62 results about "Vibration signal analysis" patented technology

The invention relates to the technical field of liquefied petroleum gas supervision, and discloses a liquefied petroleum gas safe distribution supervision method based on artificial intelligence, which comprises the following steps: eliminating temperature fluctuation interference of a tank body by dynamically calculating saturated vapor pressure, compensating and correcting vehicle bumping influence by superposing motion disturbance, and analyzing and calculating leakage suspected degree of the tank body; combining the voiceprint spectrum characteristics, the infrared temperature gradient and the vibration signals, and analyzing to obtain leakage point coordinates of the tank body; the filling compliance is verified by analyzing the dynamic balance relation of the tank body, and the leakage risk value is calculated by using the model in combination with the leakage suspected degree and the environmental parameters to judge the leakage risk; dynamically associating high-risk action recognition with environment parameters, analyzing to obtain a behavior environment risk score, and inputting the score into a global regulation and control system; and in combination with the leakage suspected degree, the behavior environment risk score and the leakage point coordinates, constructing a phase change leakage coupling factor, synchronously regulating and controlling an alarm threshold value and decision planning, performing leakage diffusion simulation, and performing full-process response.

The invention discloses a tool fracture and fatigue intelligent detection method based on vibration signal analysis, and the method comprises the following steps: S1, installing a vibration sensor, and collecting the vibration signal of a tool in real time; s2, the collected tool vibration signals are preprocessed, and noise in the signals is removed; s3, performing time-frequency analysis on the preprocessed vibration signals, and extracting time-frequency features in the signals; s4, performing deep feature learning on the extracted time-frequency features to form deep features; s5, the depth features are classified and analyzed, and the health state of the cutter is output; s6, according to the health state optimization feature extraction and prediction result of the cutter, generating learning output; s7, evaluating the health state of the cutter in real time according to the learning output, and pushing alarm information; and S8, according to the alarm information, predicting the service life of the cutter and optimizing a cutter replacement and maintenance strategy. According to the method, short-time Fourier transform and Hough transform are combined, and the extreme learning machine is applied, so that intelligent detection on the fracture and fatigue of the cutter is realized.

The invention relates to the technical field of signal correction, in particular to a nonlinear distortion correction method for a vibration monitoring signal, and provides the following scheme: carrying out broadening and sparse reconstruction on a vibration signal through dispersion Fourier transform and compressed sensing, and extracting a high-frequency local structure; on the basis of phase-space reconstruction and a Lyapunov exponent, nonlinear distortion segments are identified; further performing orbit guiding modeling and degradation path reasoning on the orbit cluster corresponding to the segment, and selecting a reference orbit matched with the structure from the residual linear segments; and constructing an affine mapping relation between orbits and back-projecting the affine mapping relation to a time domain to generate a correction signal. According to the invention, high-precision identification and low-error correction of millisecond-level local nonlinear distortion are realized, and the stability and accuracy of vibration signal analysis are improved.

The invention relates to the technical field of rail transit monitoring, and discloses a multi-sensor fusion rail transit electric passenger car bogie dynamic performance monitoring method. The method comprises the steps that multi-source heterogeneous sensing data such as bogie vibration acceleration time sequence signals, wheel-rail contact force distribution data, bearing temperature gradient data and structural strain field data are collected; analyzing the vibration signal to extract a frequency band energy feature vector, performing spatial gridding mapping on the contact force data to generate a distribution matrix, and performing time-space alignment fusion on the two to form a primary fusion feature set; positioning a bearing temperature anomaly region through a deep convolutional network, and generating a secondary fusion feature set in combination with a structural strain concentration coefficient; and inputting the two-stage feature set into a dynamic performance evaluation model, outputting a performance degradation index set of scores of axle box bearing wear degree, framework fatigue cumulant, wheel set tread damage and the like, generating a maintenance priority sequence and triggering a preventive maintenance instruction according to the performance degradation index set, and realizing accurate monitoring and efficient operation and maintenance management and control of the dynamic performance of the bogie.

The invention relates to a dental drilling hardness real-time monitoring method based on vibration signal analysis and a dental hardness sensor. The dental drilling hardness real-time monitoring method comprises the following steps: acquiring a three-dimensional vibration displacement signal; after filtering and multi-dimensional feature extraction, obtaining a vibration feature vector representing the mechanical impedance of the tooth body; inputting the hardness index and the vibration displacement into a trained machine learning model, and mapping and outputting the hardness index and the vibration displacement of the current drilling area in real time; packaging with a timestamp, and serializing and storing as a traceable historical record; the system continuously presents hardness change in a dynamic waveform and a color gradient, and prompts when detecting that the hardness changes suddenly or crosses a boundary. Compared with the prior art, the method has the advantages that the three-dimensional micro-vibration signal of the drill bit can be converted into the digital fingerprint of the mechanical impedance of the tooth tissue in real time, and a visualization-reminding closed loop is driven by the hardness index flow synchronized with the timestamp, so that the drilling hardness in-situ sensing is realized.

The present application relates to the technical field of transformer fault diagnosis, and in particular to a transformer body vibration signal analysis and fault diagnosis method, device and medium. The present application combines the kurtosis characteristics of the vibration signal, adopts a semi-soft threshold function wavelet denoising method based on the threshold selection method of the 3sigm rule, and achieves better denoising effect. Through the fault diagnosis model of the organic fusion of the two improved HHT transforms-mobilenetV2 model, combined with different feature extraction methods, it is more conducive to retaining the effective features of the vibration signal; the improved mobilenetV2 model designs a multi-scale deep convolution model, introduces a channel attention mechanism before the channel-by-channel convolution, and after multi-scale deep convolution feature extraction, a multi-source data attention mechanism is introduced; without affecting the safe and reliable operation of the transformer, the intelligent diagnosis of the vibration state fault of the transformer is realized.

The present application relates to the technical field of roller shutter door assembly wear evaluation, and particularly relates to a silent roller shutter door assembly wear evaluation method based on vibration signal analysis, which comprises the following steps: collecting a roller shutter door vibration signal, performing wavelet packet decomposition, reconstructing a denoising signal according to energy entropy and kurtosis product, and generating a window adjustment coefficient; calculating a slope ratio to construct a double-threshold band scaled by the adjustment coefficient to determine candidate pulse points; dividing pulse clusters according to time interval mutations, extracting amplitude Gini coefficient, interval discrete entropy and center frequency offset to fuse into a distortion factor; constructing a wear characteristic vector from the distortion factor cumulative slope and peak interval ratio, determining a wear grade through Mahalanobis distance comparison; recursively updating a covariance matrix based on a forgetting factor, and performing a remaining service interval penalty compression measurement. The present application overcomes the strong noise masking problem and guarantees the reliability of the prediction result.

The present application relates to a kind of powder packaging machine transmission component fault pre-diagnosis method based on vibration signal analysis, the present application is accurate to collect multiple source vibration signals, standardization processing of time-frequency characteristics, generate fault semantic anchor point library in combination with prior knowledge of dynamics and under the training method of multiple loss constraint convolutional neural network, realize the adaptive alignment of vibration signal hidden space feature and physical fault mechanism.The present application outputs the joint attribution time-frequency graph of specific fault type, and the coincidence degree of model attention area and theoretical fault frequency label is based on physical consistency check, significantly improve diagnostic accuracy and explainability, effectively assist equipment operation decision, the present application is by constructing the fault semantic anchor point library based on physical mechanism, and it is embedded in the hidden space optimization process of convolutional neural network in differentiable way, effectively overcome the problem that traditional data-driven model is generally present in powder packaging machine transmission chain fault diagnosis.

The present application belongs to the technical field of fault diagnosis, and particularly relates to a belt conveyor bearing fault diagnosis method and system based on temperature and vibration signal analysis. The method comprises the following steps: removing outliers and correcting baseline drift of the collected temperature and vibration signals respectively; using kurtosis and envelope entropy to optimize variational mode decomposition to extract fault components and generate time-frequency diagrams, and using temperature change rate to map weighting coefficients for reconstruction and high-frequency gain compensation; splicing the weighted time-frequency diagram and the two-dimensional temperature diagram into a three-dimensional tensor, inputting the residual network of the band coordinate attention and the deformable convolution branch, and using the temperature energy proportion to adjust the joint loss output to output the diagnosis result. The present application effectively enhances the multi-modal feature expression capability of the model under complex variable working conditions by establishing the physical correlation between temperature change and high-frequency vibration, and significantly improves the diagnosis accuracy and generalization performance.

The invention discloses a motor bearing fault recognition method based on vibration signal analysis. According to the method, recognition of a motor bearing of an orbital wing type wind power generation device is achieved through a hybrid method of multi-modal feature extraction and a convolutional neural network. The method comprises the following steps: firstly, decomposing a complex vibration signal by adopting an empirical Fourier decomposition (EFD) method to obtain multi-scale signal components of a plurality of frequency bands, thereby effectively revealing information of an internal structure and different frequency levels in the signal; then, multi-modal feature extraction is carried out on each component in a time domain, a frequency domain and a time-frequency domain, a time-frequency signal obtained through S transformation can further capture transient features of a bearing fault, background noise and working condition disturbance are effectively inhibited, and key state change information is extracted from a non-stationary signal; and finally, the structure of the convolutional neural network is improved, multi-scale and multi-modal feature information is fused, an efficient and robust classification model is constructed, and rapid and accurate recognition of different types of bearing faults is realized.

The invention relates to the field of new energy automobile shaft part detection, and discloses a new energy automobile intermediate shaft full-automatic detection system and method.The system comprises an intermediate shaft mechanical arm, and a feeding station, an information collection station, a meshing detection station and a coaxiality detection station are arranged within the stroke range of the intermediate shaft mechanical arm according to procedures; a visual recognition device is arranged above the feeding station and used for recognizing and marking the spatial position where the intermediate shaft is located and the position and path where the intermediate shaft manipulator clamps the intermediate shaft, the information collection station is used for recognizing a scanner of a two-dimensional code, and the meshing detection station detects gear meshing to generate a vibration signal and analyzes whether meshing of the intermediate shaft is qualified or not; the coaxiality detection station collects the outer contour of the intermediate shaft and is used for detecting the coaxiality and the outer contour size of the intermediate shaft. All the stations are automatically executed, the device is suitable for detection of large-batch intermediate shafts in factories, and the detection precision and efficiency of the intermediate shafts can be improved.

The application discloses a periodic pulse feature extraction method based on vibration signal analysis and relates to the technical field of mechanical pulse extraction, and comprises the following steps: S1, collecting the vibration acceleration impact signal of a gear or a bearing by using an acceleration sensor and performing RMS envelope processing; S2, finding the position of each pulse center and the original signal amplitude of the corresponding position based on the envelope signal in the step S1, and then determining the pulse width through the pulse center position; the application is different from the traditional feature extraction method which calculates and statistically processes the overall vibration signal, the method focuses on the more interesting part of the signal, describes the running state information contained in the signal from the morphology, has stronger interpretability, and the extracted periodic pulse feature is a relative feature based on the signal itself, and the interference of the overall signal level is greatly weakened.

The invention relates to the technical field of engine detection, in particular to an engine knock fault detection method based on vibration signal analysis. The method comprises the following steps: acquiring and preprocessing a voltage vibration signal and a pulse square wave signal of an engine to obtain a vibration digital sequence and a pulse timestamp set; a difference value of adjacent timestamps is extracted from the pulse timestamp set as a time span, a rigid body kinetic equation is constructed in combination with a physical mechanical angle interval, a transient angular acceleration is calculated, a vibration digital sequence is mapped to a crankshaft rotation space, and an equal-angle vibration sequence is obtained; frequency domain conversion is conducted on the equal-angle vibration sequence, mechanical noise is filtered out, a knock residual signal is obtained, knock characteristic energy in the monitoring window is calculated, and when the knock characteristic energy exceeds a preset safety threshold value, a control adjusting instruction is issued. According to the method, the mechanical operation state under the transient variable load is restored, the order fuzzy defect is effectively restrained, and the bottom layer operation load and response delay are reduced.

The present application relates to liquefied petroleum gas supervision technical field, disclose a kind of based on artificial intelligence's liquefied petroleum gas safety distribution supervision method, comprising: by dynamic calculation saturated vapor pressure eliminates the temperature fluctuation interference of tank body, superimposed motion disturbance compensation correction vehicle jounce influence, and the leakage suspiciousness of tank body is analyzed and calculated, in combination with voiceprint spectrum features, infrared temperature gradient and vibration signal, analysis obtains the leakage point coordinate of tank body;By analyzing the dynamic balance relationship of tank body, verify filling compliance, in combination with leakage suspiciousness and environmental parameters, leakage risk value is calculated using model to judge leakage risk;High-risk action recognition is dynamically associated with environmental parameters, and behavior environmental risk score is obtained by analysis, and input global control system;In combination with leakage suspiciousness, behavior environmental risk score and leakage point coordinate, build phase change leakage coupling factor, synchronous control alarm threshold and decision planning, and carry out leakage diffusion simulation, carry out whole-process response.

The application relates to a weak vibration signal amplification device based on bionic vision, which is used for capturing and amplifying the vibration signals of a vibrating object and comprises a laser light source arranged on the vibrating object, a vibration signal amplification and capturing module, a vibration signal receiving module and a vibration signal analysis module, the vibration signal amplification and capturing module and the vibration signal receiving module are respectively arranged on opposite sides of the vibrating object, and the vibration signal amplification and capturing module is connected with the vibration signal analysis module. Compared with the prior art, the application has the advantages of micro-vibration measurement, low equipment cost and the like, and solves the defects that the traditional contact type vibration measurement is difficult to measure small-amplitude equipment and has high measurement operation difficulty.

The invention relates to a vibration signal analysis knowledge-driven rotary machine zero sample intelligent fault detection method and device. The method comprises the following steps: carrying out quantitative scoring on multiple time-frequency analysis methods by adopting a multi-index evaluation mechanism; based on the similarity of the same-modal time-frequency maps of continuous normal state data and the dissimilarity of different-modal time-frequency maps, a zero-fault sample loss function is obtained based on similarity comparative analysis; a high-dimensional feature extractor based on Vision Transform is constructed, and a normal state sample and a constructed contrast loss function are utilized to realize model training; and calculating the similarity of various time-frequency spectrum high-dimensional features between the to-be-detected state data sample and the normal state data sample of the rotating machine, and realizing online detection of the to-be-detected state of the rotating machine. According to the method, the dependence of a traditional data-driven fault detection method on fault samples is broken through, the rotating machinery intelligent fault detection model can be constructed by using the normal state samples, and high-precision fault detection is realized.

The invention relates to the technical field of coal mine fully-mechanized coal mining equipment, and discloses a digital twinning-based cutting drum cutting pick monitoring method, which comprises the following steps: data acquisition: acquiring three-way stress, temperature signals and vibration signals of each welding position; building a multi-channel diagnosis model, specifically comprising a vibration signal analysis model and a temperature signal analysis model, and training the models; building a virtual model, including a geometric model, a cutting pick-coal rock interaction physical model and a drum dynamics model, performing inversion and fusion on the models, and then performing model updating and application; and based on the vibration signal analysis result, the temperature analysis result and the overall stress condition of the cutting pick and the cutting roller, whether the cutting pick is abnormal or not is judged, and monitoring is achieved. According to the method, through organic combination of multi-sensor fusion, artificial intelligence diagnosis and digital twinborn technologies, the core pain points of poor real-time performance, weak scene adaptation, lack of predictive ability and the like of a traditional method are solved, and the method has extremely high engineering application value and popularization prospects.

The embodiment of the invention discloses a harmonic component suppression method in structural vibration response, relates to the field of mechanical structure vibration signal analysis, and can suppress pseudo-resonance components generated by periodic excitation force in the mechanical structure response so as to improve the reliability of structural modal parameter identification. The method comprises the following steps of: providing a harmonic component suppression method aiming at the characteristic that the vibration response of a mechanical structure containing a rotating part in a working state contains periodic response, namely calculating a vibration response logarithmic spectrum and designing a logarithmic spectrum filter window function, and eliminating or weakening the harmonic component through harmonic frequency filtering of the logarithmic spectrum. The method is particularly suitable for vibration response signal processing and modal parameter identification of the rotating mechanical structure in the working state.

This invention discloses a method, device, terminal equipment, and medium for monitoring DC bias magnetization in transformers. By analyzing the vibration signal of a target transformer, average vibration data is obtained. Based on this average vibration data and standard vibration data, vibration increment data is calculated. This increment data is then substituted into a bias magnetization calculation formula to obtain the bias magnetization coefficient. Finally, by comparing the bias magnetization coefficient with a preset bias magnetization evaluation range, the degree of DC bias magnetization in the target transformer is determined, thus achieving the monitoring of DC bias magnetization in transformers. This invention, based on the vibration signal of the target transformer, analyzes and obtains vibration increment data. Using this incremental vibration data for bias magnetization calculation and evaluation reduces errors caused by environmental factors affecting vibration amplitude, thereby improving the accuracy of DC bias magnetization monitoring in transformers.

The invention discloses a balance test bench and method based on a new energy automobile lightweight aluminum alloy pull-off block, and relates to the technical field of new energy automobile pull-off block tests. According to the balance test bench and method based on the new energy automobile lightweight aluminum alloy pull-off block, through cooperation of the bearing disc mechanism, the test driving mechanism and the plurality of balance test rack mechanisms, the new energy automobile lightweight aluminum alloy pull-off block to be tested can be placed on the balance test rack mechanisms in advance; when the first servo motor drives the balance test rack mechanism to rotate to the rearmost end, the first servo motor stops working, the test driving mechanism works to drive the pull-off block to rotate, and in the process, the balance test rack mechanism can quickly and accurately detect a vibration signal of the pull-off block in a rotating state and transmit the detected signal to the central processing unit; and the vibration signals are analyzed, so that the magnitude and the position of the unbalance amount of the pull-off blocks can be quickly determined, continuous testing of the plurality of pull-off blocks is realized, and the balance testing efficiency is improved.

The application provides a fan tower prestressed anchor bolt loosening alarm monitoring method and device, comprising: obtaining vibration signals of the fan tower under different wind speed sections through a vibration sensor and a vibration spectrum analysis system; analyzing the vibration signals to establish a vibration low-frequency component database; comparing the integral of the low-frequency component in the vibration low-frequency component database with a set standard, and when the integral of the low-frequency component exceeds the set standard, an alarm is given; if the alarm is detected, a tracking analysis and offline monitoring flow is started to determine a first loosening position; the bolts at the first loosening position are photographed to obtain bolt detection images with preset markers; the preset markers are used to indicate whether the bolts are loosened; it is judged whether the preset markers in the bolt detection images are displaced, and if yes, it is determined that the bolts corresponding to the displaced preset markers are loosened, and the specific positions of the loosened bolts are determined; and the loosened bolts are found in time, and safety risks are reduced.

The invention relates to a vibration signal analysis method and device, an upper computer and a computer storage medium, and belongs to the technical field of optical fiber sensing, and the vibration signal analysis method comprises the steps: obtaining an optical fiber vibration sensing signal of a moving object, and calculating an envelope curve of the optical fiber vibration sensing signal, carrying out smoothing processing on the envelope curve by adopting a preset smoothing algorithm to obtain a smooth envelope curve; an extreme point of the smooth envelope curve is determined, a peak reserved value of the extreme point is determined based on a preset sliding window, and the peak reserved value is used for representing characteristics of the optical fiber vibration sensing signal within a preset range of the extreme point; and arranging the peak reserved values according to the time and space sequence of the optical fiber vibration sensing signals corresponding to the peak reserved values, generating spatio-temporal data of the optical fiber vibration sensing signals, and determining the spatio-temporal position of the moving object based on the spatio-temporal data. According to the invention, the space-time position of the moving object can be accurately determined in real time.

The application relates to a vibration signal analysis knowledge-driven rotating machinery zero-sample intelligent fault detection method and device, which comprises the following steps: adopting a multi-index evaluation mechanism to quantitatively score a plurality of time-frequency analysis methods; based on the similarity of continuous normal state data and the same mode time-frequency spectrum and the dissimilarity of different mode time-frequency spectra, a zero fault sample loss function based on similarity comparative analysis; constructing a high-dimensional feature extractor based on a Vision Transformer, and realizing model training by using normal state samples and the constructed contrast loss function; calculating the similarity of a plurality of time-frequency spectrum high-dimensional features between rotating machinery to-be-detected state data samples and normal state data samples, and realizing online detection of the rotating machinery to-be-detected state. The application breaks through the dependence of a traditional data-driven fault detection method on fault samples, can construct a rotating machinery intelligent fault detection model by using normal state samples, and realizes high-precision detection of faults.

The application discloses a closed-loop electric stimulation control system based on multi-modal vibration and model prediction, comprising: initializing the system; obtaining personalized parameters and stimulation parameters and determining a basic electric stimulation intensity in combination with a preset function; collecting high-frequency vibration signals and low-frequency vibration signals in real time, performing signal preprocessing, obtaining piezoelectric film signals and MEMS accelerometer signals and analyzing to determine corresponding muscle movement intensity characteristics; weighting and fusing the muscle movement intensity characteristics to obtain a basic muscle movement intensity; predicting and solving the basic muscle movement intensity to obtain a predicted muscle movement intensity; determining an optimal electric stimulation intensity based on the predicted muscle movement intensity, the basic electric stimulation intensity and the basic muscle movement intensity; applying stimulation to a preset position and repeatedly updating the optimal electric stimulation intensity until the system ends running. The application dynamically optimizes the electric stimulation intensity through multi-modal vibration signal analysis and model prediction, and realizes personalized closed-loop electric stimulation control.

The invention relates to the field of vibration test experimental equipment, and discloses an intelligent vibration frequency test system for constructing a coal assembly, when a cam is driven to rotate around a central shaft, the cam drives a driving lifting slide block to move up and down through a transmission connecting rod, and meanwhile, the driving lifting slide block flexibly drives a driven lifting slide block to move up and down through a spring; the force hammer synchronously moves along with the driven lifting sliding block so as to knock the tectonic coal combination body on the coal sample placing frame, and vibration signals generated by the tectonic coal combination body are collected by the vibration signal analyzer through the acceleration sensor. According to the test system, a vibration frequency-acoustic emission-mechanical loading multi-parameter collaborative framework is constructed; multi-signal synchronous acquisition is performed: an acceleration sensor acquires vibration signals, and the vibration signals can be synchronously accessed to an acoustic emission sensor and a pressure sensor, so that millisecond-level synchronous recording of vibration frequency, the number of acoustic emission events and coal body stress-strain data is realized; and the fracture expansion time sequence is completely restored.

The application provides a power distribution automation equipment supervision acquisition system, which comprises the following steps: time synchronization and space registration are realized by adopting a space-time alignment algorithm on infrared signals, vibration signals and electrical signals; thermal imaging analysis is performed on the infrared signals after synchronization and registration, an overheated area is automatically framed and selected, and a temperature gradient is calculated; vibration signal analysis is performed on the vibration signals after synchronization and registration, vibration entropy is calculated; partial discharge analysis is performed on the electrical signals, and the number of discharge pulses is obtained; the temperature gradient, the vibration entropy and the number of discharge pulses are weighted and fused, the fused features are input into an LSTM model for time sequence correlation analysis, the fault type and the fault type probability are obtained, and the 750KV substation is warned according to the fault type probability. The application provides an automation equipment supervision acquisition system for the 750KV substation, and has the advantages of low missed detection rate and high accuracy.

This invention provides a method for visualizing the synthesized spectrum of vibration signals, comprising the following steps: First, the acceleration signal is integrated twice to obtain the velocity and displacement signals respectively; Fast Fourier Transform (FFT) is performed on the acceleration, velocity, and displacement signals to obtain the spectra of the three signals. Based on the spectra of the acceleration, velocity, and displacement signals, the cutoff frequency range of the synthesized spectrum is determined using the Akaike Information Criterion (AIC). The amplitudes of the three spectra are appropriately scaled according to the cutoff frequency range, finally yielding a synthesized spectrum containing information from the acceleration, velocity, and displacement signals. This invention overcomes the problem that analyzing a single high-frequency acceleration signal and low-frequency velocity and displacement signals makes it difficult to comprehensively capture spectral information when analyzing vibration signals. According to practical needs, the synthesized spectrum visualization provides a more comprehensive display of the spectral information of vibration signals, making vibration signal analysis more convenient and efficient.

The application relates to the technical field of circuit breaker fault diagnosis, and discloses a circuit breaker internal defect online detection method based on vibration signal analysis. According to a standard sequence of circuit breaker vibration acceleration, opening and closing coil current and time data, and in combination with a vibration characteristic analysis platform, time domain, frequency domain and time-frequency domain characteristic data of a circuit breaker operating parameter are classified and accurately extracted, so that the mechanical operating state of a circuit breaker internal structure is deeply mined in multiple dimensions based on time domain, frequency domain and time-frequency domain of circuit breaker vibration signals. According to circuit breaker vibration time domain data, circuit breaker vibration frequency domain data and circuit breaker vibration time-frequency domain data, and in combination with an artificial intelligence algorithm and different structure fault standard vibration signal characteristic data of a circuit breaker established based on big data, comprehensive intelligent diagnosis of a circuit breaker internal structure fault is carried out, so that intelligent and accurate diagnosis of a circuit breaker internal structure fault based on circuit breaker vibration signal characteristics is realized.

The invention provides an elevator abnormal state on-line monitoring method and system based on vibration signal analysis, and the method comprises the steps: introducing a deep residual shrinkage network DRSN-CW, and replacing a convolutional layer in the DRSN-CW network with a deep separable convolutional layer, thereby obtaining a DS-DRSN-CW network, and carrying out the online monitoring of the abnormal state of an elevator through the DS-DRSN-CW network. According to the network, the model calculation complexity is effectively reduced, and meanwhile, high-precision extraction and recognition of fault features are considered. On the system level, a distributed framework based on edge computing is constructed, and real-time collection and wireless transmission of original vibration signals of the elevator are achieved through sensor nodes and a communication module; at the same time, the MCU unit embedded in the DS-DRSN-CW network is used to complete fault identification, and only key data is uploaded to the cloud. Through synchronous optimization of the method and the system, real-time sensing of elevator fault signals is achieved, the accuracy is guaranteed, meanwhile, a lightweight reliable system is constructed, and an efficient solution is provided for intelligent operation and maintenance of the elevator.

The application provides a reservoir inherent frequency downhole in-situ measurement method based on neural network filtering, which comprises the following steps: (1) generating a pulse shock wave to induce reservoir rock vibration through a continuous high-voltage pulse discharge device; (2) obtaining self-vibration noise signal data of the continuous high-voltage pulse discharge device at the wellhead when the pulse discharge is performed; (3) obtaining background noise signal and feedback vibration signal data of the continuous high-voltage pulse discharge device in a static state in the wellbore; (4) filtering and denoising the high-noise background data; (5) performing vibration signal analysis; (6) obtaining a Hilbert marginal spectrum and determining the correlation coefficient of each eigenmode and the original vibration signal; and (7) determining the target reservoir inherent frequency. The application can directly measure the target reservoir inherent frequency downhole, effectively solves the problem of difficulty in downhole inherent frequency measurement or inaccuracy in laboratory inherent frequency test, and realizes efficient in-situ acquisition of the reservoir inherent frequency downhole.