888 results about "Vibration based" patented technology

The invention provides a thermal power plant thermal instrument fault diagnosis method and system based on vibration analysis, and relates to the technical field of fault diagnosis, and the method comprises the steps: collecting and processing a vibration signal of a thermal instrument, extracting an optimized vibration feature vector, constructing a hybrid neural network model, and training the hybrid neural network model to obtain an optimized fault diagnosis model; performing fault diagnosis on the vibration feature vector, and generating a fault type, possibility and confidence; and performing fault risk assessment and case reasoning, and generating a fault reason analysis report and a maintenance suggestion.

The invention discloses a grinding machine internal part temperature anomaly detection method based on vibration signal analysis, which comprises the following steps that grinding data are collected through sensor deployment, and the sensors comprise a temperature sensor, a vibration sensor, an infrared thermal imaging sensor, a magnetic resistance current sensor and an inductance type particle sensor; carrying out preprocessing and feature extraction on the collected data; model construction and training are carried out based on data of preprocessing and feature extraction; according to the method, the abnormal condition of the temperature of the part is predicted by detecting the vibration signal of the internal part, the content of metal particles in lubricating oil is detected through the oil analysis sensor, and the abrasion degree of the bearing is judged in combination with the vibration signal. And motor current harmonic characteristics are monitored, and overload or rotor imbalance problems are identified.

The invention provides an earth surface anomaly monitoring data fusion system based on vibration signal processing, and relates to the technical field of earthquake precursor monitoring, and the system comprises a data collection module which is used for carrying out the real-time monitoring of the earth surface through a distributed multi-parameter sensor network, and obtaining the time sequence monitoring data related to the earthquake precursor; the signal processing module is used for processing the time sequence monitoring data by adopting a two-parameter joint optimization algorithm and outputting a separation signal component containing precursor information; the data fusion and state inversion module is used for carrying out fusion processing on the separated signal components by adopting a dual-time scale analysis framework and outputting a multi-scale precursor state estimation result and an uncertainty quantitative index; and the monitoring and early warning module is used for outputting earthquake precursor grading early warning information through a three-layer anomaly recognition architecture and probabilistic risk assessment. According to the invention, high-precision separation and identification of earthquake precursor related signals can be realized, and the false alarm phenomenon is reduced through an intelligent early warning mechanism.

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 agricultural robot motion control, in particular to a mechanical arm posture stable adjusting method based on vibration feedback, which comprises the following steps: S1, collecting a vibration signal of an end effector of a mechanical arm in real time; s2, frequency domain analysis and amplitude-phase decomposition are conducted on the vibration signals, and vibration characteristic parameters of the mechanical arm in the X / Y / Z axis direction are extracted; s3, generating a multi-axis vibration vector in combination with the terrain inclination angle data; s4, attitude compensation parameters of all the joints are calculated; s5, a mechanical arm joint angle adjusting instruction is generated according to the posture compensation parameters; and S6, circularly executing the steps S1 to S5 until the vibration amplitude is lower than a preset threshold value. According to the method, the multi-axis vibration characteristics are extracted in real time, the joint compensation angle is calculated through the anti-offset and anti-overturning matrix, rapid and stable attitude control of the mechanical arm in the high-frequency disturbance environment is achieved, and therefore the tail end positioning precision and the overall operation reliability are remarkably improved.

The invention relates to the field of industrial pipeline systems, in particular to a displacement analysis evaluation and compensation method for optimizing pipeline thermal expansion. The method comprises the following steps: collecting multi-temperature thermal state snapshots to form a thermal state change sequence; determining a thermal expansion constraint boundary according to the thermal state change sequence, and forming boundary condition data; fixed points and free points of the pipeline system are identified according to the boundary condition data, and a degree-of-freedom distribution diagram is constructed; based on the degree-of-freedom distribution diagram, analyzing a displacement transfer relation of each pipe section, and generating displacement transfer chain data; identifying a thermal expansion concentrated release area through displacement transfer chain data, and performing node resonance detection to form vibration risk assessment data; and designing a thermal stress digestion path based on vibration risk assessment data, and setting flexible constraints to form a balanced stress network. The thermal expansion displacement transmission path is tracked in real time through displacement transmission chain analysis, the resonance frequency is synchronously recognized in combination with vibration mode detection, and dynamic association of the thermal expansion process and the vibration mode is achieved.

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.

The invention relates to the technical field of engineering mechanical structure health monitoring, and discloses a carry-scraper structural member crack real-time detection system and method based on vibration characteristics, and the carry-scraper structural member crack real-time detection system based on vibration characteristics comprises a sensing acquisition module which is used for acquiring structural member vibration response and obtaining an original signal; the signal processing module is used for carrying out wavelet denoising on the original signal and extracting effective vibration data; the feature extraction and dynamic calibration module is used for extracting multi-domain features and performing dynamic calibration in combination with working conditions; the crack recognition and intelligent diagnosis module is used for inputting an intelligent model to recognize a crack state; and the crack positioning and alarm feedback module is used for positioning cracks and giving an alarm in combination with the diagnosis result. According to the invention, the multi-modal sensor array is constructed, piezoelectric and MEMS sensors are cooperatively arranged, a magnetic snap-in type installation structure is combined, broadband response signals are stably collected under different working conditions, and then front-end signal optimization is completed in cooperation with a multi-scale wavelet denoising and standardization mechanism.

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 relates to the technical field of electrical variable measurement, in particular to a power equipment system real-time dynamic monitoring method and system based on the Internet of Things. The method comprises the following steps: acquiring different data types; forming vibration sequences for the vibration signals, and obtaining fitting curve functions of all the vibration sequences; determining a trend sequence based on the variation coefficient of the vibration sequence; combining the trend sequence and the vibration sequence with a fitting curve function to determine a vibration trend coefficient; clustering the period based on the vibration trend coefficient, respectively obtaining a local change trend and an overall change trend based on cluster data and all data, and obtaining an equipment disturbance coefficient based on the local change trend and the overall change trend; and determining a prediction function based on the equipment disturbance coefficient, and obtaining a corrected vibration signal in combination with the vibration trend coefficient difference of adjacent periods, thereby completing dynamic monitoring. According to the invention, the monitoring accuracy of the power equipment fault is enhanced.

The invention discloses a fresh concrete vibration apparent quality identification method based on a convolutional neural network, and relates to the technical field of concrete apparent quality identification, and the method comprises the steps: obtaining vibration track data, vibration frequency parameters and a concrete surface texture image of vibration equipment in an operation process, and generating an original data set; performing model training optimization based on the initial convolutional neural network model to obtain a depth feature extraction network adaptive to the vibration process; analyzing time sequence detection data in the vibration process, extracting vibration energy density distribution characteristics, aggregate distribution uniformity indexes and slurry wrapping degree parameters, and constructing a vibration quality correlation characteristic matrix; in the depth feature extraction network, the vibration quality correlation feature matrix and vibration sensing data collected in real time are fused, and a quality evaluation system is established; according to the quality evaluation system, the vibration operation process is detected, and a visual evaluation result is obtained; the fresh concrete vibration apparent quality identification method provided by the invention is more intelligent and accurate.

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 discloses a two-phase cold plate heat dissipation control method and system based on vibration and flow coordinated regulation and control, and relates to the technical field of liquid cooling heat dissipation. The two-phase cold plate heat dissipation control method and system based on vibration and flow collaborative regulation and control comprises the steps that S1, cold plate collaborative heat dissipation data is collected in real time, and the cold plate collaborative heat dissipation data is preprocessed; s2, the heat dissipation degradation degree of the two-phase cold plate is evaluated, heat dissipation state classification judgment is executed, and a regulation and control instruction is issued; s3, executing the control instruction, evaluating the regulation and control response state of the cooling path, judging the regulation and control effect, and triggering a time-sharing coordinated regulation and control strategy; and S4, whether heat dissipation of the cooling path fails or not is judged, the thermal degradation trend of the two-phase cold plate is evaluated, and a liquid path switching strategy is executed. The problems that in an existing cold plate liquid cooling heat dissipation technology, oil films are generated by cooling liquid due to temperature changes, the oil films are prone to being attached to the inner walls of micro-pore channels, flowing is blocked, the heat exchange efficiency is reduced, and local overheating and unstable equipment performance are further caused are solved.

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 provides a municipal bridge damage defect detection method and a municipal bridge damage defect detection system, which are used for solving the problem that the quality and the accuracy of acquired data are difficult to guarantee due to the fact that vibration signals are easily interfered by environmental noise, such as vehicle load, wind load and the like. A bridge key part vibration signal data set is obtained, and a final damage area range is determined based on the vibration signal data set; whether the bridge is damaged or not is judged based on recognition of the vibration signals, so that the signal-to-noise ratio of monitoring data and the damage positioning precision are improved, a more accurate and reliable decision basis is provided for a bridge management department, and finally maintenance and reinforcement work of the bridge is effectively guided.

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 an engineering equipment operation intelligent monitoring and automatic adjusting system and method, and the method comprises the steps: collecting the multi-dimensional operation data of equipment in real time, constructing a three-dimensional operation state evaluation model through an intelligent algorithm, accurately recognizing a stable operation region, and generating a dynamic control reference and a feedback adjustment loop; a control channel decoupling and selective reconstruction technology is adopted, a beneficial synergistic effect is reserved while harmful coupling is eliminated, potential risk points such as resonance are identified through spectral analysis, and a safe operation boundary for perfecting a is established; the analysis equipment responds to dead zone characteristics, the dead zone characteristics are converted into an energy-saving working zone through energy analysis, an optimized power distribution strategy is made, and a multi-stage emergency reserve system is established; a fault early warning mechanism based on vibration characteristics is established, and rapid detection and power compensation of an abnormal state are realized; through equipment response matrix construction and time delay compensation analysis, an accurate real-time control instruction is generated, and the operation efficiency, the stability and the energy-saving level of engineering equipment are comprehensively improved.

The invention relates to the technical field of flutter, and particularly provides a flutter stability prediction method and device, equipment, a storage medium and a program product. The flutter stability prediction method comprises the following steps: dividing a flow field grid and a solid grid based on a gas compressor geometric model; on the basis of the solid grid, finite element modal analysis is executed to obtain intrinsic mode data, and the intrinsic mode data comprises inherent frequency and intrinsic mode distribution thereof; based on the flow field grid, the intrinsic mode data and preset circumferential air inlet distortion parameters, executing unsteady numerical simulation so as to determine first aerodynamic work under single-node diameter vibration; based on the phase angle between the blades and the first aerodynamic work, second aerodynamic work under all node diameter vibration is determined, and aerodynamic damping under all node diameter vibration is determined based on the second aerodynamic work; based on the aerodynamic damping, flutter stability is predicted. According to the technical scheme, the prediction speed of the flutter stability can be increased.

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 discloses an automatic medium-pressure online detection and closed-loop control method for die steel, relates to the technical field of medium-pressure online detection, and is used for solving the problems that the probability of production deviation is increased and the precision of medium-pressure interval range adjustment is reduced due to abnormality discovery lag. Collecting a pressure fluctuation value and an off-center peak value to comprehensively judge whether the conditions are abnormal; when an abnormal instruction is generated, entering a pressure test stage, calculating a pressure scaling interval based on vibration deviation and fan frequency, adjusting a default pressure interval to obtain a new pressure control interval, and comparing the new pressure control interval with a pressure value; and when the temperature exceeds the range, the surface temperature uniformity of the steel billet is detected, a fault-tolerant proportional adjustment pressure scaling interval is calculated, a control interval is updated and subjected to secondary comparison, whether an abnormal instruction is eliminated or not is determined, production abnormity is found in time, the abnormity judgment accuracy is improved, the abnormity is found in time, and the dynamic adjustment precision of the pressure control interval is improved.

The application discloses a lithology identification method, device and system based on a vibration signal and a storage medium. The method comprises the following steps: acquiring a vibration signal sample of a drill bit when the drill bit is used to drill a rock sample in a work area, and extracting a signal characteristic parameter from the vibration signal sample; establishing a probability distribution relationship model between the lithology of the rock sample and the signal characteristic parameter of the vibration signal of the drill bit according to a corresponding relationship between the lithology of the rock sample and the signal characteristic parameter of the vibration signal sample; acquiring a vibration signal generated when the drill bit breaks the rock during drilling in the work area, and extracting a signal characteristic parameter from the vibration signal; and according to the signal characteristic parameter of the vibration signal, the lithology of the stratum in the work area drilled by the drill bit is inferred by using the probability distribution relationship model between the lithology of the rock in the work area and the signal characteristic parameter of the vibration signal of the drill bit.

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 discloses a sliding bearing health management system and method based on vibration analysis, and relates to the field of sliding bearing health management, and the method comprises the steps: firstly obtaining an original vibration signal and a real-time working condition parameter of a sliding bearing, and carrying out the multi-domain feature extraction of the vibration signal after time sequence alignment and windowing processing; and then introducing a double-flow attention coding mechanism, performing deep interaction and joint coding processing on the extracted vibration multi-domain features and working condition feature vectors, and further performing adjustment and decoding reconstruction on the joint potential space representation by taking the real-time working condition feature vectors as dynamic conditions, thereby generating a theoretical healthy vibration mode under the current working condition. And comparing the vibration with the actual vibration to calculate the health index. In this way, dynamic self-adaption of the health assessment standard can be achieved, and therefore the accuracy and robustness of health management under complex and changeable working conditions are remarkably improved.

The invention discloses a dam safety monitoring and crack prediction method and system based on vibration sensing data, relates to the technical field of data processing, and solves the problems of incomplete data acquisition, data processing lag and insufficient prediction capability in existing dam safety monitoring. Adaptive normalization processing based on a dynamic window is carried out on the dam vibration data; a prediction model is constructed, an LSTM network captures time dependence of dam vibration data, an attention mechanism network fuses LSTM network output and external environment variables to extract key time steps, and an output layer predicts the probability of crack generation according to the key time steps; inputting the preprocessed dam vibration data into a prediction model to obtain a crack generation probability; calculating a dynamic threshold according to the external environment factor, and comparing the crack probability to obtain a crack prediction result; by collecting dam vibration data at multiple positions, the crack risk is predicted in advance based on the prediction model.