3241 results about "Ultrasonic detection" patented technology

The invention discloses a polyethylene plastic hollow plate defect detection method and system, and relates to the technical field of nondestructive testing, the system is composed of a plurality of functional modules, and the system comprises: a multi-mode sensing module for collecting multi-source data including a hollow plate surface image, three-dimensional morphology data and an ultrasonic reflection signal; the historical database construction module is used for performing space-time alignment and feature level fusion on the multi-source data to generate a composite defect feature matrix; collecting ultrasonic signal waveforms of known defects, extracting characteristic parameters, and establishing a historical waveform library; calculating a characteristic parameter classification range of each type of defects based on a historical waveform library; judging whether the detection waveform parameter falls into a classification range corresponding to the processing noise or not, and if so, rejecting the signal; otherwise, keeping; and the defect area identification module is used for dividing the surface of the hollow plate into uniform grids based on the surface image of the hollow plate, and counting the density of the residual ultrasonic detection sources in each grid.

The invention relates to the technical field of defect detection, and discloses a flange forging defect detection method and system. The method comprises the following steps: performing three-dimensional scanning and material acoustic characteristic measurement on a to-be-detected wind power flange to obtain layer partition sound isolation path data; generating an array element excitation control file of the double-array phased array ultrasonic detection system; applying the array element excitation control file to a double-array phased array ultrasonic detection system, and performing micro defect feature enhancement on a received echo signal to obtain a feature-enhanced signal data set; performing ultrasonic emission and data acquisition on the wind power flange to obtain a global detection data set; and inputting the global detection data set into the two-stage defect detection model for defect feature extraction and classification evaluation, and generating a defect detection evaluation report. According to the method, the detection rate and the classification accuracy of the micro forging defects are improved.

The invention discloses a railway track damage detection method, and belongs to the technical field of railway track detection. According to the method, an image acquisition module and an ultrasonic detection module are installed at the bottom of a track detection vehicle, the detection vehicle is controlled to run, and track top face and side face image sequences and ultrasonic reflection signals are acquired; preprocessing the image, and respectively inputting the image into a deep convolutional neural network model and a support vector machine classifier to obtain a crack identification result and a wear level; processing an ultrasonic reflection signal, and judging a layering defect; and finally fusing the data, marking a damage position and generating a structured detection report. According to the method, the problems of incomplete detection, low precision and the like in the existing railway track damage detection are solved, efficient detection of track surface cracks, side abrasion and internal layering defects is realized through collaborative acquisition of the multi-modal sensor, intelligent algorithm processing and data fusion, and the comprehensiveness and reliability of detection are improved.

The invention relates to the technical field of data processing, and discloses a steel structure welding seam quality ultrasonic detection method and system. The method comprises the following steps: scanning a welding seam to obtain digital parameters, constructing a virtual model to generate an ultrasonic response database, optimizing a detection parameter forming scheme, executing distributed acquisition to obtain a signal matrix, extracting defect features by using a neural network, and outputting a digital detection report through transfer learning. According to the method, automatic optimization of detection parameters and accurate extraction and intelligent analysis of defect features are realized, the accuracy, efficiency and reliability of detection are improved, and the dependence on artificial experience is reduced.

The invention relates to the technical field of nondestructive testing, and discloses a steel structure weld defect ultrasonic detection and identification system. A data acquisition module of the system acquires an original ultrasonic signal of a steel structure welding seam through ultrasonic detection equipment and acquires geometric attribute data of the welding seam; the model construction module constructs a welding seam three-dimensional digital model based on the data; a feature extraction module performs feature mining on the three-dimensional digital model and extracts a weld defect feature index set; the difference analysis module carries out deviation calculation on the characteristic index set and a reference index set in a standard welding seam characteristic database, and an abnormal area is identified; the risk assessment module calculates a defect sensitivity index according to the abnormal region in combination with real-time environmental parameters, and assesses a defect risk level; and the report generation module formulates a detection scheme according to the defect risk level, generates a detection instruction, executes ultrasonic scanning, collects performance data and generates a defect detection report. The system has the advantages of high detection precision, high reliability, automatic and standardized process and the like.

The invention relates to the technical field of stone structure ultrasonic detection, in particular to a stone structure building detection method based on a concrete ultrasonic fault detection scanner, and the method comprises the steps: obtaining the initial signal intensity when an ultrasonic signal is transmitted by using the ultrasonic fault detection scanner; obtaining an ultrasonic reflection signal after the ultrasonic signal is reflected at each reflection point under each scanning layer, the intensity of the reflection signal after reflection, and the signal propagation duration; the method comprises the following steps of: obtaining a signal propagation time difference, a signal intensity difference, a signal difference index, a signal disorder degree, a defect evaluation coefficient, a seam recognition degree and a seam correction coefficient of each reflection point under each scanning layer, correcting the intensity of a reflection signal, performing three-dimensional reconstruction on a constructed tomography graph, and detecting internal defects of a stone structure building. According to the method, the interference of the reflection points on the splicing seams during stone splicing is reduced, the accuracy of detecting the internal defects of the stone structure building is improved, and the stability detection of the stone structure building is improved.

The invention provides a double-mechanism cooperative type curved surface component water immersion ultrasonic intelligent detection method and system, and relates to the technical field of ultrasonic detection, and the method comprises the following steps: scanning a workpiece by using a double-mechanism water immersion ultrasonic detection system, collecting and processing an ultrasonic echo signal, and generating an ultrasonic C scanning image. And performing defect identification on the ultrasonic C scanning image by using the constructed multi-scale feature fused deep convolutional neural network model to obtain the type, position, size and confidence of the defect. Quantitative evaluation is carried out based on the defect recognition result, a report is generated, and meanwhile visual display and database management of the defect recognition result are achieved. Through double-mechanism cooperative scanning and deep learning intelligent identification, the precision and efficiency of curved surface component defect detection are improved, and automatic identification, quantitative evaluation and visual display of defects are realized.

The invention relates to the technical field of artificial intelligence, and discloses an AI-based composite insulator internal defect ultrasonic detection method, which comprises a multi-mode ultrasonic probe array module, a signal preprocessing module, an AI defect analysis module, a dynamic parameter optimization module, an edge calculation module and a visual report module, the method comprises the following steps: acquiring a full-dimensional signal through a multi-modal ultrasonic probe array, and inputting the full-dimensional signal into a deep space-time convolutional neural network for defect recognition after adaptive noise reduction and feature fusion; the detection precision is improved by combining dynamic waveform matching and multi-physics coupling analysis; model lightweight and real-time processing are realized by adopting transfer learning and edge calculation. The system integrates the functions of parameter adaptive optimization, three-dimensional visualization and Internet of Things cooperation, solves the problems of low efficiency and high false detection rate of a traditional detection method, and improves the intelligent level and engineering applicability of composite insulator defect detection.

The invention relates to the technical field of nondestructive testing, in particular to a multi-modal fusion metal microcrack ultrasonic detection system and method, and the system comprises an ultrasonic signal acquisition module, a signal preprocessing module, a feature alignment network module, an image segmentation module, a time-frequency analysis module and a segmentation result fusion module. According to the system, three modes of electromagnetic ultrasound, laser ultrasound and phased array ultrasound are utilized to detect a metal sample, and real-time nondestructive signal data acquisition and pretreatment are realized. The feature alignment network aligns and fuses the three modal signal features, and the U-Net segmentation neural network performs submillimeter crack segmentation on the fused features. And the time-frequency analysis module extracts signal frequency and amplitude information, and evaluates the breaking strength in combination with the crack label. Finally, a segmentation result fusion module integrates crack labels and time sequence features, and whether cracks exist or not is classified through a weighted convolutional neural network. According to the system, the detection performance, the practicability and the adaptability are remarkably improved, and a new scheme is provided for precise detection of metal microcracks.

The invention relates to a settlement data analysis and early warning method based on a cloud platform, and the method comprises the steps: collecting settlement data of a basic part of a main transformer in real time through a high-precision electronic settlement observation device array, obtaining deformation stress data through data preprocessing and coordinate calculation, and uploading the deformation stress data to the cloud platform; transmitting an ultrasonic pulse signal to the stress concentration position of the local area through ultrasonic detection equipment, receiving reflected wave data, and identifying the depth and range of the crack initiation position according to the reflected wave data; a sensor array is arranged on the basic surface of a crack initiation position, sound wave signals released in the crack propagation process are captured, and a time sequence of the crack propagation direction and speed is determined; and detecting crack depth data of the repair priority region through ultrasonic waves, calculating an adjusted basic health state value, comparing the adjusted basic health state value with a preset safety threshold, and generating a corresponding safety early warning signal.

The invention relates to a lightweight component surface defect quantitative evaluation method, system and device, and the method comprises the steps: obtaining a multispectral surface image, laser three-dimensional data and ultrasonic detection data of a lightweight component, and carrying out the fusion registration based on a preset multi-sensor synchronization protocol, and obtaining a component joint data set; performing dynamic pruning analysis on the component joint data set to obtain geometric contour features, and performing internal feature extraction on the component joint data set to obtain internal abnormal response features; stress gradient distribution data and volume ratio parameters are carried out on the geometric contour features and the internal anomaly features; and obtaining an original working condition data set of the lightweight component, carrying out service working condition coupling analysis on the multi-dimensional prediction matrix, and carrying out multi-dimensional quality l parameter identification to obtain component comprehensive quality information. According to the method, the morphology mechanical coupling feature extraction precision of composite defects such as cracks and holes can be improved.

The invention relates to the technical field of cylindrical lithium ion battery production, and discloses a method for detecting insufficient welding of a cylindrical lithium ion battery, and the method comprises the following steps: S1, appearance inspection; s2, initial internal resistance benchmark test; s3, applying a short-time large-current pulse to the battery by using a pulse generator, simulating an actual load condition, and recording an instantaneous voltage drop and current response curve of the voltage of the battery end during the pulse period; s4, performing infrared thermal imaging identification and data processing; s5, learning the picture and establishing a model; s6, comparing the test picture and the data with the model; s7, dynamic internal resistance calculation; s8, cold solder joint query and sorting processing; and S9, retesting the electrical performance. According to the invention, the technologies of dynamic internal resistance measurement, nondestructive X-ray or ultrasonic detection, rate discharge test and the like are adopted, so that efficient, accurate and nondestructive detection of the cold solder joint of the cylindrical lithium ion battery is realized, and the production efficiency and the detection precision are remarkably improved.

The invention provides a joint denoising method and system based on multi-source noise suppression and sparse representation, and belongs to the technical field of noise suppression in ultrasonic detection of power equipment, and the method comprises the steps: synchronously collecting an ultrasonic echo signal, a vibration signal and an electromagnetic signal of a composite insulator; decomposing the ultrasonic echo signal into a plurality of modal components and undecomposed signal margins; extracting a dominant frequency component in the vibration signal, and identifying a resonance noise feature overlapped with the frequency band of the ultrasonic signal; detecting a pulse interference event in the electromagnetic signal, marking occurrence time and a frequency range, and generating a pulse event feature; performing frequency band suppression and time domain threshold processing on the modal component according to the resonance noise characteristics and the pulse event characteristics; performing sparse representation coding on the processed modal components, executing multiple rounds of iterative screening and orthogonalization processing, and screening an atom set matched with defect echoes; and reconstructing the de-noised ultrasonic signal by using the screened atom set, and combining the de-noised ultrasonic signal with the undecomposed signal margin to output a final result.

The invention discloses a bearing defect detection method and system based on machine vision and ultrasonic detection, and particularly relates to the technical field of industrial automatic detection, and the method comprises the steps: S1, a synchronous collection module: carrying out pulse triggering synchronous collection, and generating a time-space reference table; s2, a feature extraction module: performing image noise reduction segmentation and ultrasonic frequency domain decomposition, and outputting a defect feature vector; s3, a fusion identification module: performing cross-modal feature alignment fusion to generate a defect classification conclusion; s4, a size measurement module: performing contour fitting to calculate inner and outer diameters, and outputting a size deviation value; and S5, a comprehensive judgment module: carrying out threshold comparison logic judgment, and generating a multi-modal detection report. According to the method, a space-time reference is established through an encoder, images are segmented in a self-adaptive mode, features are extracted through wavelet decomposition ultrasound, feature weights are re-calibrated through a parallel network and an attention mechanism, composite defects are recognized through cross-modal fusion, comprehensive judgment is conducted in combination with dimensional deviation, and a multi-dimensional quality evaluation system is achieved.

The invention discloses an ultrasonic transducer characteristic parameter test system, which belongs to the technical field of ultrasonic detection and comprises an excitation signal generation module, an impedance matching adjustment module, a resonance characteristic extraction module, a signal acquisition and processing module, a temperature control loading module, a physical vibration isolation module and a frequency spectrum calibration module. The excitation signal generation module is combined with an off-chip frequency locking feedback loop through an on-chip oscillator to generate a multi-frequency-point sweep frequency pulse array, the multi-frequency-point sweep frequency pulse array is connected to the resonance characteristic extraction module after passing through the impedance matching adjustment module, the multi-frequency-point sweep frequency pulse array and the frequency spectrum calibration module construct a bidirectional feedback path and share harmonic node synchronous calibration logic, and coupling offset compensation is achieved; the frequency spectrum calibration module performs bidirectional real-time error correction on the excitation output frequency and the resonance input phase; and the transducer is arranged in the thermal convection temperature equalizing cabin of the temperature control loading module and is overlapped with the physical vibration isolation module. The method has the advantages that high-precision, low-interference and full-frequency-domain dynamic testing of characteristic parameters of the ultrasonic transducer is achieved, and testing stability is improved.

The invention belongs to the field of material performance testing of mechanical structure strength, and particularly relates to an ultrasonic fatigue experiment monitoring method based on digital twinning. According to the method, on the basis of an ultrasonic fatigue testing machine platform, an ultrasonic fatigue digital twin model of a tested object is established by collecting frequency, temperature, load and other test data, and real-time online monitoring of ultrasonic fatigue and life prediction are carried out in combination with a fatigue life prediction model. Compared with a traditional test method, the method has the advantages that ultrasonic detection data and a digital twinborn model technology are combined, the fatigue life of the material is predicted and evaluated, the test efficiency is improved, the long-life test cost is reduced, and the accuracy and reliability of test data are improved.

The invention discloses a welding seam quality detection method and a welding seam detection system. The detection method comprises the following steps: acquiring an image of a detection area of the welding assembly by adopting a visual identification device; an ultrasonic detection device is adopted for scanning along the weld joint, and the ultrasonic detection area of the detection area is obtained; wherein the ultrasonic detection area is the welding area of the second welding piece and the concave bottom wall; comparing the ultrasonic detection area with an area threshold value; if the ultrasonic detection area is larger than or equal to the area threshold value, it is judged that the weld quality is qualified; if the ultrasonic detection area is smaller than the area threshold value, the welding seam quality is judged to be unqualified. The welding seam quality can be quantitatively evaluated, and the welding seam quality detection precision is improved.

The invention discloses an intelligent and efficient detection method for concrete-filled steel tube arch bridge arch rib void. The method comprises the following steps: 1, detecting a full bridge arch rib through laser scanning, obtaining three-dimensional point cloud data, identifying a suspected void area, and generating a bridge three-dimensional model; 2, detecting a full-bridge arch rib through infrared thermal imaging, recording a temperature distribution diagram, identifying a temperature abnormal region, and confirming a suspected void region; 3, echo signal characteristics of the suspected void area are detected through ultrasonic waves, and the specific position, depth and range of void are determined; 4, carrying out fusion analysis on laser scanning, infrared thermal imaging and ultrasonic detection data to generate a three-dimensional void distribution model, and generating a comprehensive detection report in combination with multi-source data; and 5, the arch bridge is monitored in real time, trend analysis and prediction are performed in combination with historical data, and a void expansion early warning mechanism is formed. According to the method, the void position, depth and range of the arch rib can be detected in a high-precision and non-destructive manner, and a reliable guarantee is provided for bridge safety.

The present invention relates to the technical field of dredging assessment, and in particular to a dredging and repair assessment method, device and dredging and repair robot for drainage pipes. The method comprises the following steps: obtaining a multi-directional image of the interior of a drainage pipe after dredging and a pipe ultrasonic detection signal; performing visual recognition of pipe components and internal geometric structure arrangement analysis on the multi-directional image of the interior of the drainage pipe to construct a three-dimensional pipe panoramic model; performing deep residual sludge recognition on the multi-directional image of the interior of the drainage pipe, and performing sludge spatial distribution analysis to generate residual sludge spatial distribution data; performing reflection deviation compensation optimization on the pipe ultrasonic detection signal, and performing sludge morphology calculation one by one to generate the morphological characteristics of each residual sludge; performing sludge composition characteristic analysis based on the pipe ultrasonic detection signal to obtain the composition characteristics of each residual sludge. The present invention realizes efficient and accurate dredging and repair assessment.

The invention relates to the technical field of metal ultrasonic detection, and discloses a method for performing nondestructive flaw detection on a casting by using ultrasonic waves and a flaw detection system.The method comprises the following steps that rapid modeling and acoustic reference calibration are performed on equipment and a workpiece, and a unified coordinate system, a sound velocity delay reference and an optimal sound beam path are obtained; acquiring and caching multi-view ultrasonic echoes in real time to obtain a high-integrity original data stream; implementing adaptive noise diffusion based on the data stream, and constructing a diffusion data stack matched with scattering characteristics; performing scattering compensation inverse diffusion and super-resolution fusion on the diffusion stack to obtain a high-resolution purified echo image; performing closed-loop regulation and control on image quality indexes, and optimizing scanning parameters in real time; and combining the optimized image and scanning parameters, and adopting a segmentation network and online increment fine tuning to realize defect identification and automatic grading. The system has high resolution, real-time performance and self-adaptive closed-loop control, and the reliability and the accuracy of casting defect detection are remarkably improved.

The invention discloses a hydropower station diversion tunnel longitudinal crack depth detection method, which comprises the following steps: arranging ultrasonic measuring points above and below a diversion tunnel longitudinal crack to obtain a reinforced concrete sound velocity representative value; arranging ultrasonic measuring points within the length range of the crack; performing ultrasonic detection, and judging the depth of the crack through ultrasonic waves; adjusting the position of the ultrasonic measuring point according to the depth of the fracture until the ultrasonic shape is in a suitable state; and calculating the depth of each detection point of the longitudinal crack. According to the technical scheme, by means of the method for detecting the longitudinal crack depth of the reinforced concrete in the diversion tunnel of the hydropower station through the ultrasonic waves, crack depth detection and calculation of the circular tunnel are completed, and in order to judge the development condition and the stability state of the crack, the crack depth can be regularly detected in the overhaul period of a hydropower station generator set; therefore, monitoring of longitudinal crack expansion and extension is realized.

The invention relates to the technical field of pipeline detection, in particular to a buried oil and gas pipeline excavation defect detection device which comprises a fixed part, the fixed part comprises two fixed supporting rings, and movable detection parts are arranged on the two sides of the fixed supporting rings; the moving detection part comprises a plurality of moving rings, the moving rings are internally provided with grooves and fixedly connected with annular air bags, clamping assemblies slidably connected with the side walls of the moving rings are arranged below the annular air bags, moving wheels are arranged at the bottoms of the clamping assemblies, and pneumatic assemblies used for pneumatically propelling the moving rings to move are further arranged in the moving rings; the sides, close to the fixed supporting ring, of the movable rings are fixedly connected with detection assemblies, and the sides, away from the fixed supporting ring, of the movable rings are fixedly connected with cleaning assemblies. By designing the control system with the air pressure balance module, the buckling threshold value adjusting module and the ultrasonic detection module, flexible adaptation and high-precision detection of pipelines with different pipe diameters are achieved, and the detection efficiency and accuracy are improved.

The invention discloses an unmanned aerial vehicle inspection device and method, the unmanned aerial vehicle inspection device comprises an unmanned aerial vehicle, a laser ranging camera, a holder, a telescopic device and an ultrasonic detection device, the telescopic device is installed on the unmanned aerial vehicle, and the ultrasonic detection device is connected with the telescopic device through the holder. The laser ranging camera is connected with the first end of the unmanned aerial vehicle through the holder, and the laser ranging camera and the ultrasonic detection device are both located below the unmanned aerial vehicle. The invention belongs to the technical field of unmanned aerial vehicles, and aims to solve the problem that in the prior art, iron tower inspection cannot inspect the nut loosening condition. The technical effects are that the anti-loosening condition of the nut can be reliably and accurately detected, the loosening condition of the nut can be timely detected, and the stability of an iron tower can be guaranteed.

The invention relates to the technical field of ultrasonic detection, in particular to a building steel structure strength ultrasonic phased array nondestructive testing method which comprises the following steps: acquiring morphology data of the surface of a building steel structure; analyzing coating thickness distribution and weld surface roughness of the surface of the building steel structure, and constructing a surface information matrix; automatically adjusting ultrasonic wave beam parameters of the ultrasonic phased array by using the self-adaptive parameter regulation and control model, and performing ultrasonic phased array detection to obtain a preliminary ultrasonic echo signal; adopting an echo signal compensation model to perform secondary compensation on the preliminary ultrasonic echo signal through the difference between the reference signal and the preliminary ultrasonic echo signal to obtain an optimized ultrasonic echo signal; fusing the optimized ultrasonic echo signal, the morphology data and the surface information matrix, and performing defect detection on the building steel structure by using the building steel structure detection model; evaluating a detection result, and dividing a confidence level; and carrying out local re-detection and parameter adjustment on the low-confidence region to realize closed-loop optimization.

The invention relates to the technical field of nondestructive testing, in particular to a weld joint ultrasonic testing parameter optimization method based on particle swarm optimization, which comprises the following steps: acquiring a weld joint path three-dimensional coordinate sequence, combining structure prior and boundary conditions, removing abnormal points by self-matching cubic splines to regulate and control node density, generating a curvature data set, and optimizing weld joint ultrasonic testing parameters according to the curvature data set. Smooth processing is carried out to extract curvature derivative variation; a key section is screened; a parameter set is constructed and dense sampling is carried out; feature points are detected to generate a focusing area, a matching degree function is established to execute parameter optimization, and an optimal combination set is output. According to the method, on the basis of welding seam path coordinates and structure prior, self-matching cubic spline interpolation is improved to eliminate abnormal regulation density, curvature data is generated, filtering and derivative screening focusing sections, Latin sampling difference distribution parameters and derivative feature extension paths are performed, a matching degree function is constructed to drive particle swarm optimization, and parameter reinitialization matching energy is performed; the signal-to-noise ratio and the detection rate are improved, and the resource allocation efficiency is optimized.

The invention discloses a panel vertical seam water stop structure repair effect evaluation method, which comprises the following steps of: firstly, synchronously obtaining surface crack and internal defect data through cooperative detection of infrared thermal imaging and ultrasonic flaw detection; and carrying out space alignment on point cloud data generated by three-dimensional laser scanning and an ultrasonic detection result, and establishing a BIM model containing a crack three-dimensional form. A practical engineering environment is simulated in a laboratory to carry out material accelerated aging and test key indexes such as an elastic recovery rate, a base surface state is evaluated through a temperature and humidity sensor array in a construction stage, a six-axis grouting robot is adopted to execute accurate filling, a curing process is monitored by a synchronous pre-embedded optical fiber sensor, and after aging is simulated by combining a hydraulic test with climate, a construction period is shortened. A distributed optical fiber network is used for continuously collecting deformation data, a comprehensive evaluation model is constructed based on mechanics, durability and sealing indexes, and finally material selection and process parameters are optimized through machine learning.

The invention relates to the technical field of ultrasonic detection, in particular to an ultrasonic detection method, system and device for integrated circuit plastic package. The method comprises the following steps: analyzing a distribution suspected index from a distribution position according to a proportion of a suspected defect area in an ultrasonic detection image of each sample and distance distribution of the suspected defect area and a module area, and analyzing an air hole characteristic index from internal distribution similarity in combination with a similar condition of ultrasonic signal fluctuation between pixel points in the area; obtaining a pore defect possible index; and correcting the air hole defect possible indexes by combining the overlapping expression of the suspected defect areas among the plurality of ultrasonic detection images, and obtaining the corrected possible indexes for air hole detection. According to the method, through the similarity characterization degree and the overlapping distribution degree of the defect features in the defect areas among the ultrasonic detection images of the multiple samples, the distinguishing accuracy of the electromagnetic noise and the pore defects is improved, the defect detection is more accurate, and then the optimization reliability of subsequent process parameters is improved.

The invention discloses a defect detection system for a metal-composite material mixed structure device. The defect detection system comprises an industrial control computer, a mechanical scanning arm, a polarization detection probe, an ultrasonic detector assembly and a power supply, the invention further provides a defect detection method for the metal-composite material mixed structure device. The defect detection method specifically comprises the following steps: acquiring polarization image information of the metal surface; solving polarization image parameter information; carrying out surface defect detection; metal internal ultrasonic detection information is obtained; performing internal defect detection; and performing multi-mode data fusion detection. According to the invention, two information sources with complementary advantages, namely polarization and ultrasound, are selected as detection information sources of the multi-modal detection method, so that all-directional defect detection and visualization on the surface-interior of the metal-based composite material mixed structure device are effectively realized; and a feasible technical method is provided for engineering application of polarized light / phased array ultrasonic multichannel integrated detection.

The invention discloses a transformer fault ultrasonic detection system, which comprises an ultrasonic sensor module, a signal conditioning module, a data acquisition and processing module, a data fusion module, a learning module, a diagnosis decision module and a human-computer interaction interface, according to the ultrasonic sensor module, 6-8 ultrasonic sensors are arranged in a transformer shell in an array mode, piezoelectric ceramic sensors with the resonant frequency of 40 kHZ are adopted as the ultrasonic sensors, the sensitivity of the ultrasonic sensors is-65 dBV / uBar, and effective capture is weak. According to the ultrasonic detection system for the transformer fault, single ultrasonic detection is easily influenced by accidental interference, the system is synchronously connected into equipment such as an infrared thermal imager and a vibration accelerometer, and a multi-physical-quantity correlation analysis model is established. When high-frequency ultrasonic pulses are detected, the system automatically calls temperature data of the corresponding position, and if the temperature gradient exceeds 2 DEG C / cm, it is confirmed that discharging heating is conducted; and if the temperature rise does not exist and the low-frequency vibration of 10-200Hz is accompanied, the mechanical looseness is judged.

The utility model discloses a pipeline inspection robot, which belongs to the technical field of pipeline inspection and comprises an installation platform, ultrasonic detectors are symmetrically and fixedly installed on two sides above the installation platform, round holes are symmetrically formed in two sides of the installation platform, rubber ferrules are fixedly installed in the round holes, and sleeves are sleeved in the rubber ferrules. A threaded hole is formed in the sleeve in an up-down penetrating mode, an adjusting rod is in threaded connection with the interior of the threaded hole, an ultrasonic probe is fixedly connected to the lower end of the adjusting rod, an adjusting gear is fixedly installed at the upper end of the adjusting rod, an adjustable platform is hinged to the upper portion of the installation platform, and an inspection camera is fixedly installed above the adjustable platform; the height-adjustable advancing assembly is fixedly mounted above the mounting platform, during use, the detection height and the detection angle of the inspection camera can be conveniently adjusted through the adjustable platform, so that comprehensive detection is realized, and the mounting platform can be lifted through the advancing assembly when water is accumulated in a pipeline, so that the pipeline can be conveniently detected. Therefore, important instruments are prevented from being damaged after being immersed in water.