1849results about "Material magnetic variables" patented technology

The invention relates to the technical field of aircraft structure detection, and discloses an aircraft structure crack intelligent identification method based on deep learning. The method comprises the following steps: acquiring original vibration response signals and electromagnetic field distribution data on the surface and inside of an aircraft structure in parallel through a multi-source sensor network; synchronously processing the data by using a multi-scale convolutional neural network, and extracting time-frequency domain abnormal fluctuation features and space magnetic field distortion features; constructing a cross-modal correlation model, analyzing a topological dependency relationship of the two types of features through a graph attention mechanism, and generating a fused damage sensitive feature vector; inputting the vector into a pre-trained deep belief network to obtain a probability distribution mapping relation for different crack types; and according to the mapping relation, carrying out adaptive weighted fusion on original multi-sensor data, inhibiting environmental noise and structural background interference, and separating and reconstructing an accurate three-dimensional morphology map of the target crack. According to the method, multi-source data information can be effectively fused to improve the accuracy of aircraft structure crack identification.

The invention provides an oil and gas pipeline defect three-dimensional contour determination method and device. Acquiring a three-axis magnetic flux leakage detection signal of a to-be-detected target oil and gas pipeline and corresponding space coordinate information of the three-axis magnetic flux leakage detection signal; constructing multi-channel input data according to the three-axis magnetic flux leakage detection signal and the space coordinate information; determining a defect contour prediction result of the target oil and gas pipeline according to the multi-channel input data by using a pre-trained defect contour inversion model; wherein the pre-trained defect contour inversion model comprises a multi-axis feature extraction and fusion module, a feature coding module and a multi-task decoding module; the defect contour prediction result is used for indicating whether the target oil and gas pipeline has defects or not, and the defect contour prediction result is further used for indicating the three-dimensional contour shape of the defects under the condition that the target oil and gas pipeline has the defects. Therefore, high-precision visual reconstruction of the defect position and the three-dimensional form of the oil and gas pipeline is realized, and the accuracy of defect identification and the reliability of evaluation are improved.

The invention provides an intelligent eddy current nondestructive testing method and system based on a flexible GMR sensor array, and relates to the technical field of nondestructive testing. According to the invention, a GMR sensor array and a micro excitation coil array are integrated on a flexible substrate, and synchronous acquisition under multi-frequency and multi-phase excitation is realized by establishing a probe-workpiece unified space coordinate system and a time synchronization reference; performing temperature drift compensation, attitude correction and gap normalization processing on the original response tensor to generate a corrected response tensor; the intelligent inversion model based on the prior constraint of the electromagnetic field jointly identifies the conductivity, the thickness and the crack parameters, and outputs a defect parameter map and a confidence map; and a detection report containing the position, the size, the depth and the confidence coefficient is generated through spatial clustering and connectivity analysis, so that high-sensitivity and high-reliability nondestructive detection under a complex curved surface structure is realized.

The invention relates to the technical field of magnetic ring detection, discloses a magnetic ring multi-dimensional nondestructive detection system and method based on multi-frequency composite electromagnetic excitation, and solves the defects of existing magnetic ring detection in the aspects of information acquisition, composite excitation and comprehensive diagnosis. The method comprises the following steps: applying multi-frequency composite excitation, and collecting and preprocessing multi-dimensional response; and extracting features, and carrying out deep learning fusion inversion to obtain magnetic characteristics, material components and defect information. The system comprises a composite excitation module, a multi-mode sensing module, a data acquisition preprocessing module, a data fusion inversion module and a control module. By adopting the technical scheme, comprehensive and efficient nondestructive detection of multi-dimensional electromagnetic information of the magnetic ring is realized, the existing limitation is overcome, and the detection precision and the comprehensive diagnosis capability are remarkably improved.

The invention discloses a pulse eddy current signal late period slope feature extraction method and system based on segmentation fitting, and belongs to the technical field of nondestructive testing. The method comprises the following steps: performing logarithmic transformation and filtering processing on a to-be-detected eddy current signal; intercepting a characteristic signal segment based on the filtering signal; dividing the feature signal segment into a plurality of sub-segments, performing piecewise linear fitting, determining a linear segment, and extracting a slope as a feature value; repeating the steps to obtain characteristic values of the test pieces with different thicknesses, performing curve fitting according to the characteristic values of the test pieces with different thicknesses and the corresponding thicknesses, and calculating a fitting error; and finally, iteratively optimizing the division number of the sub-segments by taking the minimum fitting error as a target. Through adaptive filtering, segmentation fitting and an intelligent optimization algorithm, the anti-interference performance, the linearity and the measurement precision of pulse eddy current signal feature extraction are remarkably improved, and efficient and reliable thickness detection of the high-temperature and high-pressure industrial pipeline under the condition that a heat preservation layer does not need to be disassembled is achieved.

A wide-field dynamic magnetic imaging device and method. The wide-field dynamic magnetic imaging device comprises: a probe assembly (1), comprising a diamond (101) containing NV centers; a microwave magnetic field assembly (2) configured to generate a uniform microwave field and a uniform bias magnetic field in the vicinity of the diamond (101), so as to regulate the spin energy level of the diamond (101) and the quantum state evolution process; a laser modulation assembly (3) configured to output an initial uniform pulsed laser to excite the diamond (101) to emit fluorescence; a fringe light modulation assembly (4) configured to modulate the initial uniform pulsed laser to output a fringe pulsed laser, causing the fluorescence emitted by the diamond (101) to be fringe fluorescence, or to modulate the fluorescence emitted by the diamond (101) into fringe fluorescence; a fluorescence detection assembly (5) configured to perform spatial imaging on the fringe fluorescence to obtain magnetic field information of a sample under test (7), thereby obtaining an initial magnetic image of said sample (7); and a data processing assembly (6) configured to perform Fourier transform and inverse Fourier transform on the initial magnetic image to obtain a first magnetic image.

The invention relates to the technical field of nondestructive testing, and discloses a bimetal composite pipe three-dimensional reconstruction method and system based on multi-source data fusion. The method comprises the following steps: acquiring magnetic flux leakage signals and thickness data of the bimetal composite pipe in a high-pressure environment, and preprocessing the magnetic flux leakage signals and the thickness data to obtain a clean multi-source signal data set; performing time domain and frequency domain feature alignment on the data set to generate a fusion data matrix; extracting a preliminary defect feature set through multi-layer convolution processing; performing classification training on the defect features to obtain a defect type classification result containing confidence scores; if the crack exists, depth fitting is carried out to quantify the crack depth; if the preset risk threshold value is exceeded, generating a three-dimensional defect distribution model and evaluating connectivity; and finally, outputting a quantitative evaluation report of the pipeline risk level. According to the method, efficient fusion of multi-source data, intelligent identification of defect types and three-dimensional visual reconstruction are realized, and the accuracy and evaluation efficiency of pipeline defect detection are remarkably improved.

The invention discloses a precise positioning method of an automatic transmission conductor breaking device, and relates to the technical field of line detection, and the method comprises the following steps: obtaining eddy current detection signal data of the surface of a transmission conductor; performing sliding window segmentation processing on the eddy current detection signal data, and extracting a transient impedance sequence; the transient impedance sequence is processed through a phase sensitive demodulation circuit, and impedance real part information and impedance imaginary part information are separated; impedance real part information features are extracted, impedance imaginary part information frequency domain energy is deconstructed, and an impedance track feature vector is generated; and carrying out space segmentation on the impedance track feature vector, and calculating defect position information and severity level based on the offset of the track center point. The problem that an existing wire detection technology is insufficient in automation level and difficult to meet the requirement for accurate defect positioning of an automatic wire breaking device is solved.

The invention relates to the technical field of pipeline defect detection, and discloses a magnetic flux leakage data defect identification and detection method and system. The method comprises the following steps: acquiring a multi-channel magnetic flux leakage detection signal of a to-be-detected pipeline and spatial position information of each detection node; determining time sequence offset according to spatial position correlation characteristics of adjacent detection nodes and magnetic flux leakage signal propagation characteristics, performing space-time alignment on the magnetic flux leakage signals, and extracting signal distribution characteristics in a preset analysis window; then combining the signal distribution characteristic difference degree of the adjacent detection nodes and the defect characteristic fluctuation factor to construct a defect characteristic incidence matrix, analyzing the matrix to obtain defect probability distribution of different frequency bands of each detection node, and extracting a target defect characteristic frequency; secondly, calculating a defect characteristic nonlinear error probability according to the spatial distribution consistency of target defect characteristic frequency, and dynamically adjusting a classification decision threshold parameter of a defect recognition model according to the defect characteristic nonlinear error probability; and finally, completing automatic identification and classification of pipeline defects by using the adjusted model.

The invention discloses a crack defect detection method and system based on a pulse neural network. The method comprises the following steps: collecting an eddy current detection signal of a turbine blade and converting the eddy current detection signal into a pulse sequence; performing feature extraction of different scales on the pulse sequence in sequence, and fusing the extracted features of different scales to obtain a feature extraction pulse sequence; carrying out attention enhancement processing on the feature extraction pulse sequence in a time dimension to obtain a time enhanced pulse sequence; converting the spatial features of the time enhanced pulse sequence into frequency domain features, and weighting the frequency domain features to obtain weighted frequency domain features; converting the weighted frequency domain features into spatial features to obtain pulse signals; and performing crack defect detection according to the pulse signal. According to the method, the feature representation capability is enhanced through multi-scale feature extraction and fusion, the feature representation efficiency is improved through enhancement of time, space and frequency domains, and the training complexity is reduced while the detection precision is ensured.

An inspection system detects defects within a component using eddy current thermography. An infrared camera detects heat from a defect within the component generated by the eddy current interfering with the defect. The eddy current is generated in the component by a magnetic field applied to the component by an inductor assembly. The inductor assembly includes a magnetic core having magnetic core material and two arms, each arm including a coil to generate the magnetic field. The inductor assembly is configured to apply the magnetic field at an angle offset from a horizontal axis of the magnetic core such that the eddy current flows at an angle within the component relative to the offset angle. Further, the magnetic core material can be shaped to apply the magnetic field at the offset angle or to accommodate complex component shapes.

The invention discloses a steel wire rope nondestructive testing method and system based on sensing and physical feature fusion, and belongs to the technical field of steel wire rope nondestructive testing. The method comprises the following steps: firstly, carrying out saturation magnetization on a steel wire rope through an axial Gaussian difference excitation probe so as to improve the signal-to-noise ratio of a defect magnetic flux leakage signal; and meanwhile, the instantaneous acceleration is synchronously acquired through the inertial measurement unit. And establishing a speed-signal model based on the law of electromagnetic induction, and performing dynamic speed compensation on the magnetic flux leakage original voltage signal. And then, quantitatively extracting characteristic parameters with clear physical significance, such as peak voltage, maximum gradient, full width at half maximum and the like, from the standardized magnetic flux leakage signal, fusing the characteristic parameters with deep characteristics extracted by the deep learning model, and inputting a fused characteristic vector into a full-connection neural network and a classifier to realize defect type identification and degree evaluation. According to the invention, high-precision and interpretable intelligent detection and residual life prediction of steel wire rope defects can be realized, the detection reliability is obviously improved, and predictive maintenance is supported.

The invention belongs to the field of sensors and signal processing, and particularly relates to a wavelet kernel scale sensitivity oriented abrasive particle induced voltage signal noise reduction method, which comprises the following steps: acquiring an abrasive particle induced voltage signal, and carrying out harmonic elimination on the abrasive particle induced voltage signal to obtain a preprocessed signal; constructing a wavelet kernel function, and calculating the wavelet kernel function and the preprocessed signal to obtain a kernel scale guiding spectrum; constructing a sparse joint noise reduction model based on the kernel scale guiding spectrum; processing the sparse joint noise reduction model to obtain a convex optimization objective function; solving the convex optimization objective function by combining a self-adaptive step gradient descent method and a self-adaptive iterative shrinkage threshold method to obtain a weight vector representing the distribution of the abrasive particle characteristic signals; carrying out binarization processing on the weight vector representing the distribution of the abrasive particle characteristic signals to obtain a characteristic indication vector; carrying out Hadamard product on the feature indication vector and the preprocessed signal, and then carrying out low-pass filtering to obtain a noise reduction signal; according to the method, the abrasive particle characteristic signals can be self-adaptively subjected to non-destructive enhancement and noise reduction processing in a strong interference environment.

The invention discloses a nondestructive flaw detection method in a seamless steel tube production process, which comprises the following steps of: initially acquiring a seamless steel tube image, quickly judging whether a crack exists or not, quickly detecting and judging a seamless steel tube product, positioning the crack position of the seamless steel tube according to the initially acquired seamless steel tube image, and spraying dry magnetic powder. Therefore, the seamless steel tube image data after spraying of the dry magnetic powder is collected for the second time, and the edge contour information of clearer magnetic traces at crack positions in the seamless steel tube image data is extracted. And finally, classifying the cracks and generating accurate pixel-level masks of the cracks by adopting a crack classification model. Inputting the cracks into a preset crack generation fault table in a classified manner to inquire fault generation steps and equipment in the seamless steel pipe production process; and the staff can quickly check faults and complete repair according to the inquired and output fault generation steps and equipment in the seamless steel pipe production process, so that the production recovery of the seamless steel pipe production line is accelerated.

The invention discloses a pulsed eddy current sandwich gold plate purity evaluation method and device based on temperature compensation, computer equipment and a medium. The method comprises the following steps: selecting test gold; collecting original data in a first environment temperature range, and collecting second original data of the test gold in at least one second environment temperature range; standardizing the first raw data; inputting the standardized original data into a classification model for training to obtain a trained classification model; collecting to-be-detected data of to-be-detected gold; standardizing the to-be-measured data according to the coincidence range of the environment temperature and the number of the to-be-measured gold; and inputting the standardized to-be-detected data into the classification model, and converting according to an output result of the classification model to obtain the predicted purity of the to-be-detected gold. According to the method, a closed-loop data preprocessing system is constructed, so that the processed data is directly adaptive to an existing classification model, and the influence caused by temperature drift can be effectively counteracted without modifying a model structure or retraining the model.

The invention provides a steel wire rope nondestructive detection device and method, the detection device comprises a shell, a signal processing module and a plurality of probes, the shell is provided with a detection cavity, the detection cavity penetrates through the shell along the axial direction of the shell, and the detection cavity is used for placing a steel wire rope; the plurality of probes are arranged on the inner wall of the detection cavity, and each probe is connected with the signal processing module through a wire; the probe comprises an excitation coil and two detection coils, the two detection coils are located on the two sides of the excitation coil, and the excitation coil and the detection coils are coaxially arranged, so that the detection coils can be most effectively coupled with eddy current magnetic field signals generated by the excitation coil and modulated by defects, and the signal acquisition efficiency is improved.

A magnetic adsorption wheel type cleaning, double-excitation magnetic flux leakage and ultrasonic thickness measurement detection robot comprises a vehicle body and an upper cover. A cleaning device for cleaning the wall surface is arranged at the front end of the vehicle body; the upper cover is provided with a shear type lifting mechanism used for adjusting the distance between the magnetizing device and the detected object, and the shear type lifting mechanism changes the distance between the magnetizing device and the detected object, so that the magnetic field intensity is adjusted; the magnetizing device is carried in the magnetic adsorption wheeled robot body and is responsible for magnetizing a detected object; the rear end of the vehicle body is provided with a thickness measuring module for detecting the thickness of a detected object. The vehicle body chassis is provided with a magnetic wheel adsorption device, and the magnetic wheel adsorption device drives the magnetic adsorption wheel type magnetic flux leakage detection robot; a probe fine adjustment mechanism is arranged on the side face of the vehicle body, the probe fine adjustment mechanism achieves accurate movement of the probe in the vertical direction, and therefore the detection probe and the detected surface are located at the optimal detection distance. According to the invention, the sensitivity, accuracy and adaptability of magnetic flux leakage detection can be improved, so that wall surface defects can be detected.

The invention relates to the technical field of defect detection, and discloses an insulating sleeve defect detection method and device based on electromagnetic induction, and the method comprises the steps: employing a main detection coil to excite a composite insulating sleeve to generate an electromagnetic field, and employing an auxiliary compensation coil to receive a mixed induction signal; performing interface reflection component extraction on the mixed induction signal, and calculating a dielectric parameter value of the composite insulating sleeve; calculating an interface compensation parameter of the auxiliary compensation coil based on the dielectric parameter value; generating a compensation magnetic field opposite to an interface reflection phase by using an auxiliary compensation coil according to an interface compensation parameter, and receiving a compensated detection signal by using a main detection coil and performing phase gradient analysis to obtain a position coordinate and a size parameter of an internal defect of the composite insulating sleeve; according to the invention, hardware-level separation of electromagnetic excitation and interface compensation is realized, active inhibition of interface reflection is carried out from a signal source, and automation and intelligentization of defect detection of the composite insulating sleeve are further realized.

The invention provides an imaging device and method based on a diamond NV color center, and the device comprises a diamond containing the NV color center, an objective lens assembly, an optical detection module, a displacement platform, and a microwave module. The objective lens assembly comprises an objective lens turntable, and an objective lens and a suction tube which are mounted on the objective lens turntable, the objective lens turntable can switch the objective lens or the suction tube to a working position, and the suction tube is used for introducing negative pressure; the displacement platform is located below the objective lens assembly, and the upper surface of the displacement platform is used for placing a to-be-tested piece and can regulate and control the position of the to-be-tested piece; the diamond is used for being placed on a to-be-tested piece and located below the working position of the objective lens assembly, when the suction pipe is switched to the working position, an opening in the lower end of the suction pipe faces the diamond, and the diamond can be adsorbed or released through negative pressure in the control pipe. And the implementation mode is simple and convenient, the use of other auxiliary equipment is reduced, the occupied space is simplified, and accurate and efficient operation of adsorbing and releasing the diamond can be ensured. By using the device, multiple times of imaging scanning of a large-size to-be-measured piece can be realized.

The invention discloses a steel box girder hidden damage identification method based on a magnetic memory effect, and relates to the technical field of bridge structure health monitoring, and the method comprises the steps: employing a magnetic memory detector, arranging a probe to detect the normal component of a leakage magnetic field on the surface of a steel box girder, carrying out the normalization processing of an original signal in order to eliminate the influence of an initial residual magnetic field, calculating to obtain a benchmark magnetic signal variable quantity; detecting lines are arranged on the upper flange, the lower flange and the web of the steel box girder at the interval of 5mm, and detecting points are arranged on the detecting lines at the interval of 1mm; carrying out graded loading on the steel box girder, synchronously acquiring strain data and magnetic memory signal data, and constructing a structured data set; a magnetic signal variation distribution curve is drawn according to the data set, the curve form is analyzed to recognize a local extreme point, and the position of the extreme point is a local buckling damage area; and verifying the consistency of the position of the extreme point and the actual buckling damage area by comparing the final damage form of the test piece, so as to realize high-precision positioning of the hidden damage.

The invention relates to an oil and gas pipeline magnetic flux leakage detection defect quantification method based on a composite convolutional neural network. The method comprises the following steps: 1) a data acquisition stage: carrying out saturation magnetization detection on a pipeline by adopting a pipeline internal detector to obtain magnetic flux leakage internal detection data containing defects; 2) a data preprocessing stage: performing normalization processing on the magnetic flux leakage data, and constructing a defect quantification data set; 3) a model construction stage: constructing a composite convolutional neural network model; 4) a model training stage: inputting a defect quantification data set sample into the model for training; and 5) a model test stage: performing feedback and iterative optimization for multiple times through a back propagation optimization algorithm of the model, finally outputting optimal model parameters in the training process, and exporting and storing the model parameters. Based on the pipeline magnetic flux leakage detection data, high-precision quantification of pipeline defect size parameters can be realized, and the accuracy and reliability of defect evaluation can be improved.

An acoustically driven ferromagnetic resonance (ADFMR) device has a piezoelectric element comprised of piezoelectric material, first and second electrodes arranged in a vertical stack with the piezoelectric element to activate the piezoelectric element to generate an acoustic wave, a radio frequency voltage source electrically connected to the first electrode, a magnet comprised of a magnetostrictive material to receive the acoustic wave, the magnet being in the vertical stack with the first and second electrodes and the piezoelectric element, wherein the acoustic wave resonates at a ferromagnetic resonance of the magnetostrictive material, and a readout circuit to detect a change in the acoustic wave by detecting g one of an output voltage amplitude, a change in impedance or a reflection of the acoustic wave in the magnet to measure an unknown magnetic field in which the ADFMR device resides and as experienced at the magnetostrictive element.

The invention relates to the technical field of pressure vessel health monitoring and nondestructive testing, in particular to an electromagnetic acoustic surface wave-based pressure vessel intergranular corrosion non-contact detection system, which comprises the following modules: a multi-field coupling electromagnetic acoustic surface wave detection module, which comprises a plurality of detection unit arrays, each detection unit comprises a magnetic biasing module and an impedance matching module and is used for generating a local magnetic field on the surface of the pressure vessel and optimizing signal excitation and receiving; and the signal acquisition and preprocessing module is used for acquiring the surface wave signal of the detection module and carrying out denoising, filtering, curved surface coupling difference correction and lift-off correction on the signal to obtain high-quality signal data. According to the method, non-contact quantitative detection of the intergranular corrosion of the pressure vessel is realized through multi-field coupling electromagnetic acoustic surface wave detection and microscopic and macroscopic mapping and inversion, so that the problem that early corrosion is difficult to quantify due to the fact that contact ultrasound is mostly adopted in traditional pressure vessel detection and grain boundary micro-damage cannot be effectively identified is solved.

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.

The invention discloses a pipeline defect detection method and device, a storage medium and computer equipment, and relates to the technical field of defect identification. The method comprises the steps of collecting a magnetic flux leakage signal of a pipeline, performing data structuring processing on the magnetic flux leakage signal to obtain an original magnetic flux leakage signal vector, and converting the original magnetic flux leakage signal vector into a two-dimensional gray matrix; based on a pseudo-color mapping function, mapping the two-dimensional gray matrix into a plurality of pseudo-color images, the image sizes of the pseudo-color images being different; and inputting the plurality of pseudo-color images into a pre-trained defect identification model to obtain defect features of the defect identification model based on the pseudo-color images of different sizes, and outputting a detection result of the pipeline. According to the scheme, the defect detection precision of the pipeline can be improved.

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.

There is provided a sensor unit for a fastener assembly, the sensor unit comprising a mounting arranged to rigidly attach the sensor unit to, or adjacent to, a fastener assembly; and a sensor, that is an optical flow sensor or an inductive sensor, configured to sense movement of the fastener assembly. The sensor unit may comprise a processor arranged to process the output of the sensor to detect loosening of the fastener assembly and to output a signal representing the status of the fastener assembly. The sensor unit may comprise a wireless communication unit arranged to communicate the signal.

The utility model relates to the technical field of bridge inhaul cable steel wire detection, and discloses a bridge inhaul cable steel wire corrosion condition detection robot device which comprises a plurality of detectors, the outer wall of one detector is fixedly connected with a transverse connecting plate, the outer wall of the other detector is fixedly connected with a longitudinal connecting plate, the outer wall of the transverse connecting plate is provided with a connecting assembly, and the outer wall of the longitudinal connecting plate is provided with a connecting rod. A conveying wheel is arranged on the outer wall of the transverse connecting plate, an adjusting assembly is arranged in the transverse connecting plate, the connecting assembly comprises a first connecting piece and a second connecting piece, and the outer wall of the first connecting piece is fixedly connected to the outer wall of the transverse connecting plate. According to the device, the push rod is pushed to drive the movable disc and the first spring and is matched with the limiting groove, so that the clamping column slides in the second connecting piece, the device can be conveniently disassembled, assembled and moved, and the problem that when an existing device is spliced, various tools need to be matched for use, and disassembly cannot be conveniently carried out is solved; and the splicing convenience of the device is improved.