687 results about "Acoustic vibration" patented technology

The invention discloses a distribution box fire early warning method and system based on multi-source information fusion, and particularly relates to the technical field of distribution box fire early warning. Temperature, smoke, acoustic vibration and current harmonic signals are synchronously acquired, time sequence alignment and amplitude normalization are performed, and a dynamic baseline is generated in real time; extracting multi-scale statistics and morphological characteristics according to an adaptive window, and calibrating an operation mode through unsupervised clustering; then retrieving a matched baseline signature in a historical feature library, and calculating a multi-modal standardization deviation of a current window; mapping the deviation index into a graph node, combining a covariance edge weight, a phase synchronization index and a smoke discrete index, obtaining a coupling risk coefficient through a fusion model, and outputting a comprehensive risk index; and finally, carrying out multi-scale rate and acceleration analysis on the comprehensive risk index, triggering three-level early warning of attention, warning and danger by adopting a dynamic threshold, and correcting the threshold by utilizing operation and maintenance feedback self-learning, thereby effectively solving the problems of early warning response lag and unknown early warning level.

The invention relates to the technical field of mold injection molding production, and discloses a fault diagnosis and prediction maintenance method and device for an intelligent injection mold, and the method comprises the steps: obtaining an original sound vibration signal and a real-time sound vibration signal; performing signal decomposition operation on the original acoustic vibration signal to obtain an acoustic vibration high-frequency component and an acoustic vibration low-frequency component; performing feature extraction operation on the sound vibration high-frequency component and the sound vibration low-frequency component to obtain a high-frequency feature set and a low-frequency feature set; inputting the high-frequency feature set and the low-frequency feature set into a detection model for model training to obtain an original detection model; inputting data acquired by different injection molding machines and molds into the original detection model for generalization learning to obtain a generalization detection model; and the real-time sound vibration signals are input into the detection model for mold state reasoning, a fault alarm type and a mold fault position are obtained, and fault diagnosis and mold maintenance of the injection mold are conducted according to the fault alarm type and the mold fault position. The method can realize fault diagnosis and maintenance of the injection mold.

The invention provides an intelligent pipe surface quality detection method and system based on machine vision, relates to the technical field of industrial automatic detection and machine vision, and aims to establish a pipe surface feature library and mark shape abnormal features. The method comprises the following steps: collecting an image of a pipe in a bright and dark composite light field, extracting gray and texture features after polarization filtering processing, reconstructing a three-dimensional point cloud covering a mortar layer and a concrete layer by matching a principal component analysis dimensionality reduction fusion feature set with a feature library, and converting the point cloud into a two-dimensional expansion graph through cylindrical projection; a mortar abnormal area and a concrete abnormal area are segmented, the hole volume is calculated through point cloud residual errors in the mortar area, and internal hollowing is detected in combination with acoustic vibration excitation and thermal response; the concrete area locates defects based on point cloud features; according to the sequence of the concrete covering process before the mortar covering process, a correlation model is constructed to match and coincide the defect sites, and the detection result is output, so that the detection automation degree and accuracy can be improved.

The invention discloses a rapid demolding device and method for a small prefabricated part, and relates to the technical field of prefabricated part production. The mold bearing table is mounted on the substrate of the demolding rack and is integrated with a multi-point temperature sensor and an acoustic vibration signal collector; the ejection demolding assembly is installed on the stand columns of the demolding rack in a liftable mode and comprises an ejection plate assembly, and an ejection force sensor and a high-frequency vibrator are arranged on the ejection plate assembly; the visual identification module is mounted on the demolding rack; and the controller is electrically connected with the multi-point temperature sensor, the sound vibration signal collector, the ejection force sensor, the high-frequency vibrator, the visual recognition module and a driving mechanism of the ejection demolding assembly. Multi-source sensing information is fused, the component condensation state and the demolding time are accurately judged, the ejection force and vibration in the demolding process are adaptively controlled, accurate and flexible control over the demolding time and process is achieved, and the demolding success rate and the component yield are remarkably increased.

The invention relates to the technical field of offshore oil safety assessment, in particular to a process pipeline fatigue damage identification and prediction method, which comprises the following steps: step 1, key pipeline primary selection and failure possibility assessment; 2, collecting and preprocessing multi-modal data; 3, performing model construction and optimization on damage identification; 4, fatigue life prediction and failure early warning are carried out; 5, carrying out correlation analysis on the stress spectrum and the service life; 6, multi-parameter dynamic early warning is carried out; and 7, integrating a real-time monitoring system. According to the method, through multi-modal evaluation of fluid-to-vibration, fluid pulsation, high-frequency acoustic vibration and the like, a stress spectrum is generated by utilizing a rain-flow counting method, the crack growth rate and the residual life are predicted in combination with an S-N curve or a Paris formula, and through correlation analysis of the stress spectrum and the life and multi-parameter dynamic early warning, the crack growth rate and the residual life are predicted. Therefore, the method has the advantages that an online system integrating vibration, acoustic emission and stress monitoring is provided, and real-time evaluation and life prediction of fatigue damage are achieved.

The invention discloses an acoustically enhanced driven parallel piezoelectric micropump and a driving method thereof. The input layer, the supporting structure and the output layer are sequentially arranged in a stacked mode. The device is characterized by further comprising a plurality of actuators arranged in the supporting structure. The actuators are arranged in a linear array or a matrix. And an input cavity which is connected into a whole is formed between each actuator and the input layer. And a plurality of mutually independent output cavities are formed between each actuator and the output layer. The actuator is used for pumping fluid through self vibration. The input layer is provided with a plurality of inflow structures staggered with the through-flow structures on the actuator. The output layer is provided with a plurality of outflow structures aligned with the through-flow structures on the actuator. The multiple pump flow units are connected in parallel, acoustic vibration is coupled with mechanical vibration, the multiple flow inlet structures are arranged at the multiple antinode points of acoustic resonance, flowing of fluid is further enhanced, and the air pumping efficiency is improved.

The invention provides an anti-interference sound and vibration detection method and device, and relates to the field of sound and vibration detection. The method comprises the following steps: acquiring target special equipment and an equipment type corresponding to the target special equipment; according to the equipment type, acquiring a multi-channel high-sensitivity sensing array combination corresponding to the equipment type from a preset inspection method database; acquiring sound vibration signal data corresponding to the target special equipment based on the multi-channel high-sensitivity sensing array combination; performing modal decomposition on the sound vibration signal data by applying an improved empirical mode decomposition algorithm, eliminating pseudo-modal components and reconstructing effective modal signal data; and inputting the reconstructed effective modal signal data into a sound-vibration collaborative recognition model, and outputting a structural state recognition result of the target special equipment through the sound-vibration collaborative recognition model. According to the method and the device, the problem of low sound vibration detection accuracy of a traditional sound vibration detection technology when mechanical impact, fluid disturbance, electromagnetic interference or multi-source background noise is accompanied in the environment where the detected equipment is located is solved.

The invention discloses a method and equipment for double-ultrasonic cooperative in-situ auxiliary electric arc additive manufacturing of a large metal component, an electric arc additive manufacturing system and an ultrasonic vibration auxiliary electric arc additive manufacturing system. The ultrasonic vibration auxiliary electric arc additive manufacturing system comprises two ultrasonic impact guns, an optical sensor, a temperature sensor and a calculation control system. The optical sensor collects optical characteristics of the deposition channel and the molten pool and converts the optical characteristics into electric signals to be transmitted to the calculation control system, and the temperature sensor is used for monitoring and collecting temperature information of the deposition channel and transmitting the temperature information to the calculation control system to draw an isotherm; the calculation control system adjusts the distance between the ultrasonic impact gun and the center of the molten pool according to the isotherm and the optical characteristics of the optical sensor mobile phone, and the ultrasonic power is adjusted; one of the ultrasonic peening guns applies ultrasound from the component side edge as materials are added to the component edge. By means of the multi-axis freedom degree and accuracy of the mechanical arm, double-ultrasonic vibration is applied to all positions of a large metal component in the material adding process, and the mechanical property is improved.

The invention discloses a conveyor carrier roller online monitoring method and device and electronic equipment. The method comprises the following steps of: firstly, acquiring a sound wave vibration signal generated in real time when the carrier roller of the conveyor moves; wherein the sound wave vibration signal is obtained based on reverse Rayleigh scattering light in the optical fiber; performing feature extraction from the sound wave vibration signal to obtain an energy feature value of each frame; performing normalization processing on the energy characteristic value to obtain a target characteristic value; judging whether the target characteristic value exceeds a preset threshold value or not: if the target characteristic value exceeds the preset threshold value, inputting the target characteristic value into a pre-constructed target neural network model, so that the target neural network model outputs a fault type corresponding to the target characteristic value; therefore, online monitoring of the conveyor carrier roller based on optical fiber sensing multi-feature fusion is completed. According to the method, the fault type can be automatically and accurately identified and output, so that the overall performance and safety of the conveyor carrier roller are enhanced.

The invention relates to the technical field of solid-state battery energy conversion, and discloses a high-safety sodium-ion battery energy conversion method based on a solid-state electrolyte, which comprises the following steps: identifying an interface dynamic mismatch risk time period in a charging and discharging process, and applying micro-amplitude high-frequency acoustic vibration disturbance to a solid-state electrolyte and a sodium negative electrode interface in the time period, sodium ion microscopic distribution is regulated and controlled through the acoustic streaming effect, contact stress is dredged, dendritic crystal early nucleation is inhibited, dynamic reconstruction and optimization of the interface state are achieved, the solid-state sodium-electricity interface is converted into dynamic self-adaption from static mismatch, the ion transmission uniformity is actively maintained through the acoustic streaming effect, and dynamic reconstruction and optimization of the interface state are achieved. And meanwhile, real-time sensing of an interface state and dynamic adjustment of acoustic vibration parameters are realized in combination with an acoustic probing mechanism, so that the interface stability and dendritic crystal inhibition capability are effectively improved, and the energy conversion efficiency and the safety performance of the battery are remarkably enhanced.

The invention relates to the technical field of switch cabinet temperature measurement, and discloses a method for measuring the temperature of a switch cabinet in real time through an infrared matrix based on multi-modal sensing. The method comprises the following steps: synchronously acquiring an infrared image sequence, an acoustic vibration signal and current waveform data of the switch cabinet through a multi-mode sensor array, and capturing equipment operation and temperature associated information; constructing a three-dimensional temperature field reconstruction mechanism model based on a heat conduction physical equation, outputting a theoretical temperature distribution value fitting a physical rule, processing multi-source data by using a spatial-temporal feature extraction network, and outputting a temperature prediction value reflecting a real-time working condition; inputting the two into a dynamic fusion module based on a Markov decision process to generate a fusion temperature distribution value, and generating a temperature anomaly positioning instruction through a rolling time domain optimization strategy according to the fusion temperature distribution value; when the deviation between the actual temperature sampling value and the fused temperature distribution value exceeds a preset threshold value, calibrating a network output layer parameter; and triggering a linkage control protocol of the switch cabinet protection system according to the abnormal positioning instruction.

The invention discloses a spiral air duct predictive operation and maintenance method and system based on digital twinning, and relates to the technical field of air duct system state monitoring and operation and maintenance, and the method comprises the following steps: deploying a high-frequency acoustic emission AE sensor and a low-frequency vibration acceleration sensor at key nodes of a spiral air duct, and taking a fan start-stop signal and operation power as working condition labels; a high-frequency acquisition mode is automatically triggered, noise reduction processing is carried out, and a multi-mode acoustic vibration original signal data set which is provided with a time sequence and a working condition label and is subjected to preliminary noise reduction is output; the high-frequency acoustic emission AE sensor and the low-frequency vibration acceleration sensor are arranged in the spiral duct, the health state of the thin-wall structure is monitored in real time through the high-frequency acoustic emission technology, weak early-stage fault signals in the duct can be accurately captured and early warning can be given out in the fault germination stage of fretting wear, sealant aging and the like of the spiral duct, and the fault early-warning effect is achieved. Real-time monitoring, early warning and accurate operation and maintenance of the spiral air pipe are achieved.

The invention discloses a portable swallowing function screening system based on multiple modes. The portable swallowing function screening system comprises a signal acquisition device, a data processing device and an interactive display device which are connected in sequence, the signal acquisition device is used for acquiring a laryngeal pressure signal, a laryngeal acoustic vibration signal and a swallowing acoustic wave signal of a patient; the data processing device is used for performing feature extraction and feature fusion according to the throat pressure signal, the throat sound vibration signal and the swallowing sound wave signal to obtain a multi-modal fusion feature, and calculating a swallowing disorder risk probability through a preset deep learning model according to the multi-modal fusion feature; the interactive display device is used for judging the swallowing function according to the swallowing disorder risk probability and displaying the screening result of the swallowing function according to the judgment result.

The invention relates to a tunnel water leakage detection method and system based on sound vibration signals, in particular to the technical field of tunnel engineering. Comprising the following steps: adjusting equipment working parameters by using a tunnel water leakage monitoring model based on environment sensing data monitored by an environment sensor through a self-adaptive module; wherein the tunnel water leakage monitoring model is established based on a machine learning model, historical environment perception data and historical equipment working parameters are taken as training samples, the tunnel water leakage monitoring model is used for predicting equipment working parameters under the environment perception data, and the equipment working parameters comprise working parameters of an acoustic vibration sensor and / or an analyzer; after the working parameters of the equipment are adjusted, analyzing and processing the environment sensing data and acoustic vibration signals collected by the acoustic vibration sensor through an analyzer to obtain analysis data; and comparing the analysis data with a tunnel water leakage state change curve through a computer, and determining a tunnel water leakage area.

An ultrasonic surgical device comprising of a proximal end and a distal end to be used in a robotic surgical system. The distal end comprises of a first jaw with an ultrasound transducer array and a second jaw with either a reflecting surface or an ultrasound transducer array. The distal end is configured to grasp a blood vessel or a living tissue. The proximal end is configured to apply a drive signal to the ultrasound transducer array. A plurality of rotary joints is connected at the distal end to facilitate its articulation. The ultrasound transducer array, generates acoustic vibrations in the grasped blood vessel, or living tissue, based on the applied drive signal by a control system of the robotic surgical system. The ultrasound transducer array, is configured to sense parameters of a target site on a grasped blood vessel or living tissue, based on the reflected acoustic vibrations.

The invention relates to the technical field of concrete bubble monitoring, in particular to an aluminum film system concrete bubble dynamic monitoring and regulating method which comprises the following steps: applying a high-frequency alternating current with a first preset parameter to an aluminum film system to induce eddy current in an aluminum film and enable the aluminum film to generate thermally induced acoustic vibration; meanwhile, an eddy current thermally induced acoustic response signal reflected by an internal interface of the concrete is obtained; based on the response signal, determining current bubble state information in the concrete, including amplitude attenuation characteristics based on the sound pressure signal, frequency spectrum characteristics based on the sound pressure signal and phase delay information; and based on the bubble state information, a regulation and control instruction of a second preset parameter is generated, the regulation and control instruction is executed to adjust the parameter of the high-frequency alternating current applied to the aluminum film system, and directional traction regulation and control are conducted on the bubbles in the concrete. According to the invention, by means of an eddy current thermally induced sound technology, dynamic distribution, including the number, the particle size, the motion trail and the like, of bubbles is captured in real time, and instantaneous changes in the pouring process can be accurately reflected.

The invention provides a safety risk control method for geological storage of carbon dioxide and electronic equipment. A distributed optical fiber sensing system is arranged in at least one injection well of the target sealing and storage area in the axial direction of a shaft and penetrates through a main cover layer and a reservoir; the method comprises the following steps: acquiring background data through a distributed optical fiber sensing system, and establishing a multi-parameter baseline model including a temperature field, a strain field and background noise; temperature, strain and sound wave vibration signals are collected in real time, and key features are extracted; the key features extracted in real time are compared with a preset risk feature library, the types of risk events are identified, and the identified risk events are graded according to the spatial range and intensity of the abnormal signals; and according to the type and the grade of the risk event, generating and executing a corresponding control instruction. According to the embodiment of the invention, the optical fibers are distributed in the whole area along the shaft and the target stratum, a three-dimensional monitoring network covering the sealing body is constructed, and the hysteresis quality of traditional point type measurement and periodic exploration is overcome.

The invention discloses a wire-powder composite ultrasonic-assisted additive and subtractive material forming device and a manufacturing method, the wire-powder composite ultrasonic-assisted additive and subtractive material forming device comprises a rack, an additive manufacturing module, a subtractive material milling module, an ultrasonic-assisted module and a forming platform, the rack comprises a Z1-axis assembly, a Z2-axis assembly and an XY plane moving platform; the additive manufacturing module is fixed to the Z1-axis assembly, and the subtractive milling module is fixed to the Z2-axis assembly. The ultrasonic auxiliary module comprises an ultrasonic generator, an ultrasonic vibration head and a mounting frame; the mounting frame is fixed on the XY plane moving platform, and a thrust cylinder is fixed in the inner space of the mounting frame; in the additive manufacturing process, the thrust air cylinder pushes the ultrasonic vibration head to move along the guide rail, so that the ultrasonic vibration head upwards penetrates through the middle hole of the mounting frame and then is vertically pressed on the lower surface of the forming platform, and vibration of the ultrasonic vibration head is controlled through the ultrasonic generator. According to the method, the performance and morphology of the part in the manufacturing process can be regulated and controlled, and therefore high-efficiency and high-quality integrated forming of a complex structure is achieved.

The invention discloses a harbor area communication optical cable intelligent operation and maintenance method and system based on the AI technology. The method comprises the steps that optical cable sound wave vibration intensity parameters, environment parameters and positioning signal parameters are obtained in real time; performing quantitative preprocessing on each parameter and generating a vibration energy entropy coefficient, an environment stress coefficient and a positioning confidence coefficient; calculating a health index according to the optical cable health assessment model fusing the coefficients; triggering corresponding response measures according to the health indexes; generating a maintenance priority list, and performing path planning of a plurality of operation and maintenance nodes based on a Dijkstra algorithm; and updating the health assessment model. The system comprises a data acquisition module, a database storage module, an intelligent operation and maintenance center and an operation and maintenance module. According to the method, multi-dimensional sensing data is collected and quantified into corresponding coefficients, the optical cable concealment damage and potential risk sensing capacity is improved, the evaluation model is established to predict and accurately judge the operation state of the optical cable, active preventive and repairable maintenance is guided to be implemented, and the comprehensive operation and maintenance capacity of the system is improved.

The invention discloses a hydrogen leakage condition multi-parameter monitoring and identification and leakage rate deduction method and device, and relates to the technical field of hydrogen safety monitoring, and the method comprises the steps: obtaining multi-parameter sensing data; carrying out noise reduction processing on the multi-parameter sensing data; based on a temperature signal, a pressure signal and a fastening force signal in the denoised data, establishing a temperature-pressure-fastening force coupled physical model by adopting a porous medium model; performing short-time Fourier transform and variational mode decomposition on sound vibration signals in the denoised data to obtain characteristic parameters; based on the characteristic parameters, establishing an initial data sample set; adding the leakage rate and the concentration signal in the denoised data into the initial data sample set to obtain a data sample library; and establishing a multi-parameter leakage prediction model by using a convolutional neural network based on the data sample library and the physical model. According to the invention, the online real-time monitoring of the hydrogen leakage state, the accurate judgment of the leakage size and the rapid prediction of the leakage development trend can be realized.

The invention discloses a crack detection device, method, storage medium and system, and relates to the technical field of diamond damage detection.The crack detection device comprises a machine table, and the upper end of the machine table is fixedly connected with an equipment mounting table; the main tower module is arranged at the upper end of the equipment mounting table; the diamond clamping modules are fixedly mounted at the lower end of the main tower module; the surface film pasting module is fixedly mounted on the left side of the upper end of the equipment mounting table; the surface crack detection module is fixedly mounted on the left side of the upper end of the equipment mounting table; the ultrasonic detection module is fixedly mounted on the right side of the upper end of the equipment mounting table. According to the scheme, based on image detection and ultrasonic vibration detection technologies, integrated automatic detection of surface crack detection and internal crack detection of the CVD diamond wafer is carried out by utilizing the main tower rotating mechanism, so that the detection process can be effectively shortened, and the crack detection efficiency of the CVD diamond wafer is improved.

The invention discloses a pipeline flow-induced and sound-induced vibration detection and acoustic modal analysis experimental device which comprises a main pipeline, one end of the main pipeline is connected with a gas source, the other end of the main pipeline is connected with a first silencer, the other end of the first silencer is connected with a replaceable experimental pipeline, and the other end of the replaceable experimental pipeline is connected with a second silencer. The main pipeline and the replaceable experiment pipeline are on the same straight line; a flow adjusting part is arranged on the main pipeline close to the gas source; a modal analysis part and a pressure detection part are sequentially arranged at the position, close to the second silencer, of the replaceable experiment pipeline in the airflow direction; the system further comprises an experimental data acquisition part which is connected with the flow regulation part, the pressure detection part and the modal analysis part. The invention further discloses a pipeline flow-induced and sound-induced vibration detection and sound modal analysis method. A pipeline vibration test result obtained through the experimental device is high in precision and high in reliability, and meanwhile, a test part is convenient to disassemble and replace.

The invention belongs to the technical field of rolling bearing manufacturing, and discloses a preparation method of a high-precision robot turntable bearing, which comprises the following steps: applying a static load simulating a preset working load to a bearing ring workpiece subjected to heat treatment, and executing a set of closed-loop acoustic vibration treatment on the workpiece in a state of keeping the static load, the processing can dynamically track the resonant frequency of the workpiece to update excitation, can monitor acoustic response characteristic parameters of the workpiece in real time, and automatically terminates when the time change rate of the parameters meets a stable condition. According to the method, the specific directivity is provided for the stress release process driven by acoustic vibration energy, so that an original disordered residual stress field in a workpiece forms an optimized form capable of actively adapting to future working loads, and the problem that the static geometric precision and the dynamic service precision of a bearing are inconsistent in a traditional process is solved.

Embodiments provide a method for acoustically testing at least one MEMS device of a plurality of MEMS devices. The method comprises a step of providing at least one MEMS device. Additionally, the method comprises a step of exciting the at least one MEMS device to an acoustic vibration. Additionally, the method comprises a step of detecting the acoustic vibration of the at least one MEMS device by at least one sound sensor. Additionally, the method comprises a step of evaluating the acoustic vibration of the at least one MEMS device detected by the at least one sound sensor to test the at least one MEMS device as to an intended functionality.

The invention relates to the technical field of cable joint diagnosis, in particular to a cable joint abnormity early warning method and system, and the method comprises the steps: carrying out the feature extraction of infrared thermal image data and acoustic vibration data, and obtaining a multi-modal feature data set; quantifying the coupling correlation degree between the hot spot diffusion gradient and the vibration frequency component according to the multi-modal characteristic data set to obtain a regional thermal vibration coupling anomaly index; calculating a regional defect risk score according to the regional defect type distribution; obtaining a modal fusion weight distribution value based on the regional defect risk score and the regional thermal vibration coupling anomaly index; performing weighted fusion on the infrared thermal image feature data and the acoustic vibration feature data according to the modal fusion weight distribution value to obtain a regional defect coupling feature vector; and obtaining the defect probability of the cable joint according to the regional defect coupling feature vector, and carrying out graded early warning according to the defect probability of the cable joint. According to the method, the infrared thermal image and the acoustic vibration data are fused, so that accurate early warning of the abnormity of the cable joint is realized.

The invention relates to the technical field of oil pipe leakage detection, in particular to an underground oil pipe leakage detection system, which comprises an underground permanent monitoring unit, which comprises at least one optical fiber sensor, and the optical fiber sensor clings to the outer wall of a production oil pipe or is integrated in an oil pipe coupling and extends along the depth of a whole shaft; the ground signal processing and analyzing unit is in communication connection with the underground permanent monitoring unit; wherein the optical fiber sensor is used as a distributed temperature sensor and a distributed sound wave vibration sensor at the same time, real-time on-line monitoring of oil pipe leakage is achieved, early warning can be given out at the initial stage of leakage, the accuracy of leakage recognition is improved through collaborative analysis of bimodal sensing data, the false alarm situation is effectively reduced, and the reliability of oil pipe leakage detection is improved. A distributed measurement mode provides meter-scale spatial resolution, the depth position of a leakage point is accurately positioned, normal production operation does not need to be interrupted during system operation, and the operation cost of an oil field is reduced.

The invention discloses an abnormal vibration monitoring method and system based on a flexible acoustic vibration sensor, and relates to the technical field of vibration monitoring. The method comprises the following steps: attaching a flexible acoustic vibration sensor to a key part of the GIS equipment of the transformer substation, capturing a vibration signal and converting the vibration signal into an electric signal in real time; selecting a proper wavelet generating function, comparing the amplitude error with the residual percentage through wavelet transformation, and selecting an optimal wavelet generating function; wavelet packet transformation is carried out on the vibration data, frequency band energy is calculated, and a frequency band with prominent abnormal energy is selected; and performing empirical mode decomposition and Hilbert transform on the abnormal frequency band signal to form a feature vector, and comparing the feature vector with a preset feature library to identify the equipment state. According to the invention, accurate diagnosis is realized, potential faults are found in time, the accuracy and the real-time performance of fault diagnosis are improved, and the downtime and the maintenance cost of equipment are reduced.

The invention discloses a robot inspection control method for textile equipment monitoring, and relates to the technical field of textile equipment monitoring, and the method comprises the following steps: a robot scans an environment along a preset path at a variable sampling rate through a mobile noise fingerprint collection method, synchronously collects working condition associated noise decomposition of textile equipment, and constructs a dynamic noise substrate; performing space-time-frequency domain constraint blind source separation by taking a dynamic noise base as a physical constraint and combining a sound vibration sensor, decoupling a background noise component, an equipment global vibration component and a residual signal, performing kurtosis detection on the residual signal, and performing preliminary screening on a fault signal by combining a preset abnormal triggering mechanism; according to the method, dynamic reconstruction of the noise base is achieved through multi-sensor cooperative working condition correlation modeling, the problems of noise time-varying characteristics and spatial isomerism are solved, an accurate reference is provided for subsequent noise suppression, the noise base serves as a physical anchor point for separation, the fault component separation purity is improved, and suspicious fault signals are effectively and preliminarily screened out.

The invention discloses a nondestructive testing device and method for the compactness of a pier based on sound wave vibration, and the device comprises a first annular track which comprises a plurality of spliced inner tracks and sleeves a concrete pier column; the second annular track comprises a plurality of spliced outer tracks, the diameter of the second annular track is larger than that of the first annular track, and fixing assemblies are arranged between the spliced inner tracks and the spliced outer tracks; a plurality of groups of crab walking mechanisms are mounted on the spliced outer track through deformable fixing assemblies; the ultrasonic detection system is mounted on the inner side of the spliced inner track; the visual detection systems are respectively mounted on the spliced outer track; and the ultrasonic detection system and the visual detection system are connected with the terminal system. According to the invention, surface defects can be captured, the internal compactness problem can be detected, and omnibearing detection of the internal and external states of the pier is realized.

The invention relates to the field of fan spindle state monitoring, and aims at synchronously acquiring sound and vibration signals through multiple channels, realizing nanosecond time alignment by adopting a precision time protocol, and performing denoising and normalization preprocessing on the signals to improve data integration and signal fidelity. Furthermore, short-time Fourier transform and continuous wavelet transform are combined to extract multi-scale time-frequency features, and a high-dimensional combined feature vector is generated in combination with cross-correlation analysis. And mapping the feature vectors to a low-dimensional manifold space through a local linear embedding algorithm, and constructing a dynamic mode reference template. Indexes such as curvature, track length and direction entropy are monitored in real time, whether the spindle has an abnormal evolution trend or not is judged through a self-adaptive curvature threshold, and abnormity judgment is achieved in combination with track backtracking verification. According to the scheme, the abnormal starting boundary of the spindle state can be caught in a refined mode, and the early warning and operation and maintenance response capacity of fan operation is effectively improved.