53 results about "Blind source separation algorithm" patented technology

The invention is suitable for the technical field of electrical equipment state monitoring, and provides an electrical equipment state monitoring method based on a non-contact leakage current sensor, and the method comprises the steps: collecting a leakage current signal of the surface of an insulating part of electrical equipment, and obtaining infrared thermal image data and an ultrasonic signal; variational mode decomposition is carried out on the leakage current signal to obtain a plurality of intrinsic mode functions; separating a leakage current effective component and an independent noise source based on a blind source separation algorithm; extracting a time-frequency characteristic of the effective component of the separated leakage current, extracting a local temperature gradient characteristic of the infrared thermal image data and a frequency spectrum energy characteristic of the ultrasonic signal, and generating a multi-modal characteristic vector; analyzing the space-time relevance of the multi-modal feature vector, dynamically distributing each modal weight coefficient for fusion, and generating a comprehensive fault feature; according to the method, the signal-to-noise ratio of the weak leakage current signal is improved, and the misjudgment rate is effectively reduced.

The invention relates to the technical field of power equipment fault diagnosis, and particularly discloses a pole-mounted circuit breaker mechanical fault diagnosis method and system, and the method comprises the steps: firstly, synchronously collecting a dynamic force-displacement signal, a high-frequency acoustic emission signal and a broadband vibration signal in the switching-on and switching-off process of a circuit breaker operating mechanism; then, the acoustic emission and vibration signals are decoupled into an impact source component and a friction source component which are statistically independent through a blind source separation algorithm; performing envelope spectrum analysis on the impact source component to extract impact characteristics, and performing energy calculation on the friction source component to obtain friction noise energy characteristics; finally, on the basis of dynamic force-displacement curve fitting analysis, in combination with impact characteristics and friction noise energy characteristics, distinguishing and delimiting of mechanical wear and lubrication degradation faults are achieved. According to the invention, the problem that the existing diagnosis technology cannot effectively identify and distinguish the coupling fault of mechanical wear and lubrication deterioration is solved, and early warning and accurate diagnosis of the mechanical fault of the pole-mounted circuit breaker are realized.

The invention belongs to the related technical field of neural rehabilitation engineering, and particularly relates to a cerebral apoplexy rehabilitation method based on fusion of a motor imagery brain-computer interface and transcranial direct current stimulation, which comprises the following steps: acquiring a corresponding artifact template based on transcranial direct current stimulation parameters; carrying out source separation on the collected electroencephalogram test times by adopting a joint blind source separation algorithm to obtain clean electroencephalogram signal components; wherein the construction mode of the artifact template library comprises the following steps: extracting electroencephalogram trial artifact samples of multiple stimulation periods, and generating a transcranial direct current stimulation artifact template through an average analysis or principal component analysis mode to obtain the artifact template library; performing motion intention decoding on the clean electroencephalogram signal component to obtain a motion intention of the patient; inputting the motion intention, pre-collected patient body state evaluation information and patient feedback comfort into a fuzzy system algorithm to obtain stimulation parameters of to-be-applied transcranial direct current stimulation, and realizing active rehabilitation through the transcranial direct current stimulation. According to the invention, algorithm-driven closed-loop rehabilitation regulation and control can be realized.

The invention relates to an array microphone noise reduction recording method based on cascade noise reduction and blind source separation, which belongs to the technical field of voice signal processing and recording, and comprises the following steps: configuring a multi-channel array microphone, ensuring that the amplitude and phase of a channel signal are consistent, and collecting an original multi-channel voice signal; a weighted kernel function blind source separation algorithm is adopted, signal-to-noise ratio distribution characteristics of signals are extracted, kernel function weights are given, and target voice and interference signal components are obtained through decoupling of an independent component analysis model; executing target-oriented adaptive cascade noise reduction, locking the voice of a keynote speaker through directional pickup, reducing noise, filtering out reverberation, and enhancing the voice of a far-field target by combining a voice mask neural network with a far-field pickup algorithm in sequence; and processing the target voice through voice feature perception lossless coding and storing the target voice. According to the invention, stable acquisition of multi-channel signals, accurate separation of mixed signals and layered suppression of noise reverberation are realized, the signal-to-noise ratio and definition of far-field voice are significantly improved, and the method is suitable for single-person speaking or multi-person dialogue scenes.

The invention provides a monitoring method and system for a cutting penetration state in a water-jet guided laser processing process based on sound signal blind source separation, and the monitoring method comprises the steps: S1, in the water-jet guided laser processing process, collecting a plurality of observation sound signals in real time, the observation sound signals being mixed sound signals composed of a plurality of independent sound source signals; s2, estimating and solving a plurality of independent sound source signals by using a blind source separation algorithm according to the plurality of collected observation sound signals; s3, the frequency spectrums of the multiple independent sound source signals are compared with sound signals generated during material laser processing, so that sound signals generated during water-guided laser processing due to material removal are determined from the frequency spectrums; and S4, the water-jet guided laser machining process is judged according to the relation that sound signals generated due to material removal change along with time. Compared with a traditional coaxial visual monitoring scheme, the method is not affected by factors such as water flow and light intensity, and can be applied to monitoring of the water-guided laser machining process.

The invention provides a multi-modal fusion unmanned aerial vehicle identification method and system, and the method comprises the steps: obtaining a radio signal, an image signal and a sound signal of an unmanned aerial vehicle; performing feature extraction on the radio signal based on a blind source separation algorithm and adaptive filtering to obtain radio features; performing feature extraction on the image signal based on image super-resolution reconstruction and a double-flow feature extraction network to obtain image features; performing feature extraction on the sound signal based on spectral analysis and harmonic feature extraction to obtain an audio feature; fusing the radio feature, the image feature and the audio feature to form a multi-modal fusion feature; and performing identification based on the multi-modal fusion features to obtain an unmanned aerial vehicle category identification result. By adopting the scheme, the accuracy, reliability and environmental adaptability of unmanned aerial vehicle identification can be remarkably improved.

The invention relates to the technical field of power equipment fault monitoring, in particular to a distribution box fault monitoring method, and aims to solve the problem that a traditional monitoring method in the prior art depends on a single filtering or fixed threshold strategy and is difficult to effectively separate fault features. According to the method, mechanical vibration and electromagnetic interference signals can be effectively separated through an adaptive blind source separation algorithm, the problem of early fault signal identification in a mixed interference environment is solved by combining multi-scale feature fusion and a dynamic environment compensation mechanism, and the method has the advantages of improving monitoring accuracy and realizing accurate quantification of fault energy.

A method for extracting effective information of a communication signal in a low signal-to-noise ratio complex environment relates to the field of communication signal processing, and comprises the following steps: firstly, obtaining a plurality of intrinsic mode functions mixed with effective signals with different frequencies and complex noise interference by using an adaptive noise complete empirical mode decomposition method, and carrying out wavelet denoising and reconstruction on the intrinsic mode functions to obtain a plurality of intrinsic mode functions; the method comprises the following steps of: obtaining a de-noised signal with a high signal-to-noise ratio, pre-processing the de-noised signal, calculating a separation matrix of an observation signal matrix after pre-processing according to a blind source separation algorithm, and extracting an effective communication signal from the original observation signal by utilizing the separation matrix so as to complete the effective information extraction of the communication signal. According to the method, noise and invalid signal interference in the signal transmission process can be suppressed under the conditions of strong environmental noise and low signal-to-noise ratio, signals containing effective information can be accurately extracted, and the target detection efficiency can be improved under the condition of high signal-to-noise ratio.

The invention discloses a machine vision-assisted method for monitoring the size accuracy of an automobile injection molded part, and relates to the technical field of machine vision, and the method comprises the steps: irradiating the injection molded part through the combination of a visible light source and a near-infrared light source, synchronously collecting a three-view image, segmenting an injection molded part region, selecting a key-sized ROI region, and carrying out the fusion to generate a multi-view fusion image; identifying the minimum key feature size from the ROI region, decomposing and fusing the image through multi-scale morphological iteration processing, extracting a real contour signal by adopting an ICA blind source separation algorithm, and generating a complete contour model through multi-view edge point fusion reconstruction; marking feature points based on a CAD standard model, positioning the feature points by adopting particle swarm optimization and a Bayesian iterative algorithm, and calculating critical dimension parameters; environment and injection molding part surface error factors are collected, a dynamic error calibration model is constructed to compensate dimensional deviation, a final size value after calibration is output, and the accuracy and reliability of automobile injection molding part size detection are remarkably improved.

The application discloses a machine vision assisted automobile injection molding part size precision monitoring method and relates to the technical field of machine vision, which comprises the following steps: combining visible light and near-infrared light to irradiate an injection molding part, synchronously collecting three-view images, segmenting the injection molding part region and framing the ROI region of a key size, and fusing to generate a multi-view fusion image; identifying the smallest key feature size from the ROI region, decomposing the fusion image through multi-scale morphological iteration processing, extracting a real contour signal by using an ICA blind source separation algorithm, and generating a complete contour model through multi-view edge point fusion reconstruction; marking feature points based on a CAD standard model, positioning the feature points by using a particle swarm optimization and a Bayesian iteration algorithm, and calculating key size parameters; collecting environmental and injection molding part surface error factors, constructing a dynamic error calibration model to compensate for size deviation, and outputting the final size value after calibration, so that the accuracy and reliability of automobile injection molding part size detection are significantly improved.

This application provides a signal processing method, chip, electronic device, and storage medium. The method includes: acquiring an input signal, wherein the input signal is a speech signal received by multiple microphones; estimating the covariance matrix of the Nth frame of the input signal to obtain a target covariance matrix of the Nth frame; updating the demixing matrix based on the target covariance matrix of the Nth frame to obtain target elements in the demixing matrix of the Nth frame; performing amplitude demixing based on the target elements in the demixing matrix of the Nth frame to obtain a target demixing matrix of the Nth frame; and performing signal separation based on the target demixing matrix of the Nth frame and the input signal of the Nth frame to obtain an output signal of the Nth frame. The method provided in this application helps to balance the performance and robustness of blind source separation algorithms and improve the signal-to-noise ratio of speech signals.

The invention discloses an inter-beam coupling signal decoupling judgment, decoupling and velocity measurement method in acoustic Doppler velocity measurement, and belongs to the technical field of coupling interference suppression in an underwater acoustic navigation technology. In order to solve the problem of signal cross coupling interference of phased array acoustic Doppler velocity measurement equipment, the method comprises the following steps: aiming at received velocity measurement beam echo signals, performing decoupling judgment on the beam echo signals by utilizing correlation between signals on a channel; and when it is judged that the speed measurement beam echo signal can be decoupled, performing preprocessing including whitening on the signal to obtain a signal, and processing a real part of the received signal by using a blind source separation algorithm to obtain a decoupled echo signal real part. And Hilbert transform is carried out on the obtained signal to obtain a complex signal for speed measurement.

The application relates to the field of radar technology and provides a method and device for resisting towed interference of an airborne radar. The method comprises the following steps: constructing sum channel data, azimuth difference channel data and elevation difference azimuth data of echo signals; constructing sum-difference three-channel data of each pulse according to the sum channel data, the azimuth difference channel data and the elevation difference azimuth data; processing the sum-difference three-channel data by using a blind source separation algorithm to obtain target echo channel data and a separation matrix; pulse compressing the target echo channel data to obtain a target distance estimation value; determining an azimuth angle and an elevation angle of a target according to the target echo channel data and the separation matrix; and constructing a target echo matrix by using target echo channel data of each pulse, and performing Doppler processing on the target echo matrix to obtain a target speed estimation value. The application can resist towed interference, determine the distance, the azimuth angle, the elevation angle and the speed of the target, and realize effective detection of the target.

This invention proposes a method for suppressing short-duration diurnal magnetic field interference based on dual-aircraft collaboration. Addressing the challenges of establishing fixed diurnal variation monitoring stations and lacking effective diurnal variation reference information in offshore dual-aircraft collaborative airborne magnetic surveys, this method utilizes airborne magnetic survey data collected during near-shore airborne magnetic surveys and synchronous observation data from diurnal variation monitoring stations. It proposes a dual-aircraft collaborative diurnal magnetic interference suppression method based on a neural network-guided blind source separation algorithm. This method first uses a deep neural network to learn and extract prior features of diurnal variation interference from the dual-aircraft observation signals. These features are then used as guiding information in the blind source separation process to separate the independent source components corresponding to the diurnal magnetic interference, ultimately achieving high-precision identification and suppression of diurnal variation interference components in the original signal. This method can efficiently separate and suppress short-duration diurnal magnetic interference, significantly improve the signal-to-noise ratio of magnetic survey data, and effectively preserve the target magnetic anomaly characteristics, providing reliable technical support for high-precision airborne magnetic surveys.

The application discloses a time-frequency domain underdetermined blind source separation method and system based on double sensors, which comprises two stages: in the first stage, a single source point with high clustering characteristics is detected by using a clustering and matching tracking algorithm, and a mixing matrix is estimated, so that high-precision mixing matrix estimation is realized; in the second stage, a time-frequency domain signal recovery problem is converted into a sparse recovery model with a relaxed sparse condition, the constraint of the number of sources on the self-source point in the traditional blind source separation algorithm is broken, and good separation effect is realized. The application is suitable for the double-sensor underdetermined blind source separation scene with more source numbers, low mixing signal-to-noise ratio and serious aliasing, and has strong applicability and practicability.

The invention discloses a drain valve fault online monitoring method and system based on voiceprint analysis, and relates to the technical field of industrial equipment state monitoring, and the method comprises the steps: employing a blind source separation algorithm based on REPET to carry out the preprocessing of a voiceprint signal, and removing the interference of environment noise; and voiceprint features are extracted by using an MFCC method, and fault diagnosis is performed by using a GMM model. According to the invention, real-time and accurate monitoring of the fault of the drain valve is realized, the accuracy rate of voice print abnormity recognition of faults such as inner leakage and blockage of the drain valve is high, the signal processing and recognition time is short, the misjudgment rate is low, the efficiency and reliability of fault diagnosis of the drain valve are effectively improved, and a powerful guarantee is provided for safe and stable operation of a steam turbine. For complex field environments such as noisy thermal power plants, narrow space and the like, the method shows excellent adaptability. The advanced voiceprint preprocessing algorithm can effectively filter environmental noise and accurately focus on the voiceprint features of the drain valve.

The invention relates to the technical field of voice processing, and discloses a device and system for doctor-patient voice separation, and the system comprises an acquisition and analysis module which separates mixed voice based on the traversal result of historical data and a blind source separation algorithm and determines dual-track sampling, a first processing module determines a sample data set, and a second processing module determines a second sample data set; the second processing module constructs a voice separation model associated with the sample data set according to the sample data set, separates dual-track samples based on the voice separation model, performs noise reduction based on the relationship between the doctor voice stream and the plurality of patient voice streams, and performs text transcription on the target doctor voice stream and all target patient voice streams to obtain a target doctor voice stream and a plurality of target patient voice streams; according to the method and the system, the medical record document of each patient is determined, the retrieval processing module extracts the text features in all the medical record documents, retrieval is performed in the record database based on the text features, the medical record document retrieval result of each patient is determined, and the stability and the reliability of mixed voice separation are ensured.

The present invention discloses an online wrist torque estimation method based on neural features and LSTM, comprising the following steps: (1) the experimenter keeps the arm still and applies torque to the torque sensor through the wrist; (2) synchronously collects the torque sensor data and high-density surface electromyography signals; (3) uses a blind source separation algorithm to decompose the high-density surface electromyography signals to obtain a motor unit spike train MUST; (4) constructs input and output vectors based on the original HD-sEMG signal and the decomposed MUST, trains the LSTM, and simultaneously performs polynomial regression on the discharge rate of the neural features and the torque; (5) calculates the real-time discharge rate DR of the CST through a sliding window for real-time torque estimation. The present invention is used for natural and real-time control of a robotic prosthesis, can provide a better interactive experience for people with disabilities, and can also be widely used in rehabilitation robots, human-computer interaction and other fields.

The application relates to the technical field of power equipment fault diagnosis, and particularly discloses a mechanical fault diagnosis method and system for a pole-mounted circuit breaker, which comprises the following steps: firstly, synchronously collecting dynamic force-displacement signals, high-frequency acoustic emission signals and wide-band vibration signals in the opening and closing process of a circuit breaker operating mechanism; then, decoupling the acoustic emission signals and the vibration signals into statistically independent impact source components and friction source components through a blind source separation algorithm; then, performing envelope spectrum analysis on the impact source components to extract impact features, and performing energy calculation on the friction source components to obtain friction noise energy features; finally, based on dynamic force-displacement curve fitting analysis, combining the impact features and the friction noise energy features, the mechanical wear and lubrication deterioration faults are distinguished and delimited; the application solves the problem that the existing diagnosis technology cannot effectively identify and distinguish the mechanical wear and lubrication deterioration coupling faults, and realizes early warning and accurate diagnosis of the mechanical faults of the pole-mounted circuit breaker.

The invention discloses an anti-interference radar signal extraction method and device and electronic equipment, and the method comprises the steps: carrying out the whitening processing of a to-be-processed radar signal through a zero-phase component analysis whitening algorithm, and obtaining a to-be-separated radar signal after the whitening processing. An initial separation matrix is obtained by performing iterative optimization processing on a preset target separation function for multiple times. And performing fine adjustment on the initial separation matrix according to a second-order blind source separation algorithm to obtain a target separation matrix. Performing signal separation processing on the radar signal and the interference signal according to the target separation matrix to obtain an initial radar signal, and performing denoising processing on the initial radar signal according to a target denoising algorithm to obtain a radar signal, the target denoising algorithm being any one of a wavelet transform denoising algorithm and an improved stationary wavelet transform denoising algorithm. The problems of high calculation complexity and low efficiency in the prior art are avoided, and the stability and accuracy of radar signal extraction are improved.

The invention discloses a bistatic multi-channel synthetic aperture radar (SAR) synchronous signal extraction method and device, and belongs to the technical field of synthetic aperture radar signal processing. According to the method, a bistatic SAR system with a plurality of receiving channels is constructed, and a synchronization signal and a scene echo signal are modeled into a linear mixed signal by using time delay and amplitude differences caused by physical position differences among the channels; a blind source separation algorithm based on high-order statistics is adopted to carry out centralization, whitening and joint diagonalization processing on multichannel received signals, and effective separation of synchronous signals and target echoes is realized. The method does not need to interrupt the imaging process, does not depend on priori knowledge or complex waveform design, remarkably improves the phase synchronization precision and the system flexibility, is suitable for various bistatic SAR systems, and has good practicability and universality.

The invention provides a moving coil diaphragm car audio, which is regulated and controlled in real time through safety margin detection, specifically, an alarm signal is extracted from a mixed sound field through an improved blind source separation algorithm, and sub-band decomposition covers a frequency band of 0-300Hz / 300Hz-5kHz / 5kHz-20kHz; an auditory discernible index ADI is calculated every 100 ms, and based on a Mel filter bank and an ISO 226 auditory weight, the spectrum difference between the alarm and the background at the frequency band of 1200-3000 Hz is quantized; when ADIlt; when the frequency range is 0.35, the frequency domain enhancement is started, the fundamental frequency offset is 50-100 Hz, the volume is increased by 6-12 dB, meanwhile, the voiceprint anchoring unit restrains the harmonic energy change to be smaller than or equal to 15%, the phase offset is smaller than or equal to 22.5 degrees, and identifiability is guaranteed.

This invention discloses an in-vehicle selective noise reduction method based on sound field reconstruction, relating to the field of in-vehicle safety warning technology. The method includes: collaboratively collecting in-vehicle acoustic and vibration data through sensors; separating the acoustic and vibration data using a blind source separation algorithm to obtain a voiceprint feature tensor; inputting the voiceprint feature tensor into a convolutional neural network for voiceprint recognition, outputting a safety sound category and a safety sound hazard level; generating adaptive noise reduction parameters based on vehicle speed and safety sound category; assigning the final adaptive noise reduction parameters to seats in different regions; and issuing a physical vibration warning based on the safety sound hazard level to complete the in-vehicle selective noise reduction. This invention significantly improves the detection and recognition accuracy of critical safety sounds in road noise environments; it balances noise suppression and active safety assurance, effectively enhancing the vehicle's perception and response capabilities to emergency sound sources while improving passenger comfort.

This embodiment provides a verification system and method for an underdetermined blind source separation algorithm based on a Yagi antenna. A blind source signal acquisition system is designed using a Yagi antenna, a superheterodyne receiver, and data acquisition software. Standard signal sources and walkie-talkies are used as experimental blind source signals. This blind source signal acquisition system has two acquisition channels. By setting the signal source and receiving frequency, it can simultaneously acquire intermediate frequency (IF) data from multiple source signals within the real-time bandwidth. The sampled IF data is used as input data for the algorithm to compare and verify the source number estimation and source signal recovery effects of various underdetermined blind source separation algorithms.

The present invention belongs to the field of computer science artificial intelligence algorithms and relates to a signal deinterleaving method for a satellite-based ship automatic navigation system. The present invention first separates interleaved signals using a multi-channel AIS signal separation neural network. After separation, the separated signal is correlated with an AIS pilot reference signal and the maximum output is taken to determine the incident angle phase of the separated signal, and the steering vector of the separated AIS signal is calculated. Finally, the separated AIS signal is beamformed using minimum variance distortionless response beamforming technology to enhance the signal. The present invention combines array signal processing with convolutional neural network technology, improving separation performance while resisting mainlobe interference. This solves the problem that traditional blind source separation algorithms suffer performance degradation when the intensity of mainlobe interference is excessive.

The invention discloses a detection method of a pipeline leakage prediction and early warning system based on distributed optical fiber acoustic sensing, and the method comprises the following steps: (1) laying a single-mode armored optical cable along the 5 o'clock direction of the outer wall of a pipeline, and collecting an acoustic vibration signal through a DAS system; (2) a blind source separation (BSS) algorithm is combined with independent component analysis (ICA) to separate pipeline noise and leakage voiceprints, and leakage feature components are extracted through non-negative matrix factorization (NMF); and (3) running a GMM-UBM model containing 64 Gaussian components in an edge server, training a universal background model (UBM) through normal voiceprint of a pipeline, injecting fault voiceprint update parameters, and calculating a voiceprint likelihood ratio (log (LR) = log (p (YH0))-log (p (YH1))). According to the method, the positioning accuracy is remarkably improved, the PHI-OTDR phase change positioning technology is adopted, the optimized voiceprint recognition algorithm is combined, the positioning error is smaller than or equal to 0.5 m, the positioning error is improved by 20 times compared with traditional manual inspection, the leakage position can be accurately determined, and the overhaul range and time are shortened.

The application provides a kind of aeroengine main bearing microdefect nonlinear phased array ultrasonic nondestructive testing method and device, it is related to the technical field of ultrasonic nondestructive testing, method includes: according to the acoustic beam focal spot diameter of the transmitting aperture of phased array excitation probe, target detection area is grid division;According to the focusing rule of phased array transmitting aperture, the focusing scanning of each grid is sequentially completed, and the ultrasonic signal of each grid is collected by phased array receiving probe in scanning process;Adaptive bilateral filtering algorithm is used to carry out noise reduction processing to ultrasonic signal;Signal blind source separation algorithm is used to decouple processing signal, and the nonlinear harmonic signal generated by microdefect is separated out, and each microdefect is positioned and quantitatively analyzed;Two phased array probes are scanned along the profile of bearing ring section, and full coverage detection of each section of bearing ring is completed.The application can nondestructively detect the microdefect of aeroengine bearing.

The invention discloses a radar main lobe anti-interference method based on space-time coding and blind source separation, and belongs to the field of radar signal processing. The method comprises the following steps: forming a plurality of equivalent phase centers by using space-time coding multiple-input multiple-output, and constructing an array configuration library containing different array configurations; simulating radar echo signals under different array configurations in an array configuration library according to the angle information of the target and interference of the current scene to obtain mixed echo signals corresponding to the different array configurations; separating the mixed echo signal by using a blind source separation algorithm to obtain independent signal components corresponding to different signal sources at a receiving end; and calculating the signal-to-interference ratio of each array configuration according to independent signal components corresponding to different signal sources at the receiving end, and selecting the array configuration with the highest signal-to-interference ratio as the optimal array configuration of the current scene. Therefore, according to the method, the optimal array configuration can be dynamically adjusted according to different interference types and specific environmental conditions, resource waste in an invalid direction is avoided, and the inhibition capability on unknown interference is remarkably enhanced.

The application relates to the technical field of brain-computer interface, and discloses a signal acquisition method for brain-computer interface control of a lower-limb exoskeleton robot. The method collects original electroencephalogram signals through a multi-channel electrode array arranged on a motor cortex of a scalp, and synchronously collects motion data of a lower-limb inertial measurement unit. In the signal processing process, differential amplification is adopted to suppress common-mode interference, adaptive narrow-band filtering is adopted to dynamically track electroencephalogram rhythms, and a blind source separation algorithm based on independent component analysis is adopted to remove motion artifacts. Finally, the processed motion intention electroencephalogram signals are output, the precision and real-time performance of intention recognition are improved, and accurate and stable control signals are provided for the lower-limb exoskeleton robot.

The invention relates to a GIS switch monitoring and early warning method and system based on blind source separation, and belongs to the technical field of power equipment state monitoring, and the method comprises the steps: collecting opening and closing vibration signals through a sensor array disposed on a GIS switch; decomposing the mixed vibration signal into a plurality of independent source signals which are strongly correlated with a specific physical action process by adopting a blind source separation algorithm; performing wavelet energy entropy calculation on each independent source signal, and extracting a characteristic quantity reflecting the state of the corresponding mechanical part; and establishing a health state evolution model based on the characteristic quantity, and performing multi-level early warning by tracking the change trend of the characteristic quantity. The problems that in a traditional method, vibration signals interfere with one another, and fault features are prone to being submerged are effectively solved, the monitoring sensitivity and the early warning capacity of tiny faults are remarkably improved, fault components can be accurately positioned, and reliable technical support is provided for predictive maintenance of a GIS switch.