2900 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.

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 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 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 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.

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 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 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 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 relates to a rolling bearing embedded lubrication state evaluation method and a computer device. The rolling bearing embedded lubrication state evaluation method comprises the steps that a temperature signal of a rolling bearing and a frequency domain spectrum amplitude sequence are spliced to generate a multi-source spectrum fusion feature vector; a random forest model is adopted to screen mean value features extracted from the temperature signals and time domain features and frequency domain features extracted from the vibration signals, the sound signals and the sound emission signals to obtain a sensitive feature set; inputting the sensitive feature set into a support vector machine model to output a first lubrication state evaluation result; inputting the multi-source spectrum fusion feature vector into a MobileNet V2 model to output a second lubrication state evaluation result; and fusing the first lubrication state evaluation result and the second lubrication state evaluation result through a D-S evidence theory to obtain a rolling bearing lubrication state evaluation result. The multi-source fusion evaluation method considering accuracy, robustness and engineering practicability is constructed, so that the problems of high limitation, insufficient fusion layers, high model complexity and the like of an existing single signal are solved.

The utility model discloses a shell device for spherical projection imaging, which comprises a transparent upper shell, a lower shell and a neck shell, the neck shell is arranged on one side of the upper shell far away from the lower shell, the neck shell and the upper shell are integrally formed, a bearing base is arranged on the inner bottom surface of the lower shell, the bearing base is provided with an accommodating groove, and the accommodating groove is communicated with the accommodating groove. A containing groove is formed in the neck shell and can contain a rolling bearing, the bearing base is in contact connection with the outer surface of the rolling bearing, a cable penetrating channel allowing a cable to penetrate is formed in the neck shell, a waterproof connector is arranged in the cable penetrating channel, the space among the waterproof connector, the cable penetrating channel and the cable is filled with sealant, and long groove holes which are oppositely formed are formed in the two sides of the neck shell. One end of the long slotted hole is connected with a suspension support through a bolt, and the other end of the long slotted hole serves as a glue pouring end. According to the utility model, the long slotted hole realizes dual purposes and convenience, can be used for being connected with a suspension bracket, and can also be used as a glue filling port, so that glue filling is accurately controlled, sealant waste is avoided, and the glue filling period is shortened.

An axial rolling bearing unit includes a first annular axial bearing washer, a second annular axial bearing washer, a bearing cage, and rolling elements arranged between the first annular axial bearing washer and the second annular axial bearing washer. The bearing cage includes an inner radial edge rim extending in an axial direction to radially guide the rolling elements, and an outer radial edge rim extending in the axial direction to radially guide the rolling elements. The inner radial edge rim or the outer radial edge rim includes an end-side bend extending in an opposite axial direction as a double-rim design, and the end-side bend has a first distal end section that holds the first annular axial bearing washer in a form-fitting manner. The other one of the inner radial edge rim or the outer radial edge rim holds the second annular axial bearing washer in a form-fitting manner.

A bearing seal provides a first sealing member to be secured to a shoulder on one side of a first bearing ring and a second sealing member that can be secured to a shoulder on the same side of a second bearing ring. A labyrinth seal gap is formed between the first sealing member and the second sealing member, which has at least one tooth-shaped turn. On the basis of the above bearing seal, the present invention further provides a rolling bearing that employs the bearing seal on an axial side thereof.

The invention relates to the technical field of structural vibration control, in particular to an adjustable electric damping-negative stiffness dynamic vibration absorber for a flexible shafting. According to the technical scheme, the damping device comprises a support and a rolling bearing, a plurality of first supporting rods used for supporting are arranged outside the rolling bearing, an electric damper used for absorbing vibration is arranged at one end of each first supporting rod, and a second supporting rod used for supporting is arranged on the other side, away from the first supporting rods, of each electric damper. A negative stiffness vibration absorber is arranged at the end, away from the electric damper, of the second supporting rod, and the electric damper comprises an auxiliary circular ring used for bearing loads. Through the arrangement of the adjusting plate, the adjusting bolt, the disc spring, the mass block, the movable magnet, the static magnet and the electric damper, the purposes of vibration absorption and vibration reduction can be achieved at the transcritical rotating speed and the supercritical working rotating speed, and the negative stiffness dynamic vibration absorption frequency and the electric damping suppression modal frequency can be rapidly adjusted according to the change of the operation working condition; and the application range and the universality are improved.

The invention provides a rolling bearing fault diagnosis method, a medium and equipment, and relates to the technical field of large-scale unit bearing fault diagnos.The method comprises the steps that collected original life cycle vibration data of a rolling bearing are obtained; performing early warning processing on the vibration data by using a Beta early warning algorithm, and determining and obtaining a required diagnosis signal according to an early warning interval; determining an input parameter of variational mode decomposition by using the diagnosis signal, performing optimal parameter selection on the variational mode decomposition by combining an improved pelican optimization algorithm, and decomposing the diagnosis signal by combining the optimal parameter with the variational mode decomposition to obtain a plurality of intrinsic mode functions; and evaluating the plurality of intrinsic mode functions by using the correlation entropy in combination with the kurtosis so as to select a proper intrinsic mode function for reconstruction, and obtaining a final diagnosis result according to envelope spectrum analysis of a reconstructed signal. According to the method, the rolling bearing fault can be early warned and accurately diagnosed, and the accuracy, the real-time performance and the intelligent level of fault diagnosis are improved.

The invention discloses a rolling bearing fault time-varying instantaneous feature enhancement and extraction method. The method comprises the following steps: acquiring an original bearing fault signal; decomposing an original bearing fault signal based on an adaptive noise complete set empirical mode decomposition method to obtain a series of intrinsic mode function components; component properties are distinguished based on a correlation coefficient hopping criterion, and denoising is carried out; mapping the bearing fault signal after noise reduction to a time-frequency chirp rate space based on a local maximum frequency chirp rate synchronous compression chirp transformation method; and extracting a time-frequency chirp rate coefficient reflecting fault time-varying instantaneous characteristics from the extruded time-frequency chirp rate space, reconstructing a bearing fault impact component, performing Hilbert envelope analysis on the bearing fault impact component, and identifying the characteristic frequency of the bearing fault. According to the method, the fault time-varying instantaneous characteristics of the rolling bearing can be clearly and accurately extracted, and the method has a good engineering application prospect.

The invention provides a rolling bearing small sample open set fault diagnosis method, and belongs to the field of fault diagnosis. The problems of insufficient fault feature extraction capability and open set threshold strategy stiffness caused by rolling bearing data scarcity in the prior art are solved. The method comprises the following steps: constructing a small sample closed set classification module, wherein the module realizes fault feature extraction and classification by fusing a dense connection network and a prototype network; an open set recognition module is designed, and the open set recognition module constructs a self-adaptive multi-class 3 sigma threshold judgment mechanism based on the self-encoder reconstruction error to distinguish a known fault class and an unknown fault class; a to-be-diagnosed bearing vibration signal is input into the closed set classification module for feature extraction and prototype matching, meanwhile, a reconstruction error is calculated through the open set recognition module and compared with a dynamic threshold value, if the error exceeds the threshold value, it is judged that the fault is an unknown fault category, and otherwise, the closed set module outputs a specific fault category. The method is applied to mechanical part detection.

The invention discloses a rolling bearing fault classification method fusing adaptive distribution perception discrimination loss (ADADL), and belongs to the technical field of rolling bearing fault diagnosis. The rolling bearing fault classification method comprises the following steps of: obtaining a rolling bearing fault, and carrying out classification on the rolling bearing fault by using the ADADL as a fusion model, and carrying out classification on the rolling bearing fault by using the ADADL as a fusion model, and carrying out classification on the rolling bearing fault by using the ADADL as a fusion model. In a complex industrial environment, classification boundary fuzziness is often caused by noise interference and feature overlapping, and the accuracy of rolling bearing fault diagnosis is reduced. According to the method, an adaptive distribution perception discrimination loss function (ADADL) is provided, and intra-class compactness and inter-class separability are improved by adjusting intra-class distance through a dynamic threshold value and optimizing inter-class distribution through an adaptive boundary. And the cross entropy loss is combined with ADADL, so that the classification precision is further optimized, the model is helped to better process samples difficult to classify, and the robustness and the adaptive ability of the model are improved. The classification performance is remarkably improved on the CWRU data set, and particularly, excellent robustness and generalization ability are shown under the conditions of class imbalance and strong noise. Feature visualization results show that ADADL can optimize clustering boundaries of different fault categories, minimize overlapping regions, and relieve the problem of fuzzy classification boundaries.

The invention belongs to the technical field of mechanical health monitoring and predictive maintenance, and discloses a rolling bearing residual life prediction method and system, and the method comprises the steps: carrying out the adaptive feature extraction, transforming a TimesFM large model structure to adapt to the performance degradation modeling of a rolling bearing, and carrying out the field fine tuning based on the bearing data; and carrying out online deployment on the trained time sequence large model, inputting online monitoring data into an adaptive feature extraction module, and finally realizing visual early warning. According to the method, a transfer learning strategy of freezing a Transform trunk and fine tuning a small sample is introduced, and only task related parameters are adjusted, so that the dependence on new data is greatly reduced; a 1D convolution + MLP parallel structure is introduced, and complementary advantages of local convolution and global modeling are combined. According to the method, starting from an actual application scene, a complete end-to-end industrial grade prediction system is constructed, a matched visual component supports real-time prediction result display and early warning feedback, and the man-machine interaction capability is enhanced.

An electrical machine for a motor vehicle drive, including a main housing body a stator, which is fastened to the main housing body by at least one fastening element and a rotor connected to a connection shaft for conjoint rotation. The connection shaft is radially and axially supported by a main rolling element bearing, either on the main housing body directly or on the main housing body indirectly the stator.