690 results about "Frequency characteristic" patented technology

The invention relates to the field of frequency converter fault detection, and discloses a frequency converter fault prediction method and system based on machine learning, and the method comprises the steps: obtaining multi-dimensional real-time data in the operation process of a frequency converter; constructing a dynamic mapping relation to obtain a basic feature set; generating a time sequence feature vector capable of reflecting the state change of the equipment based on the basic feature set; comparing, analyzing and judging whether the equipment state deviates from a normal operation interval or not based on the historical operation data and the time sequence feature vector, and outputting a state deviation index; performing abnormal fluctuation judgment on the time sequence feature vector; extracting fluctuation amplitude and frequency characteristics of the key indexes to obtain quantitative description data of abnormal fluctuation; inputting the quantitative description data of the abnormal fluctuation into an abnormal prediction model; and generating a coping strategy and a triggering condition of the coping strategy based on the risk prediction result. The method has the advantages that the abnormal state of the frequency converter is recognized in time, and potential risks are predicted.

The invention relates to the technical field of vibration monitoring, in particular to a slurry pump motion monitoring management system based on data analysis, which comprises a multi-source acquisition module, a period judgment module, a trend focusing module and a dynamic management and control module. According to the method, vibration data are collected through a three-axis acceleration sensor, a fundamental frequency amplitude is extracted through FFT, time-frequency characteristics and periodic distribution are fused in combination with an extreme value interval sequence, the data representation dimension is enhanced, the rising slope is processed through moving average, the periodic variation is calculated, random noise is restrained, and trend continuity is enhanced. Monotonicity test is combined with dynamic threshold judgment to improve anomaly recognition sensitivity; a sliding window is used for segmenting multi-cycle data; a sudden change interval is positioned based on a pressure amplification rate second derivative; the limitation of a frequency domain on transient feature recognition is broken through, a pressure amplification rate three-dimensional vector is constructed, cosine similarity is calculated, and a working condition difference degree is quantified to dynamically bind a maintenance instruction; and closed-loop feedback is formed to improve the early warning and decision matching degree.

Disclosed in the present invention are a dual-end monitoring-based partial discharge source localization method and system for high-frequency partial discharge of a high-voltage cable. The method comprises: acquiring operation data and phase velocity test data of a high-voltage cable; when a partial discharge fault occurs in the high-voltage cable, collecting partial discharge signals on two sides of the high-voltage cable by means of sensors on the two sides of the high-voltage cable; performing Fourier transform respectively on the basis of the partial discharge signals on the two sides to obtain amplitude-frequency characteristics of the partial discharge signals, and preprocessing the collected partial discharge signals on the basis of the amplitude-frequency characteristics to optimize and improve a phase difference algorithm; and by incorporating the phase velocity test data of the high-voltage cable, using the improved phase difference algorithm to perform partial discharge source localization on the faulty cable. In the present invention, dual-end monitoring is used to reduce the attenuation of signals caused by long-distance transmission, and synchronization of two sensors is performed using a PTP protocol, effectively reducing errors; and in addition, during monitoring, partial discharge source localization is performed using the improved phase difference algorithm, which is conducive to resisting external interference, and there is no need to determine the time of arrival, greatly improving the positioning accuracy.

The invention provides a low-illumination perception method and system based on space-frequency fusion, and relates to the technical field of image processing, and the method comprises the steps: obtaining a normal light image and a low-illumination image corresponding to the normal light image; performing feature extraction on the normal light image and the low-illumination image through an encoder to obtain multi-scale spatial features; decomposing the multi-scale spatial features through stationary wavelet transform to generate a low-frequency feature component and a high-frequency feature component; performing optimization processing on the low-frequency characteristic component and the high-frequency characteristic component based on a parameter learnable frequency domain adaptive filtering mechanism; fusing the optimized low-frequency feature component and the optimized high-frequency feature component by introducing a cross attention learning mechanism to obtain a frequency domain feature; performing adaptive complementary fusion on the frequency domain features and the multi-scale spatial features to generate fusion features; step-by-step up-sampling is carried out on the fusion features through a decoder; and outputting a segmentation result of the low-illumination image through a sensing head according to the fusion features after up-sampling.

The invention provides an underground structure boundary identification method and system based on distributed optical fiber sensing, and relates to the technical field of underground structure detection and boundary identification. The method comprises the following steps: firstly, arranging a sensing array in a to-be-detected area, establishing a channel corresponding to a space coordinate, and collecting a non-excitation base line; medium and noise are estimated through low-energy probe, excitation frequency band, energy and repetition rate are optimized, and the signals are emitted by an adaptive seismic source; reflection and transmission responses are synchronously collected under the unified time reference; performing wavelet denoising, temperature and dispersion compensation and time alignment on the data, and extracting amplitude, phase and frequency characteristics; gradient is calculated on the feature field, non-maximum suppression is carried out, and stable boundary points are obtained by adopting self-adaptive double thresholds based on local statistics and combining time continuity and space connectivity constraints; a two-dimensional section is obtained through spline fitting, a three-dimensional model is reconstructed under the constraint of multi-section consistency, a section map and the three-dimensional model are output, and high-precision, real-time and visual detection of small-size boundaries is achieved.

The invention belongs to the technical field of hydroelectric generating set fault diagnosis, and particularly discloses a hydroelectric generating set cavitation fault diagnosis method based on multi-channel acoustic emission signal fusion. Through the technical means of acquiring the acoustic emission signals at multiple positions, a more comprehensive cavitation phenomenon data basis is provided; the representation of time-frequency characteristics is optimized through the Mel time-frequency diagram, and the problem that the dynamic characteristics of acoustic emission signals cannot be effectively captured through a traditional method is solved; multi-channel feature extraction is performed on the Mel time-frequency graph through a preset deep convolutional neural network model, so that automatic learning of deep cavitation working conditions is realized; and multi-channel fusion and classification identification are carried out on a feature extraction result through the model, so that effective integration and fault classification of features are realized. Compared with the prior art, a more accurate and comprehensive cavitation fault diagnosis effect is realized, and the diagnosis precision is improved.

The invention discloses a signal detrending method based on sliding window statistics and dynamic wave crest correction. The method comprises the step of realizing accurate extraction of signal trend and fluctuation characteristics through a double-window dynamic adjustment mechanism of a mean value window and a standard deviation window. A detection threshold value is dynamically adjusted by analyzing the change trend of the historical wave crest amplitude, and the real-time tracking capability of signal fluctuation is ensured; meanwhile, different parameter configurations are preset for different application scenes, the problem of missing detection or misjudgment generated when the amplitude of a fixed threshold algorithm changes suddenly is solved, and the wave crest recognition accuracy is improved. Based on a dynamic threshold algorithm, wave peak start and end points are automatically identified, a linear interpolation technology is adopted to calculate slope of each interval, fitting parameters are dynamically adjusted through residual analysis, and finally low-frequency trend terms and high-frequency characteristic components are effectively separated through difference operation of original signals and fitting signals. The problem of fitting deviation caused by non-uniform sampling can be effectively solved.

The invention provides a method for extracting electrical characteristic signals of electrical equipment in a complex working condition environment, and belongs to the technical field of electrical equipment detection.The method comprises the steps that a multi-channel ultrasonic sensor array is arranged to collect partial discharge signals, background noise is eliminated through adaptive noise cancellation processing, and an ultra-sparse frequency band energy distribution vector is constructed; the method comprises the following steps: calling a self-adaptive time-frequency analysis model to extract instantaneous frequency, amplitude and phase parameters to form a micro-hour-frequency characteristic matrix, separating independent source signals through independent component analysis, calculating a kurtosis value and a skewness value, fusing multi-domain characteristics to construct a transient stationary comprehensive characteristic vector, matching with a standard discharge characteristic vector library to identify the discharge type and intensity, and calculating the discharge intensity. A corresponding prediction algorithm is selected according to the discharge mode, multi-parameter coupling optimization adjustment is started under a certain condition, an electrical characteristic signal description vector is finally constructed, and the technical problem that the partial discharge signal of the power equipment is difficult to accurately extract under a complex working condition environment is solved.

The invention provides a grid-connected inverter impedance detection method based on an L-shaped filter, and belongs to the technical field of grid-connected inverter impedance detection. A multi-frequency-point disturbance signal is injected into a preset grid-connected inverter working point, time domain data of inverter side current and common coupling point voltage are collected, fast Fourier transform analysis is carried out on the collected data, amplitude and phase information corresponding to each injection frequency point is extracted, and a frequency domain feature vector matrix is established; and establishing a frequency compensation matrix model to compensate and correct high-frequency band measurement data based on a frequency characteristic attenuation vector, calculating to obtain optimal estimated values of power grid impedance and grid-connected inverter output impedance, performing error analysis and precision verification on an impedance detection result, and outputting a verified impedance parameter value. The technical problem of insufficient power grid impedance detection precision caused by high-frequency band attenuation of the L-shaped filter is solved.

The invention provides an abnormal sound detection method based on multi-scale time-frequency feature perception, and relates to the technical field of acoustic detection for industrial machine state monitoring, and the method comprises the steps: inputting an original sound signal, and carrying out the dual-branch feature extraction to generate a time domain coding spectrogram and a logarithmic Mel spectrogram; executing hybrid data enhancement; splicing the enhanced spectrogram and inputting the enhanced spectrogram into a dense encoder for compression modeling; extracting features through a two-stage multi-scale time-frequency sensing network, processing along a time dimension in the first stage, and processing along a frequency dimension in the second stage; inputting the high-order features into a lightweight classifier to output an abnormal score; and comparing the abnormal score with the gamma distribution threshold to judge abnormity. According to the method, the weak anomaly detection rate and the cross-equipment stability are remarkably improved, and the perception and discrimination capability of the model on the multi-scale time-frequency characteristics is effectively enhanced.

The invention relates to the technical field of rotating equipment fault diagnosis, in particular to a rotating equipment fault diagnosis method based on time-frequency characteristic decoupling, and the method comprises the steps: S1, obtaining a vibration signal sequence of rotating equipment; s2, respectively extracting time domain features and frequency domain features of the vibration signal sequence, and constructing a time-frequency feature set; s3, performing feature screening on the time-frequency feature set based on a multi-dimensional feature index to obtain fault sensitive features; and S4, inputting the fault sensitive features into the trained fault prediction model, and outputting a corresponding fault prediction value. According to the method, accurate matching of features and fault types is realized through a collaborative mechanism of two-dimensional feature extraction and multi-index screening, and meanwhile, the recognition capability of the model on minority types of faults is improved through multi-scale feature extraction and feature distribution conversion and sampling, so that the accuracy and reliability of fault diagnosis of the rotating equipment are improved.

The invention relates to the technical field of generator set intelligent control, and discloses a generator set intelligent control method and system, and the method comprises the steps: obtaining the sensor data and vibration signals of a generator set; obtaining a corrected frequency characteristic value according to sensor data screening, denoising and frequency characteristic correction; generating and iteratively updating a control decision instruction sequence according to the real-time change of the vibration signal and the corrected frequency characteristic value to obtain a system stability index; and combining system stability index feedback adjustment to obtain a control decision reliability index. The method can solve the problem of noise interference under a dynamic working condition, realizes fault early warning, parameter optimization and system stability, and improves energy efficiency and operation reliability.

The invention relates to the field of solid waste crushing equipment, and discloses a solid waste crushing equipment working state monitoring method and system, and the method comprises the steps: collecting a current signal, a vibration signal and a sound signal during the operation of solid waste crushing equipment; when the duration of the peak value of the current signal is smaller than a preset duration threshold value, the impact frequency characteristic of a first vibration frequency band exists in the frequency spectrum of the vibration signal, and the decibel peak value of the sound signal in the preset first frequency band is greater than a second preset threshold value, judging that the current signal is instantaneous load fluctuation; when the duration of the peak value of the current signal is greater than a preset duration threshold value and the vibration signal frequency spectrum presents continuous abnormal fluctuation with the period greater than a first preset duration, determining that a continuous overload fault occurs; if so, judging that the load fluctuates instantaneously; if yes, a feeding mechanism of the solid waste crushing equipment is controlled to reduce the feeding speed, and first prompt information is generated. According to the technical scheme, the monitoring accuracy is improved.

The invention discloses a partial discharge signal multi-source cooperative detection method and system based on time-frequency fusion analysis, and relates to the technical field of multi-source cooperative detection, and the method comprises the following steps: obtaining partial discharge original signals of an electric signal channel and an ultrahigh frequency channel, respectively carrying out the time-frequency transformation, and generating a multi-source time-frequency characteristic spectrum; extracting high-frequency noise modes of different channels under a non-partial discharge condition based on the multi-source time-frequency characteristic spectrum, and constructing a cross-source interference fingerprint database; based on the cross-source interference fingerprint database, carrying out frequency-band-by-band difference mapping calculation on actually acquired multi-source time-frequency characteristics to generate a difference characteristic matrix; and introducing reverse consistency constraint into the differential feature matrix, carrying out weighted fusion to obtain multi-source coupling time-frequency feature mapping, and outputting a detection result of the partial discharge signal. According to the method, the multi-source time-frequency characteristic spectrum is constructed to carry out band-by-band differential mapping on the actual signal, so that cross-source noise interference is inhibited, and the accuracy and robustness of partial discharge detection in a strong noise environment are improved.

The invention provides an underwater manipulator motion balance real-time monitoring method based on acoustic sensing, which comprises the following steps: if a frequency shift range exceeds a preset range threshold, filtering a signal data set, and separating superposed components of a friction sound signal and a water flow interference sound to obtain a preliminarily separated friction sound signal; performing short-time Fourier transform processing on the preliminarily separated friction sound signals, extracting time-frequency features of the friction sound signals, and obtaining a friction sound feature set; calculating the signal energy ratio of the friction sound signal to the water flow interference sound by analyzing the frequency spectrum difference between the friction sound feature set and the water flow interference sound, screening the interference sound according to the signal energy ratio, and rejecting the screened interference sound to obtain a separation signal; and when it is recognized that the manipulator executes the complex track task, track complexity is recognized, the position of the acoustic sensor array is dynamically adjusted according to the track complexity, the dynamic layout of the acoustic sensor array is optimized, and array configuration is obtained.

The invention discloses a cable high-frequency impedance characteristic analysis method based on bipolar trapezoidal wave excitation, and belongs to the technical field of power equipment state monitoring and fault diagnosis. In order to solve the problems of low test efficiency, insufficient high-frequency parameter modeling precision and weak anti-interference capability in cable high-frequency impedance characteristic analysis of a traditional sine wave excitation method, the method comprises the following steps: generating an odd-symmetry bipolar trapezoidal wave excitation signal with specific frequency and amplitude, and injecting the signal into the head end of a cable to be tested through a coupling device after power amplification; synchronously acquiring a high-frequency response signal of the key position of the cable under the excitation of the bipolar trapezoidal wave by using a data acquisition system; preprocessing the high-frequency response signal, converting the high-frequency response signal into a frequency domain through Fourier transform, calculating input impedance of the cable under different harmonic frequencies, and synthesizing a broadband impedance spectrum; based on the broadband impedance spectrum, the amplitude-frequency characteristic and the phase-frequency characteristic of impedance are analyzed, and cable state evaluation, partial discharge detection, insulation defect diagnosis or fault point positioning are realized. The method is used for an electric power system.

The invention relates to the technical field of motion control, in particular to a stepping motor monitoring control method and system, and the method comprises the following steps: obtaining a target and feedback signal rising edge sequence, analyzing a periodic change direction, judging a time migration trend, extracting a voltage and current response section, recognizing a disturbance time slice, and positioning high and low level boundaries. And screening signals with consistent frequency, extracting a running rhythm time sequence, and comparing a rhythm sequence to obtain a monitoring control behavior. According to the method, the time sequence of rising edges of a target and a feedback signal is compared, the displacement deviation trend is judged in combination with the periodic change direction, an interference section is identified by utilizing voltage abrupt change and current fluctuation characteristics, a control boundary is delimited according to the distribution of the interference section in high and low levels, and signal segments with consistent frequency characteristics are extracted to construct an operation rhythm. The driving behavior is extracted in combination with the signal time sequence and the rhythm sequence, the responsiveness to rhythm change is enhanced, and the problems of interference fluctuation and control rhythm disjunction caused by rhythm dislocation are relieved.

The invention relates to the technical field of heat supply pipe networks, in particular to a small sample learning pipeline leakage identification method based on multi-dimensional acoustic feature fusion, which comprises the following steps of: acquiring sound pressure data based on a main line of a heat supply pipe network, analyzing signal amplitude and frequency features by a sliding window, constructing a multi-dimensional acoustic feature set, screening and aggregating leakage and normal state samples, and identifying leakage and normal state samples. Comparing the difference between the new data and the prototype, quantifying the feature distance, and judging the leakage state according to the minimum attribution principle. According to the method, multi-angle behavior expression is established, a typical category prototype is generated through feature induction, multi-category feature center aggregation and dynamic measurement are combined, the high-reliability judgment capability of the leakage state is improved, compared with a traditional technology, leakage signals are comprehensively represented through multi-dimensional features, and the reliability of the leakage state is improved. The adaptability to environmental noise, sudden interference and a complex pipe network structure is effectively enhanced, misjudgment is reduced, the dependence on a large amount of labeled data is greatly reduced through the small sample learning technology, and the data collection and labeling cost is remarkably reduced.

The invention relates to the technical field of radio frequency measurement, and discloses a method for measuring the high-frequency characteristic of a radio frequency coaxial probe. The method comprises the following steps: acquiring high-frequency response data of a radio frequency coaxial probe, segmenting a full frequency band based on a predefined frequency band division strategy, and identifying a key frequency band region; according to the response amplitude and phase distribution of the key frequency band region, screening a reference frequency band and extracting an impedance reference value, optimizing impedance matching parameters of a non-reference frequency band based on the reference value, and generating a frequency band impedance optimization parameter set; recombining a full-band impedance distribution sequence according to the parameter set to generate an initial impedance matching scheme; and finally, monitoring the high-frequency transmission deviation under the initial scheme in real time, counting the impedance fluctuation quantity and the phase offset, and generating an impedance correction instruction. The method effectively improves the stability and reliability of the measurement of the high-frequency characteristic of the radio frequency coaxial probe by accurately identifying the key frequency band in a segmented manner, guiding the impedance parameter optimization by the reference value and combining a dynamic correction mechanism.

The invention provides a coding and decoding three-dimensional face recognition method and system based on frequency feature assistance, and belongs to the technical field of face recognition. And processing the acquired to-be-recognized face image by using a pre-trained recognition model to obtain a three-dimensional face depth map. According to the method, a coding and decoding network is utilized, a multi-scale encoder structure is adopted, hierarchical features of a face depth map are extracted in a gradual down-sampling process, and key geometric information including skin textures and five sense organ contours is effectively reserved; in the decoding stage, the network selectively fuses features of different levels through jump connection and an attention mechanism, so that redundancy is remarkably reduced, and the efficiency of the face depth map is improved; dynamically selecting the frequency characteristics by adopting an attention mechanism, and selecting the frequency characteristics with relatively large information amount so as to enhance the denoising capability; and coding features and decoding features are effectively fused, so that the feature refining effect is improved.

The invention provides a probe frequency response calibration parameter determining method, a probe frequency response calibration parameter measuring system and a storage medium, and the probe frequency response calibration parameter determining method introduces a composite test signal including a steady-state frequency sweep signal and a transient pulse signal. De-embedding calculation is carried out on a data set which is obtained under the two conditions that a probe is not connected to a calibration system and the probe is connected to the calibration system and responds to the same test signal, namely amplitude division is carried out to eliminate link gain deviation, phase subtraction is carried out to strip channel phase deviation, accurate extraction of the independent frequency response characteristic of the probe is achieved, and the accuracy of the independent frequency response characteristic of the probe is improved. Therefore, the frequency response calibration parameter of the probe is determined. According to the invention, system errors introduced by a test environment and a rear-end measurement link are eliminated, the calibration precision of the amplitude-frequency characteristic and the phase-frequency characteristic of the probe in a full working frequency band is improved, and a reliable basis is provided for implementation of accurate real-time anti-distortion compensation in actual signal acquisition of measurement equipment such as an oscilloscope and the like; and the reduction authenticity of the whole measurement system in the high-frequency and broadband signal test is ensured.

The present application relates to a kind of seismic data amplitude compensation method and system.The method includes obtaining seismic data;The seismic data obtained is carried out surface consistency amplitude compensation, and the analysis is carried out to the stack section of the compensated seismic data, determines that energy lower area and energy higher area, at least one single shot data is extracted in energy lower area and energy higher area respectively, according to the frequency characteristics of each single shot data, according to the principle that the frequency characteristics trend of each single shot data in each frequency band is consistent, the frequency of seismic data is divided;The seismic data of each frequency band is respectively carried out surface consistency amplitude compensation.In the present application, the problem that the amplitude compensation effect is not obvious in prior art can be solved.

The invention discloses a multi-bandwidth spectrum data signal adaptive extraction method based on a signal-to-noise ratio threshold, and relates to the technical field of spectrum signal extraction. The frequency spectrum data collection covers 30MHz-6GHz, the sampling rate is 20MHz, 16-bit quantization is realized, and the hardware fluctuation is eliminated through the dual-channel design; according to noise floor estimation, 50 frames of no-signal samples are taken, mean value calculation is carried out through a sliding window, then median filtering is carried out, and the noise power and confidence interval of each frequency point are obtained; basic values are set for a signal-to-noise ratio threshold according to frequency bands, dynamic adjustment is performed in combination with real-time noise standard deviation, and compensation is added to a known frequency band; for multi-bandwidth detection, wavelet packet decomposition is adapted to 100kHz-5MHz, candidate signals are marked when energy detection exceeds a threshold, and a range is determined by morphological filtering; extracting parameters such as center frequency, analyzing time-frequency characteristics through short-time Fourier transform, and calculating 4-order cumulants to establish feature vectors; coherent accumulation times are adjusted according to a signal-to-noise ratio, energy is superposed through phase alignment, and a purified signal is output through band-pass filtering. According to the invention, the signal detection precision and adaptability in a complex environment are improved, and misjudgment is reduced.

The invention relates to the technical field of cable insulation monitoring, in particular to a power cable insulation performance monitoring method and system. The method comprises the following steps: obtaining a dielectric loss angle tangent value signal of a power cable, and carrying out blind source separation on the dielectric loss angle tangent value signal; for each independent component of blind source separation, two composite discrimination indexes are constructed: a component fluctuation spectrum index, which is used for quantizing the stability of the signal from the frequency characteristic; and the environment coupling coefficient is used for judging a signal source from the correlation with the temperature and the load and the change trend of the environment coupling coefficient. And an aging discrimination factor is calculated by fusing the two indexes, and a real aging trend signal is identified from a plurality of independent components, so that the insulation performance of the power cable is monitored. According to the invention, the automation level and the anti-interference capability of cable insulation monitoring are obviously improved, and misjudgment caused by environmental interference is effectively avoided.

An object of the invention is to provide an electromagnetic noise analysis device capable of analyzing a propagation path in a short time even for electromagnetic noise in a high frequency band and securing a certain level of reliability of an analysis result. For this purpose, an electromagnetic noise analysis device according to the invention includes: a propagation characteristic estimation unit configured to statistically estimate an electromagnetic field characteristic in a housing based on information about a housing structure and an electric wire path to output propagation characteristic probability distribution data; a propagation amount calculation unit configured to statistically estimate a noise propagation amount propagated from a noise source to an affected device based on a frequency characteristic of noise generated by the noise source and the propagation characteristic probability distribution data to output data of noise probability distribution on the affected device; and a malfunction risk determination unit configured to determine a malfunction risk of the affected device based on the data of the noise probability distribution on the affected device.

The invention discloses a training method of a multi-sound-source direction-of-arrival estimation model, a multi-sound-source direction-of-arrival estimation method, equipment, a medium and a product, and relates to the technical field of signal processing and artificial intelligence crossing, and the training method comprises the steps: calculating the time-frequency characteristics and cross-correlation characteristics of original signals of a multi-channel array; respectively carrying out position coding on the time-frequency characteristics and the microphone position information; fusing the features into an input matrix, and inputting the input matrix into a backbone network of an improved Transform model; calculating the attention score of the input matrix and generating head output by using the first multi-head self-attention layer, inputting the head output into the second multi-head self-attention layer, calculating the attention score and generating head output, and inputting the output into a multi-task output module to obtain a direction estimation result. The multi-head attention mechanism is introduced, long-time and multi-band complex dependence is captured, the sound source distinguishing capacity is improved, multiple heads capture direction information from different view angles, and it is guaranteed that high accuracy can still be guaranteed in the complex environment.

The invention relates to the technical field of fault detection, and discloses a rotating equipment bearing fault detection method, device and equipment and a storage medium, and the method comprises the steps: determining a vibration sampling frequency and a rotating speed sampling frequency; vibration information of the rotating equipment in the start-stop process is collected based on the vibration sampling frequency, and a vibration signal is obtained; based on the rotating speed sampling frequency, rotating speed information of the rotating equipment in the start-stop process is collected, and a rotating speed signal is obtained; associating the vibration signal with the rotating speed signal to obtain a bearing demodulation spectrum; and judging whether the to-be-analyzed bearing has a fault based on the bearing demodulation spectrum. According to the invention, the low-frequency characteristics of the early fault of the bearing can be effectively extracted, the recognizable degree of the fault signal is enhanced, and the diagnosis precision of the bearing fault is effectively improved.

An FM LIDAR system is described that includes a frequency modulated LIDAR system that incorporates a laser source that is optically coupled to a whispering gallery mode optical resonator. Light from the laser that is coupled into the whispering gallery mode optical resonator is coupled back out as a returning counterpropagating wave having a frequency characteristic of a whispering gallery mode of the optical resonator. This returning wave is used to reduce the linewidth of the source laser by optical injection. Modulation of the optical properties of the whispering gallery mode optical resonator results in modulation of the frequency of the frequencies supported by whispering gallery modes of the resonator, and provides a method for producing highly linear and reproducible optical chirps that are highly suited for use in a LIDAR system. Methods of using such an FM LIDAR system and vehicle assisting systems that incorporate such FM LIDAR systems are also described.

The invention belongs to the technical field of MEMS sensor signal processing, and discloses an MEMS sensor signal processing method and system based on an RLS-VMD fusion algorithm, and the method comprises the steps: continuously collecting an original acceleration signal at a preset sampling frequency through an MEMS sensor; offset in the original acceleration signal is eliminated through iterative calculation based on an RLS algorithm, and an offset correction signal and a residual error are obtained; a dynamic energy threshold value is obtained based on residual calculation, and frequency characteristic secondary verification is carried out on each screened modal component; carrying out superposition reconstruction to obtain an acceleration signal; and taking the purified acceleration signal as input, performing two times of integral operation based on the Newton's law of motion to realize displacement calculation, and finally outputting a displacement signal. According to the method, a correlation mechanism of RLS offset correction and VMD screening parameters is established, deep collaboration is achieved, and finally the acceleration signal processing quality and the displacement calculation precision of the MEMS sensor are improved.

The invention discloses a geophysical prospecting signal denoising method based on VMD (variational mode decomposition) combined with an improved wavelet threshold function, and belongs to the technical field of mineral exploration geophysical prospecting signal process.The method includes the steps that firstly, a decomposition mode number K of a signal is determined through VMD in a self-adaptive mode, a plurality of IMFs (intrinsic mode components) are obtained, and then according to the frequency characteristic and noise distribution of each IMF component, a wavelet threshold function is obtained; and carrying out targeted noise suppression by adopting an improved wavelet threshold function containing an adjustment parameter alpha, and finally, carrying out linear superposition reconstruction on all the processed IMF components to obtain a de-noised geophysical prospecting signal. Experimental verification shows that compared with a traditional method, the method has the advantages that the signal-to-noise ratio and the correlation coefficient of the noisy geophysical prospecting signals can be effectively increased, the root-mean-square error can be effectively reduced, the inherent defects of the traditional method are overcome, the geologic features of the geophysical prospecting signals can be effectively reserved, the method is suitable for various mineral exploration scenes, and reliable data support is provided for anomaly recognition in mineral exploration.