceprint signal of a flat pressing pipe through a voiceprint sensor, and carrying out the monitoring of the working condition of the flat pressing pipe according to the known historical data of a voiceprint signal, a liquid level signal, a vibration signal and a pressure pulsation signal of a flat pressing pipe fault; a voiceprint model library of common faults including typical voiceprint features is established and is used for carrying out rapid comparison according to the voiceprint signals of the current flat pressing pipe and identifying and judging whether the common faults occur in the current working condition state or not; and a fault model library containing voiceprint, liquid level, vibration and pressure pulsation characteristic data is constructed and is used for identifying and judging whether a known fault occurs in the current working condition state according to comparison of voiceprint, liquid level, vibration and pressure pulsation signals of the current unit. According to the invention, real-time on-line high-precision automatic identification of the fault of the flat pressing pipe is realized, the efficiency and the precision of fault diagnosis are obviously improved, and the downtime and the maintenance cost caused by misdiagnosis or missed diagnosis are reduced.

The invention relates to the technical field of robot control, and discloses a mechanical arm specified performance fault-tolerant control method based on a fuzzy logic system, and the method comprises the steps: building a kinematics and dynamics model and a fault model of an agricultural robot; establishing a relative error dynamic equation between the actual position and the expected position; designing a specified performance function, and constraining the tracking error within an expected range by using the specified performance function; on the basis, designing a virtual control law by using a backstepping method under the condition of meeting error constraints; designing an adaptive control law by adopting a fuzzy logic system based on an uncertain item, unknown environment interference and a fault model in the kinetic model; a specified performance controller is designed based on a virtual control law, fuzzy logic adaptive control and full-state feedback agricultural robot, and specified performance fault-tolerant control of the agricultural robot is realized. The stability proves that the tracking error of the agricultural robot converges to any small neighborhood near zero within fixed time.

The invention discloses a topology awareness priori driven mobile energy storage power distribution network real-time scheduling method and a topology awareness priori driven mobile energy storage power distribution network real-time scheduling system under ice disaster toughness. The method comprises the following steps: constructing a line fault space-time distribution model changing along with icing evolution, and forming a multi-period and multi-scene ice disaster dynamic fault set; on the basis of the fault set and the multi-scene power flow, combining the topology and toughness indexes of the power distribution network, and screening by using a graph convolutional network to obtain a scale-controlled mobile energy storage candidate access node set; a random scheduling model is established under the power grid-road integrated constraint, and a mobile energy storage arrangement position, a driving path and a charging and discharging plan are jointly optimized. According to the method, ice disaster dynamic fault modeling is combined with candidate set screening assisted by topology perception learning, the dimensionality of a large-scale random mixed integer optimization model containing mobile energy storage is effectively reduced while line time sequence failure characteristics are fully reflected, the calculation burden is relieved, and the calculation efficiency is improved. And the toughness of the power distribution network in a multi-ice disaster scene and the timeliness of pre-disaster scheduling are improved.

The invention discloses a fault modeling method, device and equipment based on seismic data and a storage medium, relates to the field of geological modeling, and aims to process initial seismic data by adopting a dip angle-oriented median filtering method and effectively remove noise while retaining geological structure information such as faults and the like. And then the trained fault identification model is introduced to process the target seismic data, fault information can be rapidly extracted from massive seismic data, and the identification efficiency is greatly improved. And then similarity is calculated from two dimensions of spatial distance and direction attribute based on a fault identification result, clustering is carried out by using a spectral clustering algorithm, correlation of fault lines in spatial distribution and fault extension direction characteristics are considered, and more reasonable and accurate fault line groups are obtained. And finally, constructing an initial B-spline curved surface by utilizing a grouping result, and enabling the finally constructed three-dimensional fault model not only to fit actual seismic data, but also to accord with the smoothness and direction rule of a geological structure through variation energy minimization optimization, so as to accurately reflect the real form of an underground fault.

The invention discloses a fault diagnosis device for energy storage equipment based on an artificial intelligence technology and a use method, and relates to the technical field of fault diagnosis of energy storage equipment, the fault diagnosis device comprises an energy storage unit and a fault diagnosis unit, and the energy storage unit comprises an energy storage cabinet, a battery pack, a circuit breaker and a sealing door. According to the invention, the data acquisition unit is used for acquiring the operation data during the operation of the equipment and transmitting the operation data to the microcontroller, the microcontroller transmits the operation data to the central processing unit for analysis and processing, the operation data is compared with the operation data of a normal model, and if the similarity of the two operation data is greater than or equal to a preset value, the central processing unit sends the operation data to the central processing unit. If the similarity of the two items of operation data is smaller than a preset value, the fault state is automatically judged and positioned, the central processing unit transmits the operation data to different fault models, and the fault state is judged and positioned by comparing the similarity probability of the operation data of the fault models and the normal operation data. And the fault type of the energy storage equipment can be obtained according to the similarity probability.

The invention relates to a preset performance specified time tracking control method for a mechanical arm, belongs to the technical field of intelligent robot control, and solves the technical problem of low control precision of the mechanical arm. Comprising the steps of establishing a dynamical model and a fault model, designing a preset performance specified time function, constructing a preset performance specified time tracking control law, and verifying the bounded stability of the preset performance specified time tracking control law. According to the control method, the specified time preset performance function is introduced into the control law design in an error transformation mode, synchronous adjustment of the convergence precision and the convergence speed is guaranteed, and the control method is used for the mechanical arm device and can also be used for locally spraying an end effector on the surface of an aircraft.

The invention provides a power-traffic coupling charging network toughness evaluation method and system, and belongs to the technical field of power system analysis. According to the method, a power-traffic coupling charging network is constructed, a typhoon path and a Holland wind field model are simulated in combination with a Bezier curve to calculate a wind speed, a dynamic traffic flow distribution, alternating current optimal power flow and cascade fault model is established, a fault data set is generated by utilizing Monte Carlo simulation, a fault prediction model is trained through a dynamic Bayesian network, and a fault prediction result is obtained. Inputting the actually measured wind speed, outputting a dynamic probability graph, and calculating a dynamic toughness index to evaluate the toughness of the power-traffic coupling charging network. According to the method, the problem of insufficient fault propagation and dynamic evaluation between systems is considered, and the toughness evaluation precision of the power-traffic coupling charging network under typhoon disasters is improved.

The embodiment of the invention provides an RAID fault simulation method and device, relates to the technical field of computer system structure storage and can also be used for the financial field, and the method comprises the steps that an RAID array of a corresponding level is constructed based on the preset number of RAID array hard disks and a hard disk list forming an RAID; performing parameter control on the constructed RAID array to construct a corresponding fault model, wherein the fault type of the fault model comprises at least one of a hard disk type fault, a control and configuration type fault, a logic fault and a read-write slow fault; determining a corresponding fault injection instruction and a fault recovery instruction according to the fault model, and inputting the instructions to a tested system, so that the tested system performs fault simulation; the RAID fault simulation method and device can solve the problems that the current RAID fault simulation cost is high, and hardware dependence, professional software dependence and manual operation dependence exist.

The invention discloses an intelligent fault injection and performance quantification system and method for a helicopter engineering simulator, and belongs to the technical field of flight simulation and testing, and the system comprises a hierarchical combinable fault model library module which is used for the flexible construction from a part level to a system level to a composite fault; the intelligent fault injection management module is used for realizing an intelligent fault injection strategy; the multi-dimensional performance index quantification module is used for collecting data before and after fault injection in real time and setting a performance evaluation index system; the automatic test and visual analysis module is used for comparing various quantitative indexes of different control laws or different parameters of the same control law in the same fault scene to generate a comprehensive evaluation report; according to the intelligent fault injection and performance quantification system and method for the helicopter engineering simulator, efficient, objective and deep testing of robustness, stability and efficiency of an advanced flight control law in a special condition state is realized through a hierarchical fault model, an intelligent injection strategy and automatic quantitative evaluation.

Various embodiments provide methods, systems, computer program products, apparatuses, and / or the like for assessing vulnerability of an IC design to fault injection attacks, such as through a security property-driven vulnerability assessment framework for efficiently evaluating faults with respect to certain security properties associated with the IC design. In one embodiment, a method is provided. The method includes generating, using a fault-injection technique specification, one or more fault models describing attributes of one or more faults. The method further includes selecting, using the fault models and executable security properties associated with a design file of an IC design, a fault list identifying a plurality of possible faults for the IC design. The method further includes identifying, based at least in part on performing a fault simulation on the design file with the fault list, critical locations of the IC design. The method further includes implementing protections at the critical locations.