587 results about "Vibration based" patented technology

The invention provides a pump equipment state monitoring and fault diagnosis method based on artificial intelligence, and relates to the technical field of data processing, and the method comprises the steps: obtaining a vibration signal of a target type of pump equipment based on a preset vibration sensor, and marking the vibration signal; extracting features of the vibration signal based on a preset dual-channel feature extraction model; iteratively training a preset basic fault diagnosis model based on the characteristics of the vibration signal until a preset training completion condition is reached; binding a preset number of fault diagnosis models to construct a pump equipment state reasoning model; acquiring an operation vibration signal of the pump equipment of the target category, inputting the operation vibration signal into the pump equipment state reasoning model, and outputting a fault category; through time-frequency dual-channel fusion and multi-scale perception, the fault identification precision is improved; the rationality and interpretability of the result are enhanced by using physical prior constraints; and through model integration optimization, the classification stability and reliability in a complex scene are improved.

The invention relates to the technical field of machine tool fault diagnosis and maintenance, and discloses a machine tool fault predictive maintenance method based on vibration analysis, which comprises the following steps: collecting vibration, temperature and acoustic emission signals and machine tool working condition parameters through a multi-modal sensor, extracting multi-domain features after preprocessing the vibration signals, and combining the working condition parameters through feature fusion and dimension reduction to obtain a machine tool fault predictive maintenance result. And establishing a fault classification model by using transfer learning, and performing hierarchical optimization. Model parameters are updated in real time based on an online learning mechanism, a fault early warning agent model is constructed to predict fault probability distribution, a dynamic threshold strategy is designed to avoid false report and missing report, and finally, related models and strategies are integrated to edge computing equipment. According to the method, multi-source data are integrated, multiple advanced algorithms are applied, machine tool faults can be accurately predicted, real-time monitoring and maintenance decision output are achieved, the machine tool operation reliability is improved, and the maintenance cost is reduced.

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 present application discloses a method for calculating the risk of failure of a component on the basis of vibration load simulation, comprising: inputting a determined PSD spectrum load into predetermined target software, so as to convert the PSD spectrum load into initial time domain information; executing a signal repetition operation, obtaining target time domain information, and calculating fatigue damage information; converting the information into target frequency domain data, and generating compressed spectrum data; and inputting the data into a vibration simulation model, obtaining a model output result, and determining a risk of failure evaluation result of a target component.

The invention relates to the technical field of combustion equipment state monitoring, in particular to a combustion state monitoring method and system based on vibration and noise fusion analysis, and the method comprises the steps: collecting a combustion signal in real time through a vibration sensor and a broadband noise sensor, extracting a characteristic frequency band through noise reduction and filtering, and obtaining a combustion state; fPGA hardware-level clock synchronization is adopted to realize time alignment of bimodal signals, a vibration-noise joint feature matrix is constructed and multidimensional coupling is carried out, a combustion state feature vector is generated through a mixed deep learning model, a combustion state is determined based on a bimodal confidence weighting decision, and a linkage control instruction is generated during interruption. The system comprises a signal acquisition synchronization unit, a feature fusion and modeling unit, a state decision and instruction generation unit and a safety linkage execution unit, executes a method and activates a standby safety system, solves the problems of asynchronous bimodal data and insufficient monitoring precision, and improves the combustion state monitoring accuracy and safety response efficiency.

The invention discloses a bionic TENG sensor unit for an artificial throat and a preparation method thereof, and relates to the field of artificial throats, and the sensor unit comprises a gradient micro-dome friction layer, a strain amplification layer, an electrode layer and a back cavity substrate layer. The gradient micro-dome array realizes gradual contact through gradient change of height and diameter; the strain amplification layer converts vertical vibration into horizontal strain by using a cantilever beam-sliding block structure; the back cavity array selectively amplifies vibration at a specific frequency based on the Helmholtz resonance principle. The invention further provides a corresponding preparation method. The preparation method comprises the steps of back cavity forming, electrode deposition, microstructure manufacturing, surface treatment and the like. The sensor has the advantages of high sensitivity, good frequency selectivity and strong durability, and is particularly suitable for detecting weak laryngeal vibration signals of laryngeal patients.

The invention discloses a robot track online compensation and precision control method and system based on multiple sensors, and belongs to the technical field of robot track control. The method comprises the following steps: acquiring target trajectory data and original data of each sensor, and performing time alignment on the original data of each sensor to generate a fusion data set comprising vibration acceleration data and pose data; performing time-frequency feature extraction based on the vibration acceleration data to obtain a dominant vibration frequency spectrum; constructing a mechanical transfer function model of the joint driving torque and the tail end vibration displacement; based on the mechanical transfer function model, constructing an adaptive notch filter to generate a feed-forward compensation sequence; according to the target trajectory data, the pose data and weight parameters, updated in real time, of the sensors, a trajectory fusion deviation value is obtained through calculation; and after the feedforward compensation sequence and the trajectory fusion deviation value are integrated, a joint driving current instruction is generated based on a fuzzy PID control algorithm. The robot trajectory control precision can be improved.

The invention discloses a numerical control machine tool vibration suppression self-adaptive control system and method, and relates to the technical field of intelligent vibration control, and the method comprises the steps: building a current-modal dynamic mapping relation between an electrical drive characteristic and a mechanical structure modal response based on a vibration state feature vector; real-time machine tool modal state vectors are obtained through online recursive estimation; the machine tool modal state vector is used as a boundary condition to be injected into the virtual prediction model; solving to obtain an optimal control parameter sequence and a corresponding vibration prediction result in a future time domain by taking multi-performance index collaborative optimization as a target through a rolling optimization control strategy; translating the control parameter sequence into a specific execution instruction and issuing the specific execution instruction; monitoring an execution process in real time and recording actual response data; according to the method, the accuracy of vibration prediction and the real-time performance of control response are improved by establishing the dynamic mapping relation from the electromagnetic driving characteristics of the servo main shaft and the feed motor to the dynamic response of the machine tool structure.

The invention provides a bridge expansion joint state monitoring method, device and system based on vibration voiceprint collaborative awareness, and the method comprises the steps: collecting a vibration signal sequence and a voiceprint signal sequence of a bridge expansion joint, generating an original monitoring data set, carrying out the time-space correlation modeling of the vibration signal sequence and the voiceprint signal sequence, and generating a time sequence collaborative evolution field. And performing causal sequential difference deconstruction on the time sequence co-evolution field, separating a causal lag mode of the vibration signal sequence relative to the voiceprint signal sequence in a target frequency band range, mapping the causal lag mode to a preset physical force transmission path network, generating a hidden damage topological graph for describing structural performance weakening characteristics, and determining the structural performance weakening characteristics. And based on connectivity characteristics of nodes and strength characteristics of edges in the hidden damage topological graph, quantitatively evaluating the integrity state level and the local damage degree of the bridge expansion joint, and generating a monitoring result containing a state level identifier. According to the invention, the monitoring efficiency can be improved, and the stability and applicability in a complex operation environment can be ensured.

The invention provides a motor multi-band vibration suppression method and a motor multi-band vibration suppression device based on electromagnetic-mechanical cooperative regulation and control. According to the method, vibration signals are collected in real time, and spectral analysis is carried out to identify vibration frequency distribution; dividing the vibration signal into a low-frequency band, a middle-frequency band and a high-frequency band according to a spectral analysis result, and respectively corresponding to a mechanical vibration source, an electromagnetic excitation source and a structural noise source; a control strategy is dynamically selected based on a vibration frequency band, a low-frequency band triggers a mechanical regulation and control module to output a reverse force to perform vibration suppression, a middle-frequency band triggers an electromagnetic control module to optimize a current waveform and a PWM modulation mode, and a high-frequency band starts a cooperative control strategy of the electromagnetic and mechanical control module; through real-time communication between the controller and each module, vibration feedback signals are continuously monitored, and control parameters are dynamically adjusted according to working condition changes so as to maintain the vibration suppression effect. According to the invention, through cooperative adjustment of electromagnetic control and a mechanical structure, active suppression of vibration of the motor in multiple frequency bands is realized.

The invention discloses a robot joint hidden fault detection method and system based on artificial intelligence, and relates to the technical field of robot fault diagnosis, and the method comprises the steps: collecting a vibration signal data sequence of a robot joint; performing frequency domain decomposition on the vibration signal data sequence to obtain vibration components of a plurality of frequency bands; generating a vibration spectrum based on the vibration component, calculating an energy mean value of each frequency band in the vibration spectrum, and determining a characteristic frequency band; counting time points with abnormal energy in the characteristic frequency band, and analyzing probability distribution characteristics of a time interval sequence of the time points to obtain a fault characteristic probability; and according to the change trend of the fault feature probability, determining that the robot joint has a hidden fault risk and outputting fault risk early warning information. According to the method, the abnormal energy distribution rule of the vibration signal characteristic frequency band is analyzed, the robot joint hidden fault is early warned, the equipment fault can be effectively prevented, and the equipment operation reliability is improved.

The invention relates to the technical field of coal mine fully-mechanized coal mining automation control, and discloses a roller cutting track calibration method based on IMU, radar and visual fusion, which comprises the following steps: establishing a static transformation matrix and a vibration mode external parameter compensation library; iMU, radar and visual data are collected in real time; performing external pre-correction based on an IMU vibration characteristic index compensation library; projecting radar and visual data to a fuselage coordinate system to calculate a spatial geometric residual error, and iteratively optimizing an external parameter by taking an IMU real-time attitude as a fixed constraint when the spatial geometric residual error exceeds a threshold value; and based on the geometric consistency confidence coefficient, carrying out adaptive weighted fusion on multi-source data, and reconstructing a coal wall model and a roller cutting track. Through combination of vibration mode feed-forward compensation and geometric consistency closed-loop feedback, dynamic calibration of external parameters of the sensor in a strong vibration environment is realized, the problems of multi-source data space registration deviation and fusion instability are solved, and the accuracy of fully mechanized coal mining face environment perception and cutting track calibration is improved.

The invention provides a detection method and system for an automatic drain valve, and relates to the technical field of equipment testing. The method comprises the following steps: collecting working data of a drain valve to be detected; determining a flow peak change trend and a drainage efficiency attenuation degree based on flow data of continuous multiple drainage records, and generating an impurity deposition index; generating an impurity stripping index based on the instantaneous change rate of the flow velocity data and the variable quantity of the flow data, and determining a first impurity deposition degree in combination with an impurity deposition index; determining a vibration anomaly factor and an acoustic anomaly factor based on the vibration data and the acoustic data, and determining an impurity anomaly score in combination with the first impurity deposition degree; determining the working intensity based on the vibration data, the pressure data and the liquid level data, obtaining the drainage frequency of the drainage valve to be detected after last maintenance, and determining the fault probability based on the working intensity, the drainage frequency and the impurity anomaly score; and predicting the fault condition of the to-be-detected drain valve based on the fault probability. The fault detection accuracy is improved.

The invention relates to the technical field of reliability evaluation of a composite material structure, and particularly discloses an evaluation method for vibration damage mechanical properties of a composite material. The method comprises the following steps: firstly, clamping a composite material test piece on a special test clamp which can be stable and durable in a vibration environment, and carrying out parameter-controllable vibration excitation on a vibration table to simulate damage; then carrying out mechanical property test on the damaged test piece; and finally, based on the vibration data and the mechanical property data, establishing a vibration parameter-performance degradation correlation model by calculating the performance degradation rate, and performing quantitative evaluation on the vibration damage state of the composite material in combination with microscopic damage analysis. According to the invention, the problem that the vibration test and the performance evaluation process are separated and cannot be quantitatively associated in the prior art is solved, the integrated and quantitative analysis from damage simulation to state evaluation is realized, and the environment temperature influence factor is introduced; accurate data support can be provided for safety design and life prediction of the composite material structure in a specific vibration environment.

The invention relates to the technical field of bearing monitoring, in particular to a wind generating set bearing monitoring method based on vibration data analysis, and the method comprises the steps: dividing a mixed signal into narrow-band components with independent energy through variational mode decomposition, separating dynamic responses of different frequency bands, weakening the interference effect of random noise, and enhancing the recognizability of a weak characteristic signal; the difference between the inner and outer ring load effects is displayed in a time sequence matrix in an offset trend, quantitative characterization of the energy and offset relation is enhanced, time alignment is carried out on the raceway load and the frequency amplitude by executing dynamic time warping, time sequence offset caused by rotating speed fluctuation is corrected, and the synchronization characteristics of energy and phase in the structure are displayed. Response abnormal characteristics under working condition changes are extracted, and signal de-aliasing, time sequence reconstruction and structure response accurate matching are realized, so that vibration signal identification sensitivity is improved, energy and phase change correlation reliability is enhanced, and abnormal part identification certainty is improved.

The invention relates to the technical field of oil level monitoring, in particular to a transformer oil level monitoring device and system based on dual-mode ultrasonic sensing. The method comprises the following steps: acquiring transformer oil level and vibration acceleration; threshold value differences are obtained through oil level comparison, and the oil levels are divided; based on different types of oil level fluctuations, quantities and intervals, oil level anomaly comprehensive indexes are obtained; after the over-limit oil level is screened, an oil level abnormal factor is determined, and an oil level abnormal value is obtained in combination with the oil level abnormal comprehensive index; determining a change moment and a reaction time range based on the vibration acceleration; an acceleration oil level influence factor is determined by analyzing correlation, and a fault characteristic value is obtained in combination with an interference factor; and monitoring the oil level through the fault characteristic value. The reliability of the whole monitoring system is improved.

The invention provides a crimping quality online detection method and system based on acoustic emission signal analysis, relates to the technical field of industrial detection, and solves the technical problems that an existing detection method is not sensitive to early defects and is insufficient in adaptability under complex working conditions. The method comprises the following steps: applying mechanical excitation to the crimping structure, exciting the crimping structure to generate vibration response, and synchronously collecting a vibration response signal and an acoustic emission signal; wherein the mechanical excitation represents sweep frequency excitation or pulse excitation generated by a piezoelectric vibration exciter or an impact hammer so as to excite a plurality of co-frequency offset vibration modes of the crimping structure; calculating the resonant frequency of the crimping structure based on the vibration response signal; calculating frequency offset characteristic parameters according to the resonant frequency based on an inversion method of a physical model; calculating acoustic emission signal characteristic parameters based on the acoustic emission signals; and identifying the type and severity of the crimping defect by utilizing a D-S evidence theory and combining the frequency offset characteristic parameters and the acoustic emission signal characteristic parameters.

The invention relates to the technical field of bridge structure engineering, in particular to a new and old bridge joint vibration disturbance control method, which comprises the following steps: S1, arranging a sensor at a new and old bridge joint, and collecting vibration data of the part; according to the method, a multi-source information identification model is constructed by fusing a physical mechanism and deep learning, robust identification of a vibration source and dynamic reconstruction of a transmission path are realized in combination with vibration transmission entropy and Granger causal test, an accurate basis is provided for subsequent vibration reduction scheme design, and an adaptive scheme is screened based on vibration characteristics, so that the accuracy of vibration reduction is improved. A four-dimensional coupling model is constructed through finite element simulation, an improved genetic algorithm is adopted to solve a Pareto optimal solution under the constraint of cooperative work performance of new and old bridges, parameters are selected according to engineering priorities, and cooperative optimization of the vibration reduction effect, structural safety, cost control and durability is achieved. Therefore, the accuracy and engineering applicability of vibration disturbance control at the joint of the new bridge and the old bridge are remarkably improved.

The invention discloses a vertical superfinishing machine intelligent servo control method based on dynamic parameter identification, and relates to the technical field of servo control. Comprising the steps that first sensing equipment is arranged on a key motion axis of target equipment and used for obtaining vibration data of the motion axis, and vibration information items are obtained; and second sensing equipment is arranged on the oilstone unit, is pressure and temperature integrated sensing equipment and is used for acquiring real-time pressure data and temperature data of the oilstone to obtain a pressure information item and a temperature information item. According to the method, damage sorting is combined, low-damage combinations with different oscillation frequencies and feeding speeds are preferentially selected to serve as equipment execution, updating information of vibration information items, pressure information items and temperature information items is deeply obtained, secondary adjustment is conducted on initial information items, and the accuracy of the equipment is improved. Therefore, oilstone driving control based on dynamic parameter identification of vibration data, temperature data and pressure data combination is realized.

The invention discloses an intelligent loading control system for a silo, and relates to the technical field of loading control, and the system comprises a data collection module which is used for collecting a sensor data flow; the disturbance compensation module is used for carrying out vibration coupling grade division on the basis of the sensor data flow and the vehicle vibration amplitude obtained through prediction of the vehicle vibration short-term prediction model, and formulating a corresponding coupling disturbance compensation instruction on the basis of the vibration coupling grade; the valve opening degree module is used for calculating the opening degree of a basic gate based on the set target loading weight and the collected real-time loading weight; based on the vibration coupling grade and the coupling disturbance compensation instruction, the basic gate opening degree is adjusted, and the initial adjustment opening degree is obtained; correcting the initial adjustment opening degree in combination with environment wind speed data collected in real time to obtain a corrected gate opening degree; the completion judgment module is used for closing the gate to complete loading when the real-time loading weight reaches the target value; material scattering and loss in the loading process can be reduced, and efficiency and stability are improved.

A generator fault identification method, device, product and medium relate to the technical field of generator fault category identification. The method comprises the following steps: acquiring a vibration signal and a noise signal; performing multi-mode decomposition processing on the vibration signal and the noise signal to obtain a first sub-mode component and a second sub-mode component; determining a vibration signal sequence based on each first sub-mode component and the vibration signal; determining a noise signal sequence based on each second sub-mode component and the noise signal; determining a vibration characteristic matrix based on the vibration signal sequence; determining a noise feature matrix based on the noise signal sequence; determining a first recognition result and a first confidence coefficient based on the vibration feature matrix; determining a second recognition result and a second confidence coefficient based on the noise feature matrix; when the identification results are inconsistent, determining a fault identification result according to the confidence coefficient; and when the identification results are consistent, determining the first identification result as a fault identification result. The method is advantaged in that generator fault identification accuracy is improved.

The invention relates to the technical field of iron tower detection, in particular to a method for detecting hidden defects of an iron tower based on vibration acceleration, and the method comprises the steps: building a finite element simulation model of a power transmission tower, carrying out the comparative analysis of modal and vibration response before and after damage, and determining a preliminary point distribution position; performing iron tower damage and health state vibration detection experiments on the experimental power transmission tower, setting damage points, arranging vibration acceleration sensors according to initial point distribution positions, applying unit load, drawing a frequency response curve according to data acquired by the sensors, and analyzing the frequency response curve; and if the analysis result does not meet the requirement on the accuracy of identifying the hidden defect of the power transmission tower, adjusting the point distribution position to obtain a final point distribution position. According to the method, efficient and accurate recognition of the hidden defects of the iron tower is achieved, remote monitoring can be conducted, safety, economical efficiency and operability are achieved, the use cost is lower, reliable technical support is provided for intelligent monitoring of electric power facilities, and the obvious engineering application value is achieved.

The invention discloses a concrete vibration multi-objective optimization method, a control system and equipment, and belongs to the technical field of building construction. The method comprises the following steps: firstly, collecting concrete rheological property parameters through a sensor, wherein the concrete rheological property parameters comprise workability and environmental parameters indirectly evaluated based on vibration signals; after preprocessing, establishing an optimization model fusing quality, efficiency and cost objective functions; and the optimal vibration parameter is solved by using a multi-objective optimization algorithm, and a self-adaptive control strategy is formulated according to the optimal vibration parameter to dynamically adjust the construction process. The system comprises a data acquisition module, a processing optimization module, an intelligent control module and a vibration execution module. The problems that a traditional vibrating method cannot respond to working condition changes in real time, and quality, efficiency and cost are difficult to plan and optimize as a whole are solved creatively, intelligent and refined control over the concrete vibrating process is achieved through indirect sensing and multi-target decision, the pouring quality and construction efficiency are remarkably improved, and the comprehensive cost is reduced.

The invention provides a high-voltage circuit breaker abnormity identification method based on a vibration signal envelope line. The method comprises the following steps: acquiring an operation vibration signal of a high-voltage circuit breaker, generating vibration data by a conditional GAN, filtering the vibration signal by wavelet transform, extracting the envelope line of the vibration signal and associating an operation state label to form a sample library; an AM-CNN-based high-voltage circuit breaker abnormity identification model is constructed, and sample library data is used for training so as to predict the operation state of the circuit breaker on line; and perfecting the sample library according to the prediction state and updating the circuit breaker abnormity identification model. According to the method, the fault state of the high-voltage circuit breaker is diagnosed by automatically extracting the fault features of the vibration signals, the error state and the unknown fault state are judged through self-learning so as to continuously improve the abnormal recognition precision of the circuit breaker, power transformation operation and maintenance personnel are assisted to make decisions, and the operation state recognition efficiency of the high-voltage circuit breaker is improved.

The invention discloses a concrete vibrating process control method, computer equipment and a storage medium. The concrete vibrating process control method comprises the following steps: S1, performing multi-dimensional data acquisition on a concrete pouring area; s2, fusing the collected data, and constructing a visual three-dimensional model; s3, generating a vibration scheme based on the vibrator application rule, and establishing a quality prediction model; s4, the vibration scheme is projected to visual equipment for auxiliary vibration; s5, the completion degree of each vibration step is monitored in real time, and whether vibration reaches the standard or not is predicted through the quality prediction model; s6, detecting the quality of the vibrated concrete, establishing a vibration process database, and performing correlation analysis on the vibration process database and the data; s7, performing optimization training on the quality prediction model based on the correlation analysis data in the step S6; and S8, generating a vibration compensation implementation step, and repeating the step S4 and the subsequent steps, so that the problems that the vibration step cannot be quantified according to a specific engineering structure and the corresponding vibration completion degree cannot be quantified in the prior art are solved.

The invention belongs to the technical field of wind power generation, and particularly discloses a method for monitoring the vibration state of a transmission chain of a wind generating set, and the method comprises the following steps: firstly, obtaining vibration data of each part of the transmission chain; then abnormal values in the transmission process are eliminated through a data processing module, and false vibration signals are eliminated based on correlation analysis of the vibration signals and the temperature signals to obtain effective vibration signals; respectively carrying out time domain analysis, frequency domain analysis and time-frequency domain analysis on the preprocessed effective vibration signals; and finally, the fault diagnosis module judges the current health state of the transmission chain in combination with an analysis result. Aiming at the problem that the current fixed sampling parameters cannot meet the monitoring requirements of different rotating speed components in a transmission chain, data acquisition resource optimization is realized by dynamically adjusting the sampling frequency; the problem that monitoring requirements of different rotating speed components in a transmission chain cannot be considered by adopting unified sampling parameters when the vibration state of the transmission chain is monitored in the prior art is solved.

The invention relates to a shield tunneling machine tunneling construction method for stratums with different sand contents, which comprises the following steps of: S1, investigating characteristic parameters, including types, sand contents and mechanical parameters, of stratums to be tunneled; s2, installing an ultrasonic-assisted hob system integrated with an ultrasonic excitation module; s3, ultrasonic parameters and shield tunneling parameters are set according to the characteristic parameters of the stratum; and S4, shield tunneling is started, ultrasonic parameters and shield tunneling parameters are adjusted in real time through the cooperative control module, vibration, hob abrasion and mud cake forming conditions in the construction process are monitored, and in the tunneling process, the operation mode of the cooperative control module is that on the basis of multi-dimensional monitoring data collected by a vibration sensor, an abrasion detection device and a slag sample analysis sensor, the ultrasonic parameters and the shield tunneling parameters are adjusted in real time. Parameters are dynamically adjusted according to stratum types and sensitive area requirements, the single adjustment amplitude and adjacent adjustment intervals of the parameters are controlled, the adjustment effect is verified in real time through a sensor, and closed-loop control over parameter adjustment and monitoring is formed.

The invention provides a hydraulic turbine set valve fault diagnosis method, system, equipment and product, and is applied to the field of hydroelectric equipment fault diagnosis. On one side of the edge end, an initial voiceprint signal, an initial vibration signal and temperature data of a hydraulic turbine set valve are collected; taking the initial voiceprint signal as a reference signal, and performing noise reduction processing on the initial vibration signal based on the time domain correlation between the fluid noise in the initial voiceprint signal and the first noise in the initial vibration signal; by taking the vibration noise reduction signal as a reference signal, performing noise reduction processing on the initial voiceprint signal based on the frequency domain correlation between the mechanical resonance noise in the vibration noise reduction signal and the second noise in the initial voiceprint signal; and based on the vibration noise reduction signal, the voiceprint noise reduction signal and the temperature data, performing fault diagnosis on the hydraulic turbine set valve to obtain a fault diagnosis result. Therefore, fault diagnosis is carried out through cooperative noise reduction of the voiceprint signal and the vibration signal in combination with multi-dimensional data, and the fault diagnosis accuracy of the hydraulic turbine set valve is improved.

The invention provides an Alzheimer's disease intervention rehabilitation system based on photoacoustic magnetic vibration wave resonance, and relates to the technical field of medical health. The system comprises a data acquisition unit, a correction unit, a stimulation unit and an evaluation feedback unit. The data acquisition unit is used for acquiring multi-dimensional data such as electroencephalogram signals and pathology associated data of a user and extracting target neural oscillation features from the multi-dimensional data; a correction unit constructs a brain structure-pathology association model, and performs pathology association correction on the target neural oscillation features; and the stimulation unit generates co-stimulation containing at least two modes of light, sound, magnetism and vibration based on the corrected features, configures stimulation parameters according to a predetermined time domain or frequency domain relationship, and intervenes the coupling target. And the evaluation feedback unit evaluates the intervention effect by collecting the stimulated multi-dimensional data. According to the method, the multi-mode stimulation is bound with the pathological-neural oscillation coupling characteristics, so that accurate and collaborative intervention aiming at the pathophysiological mechanism of the Alzheimer's disease is realized.

The disclosure relates to a vehicle safety system (100), comprising a smart seatbelt assembly (102) that monitors driver alertness and actively intervenes to prevent accidents. The system (100) integrates physiological sensors (106), vibration-based haptic feedback (136), and an inflatable membrane (104) functioning as a cushion within the seatbelt. An edge artificial intelligence module (130) processes inputs from cameras (116), sensors (106), and vehicle operation data (120) to evaluate driver vigilance in real time. When impairment is detected, the system initiates multimodal alerts through vibration and audio cues to regain attention. If the driver remains unresponsive, the control unit (128) tightens and inflates the membrane (104), activates hazard lights (142), and safely decelerates the vehicle. The system operates autonomously without network dependence, with optional cloud connectivity for biometric personalization and integration with wearable health devices, thereby uniting driver monitoring and restraint protection in a single intelligent safety unit.