117 results about "Vibration signal processing" patented technology

The invention provides an earth surface anomaly monitoring data fusion system based on vibration signal processing, and relates to the technical field of earthquake precursor monitoring, and the system comprises a data collection module which is used for carrying out the real-time monitoring of the earth surface through a distributed multi-parameter sensor network, and obtaining the time sequence monitoring data related to the earthquake precursor; the signal processing module is used for processing the time sequence monitoring data by adopting a two-parameter joint optimization algorithm and outputting a separation signal component containing precursor information; the data fusion and state inversion module is used for carrying out fusion processing on the separated signal components by adopting a dual-time scale analysis framework and outputting a multi-scale precursor state estimation result and an uncertainty quantitative index; and the monitoring and early warning module is used for outputting earthquake precursor grading early warning information through a three-layer anomaly recognition architecture and probabilistic risk assessment. According to the invention, high-precision separation and identification of earthquake precursor related signals can be realized, and the false alarm phenomenon is reduced through an intelligent early warning mechanism.

The invention belongs to the field of aero-engine bearings, and provides a method and system for predicting the remaining service life of an aero-engine bearing, and the method comprises the steps: carrying out the processing of an original vibration signal collected in the operation process of the engine bearing through a Pearson correlation analysis method, carrying out feature extraction to obtain a plurality of feature sequences of common bearing RUL prediction statistics; smooth processing and cumulative transformation are carried out on the statistical feature sequence, monotonicity and tendency values are calculated, screening is carried out, and a screened cumulative feature sequence is obtained; and inputting the screened accumulated feature sequence into an attention full convolutional network (AFCN) model based on physical information to predict a life ratio, and calculating a residual service life prediction value through the life ratio. According to the method, through the full convolutional network fusing the bearing physical degradation mechanism and the attention mechanism, the key feature capturing capability and prediction precision in the long-time-sequence degradation process are improved, and technical support is provided for safe operation and maintenance of an aero-engine.

The invention relates to the technical field of gas extraction drilling machine vibration signal processing, in particular to a gas extraction drilling machine vibration signal self-adaptive noise reduction method based on multi-scale feature fusion. The method comprises the steps of collecting a vibration signal, converting the vibration signal into a two-dimensional waveform image, recognizing a drilling working condition area, constructing a multi-scale objective function to optimize VMD parameters, decomposing the signal and screening a dominant IMF component for reconstruction. Through multi-objective optimization and multi-feature fusion strategies of energy distribution and transient impact characteristics, the problems that in a traditional method, the noise reduction effect is poor, and the coal rock character recognition precision is insufficient are solved, the waveform similarity, the power spectrum density coincidence degree and the correlation coefficient are remarkably improved, and reliable guarantee is provided for coal mine safety production.

The invention discloses a distributed sound wave sensing monitoring device and method for sea wave impact force. The device comprises a vibration sensing optical fiber, an anchoring unit, a distributed optical fiber sound wave sensing demodulator, a vibration signal processing unit and a sea wave impact force inversion unit. The method comprises the following steps: fixing the vibration sensing optical fiber on a coast impacted by sea waves through the anchoring unit; the distributed optical fiber sound wave sensing demodulator transmits a monitored sea wave vibration signal to the vibration signal processing unit for preprocessing; the vibration signal processing unit transmits the processed optical fiber vibration amplitude signal to the sea wave impact force inversion unit; obtaining an optical fiber vibration amplitude signal and sea wave impact force training data set through a calibration experiment, and constructing an attention convolutional neural network model for training; and the sea wave impact force inversion unit establishes a corresponding relation between the optical fiber vibration amplitude signal and the sea wave impact force by using an attention convolutional neural network model to obtain the real impact force of the coast sea wave. According to the invention, accurate monitoring of the coastal wave impact force is realized.

The invention relates to the technical field of transformer energy efficiency optimization, and discloses a dynamic energy efficiency optimization method and system for a transformer under multiple working conditions, and the method comprises the steps: obtaining a historical vibration signal of the transformer, carrying out the feature extraction and nonlinear analysis, and obtaining a frequency spectrum feature and a nonlinear feature; according to the spectrum features and the nonlinear features, a finite element model based on core lamination gap changes is established; acquiring a real-time vibration signal of the transformer, inputting the real-time vibration signal into the finite element model, calculating gap data of the iron core laminations, and calculating an energy efficiency influence coefficient of gap change on the energy efficiency of the transformer according to the gap data; and according to the energy efficiency influence coefficient, a self-adaptive compensation mechanism is adopted to adjust the operation parameters of the transformer. According to the method, vibration signal processing, nonlinear dynamics and energy efficiency optimization are deeply combined, the evaluation precision and optimization effect of the energy efficiency of the transformer are effectively improved, an efficient solution is provided for operation and maintenance of the transformer, and the operation efficiency and reliability of the transformer are improved.

The invention discloses a time-frequency analysis method for impact signal positioning based on transient scale extraction transformation, and belongs to the technical field of mechanical vibration signal processing. The method comprises the following steps: collecting a rotating machine fault vibration signal, and reconstructing a signal model through Hilbert transform and a Dirac function; a transition matrix is generated by using a Gaussian window function traversal model, and matching and Fourier transform are carried out in combination with a discretized scale basis function; solving a frequency partial derivative of a transformation result to generate a time redistribution operator, and redefining by a Dirac function to obtain a transient scale extraction operator; based on the sub-time-frequency representation of scale-based rotation discretization, screening optimal matching results of each time center through a maximum kurtosis value, and integrating the optimal matching results into a complete time-frequency representation; and finally, redistributing a time-frequency coefficient by using a transient extraction operator to realize accurate positioning of the impact component. According to the method, the problems of serious impact energy diffusion and insufficient positioning precision in existing time-frequency analysis are solved, and the time-frequency representation readability and the impact positioning reliability are remarkably improved.

The invention discloses a bearing vibration signal processing model construction method, a model and a signal classification method, which are used for constructing time domain and frequency domain loss functions by calculating a plurality of feature vector distances so as to train an encoder. According to the method, by minimizing the difference value of different distances in the time domain and frequency domain loss functions, an encoder can be guided to extract similar feature representations in signals, so that the similar feature representations are gathered in a feature space, and dissimilar features are separated. When a trained encoder (a convolutional neural network with an attention mechanism) processes a vibration signal of an unknown fault type, some feature representations in the signal are different from those of other signals, so that the feature representations are gathered in a feature space and are far away from the feature representations of other signals, and the fault detection accuracy is improved. Therefore, the recognition of the vibration signal of the unknown fault type is effectively realized.

The invention discloses a fault identification method and device for a rotating mechanism, computer equipment and the rotating mechanism. The method comprises the following steps: acquiring rotating speed information and a time domain vibration signal of the rotating mechanism; converting the time domain vibration signal into an angular domain vibration signal based on the rotating speed information; performing feature extraction on the angular domain vibration signal based on the autocorrelation matrix of the angular domain vibration signal and the filter coefficient to update the filter coefficient and obtain a target filter coefficient; and determining fault characteristics of the rotating mechanism based on a target vibration signal obtained by processing the angular domain vibration signal based on the target filtering coefficient. According to the method provided by the invention, the fault features of the unsteady vibration signals of the rotating mechanism can be effectively extracted, so that reliable fault diagnosis of the rotating mechanism is realized.

The invention discloses a vibration signal processing method based on self-adaptive wavelet packet and deep learning fusion, and belongs to the field of sewage plant equipment fault diagnosis. The method aims at solving the problems that traditional signal processing is poor in flexibility, the non-stationary signal processing capacity is weak, the deep learning data requirement is large, and the high-frequency weak feature capturing capacity is limited. According to the method, the high-frequency acceleration sensor is adopted, the vibration signals of the sewage plant equipment are accurately collected, the self-adaptive wavelet packet decomposition technology is applied, the primary function is dynamically selected, the number of decomposition layers is optimized, self-adaptive threshold noise reduction is achieved, and the signal processing quality is improved. Meanwhile, in combination with a one-dimensional convolutional neural network and a bidirectional LSTM model, local and global features of the signal are extracted respectively, and pre-processed data are formed through gating weighted fusion. According to the method, the signal-to-noise ratio and the weak fault detection rate are remarkably improved, feature redundancy and data requirements are reduced, the calculation efficiency and diagnosis accuracy are improved, the method is suitable for sewage plant equipment fault diagnosis, and the industrial applicability is enhanced.

The invention belongs to the field of hydroelectric generating set monitoring, and discloses a set vibration signal noise reduction and feature extraction method based on IAVMD and WOA, and the method comprises the steps: constructing a comprehensive evaluation index comprising an energy difference, an average energy entropy and a center frequency variation coefficient; combining an adaptive variational mode decomposition method and a whale optimization algorithm to obtain an optimal decomposition layer number K and a penalty factor; calculating an energy entropy and a Pearson correlation coefficient of each intrinsic mode component; screening the K intrinsic mode components through a double-threshold mechanism; and calculating to obtain a denoised unit vibration signal time sequence and fault features according to the screened intrinsic mode components. According to the method, the problems of large noise interference, fuzzy characteristics, parameter dependence on experience and the like in a traditional method are solved, the precision and efficiency of unit vibration signal processing are remarkably improved, nonlinear dynamic characteristics in unit vibration signals are captured through multi-scale analysis, and the sensitivity and diagnosis accuracy of early weak faults can be improved.

The invention discloses a machine tool spindle bearing fault detection method based on adaptive characteristic mode decomposition, which comprises the following steps: S1, acquiring an original vibration signal of a machine tool spindle bearing, and extracting main frequency components of the signal; s2, adopting an artificial hummingbird algorithm for main frequency components of the extracted signals, and optimizing modulation parameters of FMD; s3, performing mode decomposition on the vibration signal, and introducing a badger optimization algorithm to screen modal components of FMD; s4, calculating a sparsity influence measurement index for each sub-signal obtained by decomposition, and screening the sub-signals based on an SIMI index; s5, performing envelope spectrum analysis on the screened sub-signals, and extracting fault feature frequencies of the sub-signals; and S6, matching and comparing the extracted fault characteristic frequency with a standard fault database, and determining the fault type of the machine tool spindle bearing. According to the method, vibration signal processing, mode decomposition and optimization and fault feature extraction and analysis technologies are combined, and machine tool spindle bearing fault detection based on self-adaptive feature mode decomposition is achieved.

The invention relates to the field of optical fiber vibration signal processing, and discloses a vibration signal identification method of an optical fiber vibration sensor in a high-noise environment, which comprises the following steps of: S1, preprocessing an optical fiber vibration signal, eliminating a direct current component, performing amplitude normalization and eliminating redundant correlation; s2, separating the preprocessed signals by using an independent component analysis technology, and extracting independent signals of different vibration sources; s3, performing time alignment on the separated signals by using a dynamic time warping algorithm, and calculating an alignment path distance between the signals; and S4, judging whether the vibration signal is abnormal or not according to comparison between the alignment path distance and a preset threshold value. According to the method, independent component analysis and dynamic time warping technologies are combined, and accurate separation and efficient identification of vibration signals and environmental noise are realized through non-Gaussian optimization and time axis dynamic alignment without depending on prior mark data and a data driving model.

The invention relates to an SIP (Session Initiation Protocol) acquisition system of a six-dimensional force sensor, and the system comprises an ADC (Analog to Digital Converter) acquisition processing module which is used for acquiring six paths of analog signals of force and torque and converting the six paths of analog signals into six paths of digital signals; the multi-mode monitoring module is used for collecting a temperature signal, an electromagnetic interference signal and a vibration signal; the MCU processing module is used for performing digital filtering and calibration on the six paths of digital signals, the temperature signals, the electromagnetic interference signals and the vibration signals, decoupling the calibrated six paths of digital signals based on a trained decoupling model, and outputting a six-dimensional force decoupling value after temperature, vibration and electromagnetic interference are removed; and the ADC acquisition processing module, the multi-mode monitoring module and the MCU processing module are packaged by adopting an SIP (Session Initiation Protocol) technology. According to the invention, the SIP technology is adopted to package each module in the system, and the trained decoupling model is deployed in the MCU processing module, so that the problem that the size of the sensor acquisition system is large is effectively solved, and the accuracy of a measurement result is ensured.

The invention discloses a multi-channel audio vibration data processing method and system based on an FPGA, and relates to the technical field of data processing. Comprising the following steps: collecting and preprocessing audio vibration signals; fPGA data vibration signal processing is carried out; and processing the vibration signal of the upper computer. The FPGA is adopted as a core control unit, accurate control over multi-channel ADC register configuration and the data acquisition process is achieved through the design of the finite-state machine, the synchronism and real-time performance of data acquisition are remarkably improved, and data loss and processing delay caused by resource conflicts are avoided. The adaptive wavelet filtering technology and the dynamic signal-to-noise ratio analysis method are combined, filtering parameters can be dynamically optimized according to signal characteristics, and efficient suppression of noise in a complex dynamic environment and high-fidelity reconstruction of signal details are achieved. An eight-channel parallel input architecture is adopted, the efficient parallel computing capacity of the FPGA is fully played, hardware resource allocation is optimized, the data processing efficiency is greatly improved, and the high expansibility and adaptability are achieved.

The invention belongs to the technical field of faults, and discloses a vehicle part image fault identification method and system based on a convolutional neural network, and the method comprises the steps: obtaining a vibration signal of a vehicle bearing in a preset monitoring period, segmenting the vibration signal, and obtaining a plurality of vibration signal segments; performing modal decomposition on each vibration signal segment to obtain a vibration component set; calculating a similarity coefficient between each vibration component in each vibration component set and the vibration signal segment, and taking the vibration component with the similarity coefficient greater than a preset coefficient as a target vibration component of the corresponding vibration signal segment to obtain at least one target vibration component of each vibration signal segment; constructing a grayscale image of the vibration signal based on the at least one target vibration component; and inputting the grayscale image of the vibration signal into a fault recognition model constructed based on a convolutional neural network algorithm to obtain a fault recognition result. According to the invention, the complexity of vibration signal processing is reduced, and the accuracy of fault identification is improved.

The invention provides an intelligent anomaly detection method and system based on a convolutional flow adversarial network, and relates to the technical field of anomaly detection, and the method comprises the steps: collecting a full-life-cycle vibration signal of equipment, processing the full-life-cycle vibration signal into a time-frequency diagram, and dividing the time-frequency diagram into a normal training set and a full-cycle test set; constructing a convolutional flow adversarial network comprising a generator and a discriminator; pre-training the generator, and alternately optimizing the discriminator and the generator; and constructing an abnormal state detection model by using the trained generator to realize full-cycle test set online detection. The method can accurately capture normal features, is sensitive to early abnormality, reduces the risk of mode collapse, is high in stability, provides a probability reference to reflect fault evolution, can filter noise, and is good in robustness.

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.

The invention relates to the technical field of signal processing, and discloses a harbor equipment vibration signal processing method, which comprises the following steps: constructing a comprehensive function, and calculating a harbor equipment vibration signal based on the characteristics of a frequency scale, a second wavelet basis function and a threshold value; the concentration ratio of energy distribution, the calculation complexity of second wavelet basis functions under the frequency scale, the orthogonal value between the second wavelet basis functions under the frequency scale, the sparse value of second wavelet coefficients under the frequency scale and the signal-to-noise ratio of time domain signals reconstructed after threshold processing are comprehensively considered. The selection of the optimized frequency scale, the optimized second wavelet basis function and the optimized threshold is realized. The innovative method gets rid of the limitation of a single or optimized algorithm, fixed parameter setting or predefined rules in the prior art, and significantly improves the precision and robustness of low-frequency vibration signal processing.

The invention discloses a fault diagnosis system for an aviation turboshaft engine, and relates to the technical field of fault diagnosis. An acquisition module acquires horizontal, vertical and axial vibration signals; the processing module is used for generating real-time feature vectors by introducing dynamic short-time Fourier transform, variational mode decomposition and singular value decomposition of an unfixed-width Gaussian window; the incremental diagnosis module calculates the matching degree between a real-time feature vector or a prediction feature vector and a known class through a dynamic expansion network, selects to belong to the known class or triggers an expansion mechanism according to an adaptive resonance theory, constructs joint loss by using a Bayesian learning framework after expansion, and performs Gaussian process modeling; loss is determined through Bayesian optimization, and network parameters are updated; the prediction module generates a prediction feature vector when there is no fault; the maintenance module sends or stores a maintenance scheme according to needs, self-adaptive recognition of known classes and new fault classes is achieved, the state can be pre-judged in advance, the operation safety of the engine is effectively guaranteed, and the maintenance cost is reduced.

The invention discloses an icebreaker mast vibration monitoring and sailing auxiliary decision-making system and method. The system comprises a vibration signal acquisition system, a vibration signal processing and analyzing system and a sailing auxiliary decision-making system. The vibration signal acquisition system is used for acquiring vibration data by arranging an optical fiber vibration sensor at a measuring point position of a mast structure; the vibration signal processing and analyzing system is used for processing the vibration data, extracting the processed vibration data and simultaneously carrying out real-time vibration monitoring and evaluation; the sailing auxiliary decision-making system comprises a ship driving operation control module, a ship navigation adjustment decision-making module and a self-adaptive sailing control module. According to the method, real-time monitoring of the vibration state of the ship mast structure and real-time adjustment of the navigation state can be ensured, ice-induced vibration can be controlled on the basis of the real-time icebreaking state, overlarge ice-induced vibration frequency and amplitude are avoided, and the icebreaking efficiency is maximized under the condition that the safety of a ship and the comfort of sailors are ensured.

The invention discloses a fracturing pump multi-cylinder vibration signal processing method and device, equipment, a medium and a product. A vibration data sequence and a key phase pulse signal are obtained, and the vibration data sequence is vibration data collected by a vibration sensor installed between adjacent cylinder bodies of a fracturing pump; the key-phase pulse signals are key-phase pulse signals collected by a phase sensor installed at the crankshaft part of the fracturing pump, then carrying out decorrelation on the vibration data sequence to eliminate the correlation of the vibration data sequence, then carrying out independent analysis on the vibration data sequence after decorrelation, outputting a plurality of vibration data sequences to be matched, and outputting the vibration data sequences to be matched; and finally, based on the key phase pulse signal, matching the plurality of to-be-matched vibration data sequences with the adjacent cylinders to obtain a target vibration data sequence corresponding to each cylinder, so that vibration data with stronger feature information can be extracted while the use amount of sensors is remarkably reduced and the cost and complexity are reduced, and the accuracy of the vibration data is improved. Therefore, the accuracy and timeliness of fault diagnosis of the fracturing pump are effectively improved.

The invention discloses a vibration signal processing method, device and equipment and a computer readable storage medium, and belongs to the technical field of signal processing. The method comprises the steps that an original vibration signal matrix is obtained, the original vibration signal matrix comprises a plurality of first matrix elements, and any first matrix element comprises a vibration signal obtained by any sampling of any sampling point at any time; the original vibration signal matrix is processed, a representative vibration signal matrix is obtained, the representative vibration signal matrix comprises a plurality of second matrix elements, and any second matrix element comprises a vibration signal of any sampling point at any time; determining a third matrix element in the second matrix elements; determining a fourth matrix element in the original vibration signal matrix according to the sampling point and time corresponding to the third matrix element; and according to the sampling point, the time and the vibration signal corresponding to the fourth matrix element, determining a fifth matrix element meeting the condition in the fourth matrix element. The method improves the processing efficiency of the vibration signal.

The application discloses a kind of transfer learning methods considering non-common category sample, it is related to bearing fault diagnosis method technical field.The method includes the following steps: first, the original vibration signal is processed into graph sample;Then it is extracted using recursive multi-head graph attention residual network;Finally, using MDD dynamic migration mode, the source domain feature is migrated to target domain, wherein, in the process of dynamic migration, non-target domain class filter is used to realize non-target domain class migration;The method can improve the utilization efficiency of big data source domain and fault diagnosis precision.

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 present invention discloses a distributed acoustic wave sensing device and method for monitoring wave impact force. The device includes a vibration sensing optical fiber, an anchoring unit, a distributed optical fiber acoustic wave sensing demodulator, a vibration signal processing unit, and an ocean wave impact force inversion unit. The method comprises: fixing the vibration sensing optical fiber to the coast impacted by waves via the anchoring unit; the distributed optical fiber acoustic wave sensing demodulator transmits the monitored ocean wave vibration signal to the vibration signal processing unit for preprocessing; the vibration signal processing unit transmits the processed optical fiber vibration amplitude signal to the ocean wave impact force inversion unit; a calibration experiment obtains an optical fiber vibration amplitude signal and an ocean wave impact force training dataset, constructs an attention convolutional neural network model for training; and the ocean wave impact force inversion unit uses the attention convolutional neural network model to establish a correspondence between the optical fiber vibration amplitude signal and the ocean wave impact force, thereby obtaining the actual ocean wave impact force on the coast. The present invention achieves accurate monitoring of ocean wave impact force on the coast.

The invention discloses a mixed-flow water turbine vibration signal processing method based on adaptive enhanced EEMD (ensemble empirical mode decomposition), and relates to the technical field of vibration signal analysis and processing. A vibration signal of the mixed-flow water turbine is collected, and wavelet noise reduction is carried out; self-adaptive frequency band weighted noise is injected into the noise-reduced signal, and a noise-added signal is obtained; eMD decomposition is carried out on the signal after noise adding, a stopping condition is modified into a double-constraint mode stopping criterion in the EMD decomposition process, and an IMF component set and a residual error are obtained by using double constraints of the number of extreme points and the residual error energy change rate; and effectively screening IMF components of the IMF based on a fault sensitivity index, and introducing a compensation item into a residual error to obtain a final decomposition result as a vibration signal processing result. Background noise is effectively suppressed through frequency band weighted noise injection, and modal aliasing is effectively suppressed through a double-constraint stop criterion and fault sensitivity screening, so that fault features can be separated more accurately, and the signal-to-noise ratio can be improved.

The invention discloses a power equipment vibration signal processing method based on an interpretable wavelet threshold network. The method comprises the following steps: acquiring vibration data of key parts of power equipment; inputting the vibration data into a pre-trained fault diagnosis model, and detecting a fault feature matrix of each frequency band channel of the vibration data by using each wavelet threshold network; integrating the fault feature matrixes of different channels by using a channel-frequency dual-domain collaborative attention network, and performing weighted fusion on the fault feature matrixes of different frequency bands to obtain fusion features; using a global energy pooling layer to extract an average energy feature of each frequency band channel from the fusion feature as a discriminative feature; processing the discriminative features by using a fault classification layer, and predicting a fault type classification result; according to the method, noise robustness enhancement and fault sensitive feature depth extraction can be realized, and the fault classification precision and the reliability of a diagnosis result are remarkably improved.

The invention provides a power transmission tower positioning system and method based on a vibration signal frequency spectrum and a laser point cloud. The system comprises a micro-vibration sensor, a data storage and processing module, a communication module, a power supply module and a solar photovoltaic panel. The laser point cloud acquisition module is used for acquiring laser point cloud data of the iron tower and constructing a point cloud model of the power transmission iron tower; and the monitoring center is used for receiving the vibration signal processing result. The method comprises the following steps: collecting a vibration signal; performing noise reduction processing on the collected vibration signals by using an autocorrelation-VMD noise reduction decomposition algorithm; carrying out VMD decomposition on the vibration signal after noise reduction processing, and calculating a correlation coefficient and a center frequency; reconstructing the vibration signal, and performing spectrum analysis; and judging whether the iron tower is loosened or not according to a spectral analysis result, and if so, determining a fault position.

The present invention proposes a bionic quadruped positioning vibration sensing system based on a water strider drum structure, including a vibration signal acquisition device and a vibration signal processing device; the vibration signal acquisition device includes a first sensor, a second sensor, a third sensor and a fourth sensor deployed on a plane, the first sensor, the second sensor, the third sensor and the fourth sensor are uniformly distributed along a preset circumference in sequence, and the first sensor, the second sensor, the third sensor and the fourth sensor are all sensors that imitate the water strider drum structure; the first sensor, the second sensor, the third sensor and the fourth sensor are all electrically connected to the vibration signal processing device. The detection accuracy of the vibration signal is high, the detection sensitivity is high, and the perception range is wide. In addition, the present invention proposes a vibration sensing positioning method based on the above-mentioned vibration sensing positioning system.

The invention discloses a vibration signal de-noising method and device, and belongs to the technical field of vibration signal processing, and the method comprises the steps: determining an initial de-noised slice signal of a slice signal, a frequency domain feature of the slice signal, and a frequency domain feature of the initial de-noised slice signal according to a plurality of continuous slice signals of a target vibration signal; determining a noise spectrum of each slice signal according to the frequency domain characteristics of the slice signals and the frequency domain characteristics of the initial de-noised slice signals, and iteratively optimizing de-noising parameters according to the noise spectrums to obtain optimal de-noising parameters; and performing denoising reconstruction on the slice signal according to the optimal denoising parameter to obtain a denoised signal of the target vibration signal. According to different frequency domain characteristics of each slice vibration signal, the denoising parameters are dynamically adjusted, and the accuracy and efficiency of vibration signal denoising are improved.