605 results about "Damage detection" patented technology

The invention provides an alloy bearing unsteady-state damage detection and evaluation method based on digital twinning. The method comprises the following steps: constructing an alloy bearing multi-physical field digital twinning model; the method comprises the following steps: acquiring operation state data of a bearing under an unstable working condition based on a sensor network, preprocessing the acquired data to obtain multi-source features, and fusing the multi-source features to generate a comprehensive health index; inputting the preprocessed data into a digital twin model for forward simulation, generating a prediction observation vector, comparing the prediction observation vector with a sensing measurement value to generate a residual error, and performing damage state assimilation based on the residual error; and based on the assimilated state, predicting a damage evolution trajectory under an unsteady state working condition, and evaluating probability distribution of residual life. According to the method, the reduced-order proxy model for dynamic mode switching is constructed, the mode basis is automatically expanded and the sub-models are switched when the load suddenly changes or the rotating speed jumps, and the problem of feature drift of a traditional fixed basis function model under variable working conditions is effectively solved.

The invention relates to the technical field of concrete pole damage monitoring, and discloses a concrete pole damage monitoring method based on modal recognition, comprising the following steps: S1, multi-modal data acquisition; s2, edge side real-time processing and micro-damage identification; s3, performing model diagnosis based on cloud depth; s4, performing digital twin driven damage evolution prediction; and S5, performing dynamic risk assessment and decision support. According to the method, through synchronous acquisition and fusion of multi-source data, the limitation of a single sensing mode is overcome, multi-dimensional and cross-modal collaborative recognition of the microcrack germination stage is realized, and the sensitivity and reliability of early damage detection are remarkably improved; a lightweight damage identification model is deployed on the edge side, so that the instant early warning requirement of micro-damage germination is met; damage positioning and degree evaluation are carried out through the cloud based on a multi-modal fusion deep learning model, an efficient collaborative architecture of edge real-time early warning and cloud accurate diagnosis is formed, and the system response speed and decision accuracy are greatly improved.

The invention discloses a multi-modal data fusion-based steel wire rope detection and real-time transmission method and system, and the method comprises the steps: starting a detection system, collecting a steel wire rope damage detection signal, collecting equipment position and operation state data, constructing an original data set, carrying out the preprocessing, building an incidence matrix, dividing a processing unit, and then extracting features, and forming an initial feature data set. And extracting damage features through a multi-branch network, strengthening the weight of a key region, fusing position and equipment state features through a multi-modal fusion module, generating fusion vectors, classifying and identifying, and outputting a structured detection result. And the acquisition equipment end performs grading processing on the data, distributes transmission channels according to priorities, dynamically adjusts parameters, adds integrity and time sequence identifiers, and the upper computer verifies the data integrity and triggers abnormal supplementary transmission. And the upper computer decodes the data, reconstructs the waveform, gives an alarm in real time in combination with preset parameters, and generates details of damage key information. Reliable technical support is provided for safe operation and maintenance of the steel wire rope in industrial scenes.

The invention discloses a road culvert inner wall damage image detection and traffic capacity evaluation method and system, and relates to the technical field of road engineering detection and computer vision, and the method comprises the steps: collecting multi-source data through an annular camera, a laser section scanner and a mileage encoder carried by a detection robot, and carrying out the panoramic expansion and time-space registration; using a multi-scale attention panoramic segmentation network to identify damages such as lining cracks, joint slab staggering, damage and chipping, steel bar exposure and water leakage; calculating the clearance size and the deposition thickness of the culvert by adopting an ellipse fitting method; according to the method, the culvert detection speed reaches 50 m / h, and the damage detection accuracy reaches 89% or above.

The application discloses a curtain wall structure adhesive damage detection method and device based on modal difference and digital twinning, and belongs to the technical field of digital twinning structure adhesive damage detection and evaluation. The application solves the problem that the traditional glass curtain wall structure adhesive damage detection and evaluation method based on the first-order natural frequency cannot locate the specific position of damage due to the insensitivity to local boundary delamination. The application collects vibration data of the test group of hidden frame glass curtain walls and the non-damage state group of hidden frame glass curtain walls to obtain normal acceleration time history data. The second-order and third-order natural frequencies are preliminarily screened, and whether the structure adhesive is damaged is judged through the difference constraint condition. The boundary relative curvature modal difference is used to further locate the damage, and whether the current test point is a damage point is judged. A digital twinning model and damage early warning are constructed, and a parameter report and a visual damage evaluation result are output. The application effectively improves the boundary damage positioning accuracy and can be applied to glass curtain wall structure adhesive damage detection.

The invention discloses a fan blade monitoring system and method based on multi-modal data fusion analysis, and belongs to the technical field of wind power generation. The method comprises the steps that multi-modal characteristic data of a fan blade in operation are acquired; inputting the multi-modal feature data into a pre-constructed multi-modal feature fusion anomaly detection model, a damage factor quantitative calculation model and a health trend prediction model, and respectively performing comprehensive anomaly detection, comprehensive damage detection and health degree prediction on the operation state of the fan blade; based on calculation results of comprehensive anomaly detection, comprehensive damage detection and health degree prediction, comparing the calculation results with a corresponding normal state threshold range; and triggering early warning when any calculation result deviates from the normal state threshold range. According to the method, the multi-modal feature information in the operation of the fan blade is obtained, and the deep learning and machine learning algorithms are combined, so that the early fault under the complex operation condition is effectively identified, and the intelligent operation and maintenance requirements of a modern wind power plant are met.

The invention belongs to the technical field of numerical simulation and damage intelligent detection, and relates to an aircraft surface structure damage infrared feature simulation and sample generation method. According to the method, through the design of the virtual interface layer and the secondary homogenization, the magnitude order improvement of the full-size transient thermal simulation efficiency is realized while the physical precision is ensured, and batch simulation of a large number of damaged working conditions becomes possible. The physical effect of a sensor of the infrared camera is simulated through high fidelity, so that a simulation image is highly consistent with real external field data in the aspects of noise, texture and other characteristics, and the'simulation-reality 'domain difference is effectively reduced. By directly associating the simulation process with the preset damage parameters, automatic, accurate and zero-error generation of the truth value information is realized, and the problems of high manual labeling cost and poor consistency are fundamentally solved. According to the method, an efficient and reliable data generation way is provided for training a high-precision and high-robustness external field intelligent detection model, and the method has a definite engineering practical value.

The invention discloses an electric power pipeline external damage prevention alarm method and device, and relates to the technical field of electric power pipeline monitoring and external damage prevention. A preset vibration feature extraction model is used for carrying out feature engineering processing and suspected node screening on data, a peg device node network is constructed in combination with multi-node space correlation characteristics, confidence quantitative evaluation is carried out, and after high-precision alarm coordinate information is generated, secondary decision and external damage event judgment are completed through a preset external damage detection model.

The invention discloses a stamping die process data processing method and system based on big data analysis, and relates to the field of die management, and the method comprises the steps: outputting a crack feature vector; extracting standardized ultrasonic detection data of the stamping die; forming a process comprehensive damage detection result of the stamping die; and dynamically adjusting detection parameters of the electromagnetic field sensor and the ultrasonic sensor. The electromagnetic field frequency and intensity, the ultrasonic transmitting power, the pulse width and the beam focusing are dynamically optimized, adaptive adjustment of parameters of each region is achieved, and it is ensured that the optimal detection effect can be obtained under different material conditions. Meanwhile, closed-loop feedback and multi-source fusion ensure that the system is stable and reliable, the risk of false detection and missing detection is reduced, the controllability and practicability of the detection process are improved, and reliable technical support is provided for efficient and intelligent detection of the stamping die.

The invention relates to the technical field of steel wire rope damage detection, and discloses a steel wire rope surface damage recognition device which comprises a steel wire rope and an electromagnetic detector, supporting frames are fixedly connected to the two sides of the electromagnetic detector, guide rollers are arranged in the supporting frames, and a supporting cylinder is arranged on the side, opposite to the electromagnetic detector, of one guide roller. The side end of the steel wire rope sequentially passes through the middle of the supporting cylinder, the bottom of the guide roller and the middle of the electromagnetic detector to be detected, a cleaning mechanism is arranged in the supporting cylinder, and the cleaning mechanism comprises a cleaning cylinder. The technical means that the electromagnetic detector, the guide roller, the supporting cylinder and the cleaning mechanism are matched is adopted, the defect that in the prior art, a steel wire rope used in a mine for a long time is corroded by water vapor and then is rusted to cover the surface of the steel wire rope is overcome, and magnetic field signals, sensitivity and signal accuracy are improved through detection after cleaning; and the detection data is more accurate.

This invention discloses a preventive zoning method and system for resilient distribution networks considering the uncertainty of typhoon paths. First, a set of scenarios considering fault conditions under different typhoon paths is constructed. Then, through multi-round detection, the damage status of nodes and lines under different typhoon path scenarios is identified, and a node and line damage status detection model considering fault propagation is constructed. Next, combined with the node and line damage detection model, a preventive zoning model for resilient distribution networks considering the uncertainty of typhoon paths is constructed based on a stochastic optimization method. Finally, the constructed model is linearized to reduce the solution difficulty and then solved to obtain optimized distribution network islanding and node power supply strategies. The technical solution of this invention can properly handle the uncertainty of typhoon paths during distribution network islanding, effectively improving the risk resistance capability of post-disaster distribution network power supply restoration schemes.

The invention belongs to the technical field of ground wire steel core detection, and particularly relates to a ground wire steel core detection system based on digital enabling, which comprises a detection device module, a data acquisition and processing module, a data analysis and intelligent identification module and a visualization and early warning platform module, the detection device module is integrated with a multi-mode sensing unit (a magnetizing device, a Hall array sensor, an ultrasonic guided wave transducer and an optical fiber sensing array) and a displacement positioning assembly, so that comprehensive acquisition and accurate positioning of damage signals are realized; the data analysis module is fused with a CNN-LSTM deep learning model to complete defect automatic identification and residual life prediction; the visual platform realizes hierarchical display and intelligent early warning based on digital twinning, and is also provided with a block chain module to guarantee data credibility; the system is compatible with portable inspection and on-line monitoring, is adaptive to the existing AEMR-I system, can improve the damage detection precision, the intelligent level and the operation and maintenance efficiency, and provides support for the safe operation of a power grid.

The invention discloses an elevator steel belt damage detection and quantitative analysis system based on eddy current and magnetic flux leakage dual-mode detection, and aims to solve the problems of single-mode information loss and serious industrial field strong noise interference in the existing steel belt detection. The system synchronously integrates an eddy current sensor, a magnetic flux leakage sensor and an encoder through a multi-probe array adapter; and multi-dimensional damage physical information in the steel strip is obtained. An improved wavelet packet transform-empirical mode decomposition (WPT-EMD) collaborative noise reduction algorithm is adopted, and in combination with a sub-band energy entropy and a self-attention mechanism, non-stationary mechanical noise is effectively filtered out. A multi-rule feature extraction engine is used for extracting smooth residual errors, derivative mutation and other features, a support vector machine (DE-SVM) model introducing physical priori knowledge weights is constructed, and the small sample recognition problem is solved in combination with a virtual sample generation technology. The method can realize high-precision positioning and quantitative evaluation of steel strip damage under complex working conditions, and has the characteristics of strong anti-interference capability, high identification accuracy and good generalization performance.

The invention discloses a track equipment damage detection method fusing vision and sensing data, and relates to the technical field of railway track damage detection, and the method comprises the following detection steps: multi-source data acquisition and synchronous processing; data preprocessing and feature extraction; carrying out multi-modal data information fusion processing; identifying, judging and positioning the damage; according to the method, original data are processed through data preprocessing and feature extraction, the data quality is improved, key features which can best represent the health condition of equipment are extracted from the original data, comprehensive analysis is carried out on the processed visual feature data and sensor data through multi-modal data information fusion, and the visual feature data and the sensor data are analyzed more accurately. The accuracy and reliability of damage detection are improved, damage, damage types and damage positions can be accurately judged based on fused information through damage identification, judgment and positioning, and detected damage results can be conveniently presented in a clear and available form through damage result output and post-processing.

The invention relates to the technical field of photovoltaic equipment fault detection, and provides a damage detection method for a photovoltaic group string board, and the method achieves the precise detection through the three steps: 1, collecting the data of three dimensions, i.e., electrical, physical images, environment and operation, and carrying out the abnormal elimination and smooth optimization, thereby obtaining a detection result; forming a structured data snapshot by using a time-space alignment algorithm of millisecond-level time synchronization and three-level space identification; 2, quantitatively extracting electrical, image and time sequence fault fingerprint features, and realizing fault and cause diagnosis by adopting CNN (image recognition) + LSTM (time sequence capture) + XGBoost (multi-feature classification) fusion AI model; and 3, verifying a diagnosis result by relying on a knowledge base integrating historical data, engineer experience and industry standards and a rule engine, and performing model increment / total training and pilot verification through operation and maintenance feedback data to form closed-loop optimization.

The invention provides a structure multi-type apparent damage identification and detection report generation method based on a multi-modal large model. According to the method, the image recognition result and a natural language generation technology are fused, so that full-process automation from damage detection to report generation is realized, and the intelligent level of building structure health monitoring is remarkably improved. According to the method, automatic identification and accurate positioning of multi-type damage of the appearance of civil engineering structures such as buildings and bridges can be realized, and objective and normalized maintenance suggestions and detection reports are generated in combination with a vision-language multi-modal modeling method, so that the intelligence and automation level of structural appearance damage detection and operation and maintenance management is improved.

The invention discloses a damage detection method and system based on machine vision, and relates to the technical field of machine vision detection. A polarization structured light-thermal imaging three-mode acquisition framework of a customized machine vision array is adopted, the problems of composite noise superposition and unstable internal thermal signal diffusion caused by dynamic heterogeneity of a target surface are solved, the adaptability of damage detection to a complex environment is greatly improved, and by means of a space attention mechanism and a cascade analysis network, the damage detection accuracy is improved. According to the method, geometric measurement and heat conduction simulation technologies are combined, the problems that damage space distribution is rough in description, and there is no stable corresponding relation between surface and deep abnormity are solved, accurate space mapping and multi-dimensional quantification of damage from the surface to the interior are achieved, and through linkage of a dual optimization mechanism and an edge end improved MobileViT lightweight model, the problem that damage space distribution is rough in description is solved. Pain points where real defects and interference artifacts are difficult to distinguish are broken, stable correspondence between surface signals and deep damage is enhanced, and accuracy and practicability of damage detection in an industrial scene are guaranteed.

The invention discloses a damage safety assessment method and system for a building service glass curtain wall, and the method comprises the steps: setting a plurality of damage conditions based on a glass curtain wall standard test piece, and building a function relation between the maximum panel deflection and the inherent frequency through a regression analysis method; calculating the maximum theoretical panel deflection according to specifications, substituting the maximum theoretical panel deflection into the function relationship to obtain a critical inherent frequency, and constructing a safety evaluation model according to the critical inherent frequency; and acquiring the actual inherent frequency of the curtain wall to be tested, and evaluating the damage safety state of the curtain wall according to the model. According to the invention, rapid and efficient safety assessment can be carried out on the active glass curtain wall without damaging the rubber material body, and large-area screening and regular inspection can be realized; field test provides real data, finite element simulation supplements multiple working conditions, and data accuracy and model universality are guaranteed; the obtained inherent frequency reflects the essential attributes of the structure, and the damage detection and evaluation sensitivity is high; according to the constructed evaluation model, the result is direct and reliable through comparison between a safety threshold value and actual measurement.

The invention provides a soil body-structure interaction boundary damage quantitative testing device and method, and relates to the technical field of geotechnical engineering test.The soil body-structure interaction boundary damage quantitative testing device comprises a stress conduction boundary shear strength and rigidity combined testing structure and a testing system integrating a shaft confining pressure-temperature collaborative loading system and a monitoring collection and process control system; through an improved slurry sample preparation method, tight connection and constant contact area between the hollow columnar soil sample and the friction pile body are ensured, and the test sealing performance is enhanced through the elastic sealing design; the real-time measurement of the rigidity of different positions in the whole shearing process is realized by virtue of the excitation damage detection element capable of synchronously moving; according to the method, the constitutive model of the marine rock-soil stress conduction boundary domain is perfected, a reliable experimental basis is provided for foundation design of deep-sea engineering such as submarine pipelines and anchoring structures, and the method has important theoretical value and engineering significance for improving design scientificity and safety of deep-sea infrastructures and preventing and controlling submarine geological disasters.

The invention discloses an ultrasonic guided wave damage ellipse imaging positioning method and equipment for a layer decreasing composite material laminated plate. The method comprises the following steps: S1, acquiring thickness change direction information of the laminated plate and position coordinates of an excitation transducer and a receiving transducer; s2, according to the material characteristic parameters, simulating and calculating the propagation time of ultrasonic guided waves transmitted from an excitation transducer to any point to generate a first theoretical propagation time map, and simulating and calculating the propagation time of the ultrasonic guided waves transmitted from any point to a receiving transducer to generate a second theoretical propagation time map to generate a second theoretical propagation time map; coupling influence of thickness change and material anisotropy on guided wave propagation is considered in the simulation process; s3, transmitting an ultrasonic guided wave signal, and extracting the actual flight time of a damaged wave packet; s4, calculating theoretical total propagation time, and determining a candidate area where damage exists; and S5, on the basis of repeated operation of the multiple groups of transducer pairs, taking a plurality of candidate region intersections to determine a final damage position. The objective of the invention is to solve the positioning error problem caused by the structural characteristics of a laminated plate in a traditional algorithm, and to realize more accurate damage detection and positioning.

An old well corrosion oil casing safety evaluation and life dynamic prediction method is characterized in that based on old well basic parameters and a well bore structure, pipe mechanics, thermodynamics and electrochemical experiments are carried out, and a corrosion rate model is established; then, on the basis of the pipe corrosion morphology / contour, utilizing SOLIDWORKS to establish a three-dimensional geometric model of the casing containing corrosion damage, importing ABAQUS finite element simulation software, simulating the corrosion depth, the time-varying strength and the residual life of the casing under different service conditions, and establishing a prediction model of the corrosion depth of the casing, the time-varying strength of the corroded casing and the residual life of the corroded casing; in combination with on-site underground service conditions and casing damage detection data, an isolation forest method and a data cleaning technology are adopted to preprocess original data, then required parameters are input into a prediction model for dynamic prediction, and the method provides a basis for petroleum engineers to predict the residual life of the casing with corrosion defects according to the on-site service conditions. The method is suitable for the technical field of petroleum and natural gas drilling engineering.

The invention provides a power cable damage detection device. The power cable damage detection device comprises a conveying mechanism, a twisting mechanism, a camera shooting mechanism and a driving mechanism, the conveying mechanism comprises a rack, a chain plate and a chain wheel; the twisting mechanism comprises a clamping assembly and a control assembly. The multiple clamping assemblies are installed on the chain plate, and the control assembly controls the three or more clamping assemblies to clamp the cable and controls the clamping assemblies with the clamped middle parts to rotate, so that the two ends of the to-be-detected position of the cable are clamped, and the middle parts are twisted and then reset. The camera mechanism collects an image on the twisted cable; after the camera shooting mechanism completes collection, the control mechanism controls the clamping assembly to be separated from the cable; the driving mechanism drives the cable to linearly move. The twisting action actively amplifies the damaged position on the cable through physical deformation, fine defects originally needing to be recognized by high-precision equipment are converted into obvious features easily captured by a camera mechanism, the precision requirement for an image recognition system is lowered, and meanwhile the sensitivity of damage detection is greatly improved.

The invention relates to an ultrasonic detection and damage evaluation method and system for a curved-surface composite component, and the method comprises the steps: collecting three-dimensional point cloud data of a surface damage region of the curved-surface composite component, and constructing a detection path point sequence; the industrial robot and the ultrasonic phased array are controlled to move and scan synchronously according to time, and ultrasonic A scanning signals and robot tail end pose information are collected; three-dimensional space coordinates of all reflection points in the curved surface composite material component are calculated in combination with pose derivation, and an initial three-dimensional damage point cloud is constructed; setting a segmentation threshold to construct an initial segmentation damage point cloud; a traditional DBSCAN clustering algorithm is improved for clustering segmentation, and point clouds of all independent damage areas are obtained; and voxelizing the point cloud of the independent damage area into a three-dimensional voxel model to realize quantitative assessment of the damage. According to the method, automatic and high-precision three-dimensional damage detection and evaluation of the complex curved surface component are realized, and the problems that a traditional ultrasonic detection method depends on manual operation and is poor in adaptability, the detection result is not visual and the like are effectively solved.

The invention discloses a damage detection method, electronic equipment, a storage medium and a computer program product, and relates to the technical field of nondestructive detection.The damage detection method comprises the steps that a to-be-detected composite material is scanned through a terahertz time-domain spectroscopy system, and an original time-domain echo signal of the to-be-detected composite material is obtained, the to-be-detected composite material is a to-be-detected glass fiber reinforced polymer composite material; preprocessing the original time domain echo signal to obtain target image data; performing feature extraction on the target image data to obtain damage features; the geometric offset of the damage features is predicted, and a damage target is positioned in the to-be-detected composite material based on the geometric offset and the damage features. The technical problem that the detection effect is poor in an existing nondestructive detection technology is solved.

The invention provides a fan damage detection method of a multi-scale network based on lightweight attention guidance, and the method comprises the steps: firstly, taking an obtained wind turbine unmanned plane detection image as a data set; secondly, an LAMN-YOLO model is constructed based on a YOLOv8 network structure: firstly, in a Backbone, a CSPAFR module is utilized to replace C2f, and a C3k2 module is used in a deep layer of the Backbone to carry out residual link; secondly, in the Neck, a Dysample module is adopted to replace up-sampling, an SGConv is adopted to replace Conv, and a CSPMSEA module is inserted into the deep layer of the Neck; in the Head, a detection head of YOLOv11 is adopted to replace a detection head of YOLOv8; and finally, training, verifying and testing the LAMN-YOLO model by using the data set, and outputting an optimal detection model and an optimal detection result. According to the method, the parameter quantity is reduced, and meanwhile, high recognition precision is kept.

The invention discloses a heat-shrinkable cable shielding layer damage detection method and equipment based on pulsed eddy current, and relates to the technical field of cable shielding layer damage detection.The method comprises the steps that a pulsed eddy current probe is arranged on the outer surface of a cable, preset pulse excitation is applied, an original transient electromagnetic response signal is collected, and a reference phase signal is constructed; performing synchronous phase-sensitive demodulation, and extracting an in-phase component and an orthogonal component to obtain a phase purification signal; based on the difference of the spatial statistical characteristics of the shielding layer response and the conductor response, performing spatial joint reconstruction on the phase purification signal to obtain a purification reconstruction signal; based on the purified reconstruction signal, extracting damage characteristics, performing cable shielding layer damage judgment, and positioning a damage area; the method effectively solves the problems that the electromagnetic response of the internal conductor and the response of the shielding layer are highly overlapped and are difficult to separate, and the damage characteristics are easily covered by noise and spatial fluctuation, so that the damage identification sensitivity and positioning precision of the shielding layer are insufficient in the existing pulsed eddy current detection.

The invention relates to the field of concrete beam damage detection, in particular to an ultrasonic imaging method for concrete beam damage detection, which comprises the following steps: arranging a linear array comprising a plurality of ultrasonic probes on a single-side contactable surface of a to-be-detected concrete beam; preparing a multi-channel ultrasonic transmitting and receiving instrument, and connecting the multi-channel ultrasonic transmitting and receiving instrument with the sensor array and the control computer; a multi-channel ultrasonic device is used to work in a full-matrix capture mode, in the full-matrix capture mode, each probe is sequentially excited to emit ultrasonic pulses, and time domain signals received by all the probes of the array are synchronously recorded to obtain an original full-matrix data set. According to the method, strong scattering noise generated by the concrete aggregate can be inhibited through the deep convolutional neural network so as to improve the signal-to-noise ratio of the image, the sound velocity field conforming to the non-uniform characteristic of the concrete can be constructed through local sound velocity inversion and iteration based on the strong reflection points, the problems of imaging artifacts and form distortion are avoided, and the accuracy of the imaging process is improved. And the concrete damage detection precision is ensured.

The invention provides an automatic detection system for anti-radiation performance of a lead garment, a lead garment hanger adopts a humanoid structure design and is used for simulating the condition that the lead garment is worn in a human body posture, a plurality of X-ray detectors are arranged on the outer surface of the lead garment hanger and are used for detecting the X-ray penetration position and measuring the X-rays, and the X-ray detector is used for detecting the radiation resistance of the lead garment. The rack guide rail is detachably assembled on a CT operation site and arranged at the bottom of the lead clothes hanger, the lead clothes hanger can reciprocate along the rack guide rail through a first driving motor and can rotate circumferentially through a second driving motor, and the main control module comprises a single chip microcomputer and is used for controlling the lead clothes hanger to move horizontally and rotate. And the X-ray penetration position and the X-ray measurement in the lead clothes are determined through fitting calculation. According to the lead clothes damage detection method, the lead clothes damage detection method during wearing by simulating the human body posture can be realized, the authenticity and reliability of lead clothes damage detection are effectively improved, and unnecessary damage of rays to the human body caused by internal damage of the lead clothes is prevented.

The invention provides an engineering data monitoring and analysis method for bridge damage detection. Relates to the technical field of bridge damage detection. The method comprises the following steps: firstly, setting a data set, collecting engineering data of a bridge in normal and damaged states, and carrying out manual labeling; then, cleaning, standardization and feature extraction are carried out on the original data through feature engineering; and then, constructing a hybrid model of a deep neural network and an ARIMA model, which is used for identifying the damage state of the bridge. After model training, damage state identification can be carried out on newly collected bridge engineering data, and a threshold value is set to judge whether the data is abnormal or not, namely whether bridge damage exists or not. According to the method, the automatic feature learning capability of the deep neural network and the time sequence analysis capability of the ARIMA model are combined, and the bridge damage detection efficiency and recognition precision are improved.

Tool damage detection device (2), comprising a data acquisition unit (222) that acquires, for a period of time of a cutting feed, load data indicating a load on a spindle, rotational speed data indicating a rotational speed of the spindle, and coordinate value data indicating a coordinate value of a cutting point; an extraction unit (223) which extracts evaluation data from the load data based on the rotational speed data, which are to be used for evaluating the occurrence of damage to a tool, wherein the extraction unit (223) determines a type of machining performed by the tool based on the rotational speed data and the coordinate value data, and extracts the evaluation data according to the determined type of machining; a detection unit (225) which, using the evaluation data, detects the occurrence of damage to the tool; and an output unit (226) that outputs data indicating the occurrence of damage to the tool when the detection unit (225) has detected damage to the tool.