34 results about "Vibration signal processing" patented technology

The invention belongs to the technical field of engineering machinery intelligence and construction quality detection, and provides an intelligent compaction degree detection system and method integrated on a road roller. The detection system comprises a multi-mode sensing unit which comprises a vibration sensing module, a machine vision module, an inertia measurement positioning module and a flatness detection module; the detection method comprises the following steps: S1, synchronously collecting data; s2, vibration signal processing and compaction index calculation; s3, image analysis and apparent feature extraction; s4, a multi-source data fusion and machine learning model; s5, positioning and drawing; in conclusion, the invention provides the intelligent compaction degree detection system and method integrated on the road roller. The method is reasonable in design and simple to operate, can realize real-time, comprehensive, lossless and high-precision detection of the compaction degree, and can evaluate the construction apparent quality at the same time, so that the method is suitable for industrial popularization.

A stylus includes a vibration device, a driving circuit, a detection circuit, and a processor. The vibration device generates vibrations. The driving circuit generates a driving signal that drives the vibration device. The detection circuit detects an actual vibration of the vibration device and generates a vibration signal corresponding to the actual vibration. The processor compares the driving signal with the vibration signal, and performs feedback control on the driving signal based on a result of comparing the driving signal with the vibration signal. The processor performs the feedback control such that a phase difference between the driving signal and the actual vibration becomes substantially π / 2.

The application discloses a kind of fracturing pump multi-cylinder vibration signal processing method, device, equipment, medium and product, by obtaining vibration data sequence and key phase pulse signal, vibration data sequence is the vibration data collected by vibration sensor installed between adjacent cylinder body of fracturing pump, key phase pulse signal is the key phase pulse signal collected by phase sensor installed in crankshaft part of fracturing pump, then the correlation of vibration data sequence is eliminated by decorrelating vibration data sequence, the vibration data sequence after decorrelation is analyzed independently, output multiple vibration data sequences to be matched, finally based on key phase pulse signal, multiple vibration data sequences to be matched are matched with adjacent cylinder, the target vibration data sequence corresponding to each cylinder is obtained, can significantly reduce the amount of sensor, reduce cost and complexity while extracting the vibration data with stronger feature information, to effectively improve the accuracy and timeliness of fracturing pump fault diagnosis.

The application belongs to the field of big data learning models and provides a rotating machinery characteristic frequency extraction method under strong interference, which is based on the optimal balance parameter alpha in VMD decomposition roughly positioned according to the set reference modal correlation 最优 The balance parameter alpha of each decomposition is locally optimized by combining the sparrow search optimization algorithm. The modulus K of decomposition is adaptively determined by the iteration decomposition times, and the improved recursive VMD method avoids the influence of the inaccurate preset decomposition number and the balance parameter on the decomposition effect. The application is applied to the constructed simulation signal to achieve the optimal decomposition effect that can be achieved by the VMD method, and is applied to the pump cavitation flow-induced vibration signal processing to successfully achieve the effective extraction of the fluid machinery flow-induced vibration characteristic frequency under the condition of low signal-to-noise ratio.

The application discloses a vortex flowmeter anti-vibration signal processing method, and relates to the technical field of vortex flowmeter signal processing. The original voltage signal of the vortex flowmeter is collected, and after frame division, pre-emphasis and windowing preprocessing, time-frequency characteristic parameters reflecting the signal state are extracted and an observation sequence is formed. The sequence is input into a probability model trained by samples, and the probability distribution belonging to each predefined working condition is obtained. Finally, the most possible working condition is determined through state inference, the accurate identification of the working condition of the vortex flowmeter is realized, and the vibration interference mark is marked. The application realizes dynamic identification and tracking of the working condition by constructing a hidden Markov model, breaks through the limitation of traditional static filtering, adopts a joint time-frequency characteristic fusion method, constructs an accurate signal fingerprint from multiple dimensions, significantly improves the identification sensitivity, utilizes the time sequence characteristics of the model, decodes the state sequence, and makes the identification result more in line with the physical process law, so that the anti-interference ability and reliability are greatly improved.

The invention discloses a heading machine inertial navigation positioning measurement method, and relates to the field of image identification and positioning measurement, and the method comprises the following steps: asymmetrically arranging permanent magnet beacons along a tunnel according to a preset interval, measuring and recording accurate coordinates and magnetic characteristic parameters of each beacon, and constructing an underground magnetic fingerprint map; motion parameters, geomagnetic field data and vibration signals of the heading machine are collected through an inertial measurement unit, a magnetometer and a vibration sensor; processing the geomagnetic field data, identifying a preset magnetic beacon by detecting the sudden change of the geomagnetic field intensity, and obtaining an absolute position reference point; analyzing the spectrum characteristics of the vibration signals, and estimating the slip rate of the cutterhead according to the mapping relation between the characteristic frequency and the rock stratum hardness; and carrying out data fusion on the absolute position reference point, the slip rate observation value and an inertial navigation calculation result. The method is used for solving the technical problem that in the prior art, displacement measurement is not accurate due to slipping when a heading machine cutterhead conducts heading in a complex rock stratum.

The present application relates to the technical field of fault detection, in particular to a fault detection system for a smart wharf and a building method, the fault detection system comprises: a data acquisition module for acquiring vibration signals of port equipment; a processing module for signal processing of the vibration signals, judging whether the vibration signals after signal processing are fault data; an extraction module for extracting fault features of the fault data according to a deep learning method, judging the fault type of the port equipment according to the fault features; a maintenance module for maintaining the port equipment according to the fault type, and changing the fault data after maintenance into normal data. The present application solves the problem of not timely discovering the fault of the port equipment in the prior art, which affects the safety of the equipment and the person or causes an accident.

The utility model discloses a vibration transmitter, belongs to the technical field of instruments and meters, and particularly relates to a two-wire system vibration transmitter based on an HART protocol, which comprises a magnetoelectric vibration sensor, a vibration signal processing module, a master control CPU, a current output module and a communication module. The magnetoelectric vibration sensor collects signals, the output end of the magnetoelectric vibration sensor is connected with the input end of the vibration signal processing module, the main control CPU is connected with the output end of the vibration signal processing module, the current output module and the communication module are connected with the main control CPU, and the current output module is connected with the communication module. According to the utility model, the field programmability of the measuring range is realized through the HART protocol, a user can freely adjust the measuring range according to actual requirements, the requirement of frequently replacing a transmitter due to mismatching of the measuring range is avoided, the long-term operation cost is reduced, the operation process of measuring range adjustment is simplified, and the working efficiency is improved.

This application discloses a fault identification method, apparatus, computer equipment, and rotating mechanism for a rotating mechanism. The method includes: acquiring the rotational speed information and time-domain vibration signal of the rotating mechanism; converting the time-domain vibration signal into an angular-domain vibration signal based on the rotational speed information; extracting features from the angular-domain vibration signal based on the autocorrelation matrix and filtering coefficients to update the filtering coefficients and obtain target filtering coefficients; and determining the fault characteristics of the rotating mechanism based on the target vibration signal obtained by processing the angular-domain vibration signal using the target filtering coefficients. The method provided in this application can effectively extract fault characteristics from the unsteady-state vibration signal of a rotating mechanism, thereby achieving reliable fault diagnosis of the rotating mechanism.

The invention relates to the field of rotary mechanical equipment vibration signal processing and rotor fault diagnosis, and provides a rotor rub-impact identification method and system based on cyclic spectrum eigen orthogonal decomposition, and the method comprises the steps: continuously collecting vibration signals of a continuously operating gas turbine and a combined cycle unit support bearing at equal intervals and equal length, and carrying out the preprocessing; a cyclic spectrum correlation mapping graph of the preprocessed vibration signals of the equal-length supporting bearing is obtained, a two-dimensional cyclic spectrum correlation mapping graph is obtained through amplitude normalization, and discrete estimation is carried out; constructing a snapshot matrix and performing intrinsic orthogonal decomposition; and determining the number of reconstructed modes according to the change condition of the modal energy ratio of each order, reconstructing an intrinsic orthogonal decomposition result, carrying out full-band integration along the spectral frequency direction to obtain a fault mode demodulation spectrum, and analyzing the correlation between the fault mode demodulation spectrum and the two-dimensional cyclic spectrum coherence mapping graph to determine the fault occurrence time. According to the method, the fault mode can be accurately extracted, the fault occurrence time can be identified, and the industrial field requirement of unit vibration monitoring in a continuous monitoring scene is met.

The invention relates to the technical field of rotary mechanical vibration signal processing and fault diagnosis, and discloses a coal mine underground mechanical excitation identification method based on self-adaptive cepstrum exponential window filtering, which comprises the following steps: S1, preprocessing an original vibration signal; s2, constructing a band-pass filter to filter the preprocessed vibration signal; s3, performing equal-length segmentation on the vibration signal after band-pass filtering, and calculating a linear kurtosis value of each sub-segment; s4, obtaining an angle domain stable vibration signal; s5, adaptively determining a characteristic cepstrum boundary according to the cepstrum energy distribution; and S6, performing inversion on the cepstrum signal after cepstrum filtering to obtain a time domain forced excitation signal. The index window damping factor is automatically set by referring to the analytic relationship between the cepstrum delay and the suppression ratio, the limitation that a traditional cepstrum index window depends on empirical parameter estimation is overcome, cepstrum editing can adapt to different structures and working conditions, and the stability and effectiveness of system transfer characteristic suppression are guaranteed.

The embodiment of the invention discloses a hydroelectric generating set vibration signal processing method and a related device. The method comprises the steps of collecting a vibration signal of a hydroelectric generating set; taking the minimum information entropy as an optimization target function, and setting an optimization range of working parameters of a target decomposition algorithm according to the target function; according to a chaotic mapping mechanism, a sine and cosine optimization algorithm, a random walk perturbation algorithm and a t distribution variation strategy, an artificial chimpanzee army algorithm is improved; inputting the working parameters into an improved artificial chimpanzee army algorithm for iterative optimization, and outputting to obtain target working parameters; and inputting the target working parameter into a target decomposition algorithm, and performing modal decomposition of the vibration signal. Therefore, the decomposition precision of simulation signal analysis is improved, the noise reduction performance is optimized, noise suppression and effective information retention are realized, and a basis is provided for intelligent vibration monitoring and diagnosis of the hydroelectric generating set.

The embodiment of the application discloses a rotating equipment rotating speed estimation method based on hill peak identification and correlation analysis, belongs to the field of mechanical vibration signal processing, and comprises the following steps: collecting a vibration signal, using a Fourier transform method to calculate the frequency spectrum of the vibration signal, automatically identifying whether a hill peak exists in the frequency spectrum through a hill peak identification method, using a correlation analysis method to calculate the autocorrelation coefficient of an envelope signal or the vibration signal, using a smoothing filter to fit the autocorrelation coefficient to obtain a new autocorrelation coefficient, taking the two maximum value points in the new autocorrelation coefficient, obtaining two index position points corresponding to the two maximum value points, calculating the position difference of the two index position points, calculating the fundamental frequency according to the position difference, and calculating the rotating speed according to the fundamental frequency. The embodiment of the application can realize the rotating equipment rotating speed estimation based on the correlation analysis through the automatic identification of the modulation signal in the vibration signal waveform spectrum.

The invention discloses a vibration signal processing method, system and equipment of a tunnel boring machine. The vibration signal processing method of the tunnel boring machine comprises the following steps: obtaining tunneling parameters of the tunnel boring machine; judging whether the tunnel boring machine is in a tunneling state or not according to the tunneling parameters; if yes, original vibration signals of the tunnel boring machine in the tunneling process are collected; denoising the original vibration signal to obtain a target vibration signal; and identifying the surrounding rock category according to the target vibration signal. According to the embodiment of the invention, the vibration signal redundancy can be effectively reduced, the acquisition efficiency can be improved, the definition and the feature extraction effect of the vibration signal can be improved, and in addition, the vibration signal is utilized to perform real-time surrounding rock category identification, so that accurate classification of different surrounding rock categories can be realized.

The application discloses an aero turboshaft engine fault diagnosis system and relates to the technical field of fault diagnosis. A collection module acquires horizontal, vertical and axial vibration signals; a processing module generates real-time characteristic vectors by introducing a dynamic short-time Fourier transform of a non-constant-width Gaussian window, a variational mode decomposition and a singular value decomposition; an incremental diagnosis module calculates the matching degree between real-time characteristic vectors or predicted characteristic vectors and known classes by a dynamic expansion network, determines whether to belong to the known classes or trigger an expansion mechanism according to an adaptive resonance theory, constructs a joint loss by using a Bayesian learning framework after expansion, and performs Gaussian process modeling, determines the loss by Bayesian optimization and updates network parameters; a prediction module generates predicted characteristic vectors when there is no fault; and a maintenance module sends or stores maintenance schemes as required, so that adaptive identification of the known classes and new fault classes is realized, the state can be predicted in advance, the operation safety of the engine is effectively ensured, and the maintenance cost is reduced.

The invention belongs to the technical field of mechanical equipment fault diagnosis, and particularly relates to a DNN bearing fault diagnosis method based on multi-modal feature fusion and alpha evolutionary algorithm joint optimization, which comprises the following steps: firstly, processing vibration signals in different states into three different images of a two-dimensional time domain grey-scale map, a two-dimensional frequency domain spectrogram and a two-dimensional time frequency map; respectively extracting a time domain feature, a frequency domain feature and a time frequency feature by using the strong feature extraction capability of the CNN; then, the extracted three features are used for generating feature representations with better distinction by utilizing the powerful feature fusion capability of KPCA, and parameters and structures of DNN are optimized by adopting an alpha evolutionary algorithm; and finally, training the DNN model by using the fault feature set, and carrying out fault diagnosis on the bearing by using the trained model. On the basis, the influence of noise in the vibration signal on the fault diagnosis accuracy is effectively reduced, and the bearing fault diagnosis accuracy can be remarkably improved.

The present application relates to physiological micro-vibration signal processing technical field, specifically disclose physiological micro-vibration signal multi-dimensional time sequence feature extraction and reconstruction method and system, the method includes preprocessing physiological micro-vibration original mixed signal, obtain pretreatment signal;Based on the improved adaptive variational mode decomposition algorithm and blind source separation algorithm to the pretreatment signal processing, obtain time-frequency domain feature vector;The method of combining bidirectional long short term memory network and encoder is used to the pretreatment signal deep time sequence feature extraction and dimension reduction, obtain dimension reduction feature vector;Time-frequency domain feature vector and dimension reduction feature vector are fused, effective fusion feature vector is screened based on correlation threshold value, physiological micro-vibration signal is reconstructed based on the effective fusion feature vector screened out. Through the improved VMD-BSS algorithm, the adaptive determination of the number of modes and the penalty factor is realized, the dependence on prior knowledge is reduced, and the processing speed of signal decomposition is significantly improved.

The invention provides an active noise reduction system and method for an aircraft and a storage medium, and relates to the technical field of aviation noise reduction, and the method comprises the steps that a signal collection module is used for collecting an engine rotating speed signal of a target aircraft, a primary noise signal in a cockpit and a cabin body vibration signal of the cockpit; the ANC processing module is used for generating a secondary sound wave signal through the engine rotating speed signal, the primary noise signal, the cabin vibration signal and the filtering weight coefficient; the power amplification module is used for performing power amplification on the secondary sound wave signal to obtain a target driving signal; and the headrest type loudspeaker group or the head top type loudspeaker group is used for outputting offset sound waves under the driving of the target driving signal, so that the offset sound waves and the primary noise signal are subjected to destructive interference. The technical problem that the noise reduction effect of the aircraft cockpit is poor in the prior art is solved, and the technical effect of improving the noise reduction effect of the aircraft cockpit is achieved.

An inspiration system related symptom sensing system includes a patch and a processor. The patch includes at least one vibrator and a plurality of receivers. Each of the receivers is configured to generate a reference vibration signal corresponding to the vibrator and a vibration signal corresponding to an inspiration system of an organism. The processor is configured to determine a relative position of each of the receivers related to the inspiration system of the organism based on the reference vibration signals, the vibration signals, and a plurality of positional relationships of the vibrator corresponding to each of the receivers. The processor is configured to generate a spatial position corresponding to the inspiration system and a corresponding symptom type by using a classification model based on the abnormal signals and the relative positions corresponding to the receivers.

The application belongs to the field of aero-engine bearings, and provides a method and system for predicting the remaining useful life of an aero-engine bearing. The method comprises: processing original vibration signals collected during the operation of the engine bearing by a Pearson correlation degree analysis method, and extracting features to obtain a plurality of feature sequences commonly used for bearing RUL prediction statistics; performing smoothing processing, cumulative transformation, and monotonicity and trend value calculation on the statistical feature sequences to obtain screened cumulative feature sequences; inputting the screened cumulative feature sequences into an attention full convolution network (AFCN) model based on physical information to predict a life ratio, and calculating a remaining useful life prediction value through the life ratio. The application improves the key feature capturing capability and prediction accuracy in the long-term degradation process by fusing the bearing physical degradation mechanism and the attention mechanism full convolution network, and provides technical support for the safe operation and maintenance of aero-engines.

This invention discloses a Doppler vibration signal processing method and system for complex environments, comprising: a Doppler sensing unit, an auxiliary sensing module, a data acquisition and preprocessing module, an adaptive time-frequency domain signal decomposition and feature extraction module, an interference pattern recognition and suppression module, a target signal demodulation and parameter estimation module, a spurious frequency shift discrimination and correction module, and a system control and data output module. This invention relates to the field of building surveying technology and can solve the fundamental contradiction between weak signal detection, accurate demodulation, and effective interference suppression in existing technologies under low signal-to-noise ratio, multiple interference sources, and non-stationary backgrounds.

The application provides a solid material internal friction measuring device and a measuring method. The device comprises a vacuum cavity, a vacuum unit, a sample fixing tool, a vibration excitation assembly, a vibration detection assembly and a vibration signal processing and analysis assembly. The device is used to propose a solid material internal friction measuring method. The method can measure the internal friction of metal and non-metal solid materials at a specific vibration frequency, and has the advantages of high sensitivity and high measuring precision.

This invention belongs to the field of signal processing technology and provides a method and system for processing unbalanced vibration signals of an ultra-precision lathe spindle. The method includes: synchronously acquiring the X-axis grating follower error signal and the spindle encoder pulse signal using an ultra-precision lathe X-axis grating ruler and a spindle encoder; extracting the weak unbalance signal using a quadrature lock-in amplifier; synchronizing the quadrature reference signal with the real-time spindle angle; mixing the quadrature reference signal with the X-axis grating follower error signal; filtering the signal using a zero-phase-shift filter to obtain the DC component; and calculating the amplitude and phase of the unbalance signal; processing the amplitude and phase of the unbalance signal using the influence coefficient method to obtain the anchoring quality and angle for leveling the spindle dynamic balance. This invention can conveniently monitor changes in spindle dynamic balance, reduce the cost of spindle dynamic balance adjustment, improve the efficiency of spindle dynamic balance adjustment, and accurately calculate the phase information of the spindle unbalance.

The application discloses a kind of based on adaptive enhanced EEMD's mixed-flow water turbine vibration signal processing method, it is related to vibration signal analysis and processing technical field.Mixed-flow water turbine vibration signal is collected, and wavelet denoising is carried out;Adaptive band weighted noise is injected to the signal after denoising, and the signal after adding noise is obtained;The signal after adding noise is decomposed by EMD, and the stop condition is modified to double-constraint mode stop criterion in the EMD decomposition process, using the double constraint of extreme point quantity and residual energy change rate, obtain IMF component set and residual;IMF is effectively screened based on fault sensitivity index IMF component, and compensation term is introduced in residual, to obtain the final decomposition result, as vibration signal processing result.Through band weighted noise injection, background noise is effectively suppressed, and through double-constraint stop criterion and fault sensitivity screening, modal aliasing is effectively suppressed, so that fault characteristics can be more accurately separated and signal-to-noise ratio is improved.

This invention provides a joint noise reduction method and device for vibration signals based on an improved wavelet threshold, belonging to the field of vibration signal processing technology. This invention improves the accuracy and adaptability of signal decomposition by adaptively optimizing the number of modes and penalty factor in variational mode decomposition using a Cordyceps sinensis optimization algorithm. Then, it objectively filters the intrinsic mode function components using the Pearson correlation coefficient, effectively identifying and separating effective components strongly correlated with the original vibration signal, reducing the false rejection of useful information. Finally, it applies an improved wavelet threshold function based on adaptive adjustment of subband signal-to-noise ratio to the filtered effective components for noise reduction, achieving dynamic adjustment of noise reduction intensity within different frequency bands. This effectively suppresses noise while better preserving the detailed features and transient impact components of the signal. This invention solves the noise residue problem of traditional wavelet threshold methods and improves the signal-to-noise ratio and feature fidelity of vibration signals in strong noise backgrounds.

The invention discloses a robot diagnosis system and method fusing audio and video and vibration signals. The system comprises a mobile robot platform with a built-in laser radar SLAM autonomous navigation and positioning module, and a multi-sensor assembly. The edge calculation unit is internally provided with a multi-modal fusion diagnosis model and is in communication connection with the multi-sensor assembly, and the multi-modal fusion diagnosis model comprises a vibration signal processing branch, a sound signal processing branch, an image signal processing branch and a cross-modal attention fusion module; and the cross-modal attention fusion module performs deep fusion on feature vectors output by the vibration signal processing branch, the sound signal processing branch and the image signal processing branch, and outputs a fault diagnosis result in combination with consistency verification. The structure is simple, the fault recognition accuracy is improved, and the recognition rate of hidden faults such as bearing early fatigue and rotor bar microcracks is greatly improved compared with an existing scheme; the fault positioning precision is not more than 5cm, and the reliability is obviously improved.

The invention belongs to the field of aero-engine testing and fault diagnosis, and provides a vibration signal processing device and method based on an engine airborne sensor, and the device comprises a vibration sensing unit which is disposed on an engine and is used for collecting an original vibration signal; the original signal transmission channel is used for transmitting the signal to an engine data acquisition system for conventional monitoring; the signal shunting interface assembly is detachably connected between the sensing unit and the transmission channel in series, and the original vibration signals are shunted and conducted to the two output channels in a lossless mode on the premise that an original signal channel is not damaged; and the portable analysis unit is connected with one of the output channels and is used for carrying out real-time acquisition, digital processing and multi-dimensional analysis on the signals. On the premise that an original airborne monitoring system is not affected, refined fault diagnosis in the test run process can be achieved, and high safety, repeatability and engineering practicability are achieved.

The invention discloses a wearable multi-mode tactile rendering device, system and method, and belongs to the field of tactile feedback, and the device processes a to-be-rendered multi-channel multi-mode tactile signal through a main controller, converts the to-be-rendered multi-channel multi-mode tactile signal into a control instruction for driving a vibration feedback module and a force feedback module, and outputs the control instruction to the vibration feedback module and the force feedback module. The main controller has the functions of data analysis, time sequence management, synchronous scheduling and the like at the same time, and a multi-channel multi-mode tactile signal to be rendered can be quickly mapped into tactile output in the wearable glove through the main controller, so that real-time interaction of a virtual object or a remote operation environment is realized; a nonlinear vibration signal processing method is adopted by the main controller, and the problem of perception distortion caused by linear mapping of vibration signals is solved; a unified scheduling mechanism is adopted, a time synchronization constraint mark is introduced, the consistency of vibration feedback and force feedback in a time domain is ensured, and the problem of time sequence mismatch caused by multi-mode independent control is solved.

The application discloses a cable-stayed bridge cable force online monitoring and early warning system based on vibration frequency analysis and relates to the technical field of vibration measurement and signal processing; the system comprises a distributed vibration sensing module which collects vibration monitoring data and environmental monitoring data; an adaptive vibration signal processing module divides effective frequency bands and interference frequency bands through coherence analysis, generates purified vibration signals in combination with temperature compensation after filtering out noise; an inversion module performs frequency domain analysis on the purified signals to extract multiple-order inherent vibration frequencies and performs dynamic correction in combination with environmental data; a hierarchical early warning module generates corresponding grade early warning information through hierarchical comparison; the application realizes high-precision collection and purification of cable vibration signals, robust extraction and environmental adaptability correction of multiple-order inherent vibration frequencies under complex environments, has strong anti-interference capability and high feature accuracy, and can provide reliable high-quality vibration feature data support for cable vibration modal analysis and refined vibration monitoring.