1944 results about "Rolling-element bearing" patented technology

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 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 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 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 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.

The invention relates to the technical field of bearing measurement, and discloses a multifunctional measuring instrument for comprehensive precision of a rolling bearing. The driving device is mounted on the working platform and comprises a load block capable of axially moving and a motor assembly for driving the load block to rotate; the measuring device comprises a high-precision inductance measuring head for detecting the runout of the bearing to be measured and a pressure sensor for monitoring an axial load; the fixing device is a replaceable mandrel base and is used for supporting an inner ring or an outer ring of the bearing to be detected; wherein the driving device applies an axial load to an inner ring or an outer ring of a to-be-measured bearing through the load block and drives the to-be-measured bearing to rotate, the measuring device synchronously collects bounce and load data of the to-be-measured bearing, the brushless motor and the load block are connected through the magnetic coupling, and the centering error of the mechanical coupling is eliminated. Meanwhile, motor vibration is prevented from being transmitted to the to-be-tested bearing, the pushing force air floating shaft sleeve actively counteracts the magnetic attraction force, and the stability of the load applied to the to-be-tested bearing is remarkably improved.

The invention belongs to the technical field of rolling bearing manufacturing, and discloses a preparation method of a high-precision robot turntable bearing, which comprises the following steps: applying a static load simulating a preset working load to a bearing ring workpiece subjected to heat treatment, and executing a set of closed-loop acoustic vibration treatment on the workpiece in a state of keeping the static load, the processing can dynamically track the resonant frequency of the workpiece to update excitation, can monitor acoustic response characteristic parameters of the workpiece in real time, and automatically terminates when the time change rate of the parameters meets a stable condition. According to the method, the specific directivity is provided for the stress release process driven by acoustic vibration energy, so that an original disordered residual stress field in a workpiece forms an optimized form capable of actively adapting to future working loads, and the problem that the static geometric precision and the dynamic service precision of a bearing are inconsistent in a traditional process is solved.

The invention provides a boundary-guided rolling bearing semi-supervised fault diagnosis method and system, and relates to the technical field of bearing fault diagnosis, and the method comprises the steps: collecting a rolling bearing vibration signal data set which comprises a labeled sample and an unlabeled sample; constructing a diagnosis network; inputting the time domain and frequency domain signals into a feature extractor to extract features; performing cross-mask comparison learning on an output layer of the feature extractor, and calculating cross-mask comparison loss; carrying out asymmetric predictive contrast learning in the projection space, and calculating asymmetric predictive contrast loss; constructing a boundary guiding mechanism by using the labeled samples, and guiding unlabeled sample aggregation by monitoring loss; introducing a consistency regularization mechanism, and constructing consistency loss by using time-frequency prediction distribution of unlabeled samples; integrating each loss training network until convergence; and finally, fault diagnosis of the label-free signal is realized. According to the method, the problems of fuzzy inter-class boundary and poor feature clustering quality under limited labeling are solved, and the diagnosis precision and robustness are improved.

A method of detecting a bearing fault of a rolling element bearing mounted around a shaft, using a shaft displacement sensor providing a shaft displacement signal to detect shaft displacement, the method including: a) subtracting a modelled shaft displacement reference signal from the shaft displacement signal to obtain a residual vector, b) estimating for at least one bearing fault type a respective bearing fault frequency, and estimating, for each bearing fault type, an amplitude of the residual vector in a frequency range containing a bearing fault frequency of the associated bearing fault type, and d) determining whether a bearing fault is present based on the at least one estimated amplitude.

The invention discloses an ultra-high-temperature heavy-load self-aligning roller bearing optimization design method and system based on a particle swarm algorithm, belongs to the field of rolling bearing optimization design, and solves the problems that a traditional bearing design process is low in efficiency, and multi-target collaborative optimization is difficult to achieve. And the design method combining the empirical formula and the finite element model is poor in precision due to the strong coupling characteristic of the material nonlinearity, the thermal coupling effect and the bearing performance under the ultra-high-temperature heavy-load working condition. The method comprises the following steps: S1, determining a bearing parameter, a working condition parameter and a particle swarm algorithm parameter; s2, establishing an optimization model of the ultrahigh-temperature heavy-load self-aligning roller bearing: S21, establishing a target function; s22, determining a design variable; s23, establishing a constraint equation; and S3, optimizing corresponding parameters of the bearing by adopting a particle swarm algorithm to obtain an optimization result. The method is suitable for a self-aligning roller bearing optimization design scene.

The invention discloses a variable-shaft-diameter rolling bearing testing device and testing method, and aims to solve the problems that an existing rolling bearing testing device is difficult to adapt to bearings with different shaft diameter specifications, and a main shaft needs to be replaced frequently. The device is composed of a testing platform, a lead screw driving mechanism, an axial loading mechanism, an inner diameter variable mechanism, an outer diameter variable mechanism, a main shaft, a bearing seat mechanism, a main shaft coupler, a main shaft motor and a supporting frame. The inner diameter variable mechanism adapts to the inner diameters of different rolling bearings through a plurality of ring petals and a chuck structure, the outer diameter variable mechanism can adjust the outer diameter and the radial applied force in real time according to the outer diameter of the rolling bearing, the axial loading mechanism can apply a preset axial load to the bearing, and besides conventional constant axial force, the axial loading mechanism can also apply axial torque in different directions. The device can adapt to the testing of rolling bearings with various shaft diameter specifications without replacing the whole set of main shaft, effectively shortens the testing preparation time, and improves the testing efficiency. And the universality and the applicability are high.

The invention belongs to the technical field of mechanical fault diagnosis, and discloses a rolling bearing small sample fault diagnosis method and system based on an online soft label Gaussian prototype network. The method comprises the following steps: collecting bearing vibration signals under different working conditions to construct a data set, dividing the data set into a meta-training set and a meta-test set, and splitting the data set into a support set and a query set; constructing a Gaussian prototype network model containing an embedding module, a prototype calculation module and a classification module; in the meta-training stage, soft labels are dynamically generated by adopting an online soft label strategy, and multi-task training is carried out in combination with hard label loss to obtain optimal parameters; in the meta-test stage, a Gaussian prototype is constructed based on a support set, classification is achieved by calculating the Euclidean distance between a sample and the prototype, and prototype parameters are finely adjusted to adapt to cross-working-condition diagnosis when feature distribution drifts. According to the method, label noise interference is effectively relieved, the diagnosis precision can still be ensured under a small number of labeled samples, and the model generalization ability and the diagnosis stability are remarkably improved.

The invention discloses a non-contact mechanical sealing device for a rolling bearing, and relates to the technical field of non-contact sealing, the non-contact mechanical sealing device comprises a sealing end shell, a mounting flange, a connecting end shell and a rotating shaft, the sealing end shell is internally provided with a sealing mechanism for realizing sealing of a connecting structure of the sealing end shell and the rotating shaft; a lubricating mechanism for promoting liquid in the sealing end shell to flow back and forth is arranged on the side face of the sealing mechanism. An outer liquid sealing mechanism used for supplementing lubricating oil in the sealing end shell in real time is installed on the front side of the sealing end shell, and a liquid conveying port is formed in the outer surface of the outer liquid sealing mechanism. The sealing mechanism comprises an inner sealing ring, an annular sealing structure is additionally arranged at the joint of the side face of the sealing end shell and the rotating shaft through the side sealing ring groove and the side wedge ring, a multi-stage sealing defense line is formed by the annular sealing structure and a main sealing interface, and the overall sealing reliability between the inner wall of the sealing end shell and the rotating shaft in the axial direction and the radial direction is further improved.

Provided is a sealing device (10) for use together with a rolling bearing to at least partially seal at least one interior space (12) of the rolling bearing from an external environment (14). The sealing device (10) includes a first carrier member (16) having a passage (20) that fluidly connects an internal space (12) of the rolling bearing and an external environment (14). The sealing device (10) also includes a ventilation means (24) having at least one accommodating body (26) and at least one membrane (28). The accommodating body (26) includes a channel (32) and completely passes through the passage (20) of the first carrier member (16). The membrane (28) is at least partially disposed in the channel (32) of the accommodating body (26). The accommodating body (26) is injection molded, and the membrane (28) is overmolded and / or laser welded to the accommodating body (26) and fixed thereto.

The invention belongs to the technical field of bearing residual life prediction, and discloses a pyramid attention-based rolling bearing residual life prediction method and system. According to the method, through fast Fourier transform, wavelet transform and time domain statistical feature extraction, bearing vibration signals are processed, and a multi-feature set is constructed. A relationship between the multiple feature set and rolling bearing life prediction is captured. A Weibull distribution loss function is introduced, and convergence of the rolling bearing residual life prediction model is accelerated. Kalman filtering is introduced to carry out smoothing and noise reduction processing on a rolling bearing residual life prediction sequence, and the stability of a prediction result is further improved. The method solves the problems that an existing bearing residual life prediction method is high in operation complexity when the sequence long-time dependency relationship is captured, and the dependency relationships of different time scale ranges are difficult to capture.

The invention discloses a high-performance and long-service-life gearbox assembly which comprises a gearbox body, a gear shaft and a gear. The gear shaft is installed in a bearing sleeve through a first rolling bearing. A fan assembly is mounted on the right end shaft section of the gear shaft; the centrifugal fan is characterized in that the fan assembly comprises a shell, a first impeller, a second impeller and a second rolling bearing, the first side wall of the shell is fixedly connected with an end flange of the bearing sleeve, and the first impeller and the second impeller are installed on the right end shaft section and installed in an impeller cavity defined by the shell; the inner peripheral wall of the second impeller is connected with the right end shaft section through a second rolling bearing; the gear shaft can drive the first impeller to rotate in a torque transmission mode, and at the moment, the second impeller passively rotates under the acting force of suction airflow of the first impeller. The suction performance, flow and efficiency of the fan assembly can be improved, so that the heat dissipation effect on the bearing is improved, the heat exchange efficiency of airflow in the gearbox is improved, the operation performance of the gearbox is improved, and the service life of the gearbox is prolonged.

The invention discloses a fault self-adaptive diagnosis method for a rolling bearing for a rotating machine, and the method comprises the steps: firstly collecting a vibration signal in the operation process of the rolling bearing, and carrying out the preprocessing of the vibration signal, and obtaining the preprocessed vibration signal data; performing time sequence modeling on the preprocessed vibration signal data, extracting time sequence features, and generating a stage probability vector according to the time sequence features; mapping the stage probability vector into a stage embedded vector, introducing the stage embedded vector into a stage adaptive attention mechanism, and outputting an optimized feature vector; and finally, constructing a fault diagnosis network based on deep learning to perform fault diagnosis on the rolling bearing, outputting probability distribution of various faults of the rolling bearing, selecting the fault type with the maximum probability as a diagnosis result, and determining the severity of the fault type. According to the method, the fault stage can be automatically sensed, the evaluation standard can be dynamically adjusted, the method adapts to industrial data characteristics, the initial detection rate and the late false alarm rate are balanced, and therefore more accurate predictive maintenance is achieved.

The embodiment of the invention provides a rolling bearing fault diagnosis method, device and equipment based on digital-analog driving. According to the method, vibration signals of the rolling bearing under different working conditions are collected, the vibration signals are preprocessed and then input into a trained multi-source domain fault diagnosis model, and a fault diagnosis result of the rolling bearing is obtained. The training process of the multi-source-domain fault diagnosis model comprises the following steps: generating simulation data according to a rolling bearing dynamical model, and forming a multi-source-domain data set together with measured data; performing multi-scale feature extraction on the multi-source domain data set, and performing feature dynamic matching; identifying and discarding a specific domain sensitive channel by using a domain sensitive feature suppression strategy; a classification aggregation loss function is introduced, and representation learning with invariant cross-domain class conditions is realized on a feature space level; loss back propagation is carried out according to dynamic matching feature loss, domain sensitive feature suppression loss and classification aggregation loss, model parameters are optimized, and the accuracy and reliability of a rolling bearing fault diagnosis result are improved.

The invention relates to the technical field of rolling bearing state monitoring, and discloses a rolling bearing performance degradation evaluation method based on kernel optimal global locality preserving projection and Sinkhorn distance, which comprises the following steps: acquiring an original vibration signal of a rolling bearing, performing feature extraction on the original vibration signal, and constructing a high-dimensional feature set; calculating and screening features in the high-dimensional feature set, and constructing a sensitive feature set; performing dimension reduction processing on the sensitive feature set by adopting a kernel optimal global locality preserving projection method to obtain a low-dimensional feature matrix; in the low-dimensional feature matrix, the difference between samples is calculated based on the optimal transmission distance of entropy regularization, and a Sinkhorn distance matrix is constructed; on the basis of the Sinkhorn distance matrix, constructing a degradation index; a health threshold is set, an early degradation point is judged through the degradation index and the health threshold, rolling bearing fault recognition and performance evaluation are achieved in combination with the change trend of the degradation index value, and technical support can be provided for health monitoring and intelligent maintenance of rotary mechanical equipment.

The utility model provides a tooling device for a fatigue test of a plate, which is applied to the fatigue test of a bridge deck slab and relates to the technical field of the fatigue test of the plate, the tooling device comprises a first connecting piece, a first supporting piece and a second supporting piece, the first connecting piece comprises a sliding part, a connecting part and a rolling bearing, one end of the sliding part is connected with one end of the connecting part, and the other end of the connecting part is connected with the rolling bearing; rolling bearings are symmetrically arranged on the two sides of the other end of the sliding part, the first supporting piece comprises a first supporting plate, one end of the first supporting plate is vertically fixed to the test platform, an opening is formed in the other end of the first supporting plate, and the other end of the connecting part penetrates through the opening and is connected with one end of a panel; rolling grooves corresponding to the rolling bearings are formed in the sides, away from the panel, of the first supporting plates, the rolling grooves extend vertically, the rolling bearings abut against the rolling grooves and are used for rolling along the rolling grooves, and the second supporting pieces are fixed to the test platform and used for fixing the stiffening ribs. The bridge deck slab fatigue testing device is simple in structure and low in cost, and can reasonably reflect the actual fatigue condition of the bridge deck slab.

The invention belongs to the technical field of rolling bearing measurement, and particularly relates to a rolling bearing overturning rigidity measuring device and method, and the device comprises an upper pressing gasket, a loading device, a torque sensor, a motor, a bearing, a lower supporting gasket, a bearing supporting seat, a sensor supporting seat, a platform, a control box, a measuring meter, and a nut A; the motor, the sensor supporting seat and the bearing supporting seat are respectively fixed on the platform; the torque sensor is fixed on the sensor supporting seat, one end of the torque sensor is connected with an output shaft of the motor through a high-rigidity coupling, and the other end of the torque sensor is connected with the loading device through a flange plate and a bolt; according to the method, controllable overturning moment is accurately applied to a bearing outer ring through a high-rigidity and coaxial driving and measuring system, tiny overturning angular displacement generated by the bearing outer ring is synchronously measured, and the overturning rigidity of the bearing is obtained by calculating the slope of a moment-angular displacement relation curve.

A signal analysis method for oil-deficient bearings based on frequency centroid index and minimum entropy deconvolution is proposed. This method evaluates the lubrication condition of bearings using vibration signals. First, the signal to be evaluated undergoes kurtosis analysis and minimum entropy deconvolution to remove large external interferences and amplify the pulse components. Then, envelope spectrum analysis is used to determine whether the pulse components are random pulse signals caused by oil deficiency faults. Subsequently, bearing oil deficiency characteristic parameters are calculated, and finally, the bearing lubrication condition is judged based on a set threshold. This invention can effectively extract random impacts caused by bearing lubrication faults and identify oil deficiency faults in rolling bearings through a dimensionless oil deficiency characteristic parameter (SLF). It is also robust to changing operating conditions and has significant implications for bearing lubrication condition monitoring in engineering applications.

The invention discloses a rolling bearing intelligent fault diagnosis method based on data quality leading. The method comprises the following steps: (1) collecting a vibration signal of a rolling bearing; (2) carrying out preprocessing and data enhancement on the collected signals, specifically, (2.1) optimizing a quantitative mapping relation between a sampling length and a fault period number based on a bearing fault characteristic frequency and a periodic impact theory, optimizing a signal length to be 2048 sampling points, and setting a sliding window overlapping rate to be 30%; (2.2) constructing a five-dimensional aggressive data enhancement strategy covering Gaussian noise injection, amplitude scaling, time translation, random flipping and impulse noise disturbance; (3) carrying out fault diagnosis on the rolling bearing; and (4) according to an experiment result, selecting an optimal data quality optimization strategy and a model architecture to carry out rolling bearing fault diagnosis, and outputting a fault diagnosis result. By optimizing the data quality, the accuracy and generalization ability of rolling bearing fault diagnosis are improved, and the problem caused by insufficient attention to the data quality in the prior art is solved.

An embroidery machine head with a controllable take-up track comprises a needle bar frame connected with the embroidery machine head, an embroidery needle bar and a take-up bar are installed on the needle bar frame, the embroidery needle bar moves up and down and is controlled by a cam driving device driven by a main shaft, the take-up bar is connected with take-up teeth in an attached mode to form a take-up assembly, and the take-up assembly is connected with an installation shaft of the needle bar frame. The take-up-lever and the take-up-tooth rotate synchronously, the side edge of the take-up-tooth is rotationally connected with a rolling bearing through an arc-shaped clamping groove, a connecting shaft in the middle of the rolling bearing is connected with a first connecting rod, and the first connecting rod is connected with a linear shaft driven by an independent motor. According to the utility model, the linear shaft driven by the independent motor is used for changing a traditional crankshaft or cam control change take-up track structure, and the take-up track is controlled by combining the up-and-down movement of the needle bar of the embroidery machine with the take-up lever driven by the linear shaft, so that the take-up track is adjustable.

A fault diagnosis method for a rolling bearing of a steel belt elevator traction machine belongs to the field of mechanical equipment detection and fault diagnosis, firstly, a KAN structure is applied to a convolutional neural network to replace a traditional linear convolution kernel, so that KAN convolution is used as a network main body, parameter efficiency is ensured, and overall flexibility and adaptability of the network are improved; secondly, a GCA-ACA double-branch fusion attention module is provided, and fault features are learned by capturing feature differences and non-local operation, so that the feature representation capacity in the steel belt elevator traction machine under different working conditions is enhanced; and the FCDE method is used for dynamic enhancement of global feature key information so as to enhance the robustness of the network. According to the invention, bearing faults of the steel belt elevator traction machine under different working conditions can be accurately diagnosed.

The invention discloses a bearing performance degradation type identification method based on a vibration signal tail attenuation rate, belongs to the technical field of mechanical equipment state monitoring and fault diagnosis, and aims to improve the prediction precision of the remaining service life of a rolling bearing and improve the applicability of an intelligent prediction model by effectively identifying the bearing degradation type. The system comprises a data acquisition and preprocessing module, a tail attenuation rate calculation module, a degradation point detection module and a degradation type identification module. Firstly, feature extraction and segmentation processing are carried out on an original vibration signal of the rolling bearing; then, histogram distribution of vibration signal amplitudes is constructed, a right tail area sample is extracted, and a tail attenuation rate index representing signal extreme fluctuation attenuation behaviors is calculated through logarithmic transformation and linear fitting processing; and finally, constructing a dual judgment criterion of ratio threshold value judgment and adjacent point difference value comparison, and realizing automatic identification and classification of three typical degradation modes of bell shape, horn shape and sudden deformation.