3854 results about "Rolling-element bearing" patented technology

The invention belongs to the technical field of bearing residual service life detection, and discloses a rolling bearing residual service life prediction method and system based on a convolution white box. According to the method, time domain and frequency domain feature extraction, noise reduction processing and health state and degradation state division are carried out on an original vibration signal, and deep neural network training is carried out in combination with a Weibull-MSE loss function, so that prediction of RUL conforms to the actual degradation process of a bearing; and synchronous extraction of long-time dependence information and local degradation characteristics of the bearing signal is completed through a convolution CRATE network architecture fusing an expansion causal convolution attention mechanism DCA and a multi-scale convolution MSC. According to the method, the accuracy of RUL prediction is improved in combination with health state evaluation, the local feature extraction capability is enhanced, the local modeling capability of the model is improved through multi-scale information fusion, and the interpretability and the prediction credibility are improved through a CRATE structure.

A rolling bearing combined fault diagnosis method based on a multi-domain feature construction is provided, including: collecting vibration signals of rolling bearings, extracting time-domain and frequency-domain features of fault-sensitive vibration signals of the rolling bearings, and obtaining an in-domain feature set; performing an improved empirical mode decomposition on the vibration signals of the rolling bearings to obtain time-frequency features; constructing a multi-domain fault sample set based on the in-domain feature set and the time-frequency features; performing a fault mechanism analysis on the multi-domain fault sample set to obtain modulation features under each single fault; carrying out a feature engineering on the time-domain and frequency-domain features, and screening out relevant features, redundant features and irrelevant features; performing an unsupervised hierarchical clustering on test set fault samples, and then inputting the test set fault samples into a KNN model for a classification, and obtaining rolling bearing combined fault diagnosis results.

The invention discloses a rolling bearing fault diagnosis method based on a multi-scale residual attention network and an adaptive Transform encoder. The rolling bearing fault diagnosis method comprises the following steps: acquiring original vibration data in the running process of a rolling bearing; segmenting the collected original vibration data into samples with specified lengths, and dividing the samples into a training data set and a test data set; inputting the training data set into a multi-scale residual attention network to perform preliminary multi-scale feature extraction; inputting the feature information extracted by the multi-scale residual attention network into an adaptive Transform encoder to obtain time sequence features; finally obtained feature information is subjected to GAP processing and then is input into a Softmax layer for fault diagnosis; the forward propagation calculation and the back propagation calculation are repeatedly executed to optimize model parameters until the diagnosis accuracy and loss of the training data set reach a stable level; and inputting the test data set into the trained model for fault diagnosis, and determining the health condition of the rolling bearing. According to the method, the adaptability and the diagnosis accuracy in time sequence dependence scenes such as rolling bearing fault diagnosis are enhanced.

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.

To provide an electric corrosion preventing electric conductive cover and a rolling bearing which can prevent electric corrosion to lengthen a service life thereof.SOLUTION: An electric corrosion preventing electric conductive cover 1 has: a bearing fitting part 9 having a cylindrical part fitted in a first bearing ring 3 of a rolling bearing 2 and having electric conductivity; and an electric conductive part 10 radially extending from the bearing fitting part 9 to come into contact with a second bearing ring 4 of the rolling bearing 2 and having electric conductivity. A rolling bearing 2 includes the electric corrosion preventing electric conductive cover 1.SELECTED DRAWING: Figure 2

The invention provides a rolling bearing fault diagnosis method driven by simulation data, and the method comprises the steps: constructing a five-degree-of-freedom dynamical model of a rolling bearing according to the failure forms of different fault types of the bearing, so as to represent the dynamic response of a bearing system in a virtual space; the method comprises the following steps: collecting vibration signals under healthy and different fault conditions in a real environment through a bearing fault simulation experiment table, and establishing a data set; the feature distribution difference between the simulation data and the measurement data is considered, and four optimization targets are constructed and are respectively used for difference feature learning and domain invariant feature learning; a Transform model is used as a feature extractor, and fault diagnosis is carried out under a cross-domain condition in combination with a mixed loss function. Compared with the prior art, the model established by the invention can perform effective diagnosis under cross-domain and sparse training data conditions, solves the problems of data feature distribution difference and sparse target domain samples in engineering practice, and has certain robustness.

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 a variable-speed bearing fault diagnosis method based on nonlinear modal decomposition, and the method comprises the steps: S1, obtaining a vibration signal of a rolling bearing at a variable speed, and calculating a fault feature order according to a bearing parameter; s2, performing low-pass filtering on the vibration signal to obtain a filtered signal; s3, extracting a non-linear mode component from the low-frequency part of the filtered vibration signal by using non-linear mode decomposition; s4, calculating the instantaneous frequency of the modal component by using a Hilbert method; s5, based on the instantaneous frequency, converting the vibration signal obtained in S1 into an angular domain pseudo-stationary signal by using an angular domain resampling method; and S6, performing envelope order analysis on the angular domain pseudo-stationary signal, solving a mode of an analysis signal, solving a Fourier spectrum for the mode of the analysis signal, comparing the Fourier spectrum with a fault feature order, and identifying a fault. According to the invention, the time-frequency analysis precision in a strong noise environment is improved, the accuracy of the rotating speed signal is ensured, and the accuracy of fault diagnosis is improved.

The invention relates to a rolling bearing fault diagnosis method and device based on a composite multi-scale attention entropy and an optimized SVM, and a medium, and the method comprises the steps: collecting vibration signals of a rolling bearing in different states, obtaining the damage size grade in each state according to the vibration signals, and obtaining a fault data set; performing coarse graining processing on the vibration signal to obtain a coarse grain sequence, and calculating an entropy sequence of the coarse grain sequence under different scale factors by adopting a composite multi-scale attention entropy algorithm improved by a fractional order algorithm; entropy values of a plurality of first scale factors in the entropy sequence are selected as bearing fault feature vectors; optimizing hyper-parameters of the support vector machine by using a collaborative group optimization algorithm; and constructing a support vector machine classification model based on the optimal hyper-parameter, and performing fault prediction and judgment on the test sample. Compared with the prior art, the method has the advantages of high noise robustness, high global convergence, high adaptability and the like.

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 a rolling bearing fault diagnosis method and system based on multi-channel adaptive fusion, and the method comprises the steps: obtaining a one-dimensional vibration acceleration signal, carrying out the normalization, and converting the one-dimensional vibration acceleration signal into a two-dimensional gray-scale map; inputting the two-dimensional grey-scale map into a convolutional neural network model for processing, specifically comprising the following steps: a multi-scale feature extraction module extracts feature information of the two-dimensional grey-scale map, adaptively weights the importance weight of multi-channel information, and outputs a first multi-scale feature map; a double-attention cavity convolution module extracts the spatial position and channel information of the first multi-scale feature map, and outputs a second feature map; and the residual dense module performs deep feature extraction on the second feature map through residual connection and dense connection, and outputs a rolling bearing fault diagnosis classification result. According to the method, the problems of low model fault diagnosis effect and poor generalization ability caused by strong noise interference and large load change of the rolling bearing can be solved.

The invention provides a harmonic joint. The harmonic joint comprises a joint shell and a speed reducer assembly. A first limiting ring groove is formed in one end of the inner peripheral wall of the joint shell; the speed reducer assembly is arranged in the joint shell and comprises a rigid wheel and a rolling body. A second limiting ring groove corresponding to the first limiting ring groove in position is formed in the peripheral wall of the rigid wheel, and the second limiting ring groove and the first limiting ring groove form a limiting ring cavity. The rolling body is arranged in the limiting ring cavity, and the rolling body, the peripheral wall of the rigid wheel and the peripheral wall of the joint shell jointly form a rolling bearing structure.

The invention discloses a rolling bearing fault feature extraction method based on NRBO-FMD decomposition and reconstruction. Firstly, vibration signals of a rolling bearing are collected through a vibration sensor and subjected to standardization processing; secondly, an NRBO algorithm is adopted to optimize decomposition parameters of the FMD, self-adaptive decomposition of the vibration signals is achieved, and the vibration signals are decomposed into a plurality of intrinsic mode components IMFs; screening and reconstructing the IMFs obtained by decomposition by adopting a kurtosis-frequency domain correlation coefficient screening criterion to obtain a reconstructed signal; envelope demodulation is carried out on the reconstructed signal, an envelope spectrum is calculated, and a bearing fault characteristic frequency is extracted; and finally, comparing with the theoretical fault characteristic frequency of the bearing to determine the fault type of the bearing. Through a signal decomposition and reconstruction method, effective extraction of fault impact signals under noise and interference conditions is realized, and the accuracy of fault diagnosis is improved.

The invention provides a rolling bearing digital twinning dynamic evolution method and system based on continuous learning, and belongs to the technical field of bearing life prediction. The method comprises the following steps: processing a bearing monitoring signal through short-time Fourier transform to generate standardized time-frequency data; constructing health indexes based on the index degeneration function and dividing health levels; training a life prediction model by using the extended LSTM network and taking the time-frequency data as input; real-time data is collected through a fixed time window to predict the life, and when the root-mean-square error of a predicted value and an actual value exceeds the limit, the edge device is triggered to upload new data; evaluating parameter importance in combination with a Fisher information matrix, and dynamically adjusting a regularization intensity updating model; and monitoring the data standard deviation in real time, triggering shutdown when the data standard deviation exceeds the limit, otherwise, predicting the remaining life by updating the model, and stopping when the remaining life reaches the threshold value. According to the method, dynamic evolution of the digital twin model is realized through continuous learning, and the industrial equipment state monitoring and predictive maintenance capability is effectively improved.

The invention discloses a rotor reliability constrained rolling bearing assembly parameter robust design method. The method comprises the following steps: constructing a dynamic model for an actual rotor-bearing system; constructing an uncertainty parameter vector and a design variable vector; a target function based on robustness and a constraint function based on reliability are constructed, so that an uncertainty optimization model is obtained; constructing an augmented input variable, and establishing a candidate orthogonal polynomial basis function set; on the basis, constructing and evaluating polynomial chaos-Kriging models for the target function and the constraint function respectively, and screening out an optimal polynomial chaos-Kriging model; calculating the expectation and the standard deviation of the target function and the failure probability of the constraint function under each design variable vector; and converting the uncertainty optimization model into an unconstrained single-target optimization model, randomly generating population individuals of a heuristic optimization algorithm in a feasible region of design variables, and iteratively searching an optimal solution of the unconstrained single-target optimization model as a rolling bearing assembly scheme.

The invention discloses a rolling bearing vibration signal multi-mode fault classification method. The method comprises the following steps: collecting a vibration time sequence signal of a rolling bearing; the vibration time sequence signals are input into a time sequence branch network and a space branch network in parallel, and the time sequence branch network extracts time sequence dependence characteristics of the signals through a one-dimensional convolutional neural network and a bidirectional gating circulation unit; the spatial branch network converts the vibration time sequence signal into a Markov transform field image, and extracts spatial structure features of the image by using a two-dimensional convolutional neural network and a window Transform-based visual network; performing bidirectional interaction and weighted fusion on the time sequence features and the spatial features through a cross-modal attention mechanism to obtain fusion features; and inputting the fusion features into a classifier, and outputting a fault classification result of the rolling bearing. According to the method, the problems that a traditional single-mode fault diagnosis model is insufficient in adaptability to complex working conditions, multi-mode feature fusion is insufficient, and the generalization ability is weak due to model structure redundancy are solved.

The invention discloses a TCN-SVM rolling bearing fault diagnosis method fusing an SE attention mechanism, and belongs to the technical field of mechanical fault intelligent diagnosis. Aiming at the problems of feature redundancy, noise sensitivity, insufficient Softmax classifier generalization and the like existing in a traditional time sequence convolutional network (TCN), the invention provides a solution for collaborative optimization of a deep network and a support vector machine. The method comprises the following steps: acquiring a vibration signal of the rolling bearing; constructing a multi-fault sample set, and processing an original signal; constructing an SE-TCN feature extraction network, capturing multi-scale time sequence features by adopting expansion causal convolution, and embedding an SE module into a residual module to realize channel adaptive weighting; and a support vector machine (SVM) classifier decision function is constructed, and fault classification is completed through the RBF kernel SVM. Experiments show that the method has high fault recognition accuracy and robustness, and the problem of confusion of composite fault features of the rolling bearing is effectively solved.

The invention provides a bearing fault feature extraction method based on parameter optimization SMHD-FMD, and the method comprises the following steps: S1, collecting a vibration signal of a rolling bearing, carrying out the pre-parameter optimization of the SMHD through employing the Myceland algorithm, and carrying out the noise reduction preprocessing of the SMHD through employing the optimal parameter SMHD; s2, performing PO-FMD on the denoised signal to obtain an IMF component; s3, selecting an optimal component according to the kurtosis of each component; and S4, carrying out envelope demodulation analysis on the optimal component, and extracting a characteristic frequency to realize fault diagnosis. According to the method, an optimal parameter SMHD is used for carrying out noise reduction preprocessing on an original signal to highlight pulse characteristics, so that characteristic modal decomposition is carried out on the original signal to obtain a modal component, then kurtosis is used for selecting an optimal component, finally, fault characteristic frequency is extracted by calculating an envelope spectrum of the optimal component, and fault diagnosis of the rolling bearing is achieved.

A rolling bearing intelligent fault diagnosis method oriented to generalization in a single field is characterized in that feature extractor branches of different scales are designed, branch features which are different in distribution but contain the same fault information are mixed through adversarial training, and quantized entropy indexes are designed to weight the branch features; a feature extractor can extract robust diagnosis features with cross-branch consistency; and meanwhile, the compactness of the classification result of each branch is improved by using the minimum classification entropy, and the consistency of the classification boundary of each branch is further constrained through a norm, so that the learning effect of the classifier is maximized. According to the method, the problem of effectively generalizing to an unknown target domain for fault diagnosis under the limitation of single source domain data can be solved in a simpler way.

The invention discloses a rolling bearing lightweight fault diagnosis method and system based on multi-source signal fusion. Vibration, temperature and rotating speed signals are synchronously collected, and timestamps are calibrated; respectively carrying out denoising and normalization preprocessing; dividing and aligning windows; differential feature extraction: extracting time-frequency features of the vibration signals by using a one-dimensional residual CNN, and extracting abnormal measurement of the temperature / rotating speed signals by using an LSTM in combination with an isolated forest algorithm; carrying out self-adaptive weighted fusion on the features through an attention mechanism; the lightweight diagnosis model (through knowledge distillation, pruning and quantification) deduces and outputs the fault category, the health index and the confidence coefficient. The system correspondingly comprises an acquisition module, a preprocessing module, a feature extraction module, a fusion module and a diagnosis module. The method improves the early fault sensitivity, enhances the variable working condition robustness, supports the real-time deployment of edge equipment, and is suitable for the intelligent monitoring of industrial bearings.

The invention relates to a rolling bearing residual life prediction method based on space-time degradation characteristic decoupling, and the method comprises the steps: obtaining a vibration signal in a full life cycle operation process of a rolling bearing, and obtaining a sample sequence after preprocessing; performing complete ensemble empirical mode decomposition on the sample sequence, and extracting a plurality of IMF signals; on the basis of statistical threshold criterion in combination with energy mutation and self-correlation structure mutation analysis, identifying a degradation starting moment, adding a label to a sample sequence, and dividing a training set and a test set; training the space-time degeneration decoupling network by using the training set to obtain an RUL prediction model; testing the RUL prediction model by using the test set to obtain a prediction result; the space-time degeneration decoupling network combines a degeneration guide feature deconstruction module and a collaborative modeling strategy of a time modeling branch and a space modeling branch, captures time dynamic characteristics and a space hierarchical structure, and improves the accuracy, stability and reliability of RUL prediction.

An intelligent efficient vertical axis wind turbine with variable flaps and propeller pitches comprises blades, blade sliding rings, a main body sliding ring, a motor, a connecting rod, a connecting disc, an intermediate shaft, a motor connecting shaft and a rolling bearing. The blade comprises a flap, a flap connecting rod, a connecting rod, a rudder arm, a steering engine, a blade shaft and a blade front part, wherein the flap, the flap connecting rod, the connecting rod, the rudder arm and the rudder mechanism form a parallelogram-shaped connecting rod mechanism; the steering engine is a driving rocker, rotates by a certain angle under the driving of electric power, and can drive a driven rocker composed of a flap and a flap connecting rod to rotate by a certain angle. The invention further provides a method for controlling the variable flaps of the vertical axis wind turbine through the deep reinforcement learning algorithm. According to the invention, the pitching moment generated after the flap is driven by the steering engine to deflect is indirectly regulated and controlled. Under driving of methods such as deep reinforcement learning, the flap angle can be intelligently adjusted in a closed-loop mode along with the rotor phase and the incoming flow direction, and real-time optimization of the lift resistance and the direction of the blade is achieved.

The invention discloses a petroleum drilling PDC drill bit, and relates to the technical field of drilling PDC drill bits, the petroleum drilling PDC drill bit comprises a drill bit main body, a blade main body and a liquid conveying pipeline, an adjusting assembly is arranged between the drill bit main body and the blade main body, and the adjusting assembly is used for automatically adjusting the rotating speed and bit pressure of the blade main body; the adjusting assembly comprises a conical block fixedly installed on the inner wall of the drill bit body, a rolling bearing is fixedly installed on the inner wall of the drill bit body, and a gear disc is rotationally installed on the rolling bearing. The drilling tool has the advantages that before drilling of the blade body, the rotating speed and the drilling pressure of the blade body can be adaptively adjusted according to the drilling torque of the blade body, and meanwhile when continuous drilling of the blade body is conducted and the drilling torque is increased or decreased, the rotating speed and the drilling pressure of the blade body are adaptively adjusted in time through cooperation of the adjusting assembly and the sensing assembly; therefore, the well drilling effect of the blade body can be improved, abrasion of the blade body caused by long-time use is reduced, and the service life of the blade body is prolonged.

The invention provides a rolling bearing performance degradation evaluation method based on electrostatic monitoring. The rolling bearing performance degradation evaluation method comprises the following steps: S1, fusing electrostatic charge signal features with conventional time domain, frequency domain and time-frequency domain features to form an electrostatic monitoring system feature parameter set representing a bearing state; the invention provides a rolling bearing performance degradation evaluation method based on electrostatic monitoring. According to the rolling bearing performance degradation evaluation method based on electrostatic monitoring, nonlinear features and different degradation stages of a bearing can be better identified through a deep noise reduction auto-encoder optimized by L1 sparse constraint and a Dropout mechanism; the provided adaptive density peak value clustering model can well measure the health state of the bearing, and compared with traditional DPC and SVDD, the method provided by the invention can recognize early degradation of the bearing earlier, and has better monotonicity, robustness and tendency; compared with vibration monitoring, static monitoring has superiority in bearing performance degradation evaluation, and degradation can be found earlier.

The invention relates to an embedded rolling bearing with a source end parameter acquisition system and a manufacturing method of the embedded rolling bearing. The embedded rolling bearing comprises a bearing body, and a containing groove is formed in the peripheral face of the bearing body; the thin film sensor assembly is formed in the accommodating groove, and comprises a PDC-ITO (Poly (Propylene Diene Monomer-Indium Tin Oxide)) strain element, a PDC-Pt (Poly (Propylene Diene Monomer)-Pt) temperature element, a PDC-PZT (Poly (Poly (Propylene Diene Monomer)-PZT) piezoelectric thin film and a plurality of bonding pads which are respectively matched with the PDC-ITO strain element, the PDC-Pt temperature element and the PDC-PZT piezoelectric thin film; the acquisition microstructure is arranged in the accommodating groove and comprises a PCB (Printed Circuit Board) and a functional chip which is formed on the PCB and is electrically connected with the plurality of bonding pads through a plurality of electrical interconnection sockets; the heat insulation pouring sealant is poured into the accommodating groove; and a metal cover. Normal installation of the bearing is achieved to the maximum extent, and the defect that the appearance and the size of a traditional external sensor and a traditional collector are limited is overcome.

The invention discloses a rolling bearing generalization fault diagnosis method based on a quantum physical information neural network, and the method comprises the following steps: S1, obtaining vibration signals and working condition parameters in the operation process of a rolling bearing, and carrying out the preprocessing and domain division to obtain a meta-training set and a meta-test set; s2, constructing a quantum-physical information neural network model which comprises a quantum path and a physical path; s3, feature fusion and classification; and S4, based on physical perception element learning training, repeating inner ring-outer ring iteration until the model is converged. Compared with the prior art, the rolling bearing fault diagnosis field generalization method based on the quantum physical information neural network has the advantage that the field offset problem existing in a traditional fault diagnosis method is solved.

The invention discloses a rolling bearing depth universal domain self-adaptive cross-working-condition fault diagnosis method and device and a medium, and can be applied to the technical field of rolling bearing intelligent fault diagnosis. According to the method, the fault diagnosis model comprising the feature extractor, the binary classification network and the adversarial domain discriminator is trained through the source domain data containing the fault type label and the target domain data not containing the fault type label, and the loss function in the training process is calculated; adjusting model parameters of the fault diagnosis model according to the total loss function and a preset model parameter optimization algorithm so as to realize shared class feature distribution alignment between the source domain training set and the target domain training set, and identifying a private fault type of the target domain data set according to a confidence coefficient threshold; and when the number of training iterations of the fault diagnosis model is greater than or equal to the maximum number of iterations, the trained fault diagnosis model is tested through the target domain test set, so that the fault diagnosis accuracy of the fault diagnosis model can be improved.

The invention discloses a Bayesian optimization-based DCAE-RNN rolling bearing early fault prediction method, and the method comprises the steps: constructing a DCAE-RNN model, the DCAE-RNN model comprises a DCAE model and an RNN model, the DCAE model is composed of an SADE and a CAE, and carrying out the hyper-parameter optimization of the SDAE and the CAE through a Bayesian optimization function; the training set is adopted to train the DCAE-RNN model, and a trained DCAE-RNN model is obtained; and inputting a real-time vibration signal of rolling bearing operation into the trained DCAE-RNN model, performing residual calculation by using a predicted value output by the model and a true value, and detecting an early fault of the rolling bearing. According to the method, the running state of the rolling bearing can be well described, the early failure of the rolling bearing can be predicted earlier and more stably, the method has high accuracy and robustness, the model is convenient to deploy, and the method is very important for degradation monitoring of the running state of the rolling bearing.

The invention provides a high-stability low-noise uniform mixing oscillator. The high-stability low-noise uniform mixing oscillator comprises an oscillation platform and a base unit, the bottom surface of the oscillation platform is provided with three ball bearing surfaces which are distributed along the circumference in an array manner and at least three magnetic columns which vertically extend downwards, and the magnetic columns are sleeved with rolling bearings; a lower base body of the base unit is provided with three concave ball containing holes and at least three concave polarization track groove holes, balls are arranged in the ball containing holes, first elastic rings are arranged in the middles of the inner walls of the ball containing holes, and second elastic rings are arranged on the inner walls of the polarization track groove holes. The oscillation platform is supported through three balls with the same height, the three points form a stable supporting face, under the non-contact magnetic attraction effect, the oscillation platform can still be kept on the same plane during high-frequency oscillation, and the operation stability is improved; during oscillation, the column body of the magnetic column is firstly buffered by the contact between the elastic ring and the rolling bearing, and then the column body of the magnetic column is contacted with the inner wall of the polarization track groove hole, so that the noise during high-frequency oscillation can be obviously reduced.

The invention discloses a rolling bearing residual life prediction method based on personalized federated learning, and belongs to the field of rolling bearing residual life prediction.The method comprises the steps that rolling bearing full life cycle vibration data collected under different operation conditions are stored in a plurality of clients, and a server and the clients work cooperatively; and establishing a personalized prediction model for the client. A plurality of clients are aggregated into a global model at a server side by adopting a dynamic weighted federation aggregation strategy, so that the sensing ability of the aggregated global model to multi-working-condition data is improved; an adversarial training and self-adaptive fusion strategy is adopted at a client, a global model aggregated by a server and a local model are effectively fused, and the generalization ability of the local model is improved. The client local model adopts a multi-scale convolution and gating cycle unit parallel cross attention feature extraction module to capture spatio-temporal feature information in the data. According to the method, the residual life prediction precision and generalization performance of the rolling bearing under different working conditions can be improved.