460 results about "Bearing vibration" patented technology

The invention discloses an attention mechanism and LSTM fused industrial bearing vibration time sequence signal fault prediction method and system. The method comprises the following steps: collecting a bearing vibration signal and carrying out filtering, noise reduction and normalization preprocessing; constructing a deep learning model combining the bidirectional BiLSTM and a coordinate attention mechanism to extract bidirectional time sequence features and enhance key fault features; carrying out model training by adopting a multi-target composite loss function and an Adam optimizer, and introducing an early stop mechanism to prevent overfitting; performing fault type identification and degree evaluation on the real-time vibration signal by using the trained model, and performing quantitative analysis by fusing multi-scale spectrum kurtosis features and nonlinear kinetic parameters; and finally, outputting a fault diagnosis report, and triggering multi-stage early warning based on an adaptive threshold. The method can realize high-precision and high-reliability bearing fault prediction and health state evaluation, and is suitable for intelligent operation and maintenance of industrial equipment.

The invention relates to the technical field of air compressors, and discloses a centrifugal air compressor surge suppression method and device.The method comprises the steps that pressure pulsation feature extraction is conducted on a pressure signal of a centrifugal air compressor, and pressure pulsation features are obtained; extracting a flow statistical feature and a frequency band energy feature of the flow signal to obtain a surge early warning feature space; carrying out characteristic decomposition on the bearing vibration signal, and extracting a characteristic frequency to obtain a surge early warning characteristic set; inputting the surge early warning feature set into a three-channel neural network for surge early warning prediction to obtain a surge early warning level; according to the surge early warning grade, control parameters of a guide vane angle controller, an anti-surge valve opening controller and a return valve opening controller are generated, the guide vane angle, the anti-surge valve opening and the return valve opening are synchronously adjusted, and a surge margin value is calculated. According to the method, accurate control over the guide vane angle, the anti-surge valve opening and the return valve opening is achieved, the surge margin value is calculated, and the control accuracy of the guide vane angle, the anti-surge valve opening and the return valve opening is improved. And real-time optimization and adjustment of the control strategy are ensured.

The invention belongs to the technical field of bearing fault diagnosis, and particularly relates to a transmission casing typical small bearing fault weak vibration signal extraction method, which comprises the following steps: reconstructing a bearing vibration signal to obtain a modulation impact signal, and calculating a weighted kurtosis value based on the modulation impact signal to improve a Protugram algorithm; noise interference is added to a bearing fault signal, and extraction of a small bearing fault weak impact signal is realized by optimizing a filtering frequency band based on a weighted kurtosis value improved Protugram algorithm; bearing impact characteristics can be enhanced based on a simulation sensor resonance enhancement algorithm (SPM); the impact quantification of the bearing can eliminate the influence of different rotating speeds and load working conditions, and realizes the quantitative diagnosis of bearing faults. And finally, the fault data of the rolling body, the inner ring and the outer ring of the flight attachment case small bearing part test bed are verified, and effective extraction and enhancement of bearing fault weak signals can be realized.

The invention discloses a variable working condition rolling bearing fault diagnosis method and system, and the method comprises the steps: firstly obtaining bearing vibration signal data, temperature data and current spectrum data of a motor under different working conditions, carrying out the adaptive decomposition through employing an EEMD algorithm, and extracting a stable feature subset; then, constructing a cross-working-condition diagnosis model by utilizing transfer learning, and introducing an HMM (Hidden Markov Model) time sequence model to capture signal dynamic characteristics; during diagnosis, the working condition of the signal is firstly judged, and the corresponding model is selected for fault diagnosis. And if the diagnosis confidence is low, triggering an online updating mechanism, and performing incremental learning by using new data. Through continuous data collection and model updating, a dynamic evolution system capable of automatically adjusting the diagnosis strategy according to the working condition change is constructed, the problem that the diagnosis performance is reduced due to the working condition change in bearing fault diagnosis is effectively solved, and high-accuracy self-adaptive diagnosis is achieved.

The invention provides a bearing fault diagnosis method and system, a storage medium and a computer, and the method comprises the steps: obtaining a bearing vibration signal, and extracting time-frequency features in the bearing vibration signal according to variational mode decomposition and fast Fourier transform; performing convolution operation on the time-frequency features, and processing a convolution output result by adopting an ECA attention mechanism to extract frequency domain features in the time-frequency features; capturing a global time sequence dependency relationship in the time-frequency features based on a Transform model to extract long-distance dependent time domain features in the time-frequency features; and fusing the extracted frequency domain features and time domain features through a bidirectional cross attention mechanism, transmitting the fused features into a full connection layer for classification, outputting fault categories of the bearing, converting a classification result into probability distribution, and selecting the category with the highest probability as a diagnosis result. The bearing fault diagnosis method provided by the invention is high in fault diagnosis accuracy.

The invention relates to the technical field of electrical monitoring, provides a general generator electrical monitoring system with a fault self-diagnosis function, and aims to deeply excavate potential correlation between electrical and mechanical parameters by judging a coherence coefficient and a phase difference between a current harmonic component and a bearing vibration frequency band and marking a fault coupling identifier by using a coupling mode library. Electrical and mechanical coupling faults can be accurately identified, and the identification capability of complex faults can be greatly improved; meanwhile, the diagnosis threshold is dynamically adjusted in combination with the load rate and the winding temperature, so that the system can better adapt to different operation conditions of the generator, and the diagnosis accuracy and reliability are improved; the fault causal chain is analyzed through the causal inference algorithm, and the fault source is positioned, so that compared with the existing fault tracing mode lacking systematicness, the fault generation reason and process can be analyzed more comprehensively and deeply, the fault source can be positioned quickly and accurately, operation and maintenance personnel can take targeted measures in time, and the fault tracing efficiency is improved. And the operation safety and reliability of the generator are improved.

The invention relates to a rolling bearing early fault early warning and diagnosis method, which comprises the steps of extracting an envelope component from a bearing vibration signal, constructing a time-delay feedback stochastic resonance optimal model by taking an improved signal-to-noise ratio INSR as an optimization objective function, obtaining an output signal, obtaining a first reconstruction signal through CEEMDAN adaptive decomposition and IMF component screening, and obtaining a second reconstruction signal through the CEEMDAN adaptive decomposition and IMF component screening. Calculating the signal-to-noise ratio ISNR of the vibration signal at the fault characteristic frequency, comparing the signal-to-noise ratio ISNR with a preset initial threshold value, judging whether an early warning is given out or not, and if the early warning is given out, processing the vibration signal by using a multi-wavelet adjacent coefficient adaptive threshold value method to obtain a denoised signal; processing the denoised signal by combining a fast spectral kurtosis method and an ensemble empirical mode decomposition method to obtain a second reconstructed signal; and processing by using improved fast spectrum correlation to obtain a corresponding enhanced envelope spectrum, and comparing the enhanced envelope spectrum with a fault characteristic frequency for identification. Compared with the prior art, accurate early warning and diagnosis can be carried out on early weak faults of the rolling bearing.

The invention relates to the technical field of bearing fault diagnosis, in particular to a multi-bearing fault positioning and diagnosing method, which comprises the following steps of: acquiring bearing vibration signals of a plurality of sensing points, extracting peak time information to complete synchronous calibration, extracting high-frequency band energy and constructing a feature vector, screening deviation features and clustering and grouping, and identifying an energy abnormal channel, positioning a bearing position, extracting time domain and frequency domain features, comparing fault modes, and outputting a fault type diagnosis result. According to the method, the consistency of multi-channel data synchronization is realized by extracting the peak point of the vibration signal and calibrating the time offset, the high-frequency energy change rate is utilized to match the weight coefficient, the detection sensitivity of tiny fault features is enhanced, and the separability of a composite fault state is improved by combining feature relative deviation rate screening and density trend clustering. A frequency section energy proportion change recognition channel is adopted, accurate positioning of a fault bearing is achieved, and accurate judgment of a fault type is achieved in combination with a multi-feature comparison mode.

The invention provides a prediction method for the remaining service life of a rolling bearing based on transfer learning, and belongs to the technical field of artificial intelligence and mechanical engineering. Rolling bearing vibration signals are obtained, and a data set is constructed and divided into a source domain and a target domain; constructing a prediction model of the remaining service life of the rolling bearing; the model comprises the following steps: obtaining low-dimensional features and model weights by using a coding layer; the coding layer comprises a multi-head self-attention sub-layer used for obtaining a relationship between features and a model weight; the feedforward mechanism is used for obtaining low-dimensional mapping; predicting the residual life of the bearing by using the feature representation output by the last coding layer through the full connection layer; training the prediction model of the remaining service life of the rolling bearing: calculating the difference between a source domain and a target domain by using the data set, and constructing a loss item; weight parameters are added to the loss item, the weight parameters and the root-mean-square error loss item are added for back propagation, and the model weight is updated. And the generalization ability of predicting the bearing life by the model is improved.

The invention discloses an online monitoring method and system for the wear state of a bearing of a flour mill, particularly relates to the technical field of state monitoring of industrial equipment, and is used for solving the problem of misjudgment caused by signal characteristic deviation under a dynamic load working condition in an existing flour mill bearing vibration signal monitoring method. A grinding pressure parameter and a bearing vibration signal are collected in real time, the dynamic incidence relation of the grinding pressure parameter and the bearing vibration signal is analyzed, interference frequency band components are separated, the time-frequency ridge line track and curvature sudden change density of a characteristic frequency band are extracted, a dynamic reference standard is generated to adjust an amplitude-frequency threshold value in a self-adaptive mode, and finally a wear state evaluation result is output through amplitude-frequency joint analysis. Accurate monitoring of the wear state of the bearing under the complex working condition is achieved, and the reliability of an online monitoring system and the scientificity of maintenance decision are remarkably improved.

The invention discloses a bearing defect intelligent detection method and system based on deep learning, and relates to the field of bearing defect detection. Multi-modal data such as bearing vibration, acoustics, thermal imaging and the like are acquired by using various sensors, and a four-dimensional feature tensor is constructed through preprocessing such as noise reduction and feature extraction; features are fused through a reconfigurable multi-branch convolutional neural network, and defects are identified and a development trend is predicted in combination with a meta-learning twin network; a decision threshold is optimized by adopting a quantum heuristic algorithm, and multi-level early warning is realized; and continuous evolution of the model is completed through edge-cloud collaboration and federated learning, the functions of data calibration compensation, model dynamic optimization and the like are achieved, and efficient and accurate detection of bearing defects is achieved. The detection time is remarkably shortened, and the positioning precision is high; the novel defect response speed is high, and faults can be predicted in advance; system energy consumption is reduced, model updating is improved, stable operation of equipment is effectively guaranteed, and cost reduction and efficiency improvement of industrial intelligent operation and maintenance are facilitated.

The invention belongs to the technical field of vibration analysis and testing of bearings, and particularly relates to a cam driven bearing wear degree monitoring method and system, and the method comprises the steps: carrying out the equal-angle resampling processing of a vibration signal through a rotating speed signal, decomposing an obtained angular domain vibration signal into a plurality of mode components through a variational mode decomposition algorithm, and carrying out the measurement of the vibration signal; according to the kurtosis value of each modal component and the correlation coefficient of each modal component and the original vibration signal, evaluating the impact saliency weight of each modal component, and performing weighted summation on the energy of each modal component to obtain comprehensive impact energy; calculating to obtain a speed decoupling wear index without the influence of the rotating speed by utilizing the comprehensive impact energy and the vibration energy calculated by the physical mapping model; and the speed decoupling wear index is compared with a preset self-adaptive alarm threshold value, and the wear state of the cam driven bearing is judged according to a comparison result. According to the invention, the problems of false alarm and missing alarm under the variable-speed working condition are solved.

The invention discloses a migration working condition bearing remaining service life prediction method based on a contrast double-confrontation mechanism, and the method comprises the steps: obtaining a bearing vibration signal of a single time step and a corresponding label, decomposing the bearing vibration signal of the single time step, obtaining an intrinsic mode IMF, reconstructing the bearing vibration signal of the single time step, and obtaining a prediction result of the bearing remaining service life. Fault point detection is carried out; constructing a sample containing time sequence information; constructing a residual service life prediction model, inputting the sample into the residual service life prediction model, and outputting the residual service life corresponding to the sample; the residual service life prediction model comprises a dynamic convolution long-short memory neural network feature extractor and a predictor; and constructing a migration training framework of a contrast double-confrontation mechanism. According to the method, noise reduction processing is carried out on an original vibration signal in combination with multi-scale permutation entropy, and fault occurrence point detection in the bearing degradation process is carried out by using hidden Markov based on Bayesian reasoning. Through the technology, the influence of non-degradation data information on the residual service life after degradation is reduced, and the workload and prediction difficulty of a prediction task are reduced.

The invention relates to the technical field of bearing fault diagnosis, and discloses a fault self-adaptive diagnosis method for a low-speed heavy-duty bearing, which comprises the following steps: acquiring bearing vibration time domain data under a low-speed heavy-duty working condition, and carrying out data cleaning on the bearing vibration time domain data to obtain cleaned bearing vibration time domain data; performing spectrum analysis on the vibration time domain data of the cleaned bearing to obtain a spectrum diagnosis result; acquiring a noise reduction envelope spectrum of the cleaned bearing vibration time domain data by adopting a clustering weighted envelope spectrum method; performing envelope spectrum analysis on the noise reduction envelope spectrum to obtain a diagnosis result of the noise reduction envelope spectrum; if the frequency spectrum diagnosis result and the noise reduction envelope spectrum diagnosis result are normal, judging that the bearing is normal, otherwise, judging that a bearing fault exists; the method improves the efficiency and accuracy of bearing fault diagnosis.

The invention provides a fan bearing diagnosis method and device based on a gradient weighting type activation thermodynamic diagram, and relates to the technical field of wind driven generator bearing fault diagnosis, and the method comprises the steps: obtaining a bearing vibration signal of a wind turbine generator; carrying out noise reduction processing on the obtained bearing vibration signal; the bearing vibration signal after noise reduction is converted into a time-frequency image through short-time Fourier transform, the obtained time-frequency image serves as input, and a gradient weighting type activation thermodynamic diagram is obtained through a feature extraction unit of a fault diagnosis model; taking the obtained gradient weighting type activation thermodynamic diagram as input, performing fault diagnosis through a fault diagnosis unit of a fault diagnosis model according to the input gradient weighting type activation thermodynamic diagram, and obtaining a diagnosis result; and the fault diagnosis unit is obtained by training a preset machine learning algorithm according to historical gradient weighted class activation thermodynamic diagrams corresponding to different bearing faults of the wind turbine generator. The data processing calculation amount can be effectively reduced, and the fault diagnosis accuracy is improved.

The invention relates to the technical field of lubricating oil monitoring, in particular to a compressor lubricating oil online monitoring vibration diagnosis method and system, and the method comprises the steps: obtaining vibration signal data of a compressor bearing, a connecting rod mechanism and a piston assembly and lubricating oil medium parameters, arranging the data according to a time sequence, and generating a lubrication working condition time sequence signal data set; according to the method, through time sequence matching of vibration signal data and lubricating oil state parameters, a lubricating working condition time sequence signal data set is established, and in combination with time window division, the bearing vibration frequency offset, the piston transverse impact amplitude and the connecting rod torsional vibration change rate are extracted; and calculating the viscosity drop rate, the temperature change range and the pollutant particle growth rate of the lubricating oil, screening the correlation trend of the bearing vibration frequency offset before and after the change of the lubricating state, and enhancing the lubricating state recognition capability.

The invention discloses a bearing vibration signal processing model construction method, a model and a signal classification method, which are used for constructing time domain and frequency domain loss functions by calculating a plurality of feature vector distances so as to train an encoder. According to the method, by minimizing the difference value of different distances in the time domain and frequency domain loss functions, an encoder can be guided to extract similar feature representations in signals, so that the similar feature representations are gathered in a feature space, and dissimilar features are separated. When a trained encoder (a convolutional neural network with an attention mechanism) processes a vibration signal of an unknown fault type, some feature representations in the signal are different from those of other signals, so that the feature representations are gathered in a feature space and are far away from the feature representations of other signals, and the fault detection accuracy is improved. Therefore, the recognition of the vibration signal of the unknown fault type is effectively realized.

The invention relates to the technical field of fault diagnosis, and discloses a fault diagnosis method and system based on variational mode decomposition. The method comprises the steps of obtaining an original bearing vibration signal; introducing a sparsity penalty term into the mathematical model of the variational mode decomposition algorithm to obtain an improved variational mode decomposition algorithm; decomposing the original bearing vibration signal based on an improved variational mode decomposition algorithm to obtain a decomposition result; and obtaining an envelope spectrum of each mode according to a decomposition result, and determining a fault frequency according to a frequency corresponding to a maximum peak value of the envelope spectrum. According to the method, the sparsity penalty term is introduced into the mathematical model of the variational mode decomposition algorithm, the bearing vibration signal is decomposed through the improved variational mode decomposition algorithm, the envelope spectrum is solved for each mode obtained through decomposition, the frequency corresponding to the maximum peak value of the envelope spectrum is the fault frequency, and the fault frequency can be calculated under the noise condition. Accurate extraction of fault frequency is realized, and noise interference is overcome.

The invention discloses a bearing vibration detector pressing device and method, and relates to the technical field of bearing detection.The bearing vibration detector pressing device comprises a machining table, a lifting table is fixedly installed at the top of the machining table, and a first motor is fixedly installed on the surface of the machining table; a second motor is fixedly installed at the top of the machining table, and the clamping device comprises a sliding plate, a first telescopic elastic rod, a clamping jaw, a gear, a base, a tooth block, a lantern ring, a rotating shaft, a fixing rod, a first connecting block, a first connecting rod, a linkage rod, a second connecting rod and a second connecting block. It is ensured that the bearing is kept stable in the detection process, interference of vibration signals is reduced, and looseness during vibration detection is effectively prevented.

The invention discloses a mining ventilator bearing fault diagnosis method based on a CEEMDAN and a multi-scale spatio-temporal information fusion map neural network. The method comprises the steps of collecting a mining ventilator bearing vibration signal; the vibration signals of the mining ventilator bearing are preprocessed; cEEMDAN is carried out on the preprocessed mining ventilator bearing vibration signal, and the signal is decomposed into a plurality of IMF components through adaptive multi-scale decomposition; constructing a multi-scale space-time convolution module, and adopting convolution kernels of different scales to extract time sequence characteristics of each IMF component in parallel; introducing a channel attention mechanism to perform adaptive weighting on the multi-scale spatial-temporal features, and dynamically strengthening the characterization intensity of the key fault mode by calculating the importance weight of the channel features; and aggregating the multi-scale spatial-temporal features through GCN to generate global feature representation, and inputting the global feature representation into a classifier to output a fault diagnosis result. Through combination of the CEEMDAN and the multi-scale spatio-temporal information fusion graph neural network, the technical bottlenecks of noise covering, spatio-temporal correlation deficiency and insufficient generalization ability are broken through.

The invention belongs to the technical field of fault diagnosis, and particularly relates to a fault feature extraction method and system for bearing data, and the method comprises the following steps: S1, carrying out the multi-frequency-band division of a collected bearing vibration signal, calculating the envelope spectrum of each frequency band, selecting the frequency band with the maximum peak amplitude in the envelope spectrums, and carrying out the multi-frequency-band division; determining a main peak frequency of the envelope spectrum as a fault characteristic frequency, and performing conversion to obtain a fault impact period T; s2, calculating an autocorrelation function of the bearing vibration signal, and determining the length L of an FIR filter according to the attenuation characteristic of the autocorrelation function and the fault impact period T; and based on the fault impact period T, carrying out period synchronous averaging on the bearing vibration signal. According to the method, the problem that interference is easy to amplify or effective information is easy to lose under strong noise is solved, so that the extracted fault features are purer and more prominent on an envelope spectrum, and the recognition capability of early weak faults is enhanced.

The invention relates to the field of rotating machinery fault diagnosis, and discloses a bearing fault diagnosis method based on a physical perception KAM network, and the method comprises the steps: carrying out the discretization of a bearing vibration signal through a Gabor filter group based on physical prior initialization, and generating modal feature lexical elements with physical frequency band meanings; and inputting the lexical elements into a PC-KAM backbone network, calculating a hidden state vector by using a state space model branch, and dynamically adjusting the position of a primary function node of a Kolmogov-Arnod network branch to realize collaborative dynamic feature extraction. In the training stage, an orthogonal subspace constraint and physical perception low-rank adaptation fine tuning mechanism is introduced. And finally, searching a historical fault case, performing multi-modal fusion on the historical fault case and the deep feature sequence, mapping a fusion representation into a soft prompt, and inputting the soft prompt into a large language model to generate a diagnosis report. According to the method, the problems of poor physical interpretability of characteristics and few-sample diagnosis under variable working conditions are effectively solved, and the generalization and decision-making ability of a diagnosis system are improved.

The invention discloses a numerical control machine tool power tool apron noise reduction method based on UUSGSR, and belongs to the technical field of numerical control machine tool servo tool rest power tool apron monitoring. The method comprises the specific steps that Hilbert transform and elliptic filtering preprocessing are conducted on monitored bearing vibration signals in a tool rest power tool apron; constructing an unsaturated scaling type Gaussian potential stochastic resonance model processing signal; a high-order weighted harmonic noise ratio HWHNR is proposed as a target function to reflect signal nonlinear characteristics; optimizing the structural parameters and the damping factors by using a grey wolf optimization algorithm to obtain an optimal under-damping unsaturated scaling type Gaussian potential stochastic resonance model UUSGSR containing the model structural parameters and the damping factors, and performing stochastic resonance processing on bearing vibration signals in the power tool apron of the tool rest by using the model. Noise contained in a bearing vibration signal in the power tool apron of the tool rest is weakened, and the signal-to-noise ratio is improved. In conclusion, the method can realize noise reduction of the vibration signal of the bearing in the power tool apron of the tool rest.

The invention provides a rolling bearing fault diagnosis method based on deep learning, and relates to the technical field of mechanical fault diagnosis. According to the method, global optimization is carried out on a parameter combination in an ICEEMDAN decomposition process through a CPO optimization algorithm, the optimal amplitude of white noise and the number of times of adding the white noise are determined, an intrinsic mode function is screened and denoised in combination with a correlation coefficient, reconstruction of a vibration signal of a rolling bearing is completed, and the vibration signal of the rolling bearing is reconstructed. The problem of mode aliasing is solved while the reliability and the stability of a vibration signal of the rolling bearing are improved; a K nearest neighbor method is used to construct a graph model, an edge connection relation between nodes can be effectively established, so that a foundation is laid for subsequent deep analysis and processing of vibration signals based on a graph structure, a graph attention neural network is input, topological structure information of the nodes and edges is fully utilized, extracted node features are classified and identified, and the accuracy of vibration analysis is improved. Therefore, fault diagnosis of the rolling bearing is realized.

The invention discloses a trailer and a trailer damping method.The trailer is applied to a wheel track type belt conveyor and used for supporting and dragging a conveying belt on a track of the wheel track type belt conveyor and walking along with the conveying belt, the trailer comprises a frame and a plurality of wheel assemblies distributed on the two sides of the frame, and the frame is provided with mounting seats corresponding to the wheel assemblies one to one; the wheel assembly comprises a wheel body, a wheel axle and a bearing, the wheel axle is in transmission connection with the wheel body through the bearing, at least part of the area of the wheel axle penetrates through a mounting hole formed in the mounting base and is in clearance fit with the mounting hole, and an elastic structure located between the mounting base and the wheel axle is arranged in the mounting hole. When the trailer transports a conveying belt and materials on the track, material loads and wheel-rail force are subjected to energy storage and energy consumption when passing through the elastic structure, so that the overall vibration of the trailer is attenuated, the vibration reduction effect of the trailer during operation on the track can be greatly improved, the peak value of a bearing vibration curve can be reduced, the peak clipping effect is achieved, and the service life of the bearing is prolonged.

The invention relates to the technical field of nuclear power plant equipment safety, in particular to a bearing fault diagnosis method and device, electronic equipment and a storage medium. According to the bearing fault diagnosis method provided by the embodiment of the invention, target bearing vibration data is acquired; performing multi-scale feature extraction on the target bearing vibration data through the bearing fault diagnosis model to obtain multi-scale bearing vibration features; carrying out bearing state detection on the target bearing based on the multi-scale bearing vibration characteristics to obtain a bearing vibration state; performing feature filtering processing on the multi-scale bearing vibration features to obtain filtered bearing vibration features; and carrying out bearing fault diagnosis on the target bearing according to the multi-scale bearing vibration characteristics, the bearing vibration state and the filtered bearing vibration characteristics to obtain a bearing fault category. Therefore, the accuracy of bearing fault diagnosis can be improved.

The invention discloses a real-time fault diagnosis method, device and system for a rotating machinery bearing based on a deep learning algorithm, and the method comprises the steps: designing a data collection board card, and collecting the vibration data and sound data of a bearing in different fault states on an electromechanical fault simulation platform; carrying out normalization processing on the collected signals, and constructing a data set by adopting a sliding window method; performing multi-scale decomposition on the collected bearing vibration signal and sound signal by using a signal time-frequency analysis method to obtain a time-frequency signal with a multi-scale fault feature; taking the decomposed signals as input, training a lightweight deep neural network model and storing optimal model parameters; deploying the trained lightweight deep neural network model based on time-frequency analysis to an edge computing embedded board card; a display screen of the edge computing embedded board card outputs a bearing fault diagnosis result in real time, and an emergency alarm is given through an acousto-optic signal when a fault is detected. According to the method, the advantages of signal time-frequency analysis in processing non-stationary signals and the powerful capability of the deep neural network in feature extraction are fully combined, the bearing fault can be accurately diagnosed in real time in a complex industrial environment, and the problems of poor real-time performance, high calculation complexity and the like existing in bearing fault diagnosis in the prior art are solved.

The invention provides a bearing fault diagnosis method and system, and relates to the technical field of bearing fault detection, and the method comprises the steps: collecting bearing vibration data; the bearing vibration signals are converted into a wavelet two-dimensional time-frequency image, an angle field two-dimensional time-frequency image and a transfer field two-dimensional time-frequency image through a continuous wavelet transform algorithm, a Grubrum angle field coding algorithm and a Markov transfer field algorithm; fusing the wavelet two-dimensional time-frequency image, the angular field two-dimensional time-frequency image and the transfer field two-dimensional time-frequency image to generate a fused time-frequency image; constructing a bearing fault detection model with a Swinin-T channel, a mixed attention channel and a feature fusion module; inputting the bearing vibration data as a training set into a bearing fault detection model until the predicted loss value of the bearing fault detection model is within a preset predicted loss value interval; acquiring a vibration signal of the to-be-detected bearing; and inputting a vibration signal of the bearing to be detected into the trained bearing fault detection model, and outputting a predicted bearing fault type.

The invention relates to the technical field of cooling water abnormity monitoring, in particular to a steel slag pressure hot stuffy rolling cooling water abnormity alarm system and method.Roll gap cooling water flow, injection temperature, hot stuffy tank pressure and bearing vibration data are collected, a dynamic mapping relation between position coordinates of a spraying opening and a water inlet valve is established, temperature and pressure time sequence offset is calculated, and the temperature and pressure time sequence offset is calculated; the temperature judgment boundary is corrected by combining the pressure bearing limit value of the sealing ring and the real-time flow, and an abnormal alarm is triggered according to the temperature over-limit frequency in the continuous time period. A multi-source analysis framework is constructed by integrating temperature, flow, pressure and vibration parameters, single-point monitoring limitation is eliminated, nonlinear influence of the flow on tank pressure is quantified by adopting a dynamic weight model, a self-adaptive temperature threshold value is generated in combination with a sealing pressure bearing limit, dynamic coupling of an alarm mechanism is realized, an abnormal scene is positioned based on time sequence analysis and coordinate mapping, and the accuracy of monitoring is improved. The sealing hidden danger is pre-judged through the thermal unbalance coefficient, and the pressure rate deviation is intercepted in real time.

The invention belongs to the technical field of rail transit equipment, and provides a vibration damping device for rail transit, which comprises a vibration damping seat, the top of the vibration damping seat is vertically connected with a rail in a sliding manner, and a bearing vibration damping assembly is arranged between the rail and the vibration damping seat; the two sets of main vibration reduction assemblies are arranged on the two sides of the rail correspondingly, each main vibration reduction assembly comprises an eccentric block arranged in a vibration reduction base, a rotary driving mechanism is arranged between the eccentric block and the bearing vibration reduction assembly, and the rotary driving mechanism drives the eccentric block to swing in a reciprocating mode when the rail vibrates up and down; the hydraulic vibration reduction mechanism is in transmission connection with the rotary driving mechanism, a secondary vibration reduction part is arranged in the hydraulic vibration reduction mechanism and corresponds to the eccentric block, and when the amplitude of the track is too large, the eccentric block abuts against the secondary vibration reduction part to increase damping of the hydraulic vibration reduction mechanism. The damping device can adapt to different vibration intensities, can automatically enhance damping force under the large-amplitude working condition, and provides a better vibration reduction effect.