28 results about "Blind source separation algorithm" patented technology

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 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 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.

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.

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 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.

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.

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.

The application discloses an intelligent sleep staging and fatigue assessment system based on a multi-modal deep neural network, relates to the technical field of intelligent sleep, and comprises a cECG data acquisition module, a multi-modal decoupling filtering module, a cross-modal quality calibration module, a multi-modal deep neural network algorithm module and a result output layer. The cECG data acquisition module adopts an array capacitive sensing layout, is embedded into a daily sleep carrier, and continuously acquires original array cECG signals during user sleep. Through a space and mode double-constraint blind source separation algorithm, accurate decoupling of three types of mode signals, i.e., electrocardiogram, respiration and body movement, is realized, cross-modal quality calibration and dynamic fusion weight adjustment are combined, the signal quality and fusion accuracy are improved, and with the aid of a space, quality and attention three-dimensional mutual feedback calibration network and an adaptive state gate Mamba, synchronous and accurate execution of double tasks of sleep staging and fatigue assessment is realized.

The invention discloses a converter valve working condition diagnosis method, device and equipment based on voiceprint feature extraction, and relates to the technical field of high-voltage direct-current converter valve working condition diagnosis. The method comprises the following steps: acquiring original audio data of an operation state of the high-voltage converter valve, and carrying out framing, windowing and normalization preprocessing on a collected sound signal; separating various source signals in the mixed sound signals by using a blind source separation algorithm to obtain abnormal sound signals of the high-voltage converter valve; respectively extracting MFCC features and wavelet packet energy spectrums of the sound signals of the high-voltage converter valve by adopting a joint feature extraction method, and dynamically adjusting parameters through an improved zebra optimization algorithm ZAO; and inputting the extracted combined optimization features into a convolutional neural network, and judging the fault type of the abnormal operation state of the high-voltage converter valve. According to the method, the reliability of voiceprint feature extraction of the high-voltage converter valve and the accuracy of operation state evaluation are improved, the robustness is high, and possible potential safety hazards are avoided.

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

This invention discloses a method for visualizing structural mode shapes based on an Euler-Lagrange hybrid frame, comprising: 1. acquiring motion video data of the structure to be measured, and extracting vibration signals of discrete regions of the structure using a sub-pixel precision image matching algorithm; 2. decoupling the vibration signals using a blind source separation algorithm to obtain modal response signals of each order of the structure; 3. calculating the spatial weights of each mode of the structure based on the acquired modal response signals; 4. calculating the image grayscale values ​​associated with a single mode of the structure based on the modal response signals and spatial weights; 5. extracting the dense motion field between frames associated with a single mode of the structure using an optimized Demons algorithm; 6. visualizing the structural mode shapes using a motion compensation algorithm based on the acquired motion field. This invention can better achieve global spatial motion decoupling and improve the accuracy of the motion field, thereby reducing the complexity of the algorithm and improving the quality of visualized structural mode shapes.

The invention discloses an enhanced MIMO and FDA-MIMO single-base radar combined aperture distance and angle cooperation estimation method, and the method comprises the steps: building a dual-mode single-base radar signal model, and obtaining the echo mixed data of a target; performing matched filtering processing on the echo mixed data to obtain MIMO and FDA-MIMO radar signal echo data matrixes corresponding to each receiving array element, performing superposition to obtain final output signals of the MIMO radar and the FDA-MIMO radar, and further estimating a joint array guide matrix by using a blind source separation algorithm to calculate guide vector data of the MIMO radar and the FDA-MIMO radar so as to obtain the guide vector data of the MIMO radar and the FDA-MIMO radar. Distance phase information of the target is further obtained; and defuzzification is carried out on the distance phase information of the target, and a distance compensation matrix is designed to carry out distance compensation on the FDA-MIMO steering vector so as to obtain a cooperative radar full-aperture emission steering vector and improve the DOA estimation accuracy.

This invention belongs to the field of online detection technology for metal impurities in food, and particularly relates to an intelligent sensing-based online detection method and system for metal impurities in food. It simultaneously transmits and receives multiple discrete frequency band electromagnetic signals, generating a multi-dimensional original signal matrix by leveraging the differences in their responses to metals, food, packaging, and the environment. Based on the fusion application of an improved FastICA blind source separation algorithm and wavelet packet transform, a multi-frequency feature-blind source separation dual-layer model is constructed. The effectiveness of signal separation is verified using a multi-scenario standard signal feature library and cosine similarity matching. After feature extraction and combined dimensionality reduction processing, the output is fused through a hybrid model of traditional machine learning and lightweight deep learning. The D-S evidence theory is introduced and combined with real-time parameters from the production line to dynamically adjust the decision rules.

This invention discloses a communication signal anti-interference method based on shape parameter estimation and semi-blind source separation. The method includes the following steps: S1. A signal receiving device acquires a received signal through an antenna. The received signal is a mixed signal including a transmitted signal from a signal transmitting device and interference signals from other sources; S2. A signal shape parameter estimate of the received signal is obtained by combining deep learning and numerical analysis methods; S3. The received signal, the interference signal, and the signal shape parameter estimate are input into a signal processing system. The signal processing system uses a signal shape parameter estimation algorithm and an AuxIVA-based semi-blind source separation algorithm to acquire the target communication signal. This method can improve the accuracy of the algorithm, thereby effectively improving the anti-interference capability against co-band interference signals that cause interference in wireless communication.

The application relates to a single-channel radar main lobe interference separation method, device and computer equipment. The method comprises the following steps: receiving an observation signal through a single-channel radar, discretizing the observation signal at a first preset period to obtain a plurality of discrete signals; then, performing virtual multi-channel sampling and rearrangement on the obtained plurality of discrete signals to obtain a virtual multi-channel signal matrix; then, performing pulse compression on each virtual channel signal in the virtual multi-channel signal matrix to obtain a virtual multi-channel pulse compression signal; obtaining the number of signal sources according to the virtual multi-channel pulse compression signal; taking the number of signal sources as prior information; and adopting a blind source separation algorithm to perform main lobe interference separation on the virtual multi-channel pulse compression signal. The method can separate target signals under the coexistence of multiple types and quantities of active interference, and still has a good separation effect under a low signal-to-noise ratio.

The application discloses a cable force calculation method and system, and the cable force calculation method comprises the following steps: acquiring a first image of a cable to be detected; inputting the first image into a trained image segmentation model to obtain a second image; calculating a power spectral density graph of the cable to be detected through Hilbert transform and a blind source separation algorithm according to the second image; and calculating the cable force of the cable to be detected according to the power spectral density graph. The application improves the accuracy of initial data through the image segmentation model, improves the accuracy of frequency measurement through Hilbert transform and the blind source separation algorithm, and thus improves the accuracy of cable force calculation.

The invention discloses an effective MIMO and FDA-MIMO single-base radar distance and angle cooperation estimation method, and the method comprises the steps: building a dual-mode single-base radar signal model, and obtaining the echo mixed data of a target; through matched filtering processing, MIMO and FDA-MIMO radar signal echo data matrixes corresponding to each receiving array element are obtained, and final output signals of the MIMO radar and the FDA-MIMO radar are obtained through superposition; the method comprises the following steps: estimating a joint array guide matrix by using a blind source separation algorithm to calculate MIMO radar phase information and obtain a DOA estimation value of a target, calculating guide vector data of the FDA-MIMO radar by combining the joint array guide matrix, further calculating distance phase information under the FDA-MIMO radar, and obtaining a distance estimation value of the target after phase ambiguity solving. The estimation precision of the angle and the distance is guaranteed, the calculation efficiency is improved, and automatic pairing of multiple parameters is achieved.

The invention discloses an online sensing method and system based on vacuum OLTC multi-dimensional information, and the method is characterized in that the method comprises the steps: extracting a sound signal and a vibration signal of a vacuum OLTC; based on an SDICA blind source separation algorithm, interference signals in the sound signals and the vibration signals are filtered out; based on the Mel time-frequency spectrum, respectively calculating a sound cepstrum coefficient MFCC and a vibration cepstrum coefficient MFCC of the sound signal and the vibration signal after the interference signal is filtered out; based on an SRU neural network, performing deep learning on the sound Mel cepstrum coefficient and the vibration Mel cepstrum coefficient, and judging the defect type of the vacuum OLTC; extracting an electrical quantity signal of each transition branch of the vacuum OLTC; and identifying an action time sequence of the electrical quantity signal, comparing the action time sequence with a design time sequence, and determining a defect position of the vacuum OLTC based on a comparison result.

This application provides a distributed optical fiber monitoring method and system for tunnel support structures, belonging to the field of safety monitoring technology for carbon tunnel engineering. The method involves adapting and deploying multi-core sensing optical cables to the support structure, with the monitoring section aligned with the stress-sensitive area, and the free section fitted with a protective sleeve that is detachably connected. A frequency-agile optical comb detection sequence is transmitted through an optical frequency comb detection module to acquire full-lifecycle sensing signals. Wavelet threshold denoising and blind source separation algorithms are used to process the signals, constructing a two-dimensional feature map monitoring dataset. An improved neural network model containing an SPPF spatial pyramid pooling layer and a path aggregation network is input to achieve accurate identification and location of cracks, anchor bolt loosening, and surrounding rock loosening, as well as anomaly classification. An early warning information feedback terminal is generated, and the detection parameters and monitoring sensitivity are adaptively adjusted.