206 results about "Wavelet packet transformation" patented technology

The invention relates to the technical field of electromagnetic flowmeter calibration, and discloses an electromagnetic flowmeter online calibration system and an acquisition and storage device. The system comprises a signal acquisition module, a data processing module, an error analysis module, a parameter calibration module and a real-time monitoring module. The signal acquisition module captures an original signal waveform through a multi-channel sensor array and acquires flow signal data to generate a state set; the data processing module uses a wavelet packet transformation and adaptive filtering combined algorithm to reduce noise and extract features; the error analysis module constructs a dynamic time warping error matching model to obtain an error correction coefficient; the parameter calibration module adopts a fuzzy control and proportional integral differential composite strategy, and adjusts excitation current and electrode sensitivity according to an error correction coefficient; the real-time monitoring module establishes a rolling optimization model with time-varying constraints, and generates a calibration instruction in combination with signal stability and power consumption limitation. According to the system, online accurate calibration of the electromagnetic flowmeter is realized, and the measurement accuracy and the equipment operation stability are improved.

The invention relates to the technical field of water photovoltaic flexible support pile foundation construction data processing, in particular to a process for a water photovoltaic flexible support pile foundation, which comprises the following steps: fusing meteorological, hydrological and pile deformation multi-source data through a dynamic weight distribution mechanism, and generating a space-time associated dynamic load spectrum by adopting wavelet packet transformation and entropy feature extraction; a collaborative analysis architecture of a finite element simulation and machine learning agent model is constructed, an anchoring depth mapping relation and a real-time design parameter adjustment instruction are output in parallel, and macroscopic fluid parameters and microscopic material degradation characteristics are associated through a data cascade interaction correction mechanism; full-life-cycle data version management is achieved through the block chain technology, and finite element model re-calibration and protection strategy dynamic updating are triggered. The dynamic load response analysis precision and long-term stability of the pile foundation are improved, and the deformation coordination requirement of the flexible support in the complex water area environment is met.

The invention relates to an intelligent power distribution harmonic monitoring and dynamic compensation system which comprises a monitoring unit, a correction unit and a compensation unit. The monitoring unit continuously collects high-frequency harmonic voltage and current data in a distribution line at a high sampling frequency, extracts transient harmonic components through wavelet packet transformation and empirical mode decomposition, and generates low-dimensional feature vectors based on sparse representation. And the correction unit decodes the low-dimensional feature vector, recovers harmonic time-frequency features, calculates a phase drift rate, predicts a harmonic propagation path and an accumulation node by combining real-time power distribution network topology construction and adopting a nonlinear dynamic prediction model, and generates a correction instruction when abnormality is detected. And the compensation unit adopts pulse sequence density modulation to dynamically adjust a compensation current phase according to the correction instruction, and meanwhile, an inductive coupling device is utilized to transfer harmonic energy to a low-risk node, so that harmonic voltage distortion of a target node is quickly recovered to a stable level in a fundamental wave period after early warning.

The invention discloses an electrical fire monitoring method based on inherent residual current automatic compensation, and relates to the technical field of electrical safety monitoring, and the method comprises the following steps: S100, collecting a residual current signal, executing multi-component time-frequency deconstruction processing, extracting an energy distribution characteristic through wavelet packet transformation, and extracting a mutability index in combination with short-time spectrum entropy analysis, and constructing a preliminary distribution map of higher harmonic interference, and determining boundary features of interference signals in a time domain and a frequency domain. According to the method, high-frequency interference signals are accurately positioned through time-frequency deconstruction, wavelet packet analysis and spectral entropy indexes, non-fault harmonic components are effectively eliminated in combination with amplitude-frequency coupling recognition and a recursive rejection strategy, closed-loop control is constructed by introducing an adaptive compensation and stability backtracking mechanism, accurate recognition and dynamic correction of real electric leakage risks are achieved, and the method is suitable for large-scale popularization and application. The identification precision and the safety reliability of the monitoring system in a complex industrial environment are obviously improved, and the method has good engineering adaptability.

The invention discloses a subway tunnel intrusion monitoring and early warning system and method, and relates to the technical field of subway safety monitoring. The system comprises a vibration monitoring module, a stress monitoring module, a signal analysis unit, an alarm unit and a database module, wherein the vibration monitoring module adopts a distributed vibration sensing optical cable to collect tunnel wall surface vibration signals in real time; the stress monitoring module monitors the stress change of the tunnel structure through a spirally wound and pre-embedded fiber grating array; the signal analysis unit decomposes vibration frequency band energy through wavelet packet transformation, counts a stress standard deviation in combination with a sliding time window, and performs multi-modal feature matching with an abnormal signal database by using a dynamic time warping algorithm; and the alarm unit triggers multi-stage response according to a feature matching degree threshold value or vibration-stress space-time correlation abnormity, and train speed limiting control is linked. The problems that the single-mode false alarm rate is high, multi-source interference cannot be decoupled and stress monitoring coverage is insufficient are solved, recognition and early warning of construction intrusion events are achieved, and tunnel structure safety is improved.

According to the cooling tower early fault early warning method based on vibration state monitoring, vibration signals and working condition labels of key parts of the cooling tower are synchronously collected through multiple channels, and data quality is improved through preprocessing operation such as band-pass filtering and normalization; time-frequency features are extracted in a multi-scale mode through self-adaptive variational mode decomposition and wavelet packet transformation, signal complexity is quantized through energy entropy, and weak fault detection capacity is enhanced; the obtained features are input into a deep belief network after being subjected to principal component analysis dimensionality reduction, and automatic classification and recognition of the equipment operation state are achieved; dynamic early warning grade adaptation is carried out according to an identification result in combination with a working condition label, the environmental adaptability and stability of early warning are effectively improved, the method further has the functions of early warning sample recording and periodic model iterative optimization, and the fault identification precision and robustness in a complex noise environment are remarkably improved.

The invention provides a medical RFQ accelerator low-level signal phase stability control method and system. The method comprises the following steps: constructing a time-frequency energy spectrum feature vector based on wavelet packet transformation; extracting a second disturbance feature based on a lightweight convolutional neural network and an attention mechanism; constructing a phase dynamic trend prediction module based on a long short-term memory network, and obtaining a first prediction phase error; constructing a phase compensation module based on a residual control network to obtain a second phase compensation amount; and outputting a real-time driving control signal based on the extended Kalman filter. According to the method, the time-frequency energy spectrum feature vector based on wavelet packet transformation is constructed, accurate characterization of the multi-scale disturbance features of the low-level signals is achieved, a medical RFQ accelerator phase dynamic compensation system is established in combination with a deep learning network and an extended Kalman filtering algorithm, the control precision and the anti-interference capability of signal phase stability are remarkably improved, and the method is suitable for popularization and application. The method is suitable for a high-precision medical particle accelerator control system.

The invention discloses a power transmission line icing intelligent early warning method, system and device and a medium, and the method comprises the steps: obtaining icing state data and meteorological data, and dynamically adjusting the collection frequency and a dormancy strategy; performing data preprocessing and cleaning on the acquired data; extracting time-frequency features through wavelet packet transformation and a self-attention mechanism, and fusing the spatial dependency relationship and cross-modal interaction information of multiple monitoring points by using a graph neural network to obtain enhanced icing state characterization; performing icing risk prediction by adopting a gradient boosting decision tree model to obtain an icing risk prediction result; and analyzing an icing risk prediction result by using an interpretable tool, identifying a key factor which has the greatest influence on icing risk prediction, dynamically adjusting an early warning level according to the key factor, and generating an early warning and maintenance suggestion. Therefore, the monitoring real-time performance and the early warning timeliness are improved.

The invention relates to the technical field of intelligent buildings and energy management, and particularly discloses an intelligent building heating intelligent optimization operation method and system based on deep learning, and the method comprises the steps: collecting building environment parameters and equipment operation data in real time through a distributed optical fiber sensing network and an infrared thermal imaging system; extracting minute-level fluid transmission and distribution parameters and hour-level building thermal inertia characteristics by adopting wavelet packet transformation and a graph neural network; then, constructing a neural differential equation prediction model fused with physical constraints, and outputting high-precision thermal load demand prediction through a differentiable heat conduction operator coupling multi-time scale feature; then, a mixed integer optimization model considering equipment life loss is established, and boiler start-stop combination and pipe network flow distribution are synchronously optimized by adopting a hierarchical decision-making mechanism; and finally, closed-loop control is realized through a multi-mode actuator network, and model parameters are dynamically adjusted in combination with an online learning mechanism.

The invention provides a Lamb wave excitation and receiving optimization method for hyperbolic glass curtain wall nondestructive testing and positioning. The method comprises the following steps: acquiring curtain wall parameter information of a hyperbolic glass curtain wall to be detected and positioned; the curtain wall parameter information at least comprises geometric parameters, thickness parameters, material parameters and environmental parameters; optimizing Lamb wave parameters corresponding to the glass curtain wall according to the curtain wall parameter information; the Lamb wave parameters at least comprise an excitation frequency, a waveform coefficient and an excitation angle; performing signal emission and receiving on the hyperbolic glass curtain wall according to the optimized Lamb wave parameter, and obtaining a damage reflection signal corresponding to the hyperbolic glass curtain wall; performing time-frequency domain decomposition on the damage reflection signal according to wavelet packet transformation to obtain a damage feature vector; and obtaining a neural network positioning model containing hyperbolic geometric constraints, inputting the damage feature vectors into the neural network positioning model, and outputting damage three-dimensional coordinates to complete hyperbolic glass curtain wall nondestructive testing positioning.

The invention discloses a cross-working-condition fault diagnosis method based on knowledge embedding and multi-scale attention, belongs to the technical field of cross-working-condition fault diagnosis, designs a domain knowledge embedding signal processing method based on wavelet packet transformation, envelope spectrum analysis and statistical feature analysis, and constructs a knowledge feature matrix and a statistical feature matrix. Fault information which is not easily influenced by working condition changes is highlighted, and the dependence of a deep learning model on a target domain sample is effectively reduced; a multi-scale attention mechanism is designed to extract depth fault features in an input matrix, and compared with an original multi-head attention mechanism, multi-scale is introduced, so that the parameter quantity of a model is reduced, and the feature extraction capability is more flexible; by embedding domain knowledge highlighting domain invariant fault information into a signal processing end, the method can show excellent variable working condition fault diagnosis performance when a target domain sample is completely lacked.

The invention discloses a distribution line fault detection and positioning method based on current signal analysis, and belongs to the technical field of power system fault diagnosis. The method comprises the steps of collecting three-phase and zero-sequence current data before and after a fault, calculating current variation, judging whether the fault is an interphase short-circuit fault, and determining a first fault interval by combining an impedance method; and performing wavelet packet transformation on the transient voltage signal to obtain an actual transient signal, injecting a test signal in the first fault interval, collecting a response signal, constructing an autoregression model, solving an inverse noise function, and performing denoising processing on the actual transient signal to obtain a target transient signal. Wavelet packet decomposition is carried out on the denoised target transient signals, energy distribution and signal-to-noise ratio information of all frequency bands are combined, a corrected positioning formula is introduced, and accurate positioning of a second fault interval of the fault point is achieved. The method effectively inhibits noise interference, improves the positioning precision, and is suitable for a real-time fault detection and positioning system in an intelligent power distribution network.

The invention provides a harmonic reducer fault diagnosis method and system combining SAH with SSET under a time-varying rotating speed, and belongs to the field of harmonic reducer fault diagnosis. The invention aims to solve the problems of large time-frequency ridge line extraction error and inaccurate fault feature order extraction caused by the non-stable and signal modulation characteristics of a harmonic reducer vibration signal under a time-varying rotating speed. According to the SAH construction method based on sub-band rearrangement dual-tree complex wavelet packet transformation, a band elimination filter is designed according to an optimal frequency band, and a resonance frequency band where a fault impact component is located is filtered out; a time-frequency ridge extraction method combining SAH-SSET with a fast path optimization algorithm is provided, a frequency ambiguity phenomenon is improved, order analysis is carried out based on the frequency ambiguity phenomenon, and then the fault type of the harmonic reducer is judged according to an envelope order spectrum.

The invention provides an iron tower voiceprint intelligent detection method and system, and belongs to the voiceprint detection technology. The method comprises the following steps: firstly, outputting a sweep frequency signal in a specific frequency range, collecting iron tower vibration response data, extracting candidate frequency through spectral analysis, and dynamically adjusting excitation frequency by using a gradient descent algorithm; hardware filtering is carried out on collected sound signals, frequency band signals related to excitation frequency are reserved, and space beam forming is carried out through a microphone array to enhance iron tower voiceprint signals. Separating iron tower vibration components from the mixed signals by utilizing independent component analysis, performing time-frequency analysis, extracting an energy ratio of a specified frequency band by adopting wavelet packet transformation, and performing deep learning processing in combination with a one-dimensional convolutional neural network to generate an energy ratio and zero-crossing rate feature vector; and finally, inputting the feature vectors into a support vector data description model, setting an initial threshold value and dynamically adjusting the initial threshold value, thereby realizing graded judgment of bolt looseness and improving detection efficiency and stability.

According to the Mama network solenoid valve fault diagnosis method based on the frequency domain characteristics, the problem of solenoid valve system fault diagnosis can be solved. According to the method, a pneumatic solenoid valve fault data set is constructed by collecting operation signals such as voltage and current, and multi-scale frequency domain features of the signals are extracted by adopting Wavelet Packet Transform (WPT) and Discrete Fourier Transform (DFT). WPT-DFT preprocessing features are embedded into an improved channel-space joint attention mechanism module, and the improved channel-space joint attention mechanism module is combined with a Mamba network with extremely high sequence modeling capability to construct an end-to-end fault diagnosis model. Compared with a traditional convolutional neural network, the method can more effectively capture deep dynamic features in a time sequence and highlight a key frequency region, and experimental results show that the method has higher diagnosis precision and generalization ability and is suitable for intelligent detection of the electromagnetic valve under complex working conditions.

The invention discloses a building safety intelligent monitoring, early warning, prevention and control method, and the method comprises the steps: collecting a physical state parameter and an environment disturbance parameter of a building structure body in real time through a distributed monitoring node group disposed at a key part of the building structure body, and forming an original monitoring data flow; performing space-time alignment and noise reduction processing on the original monitoring data stream by using an adaptive weighted fusion algorithm to generate a standardized structure response data set; extracting a multi-dimensional time-frequency domain feature vector representing the health state of the structure from the standardized structure response data set based on a wavelet packet transformation and principal component analysis combination method; and inputting the multi-dimensional time-frequency domain feature vector into a pre-trained twin neural network, and outputting abnormal region positioning information and an abnormal degree quantitative index. According to the method, the building mechanics mechanism and the artificial intelligence technology are deeply fused, a full-closed-loop intelligent prevention and control system from accurate risk identification to active regulation and control is constructed, and the reliability and timeliness of building safety monitoring in a complex environment are remarkably improved.

The invention discloses a rotor acoustic anomaly detection method, system and device based on an auto-encoder and wavelet packet energy entropy and a medium, and belongs to the technical field of hydroelectric generating sets, and the method comprises the steps: collecting an original acoustic signal, carrying out the noise reduction of the original acoustic signal, and carrying out the multi-channel data fusion to obtain an integrated acoustic signal; inputting the integrated acoustic signal into a wavelet packet for transformation processing to obtain wavelet packet coefficients under different scales; calculating energy values of different frequency band signals based on wavelet packet coefficients to form an energy entropy feature vector; constructing an implicit feature model of the hydroelectric generating set rotor in a normal state according to the energy entropy feature vector; and carrying out feature reconstruction on the energy entropy feature vector, calculating a reconstruction error, carrying out dynamic statistical analysis, and carrying out real-time monitoring and abnormity judgment on the operation state of the hydroelectric generating set rotor. According to the method, the detection precision and the response speed are improved in actual hydroelectric generating set rotor acoustic anomaly detection, and automatic identification and dynamic threshold adaptive adjustment of irregular perturbation are realized.

The invention relates to the technical field of defect detection, and discloses a method for detecting defects of live cable equipment by using a high-frequency current detection method, which comprises the following steps of: exciting the live cable equipment to generate a high-frequency current signal, and judging the optimal frequency of the high-frequency current signal by adopting the minimum signal propagation loss; the method comprises the following steps: acquiring a current signal on cable equipment through a high-frequency current sensor, and performing multi-scale time-frequency analysis on the signal through wavelet packet transformation; carrying out noise reduction processing on the converted signal to remove an interference signal and extract a partial discharge signal; positioning the partial discharge signal based on the Bayesian theorem, and judging the position of a partial discharge source; and classifying the partial discharge signals by adopting a support vector machine. The excitation frequency of the high-frequency current signal enables the signal transmission loss to be minimized and the extraction efficiency of the partial discharge signal to be improved, and the signal transmission efficiency is improved in a complex cable environment, so that the defect detection precision and sensitivity are enhanced.

The invention relates to the technical field of power quality monitoring, in particular to a method and system for constructing a power quality disturbance recognition model based on multidimensional data, and the method comprises the steps: obtaining voltage and current waveform data and environmental parameter data of a key node of a power transmission line, carrying out the hardware defect self-inspection and phase compensation of the voltage and current waveform data, and obtaining a power quality disturbance recognition model; clean transmission electric energy data is obtained; and performing multi-scale noise suppression and time sequence correlation analysis on the clean transmission electric energy data, and constructing a high-fidelity disturbance sequence. According to the method, through the hardware defect self-inspection and phase compensation steps, denoising preprocessing is carried out by utilizing wavelet packet transformation, the frequency response deviation of equipment is identified through Fourier transformation, a frequency domain interpolation method is adopted to reconstruct a frequency-closed defect mark segment, the phase deviation error can be accurately compensated, and the detection accuracy is improved. And self-systematic errors of hardware are eliminated from a data acquisition source, high fidelity of clean transmission electric energy data used for subsequent analysis is ensured, and a foundation is laid for high-precision identification.

The invention relates to the technical field of hydraulic engineering safety monitoring, and particularly discloses a reservoir dam safety assessment method based on strategy optimization, which comprises the following steps: collecting sediment accumulation rate and underground water level change rate data in real time through a distributed sensor network, and extracting multi-scale features by adopting variational mode decomposition and wavelet packet transformation; constructing a sedimentary anomaly characteristic value and a water level disturbance characteristic value in combination with the energy entropy and the fluctuation amplitude; fusing the two types of features into a dam body safety feature vector, inputting the dam body safety feature vector into a deep learning model for multi-dimensional risk assessment, and outputting a dam safety level; furthermore, a feedback control loop is constructed based on a digital twinning technology and a meta-learning framework, dynamic adjustment of monitoring frequency and an early warning threshold value and automatic generation of a reinforcement strategy set are realized, a closed-loop intelligent regulation and control mechanism is formed, and the real-time performance, the accuracy and the autonomous decision-making capability of dam safety management are improved.

The invention relates to a real-time fault monitoring method for a circuit breaker. The method comprises the following steps: acquiring a current signal of a zero-sequence current sensor; whether the current signal is abnormal or not is judged, and if yes, the current signal is decomposed into multiple frequency bands through wavelet packet transformation; extracting a high-frequency component and a low-frequency component in the frequency band, and determining whether to trigger a fault early warning condition according to the energy of the high-frequency component and the low-frequency component; if the fault early warning condition is triggered, acquiring a voltage signal of a voltage sensor; and performing FFT harmonic component analysis on the voltage signal, extracting waveform features, and determining the fault type of the circuit breaker by using a trained fault feature model. According to the invention, the current is firstly analyzed to obtain the preliminary analysis of whether the circuit breaker has a fault, and then the voltage is finely analyzed, so that the fault type is determined, the fault judgment time is greatly shortened, the fault type confirmation efficiency is improved, the fault generation trend can be analyzed, and the fault can be effectively handled.

The invention discloses a lightweight online residual life prediction method, device, equipment and medium, and the method comprises the steps: building and training a teacher model based on the wavelet packet transformation of a multi-wavelet basis, and extracting the RUL information of data in different time frequency domains; on the basis of knowledge distillation, a lightweight student model of the same type as the teacher model is designed, so that knowledge transmission is facilitated; knowledge distillation is carried out in stages, the feature extraction ability obtained by training of the teacher model is transmitted to the student model of the same type, and a lightweight student model is obtained and used for online residual life prediction. According to the invention, the deep convolutional neural network based on multi-wavelet basis is adopted to extract the frequency domain features, and the feature extraction capability is distilled to the student model by using knowledge distillation, so that on one hand, the feature extraction speed of the frequency domain is accelerated, on the other hand, the student model is more interpretable, and timely calculation feedback is realized while the problem of insufficient computing power is solved, thereby improving the efficiency of the student model. And a solution is provided for rapid RUL online prediction.

The invention belongs to the technical field of voltage conversion control, and discloses a lithium battery pack voltage conversion control method, which comprises the following steps: acquiring voltage signal data, battery basic data and battery environment data; based on the voltage signal data, performing layer-by-layer decomposition by using wavelet packet transformation to obtain sub-band voltage signals; performing feature extraction based on the sub-band voltage signal to obtain feature data; based on the battery basic data, the battery environment data and the feature data, performing data fusion by using an improved PCA to obtain a fusion vector; inputting the fusion vector into a pre-trained full-connection neural network model, and predicting to obtain a health state index; setting a health state index threshold value and performing state judgment, performing voltage equalization on healthy single batteries, and cutting off the connection between unhealthy single batteries and the lithium battery pack, namely, not performing voltage equalization; performing voltage conversion based on the balanced voltage to reach the voltage required by the load; and the safety and the stability of the battery pack are obviously improved.

The invention discloses an abnormal vibration monitoring method and system based on a flexible acoustic vibration sensor, and relates to the technical field of vibration monitoring. The method comprises the following steps: attaching a flexible acoustic vibration sensor to a key part of the GIS equipment of the transformer substation, capturing a vibration signal and converting the vibration signal into an electric signal in real time; selecting a proper wavelet generating function, comparing the amplitude error with the residual percentage through wavelet transformation, and selecting an optimal wavelet generating function; wavelet packet transformation is carried out on the vibration data, frequency band energy is calculated, and a frequency band with prominent abnormal energy is selected; and performing empirical mode decomposition and Hilbert transform on the abnormal frequency band signal to form a feature vector, and comparing the feature vector with a preset feature library to identify the equipment state. According to the invention, accurate diagnosis is realized, potential faults are found in time, the accuracy and the real-time performance of fault diagnosis are improved, and the downtime and the maintenance cost of equipment are reduced.

The invention provides a data-driven saline screw compressor unit dynamic modeling method, and belongs to the technical field of industrial intelligence and predictive maintenance. According to the method, adaptive deep denoising of original data is realized through a composite signal processing flow of fusing variational mode decomposition, permutation entropy criterion and wavelet packet transformation optimal threshold denoising; meanwhile, a sectional sampling strategy is introduced to enhance the diversity of training data. Then, a wavelet multi-scale energy entropy extraction layer is used for constructing a high-information-density feature vector; furthermore, a prediction model formed by multiple layers of stacked long and short-term memory network units is adopted, and the complex time sequence dependency relationship of the system is deeply captured. According to the method, pure and stable system dynamic representation can be extracted from high-noise industrial data, the dynamic characteristics of the system in the full working condition range are accurately described, and it is ensured that the prediction result is self-consistent physically and reliable in engineering, so that the prediction precision and generalization performance of the model are remarkably improved.

The invention discloses an ultra-high voltage transmission line fault analysis system based on multi-dimensional data, and belongs to the technical field of big data analysis. The method is used for solving the technical problem that the robustness of a system is poor when an existing single-algorithm technical scheme is implemented. Through multi-source data synchronous acquisition and improved EEMD denoising, noise interference of an extra-high voltage line in a complex electromagnetic environment is suppressed; signal characteristics are dynamically tracked through adaptive Kalman filtering, space-time alignment and standardization processing are carried out, dimensional difference and time delay are eliminated, improved wavelet packet transformation, a deep belief network and adaptive morphological filtering are carried out in parallel, multi-dimensional characteristics are dynamically weighted and fused through an attention mechanism, a fault characteristic vector is constructed, and fault diagnosis is carried out. According to the method, efficient extraction and optimization of fault features are achieved, accurate reasoning of fault types and positioning can be achieved through the improved fuzzy Bayesian network, the particle swarm optimization algorithm adaptively updates parameters based on real-time errors, and the adaptive robustness of a complex power grid environment can be effectively improved.

The invention discloses a motor state monitoring method, system and equipment based on multi-sensor data fusion, and relates to the technical field of industrial monitoring, and the method comprises the steps: deploying a flexible strain-temperature composite sensor array at a key position of a motor housing, carrying out the real-time collection to obtain a multi-source fusion signal, carrying out the wavelet packet transformation, and carrying out the real-time collection of the multi-source fusion signal; extracting non-stationary fault fingerprints, calculating gear wear topology invariants, performing incremental parameter exchange with a cloud knowledge base, dynamically generating a health degree confidence ellipse in combination with real-time working conditions, and if the health degree exceeds a threshold value, triggering a brain-like decision-making unit to perform simulation fault-tolerant control. The technical problems that an existing motor state monitoring method is single in sensing data, a feature extraction shallow layer and a decision-making mechanism are solidified, the diagnosis sensitivity of early-stage composite faults is insufficient, and fault-tolerant control lags are solved, and the purposes that multi-physics field games are fused with deep fault fingerprints and cloud incremental evolution are achieved. And the technical effects of prospective identification and real-time fault-tolerant control of potential faults are realized.

The invention discloses an elevator steel belt damage detection and quantitative analysis system based on eddy current and magnetic flux leakage dual-mode detection, and aims to solve the problems of single-mode information loss and serious industrial field strong noise interference in the existing steel belt detection. The system synchronously integrates an eddy current sensor, a magnetic flux leakage sensor and an encoder through a multi-probe array adapter; and multi-dimensional damage physical information in the steel strip is obtained. An improved wavelet packet transform-empirical mode decomposition (WPT-EMD) collaborative noise reduction algorithm is adopted, and in combination with a sub-band energy entropy and a self-attention mechanism, non-stationary mechanical noise is effectively filtered out. A multi-rule feature extraction engine is used for extracting smooth residual errors, derivative mutation and other features, a support vector machine (DE-SVM) model introducing physical priori knowledge weights is constructed, and the small sample recognition problem is solved in combination with a virtual sample generation technology. The method can realize high-precision positioning and quantitative evaluation of steel strip damage under complex working conditions, and has the characteristics of strong anti-interference capability, high identification accuracy and good generalization performance.

The invention relates to the technical field of pipeline corrosion detection, in particular to a method for separating corrosion defect magnetic anomaly signals of a pipeline with a coating layer. The method comprises the following steps: acquiring a magnetic induction intensity frame sequence of the surface of a pipeline with a coating layer, performing normalization processing on data, extracting multi-scale features through wavelet decomposition, and calculating a signal-to-noise ratio, a space variance and an abnormal area proportion of an abnormal magnetic induction area; then, a target magnetic induction intensity frame combination with the highest information entropy is screened out from a large amount of data through a genetic algorithm; performing magnetic vector decomposition, wavelet packet transformation and singular value decomposition on the target frame, and constructing a continuous magnetic anomaly distribution map; and finally, calculating the three-dimensional parameters of the corrosion defect through a pre-calibrated sensor response model and an inversion algorithm. According to the method, the data processing amount is remarkably reduced while the detection precision is ensured.

The invention discloses a fault diagnosis line selection method based on data driving, and aims to solve the problems that the line selection accuracy is low under complex working conditions such as high-resistance grounding and intermittent arc, and secondary damage of equipment is easily caused by a manual line pulling method in the prior art. The method comprises the following steps: synchronously acquiring zero-sequence current and voltage data of each feeder line; performing multi-scale energy decomposition through wavelet packet transformation; extracting time sequence characteristics of each frequency band by using a parallel gating circulation unit network; constructing a feeder topological graph, and aggregating spatial correlation by adopting a graph convolutional network; and fusing the spatio-temporal characteristics through a self-attention mechanism, and outputting a fault line by a classifier. The system comprises a data synchronous acquisition module, a multi-scale decomposition module, a time domain feature extraction module, a spatial correlation analysis module, a space-time fusion module and a classification module. According to the method, high-precision, high-robustness and non-intrusive fault line selection can be realized without a power grid accurate model and depending on active intervention.