262 results about "Bearing vibration" patented technology

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

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

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

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

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

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

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

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

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

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

This invention relates to the field of data processing, and more particularly to an oscilloscope signal data processing method and system for bearing testing. The method involves acquiring bearing vibration data using an oscilloscope, extracting data from a first time moment for frequency domain analysis, obtaining its power spectrum, and dividing it into frequencies to obtain a first frequency sequence. Similarly, a second frequency sequence is obtained from the data at a second time moment. Aligning the two frequency sequences yields frequency matching pairs. Utilizing the frequency variations of neighboring frequencies within a single matching pair, the noise figure of each frequency in the matching pair is calculated, resulting in the noise figure for any frequency in the first frequency sequence. Based on these noise figures, a first mean sequence is constructed. The distribution consistency of each frequency point in the first mean sequence is obtained, and the segmentation effectiveness of each frequency point is calculated. Combined with a noise segmentation threshold, oscilloscope signal processing is completed. This invention denoises bearing vibration data acquired by an oscilloscope, improving the accuracy of the denoised oscilloscope signal data.

The invention discloses a few-sample fault diagnosis method based on a multi-scale physical information neural network, and belongs to the technical field of fault diagnosis. The method comprises the following steps: step 1, constructing a bearing vibration signal data set containing multiple working conditions, and preprocessing an original vibration signal; 2, inputting the vibration signal samples in the training set into a CCSWGAN network, generating a high-quality synthetic sample, and carrying out normalization processing on the high-quality synthetic sample; step 3, mixing the original sample and the synthetic sample and randomly disrupting the original sample and the synthetic sample to form training data, inputting the training data into an MCB-PII module, and performing multi-scale feature extraction and fault diagnosis; and 4, deploying the trained model to production equipment, and carrying out fault detection and identification on vibration signals collected in real time. According to the method, the problems of unstable performance and insufficient adaptability of the model under complex working conditions are effectively avoided, so that higher accuracy and reliability are provided for bearing fault diagnosis.

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 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 relates to the technical field of wind power technologies, in particular to a bearing vibration diagnosis method and system based on a large language model. The method comprises the following steps: acquiring bearing vibration signal data including a fault frequency spectrum signal x, a reference bearing vibration signal and a reference frequency spectrum signal; performing data preprocessing on the acquired bearing vibration signal data; constructing a fault classification model, wherein the step of constructing a feature recognition network and constructing an alignment network to train the constructed fault classification model; and performing fault prediction by using the trained fault classification model. According to the invention, the fault classification network, the feature alignment module and the large language model are utilized to combine the vibration time domain signal and the cue word together, so that the large language model can indirectly process the ultra-long data text.

The invention relates to the technical field of bearing life prediction, in particular to a bearing residual service life prediction method based on a hybrid model, which comprises the following steps: collecting bearing vibration signals under different working conditions; taking the first features as input, constructing a fusion voting mechanism based on a feature screening model, and selecting the first features to obtain second features; sequentially inputting the second feature and the actual life value into a BiGRU network, a GAT network and a full-connection network to obtain a full-connection feature vector; and inputting the full connection feature vector, the feature vector of the BiGRU network and the last time step feature of the bearing vibration signal time sequence into a super network, and inputting the output feature vector of the super network into a full connection layer to obtain a predicted life value. According to the method, the problem that the requirements of precision, generalization and interpretability are difficult to balance by an existing method is solved.

The invention provides a wind generating set bearing fault diagnosis method and system, and relates to the technical field of bearing fault diagnosis. Bearing vibration signals during operation of a wind generating set are collected; carrying out sample labeling on the bearing vibration signal to obtain an operation fault label, and fitting the operation fault label with a fault impact index of the wind generating set bearing in a cross-working condition to generate a fault identification criterion; combining the operation health data with weak fault features between similar fault feature distances in the wind generating set bearing to determine fault sensitivity probabilities, and generating a missing degradation track of the wind generating set bearing under a fault diagnosis missing working condition according to all the fault sensitivity probabilities; and carrying out classified diagnosis on the bearing fault of the wind generating set according to the fault identification criterion and the missing degradation track. According to the invention, fault classification diagnosis can be carried out on the bearing fault of the wind generating set in a complex scene in which cross-working conditions and missing working conditions coexist, so that the accuracy of fault diagnosis is improved.

The invention discloses a steam turbine shafting stability risk assessment model, particularly relates to the technical field of assessment models, and comprises a data acquisition module, a data preprocessing and visualization module and a risk assessment module. The data acquisition module acquires operation data of the steam turbine in real time; the data preprocessing and visualization module is used for carrying out data preprocessing and visualization analysis on the collected original operation data; and the risk assessment module constructs an artificial neural network to carry out risk assessment on the vibration state of the turbine bearing. According to the method, the vibration response of the steam turbine bearing can be accurately fitted, and the vibration trend of the steam turbine bearing under different load and rotating speed combinations is subjected to visual analysis and partition identification, so that the fault risk caused by the vibration of the steam turbine bearing is reduced.

The invention discloses a numerical control machine tool spindle bearing feature extraction method based on improved FMD, and belongs to the technical field of rotating machine fault diagnosis. The method aims at solving the problems that traditional feature mode decomposition is high in parameter dependency and fault feature extraction is difficult under the noise background. The core of the method is that firstly, noise is added into a collected bearing vibration signal, and an ETO-FMD model is input; secondly, using an exponential trigonometric function optimization algorithm to take weighted envelope spectrum kurtosis as a fitness function, performing adaptive global optimization on the mode number M of the FMD and the length L of a filter, and automatically obtaining an optimal parameter combination; and finally, calculating a weighted envelope spectrum kurtosis value of each IMF component after FMD decomposition, and screening out the most critical component to perform signal reconstruction so as to realize accurate extraction of fault features. According to the method, the limitation of manually setting parameters is overcome, the accuracy, the adaptability and the robustness of feature extraction are remarkably improved, and the method is suitable for diagnosis of various faults of the spindle bearing of the high-end numerical control machine tool.

The invention provides a bearing load real-time monitoring and hydraulic pressure self-adaptive adjusting system and method, and the system comprises a signal sensing unit which is used for collecting a bearing vibration signal, a hydraulic system pressure signal and a main shaft rotating speed signal; and the signal processing and calculating unit is connected with the signal sensing unit and is used for processing the collected signals, estimating the bearing load based on a pre-trained soft measurement model and generating a hydraulic control instruction. The dependence of traditional bearing load monitoring on an expensive force sensor is broken through, and by integrating operation signals such as vibration, hydraulic pressure and rotating speed which are easy to obtain, the bearing load monitoring precision is improved. According to the method, the incidence relation between signals and loads is established through off-line modeling, non-intrusive load real-time estimation is achieved, meanwhile, feed-forward and feedback composite control is adopted, hydraulic pressure is adjusted in a self-adaptive mode according to the estimated loads, traditional empirical fixed setting is replaced, monitoring, adjusting, early warning and maintenance prompting functions are integrated, and real-time monitoring is achieved. The technical problems that a traditional scheme is high in cost, blind in adjustment and dispersed in function are effectively solved.

The invention belongs to the field of industrial control, particularly relates to a cone floating sleeve drill bit bearing analysis method based on cloud computing, and aims to solve the problem that systematic errors are generated in fault diagnosis due to the fact that the actual working condition influence is ignored in the related technology. The method comprises the following steps: acquiring a bearing vibration signal; working parameters are determined, signal decomposition operation is executed according to the working parameters, and bearing characteristic components are obtained; according to the bearing characteristic component and the environmental parameters, determining an equivalent stiffness tensor of the cone floating sleeve drill bit bearing working under a preset working condition, and according to the equivalent stiffness tensor, solving a bearing system dynamic model to obtain evolution field data; determining a target feature vector according to the bearing feature component and the evolution field data, and mapping the target feature vector to a preset fault feature space; and determining the distance between the target feature vector and the plurality of typical fault feature vectors, and determining a bearing control strategy of the cone floating sleeve drill bit according to the distance. When the method is applied to bearing analysis, the accuracy is higher.

The invention discloses a diesel generating set bearing fault diagnosis system combining TTAO-VMD and IWOA-BP, and the system comprises a signal collection module which is used for collecting a vibration signal of a bearing of a monitored diesel generating set, and serves as an input signal of a TTAO-VMD fault feature extraction module; the TTAO-VMD fault feature extraction module is used for receiving the bearing vibration signal, calculating feature parameters of the bearing vibration signal in a time domain and a frequency domain according to the optimal component, and constructing a feature vector data set; the IWOA-BP fault mode recognition module is used for classifying the faults of the rolling bearing according to the feature vector data set; and the man-machine interface module is used for displaying the data analysis result and the fault type.

This invention discloses a method for storing and providing early warning of vibration acceleration data of motor bearings. The system collects vibration acceleration signals from motor bearings using a vibration acceleration sensor, and uses this data to determine the cause of bearing failures and provide early warnings. Simultaneously, a data feedback terminal provides feedback on the actual cause of the failure to supplement and correct the vibration fault database, improving the accuracy of vibration fault diagnosis and early warning. The system is characterized by including a vibration acceleration sensor, an edge cloud, a data storage backend, and a data feedback terminal. The vibration acceleration sensor establishes signal interaction with the data storage backend, the data storage backend establishes signal interaction with the edge cloud, and the data feedback terminal establishes signal interaction with the edge cloud. The vibration acceleration sensor is positioned near the bearing on the motor for continuous data collection of vibration acceleration signals. The data storage backend includes a signal conversion module and a data processing module for data conversion and comparative analysis of the vibration acceleration signals.

The application relates to the technical field of railway vehicle detection, and discloses a bearing vibration detection and identification method based on an octave and a voting mechanism, which is characterized in that bearing detection of passing vehicles is completed through a bearing running-in test platform arranged on a railway site to form actual detection data; the bearing running-in test platform comprises a contact part used for directly contacting an outer ring of a measured bearing, an acceleration sensor and a microphone are arranged on the contact part; vibration acceleration data is formed through acceleration sensor detection of bearing vibration; sound pressure level data is formed through microphone detection of bearing vibration; and the vibration acceleration data and the sound pressure level data together form vibration transmission data. The application can more accurately complete bearing vibration detection and identification and can be applied to actual railway vehicle detection and identification.

The utility model belongs to the technical field of CT machine main bearing damping, and particularly relates to a high-speed CT machine main bearing damping device which comprises a limiting block. Connecting blocks are fixedly connected between the multiple limiting blocks. Inner rings of double-row angle bearings are fixedly connected to the side walls of the multiple connecting blocks. A connecting rod is slidably connected into the limiting block. The ends of the two connecting rods are fixedly connected with fixing blocks, and the other ends of the two connecting rods are fixedly connected with centrifugal blocks. A fixed convex block is fixedly connected to the side wall of the centrifugal block; clamping wheels are fixedly connected between the corresponding fixing protruding blocks, and the multiple clamping wheels are arranged corresponding to the size of the double-row angle bearing. According to the utility model, the plurality of clamping wheels are arranged to extrude the inner ring of the double-row angle bearing which rotates at a high speed, so that the inner ring and the outer ring of the double-row angle bearing are tightly attached, the vibration condition caused by a raceway structure error and a ball size error is further reduced, the negative influence of bearing vibration on CT imaging is favorably relieved, and the imaging quality of a CT machine is improved.

The present application relates to the field of bearing test, in particular to bearing raceway surface quality and grease matching characteristic test device. The device comprises a workbench, the workbench is provided with a driving shaft, the driving shaft is provided with a sleeving section for sleeving the bearing on the driving shaft and synchronously rotating the inner ring of the bearing and the driving shaft, the workbench is provided with a fixing structure outside the radial direction of the driving shaft for fixing the outer ring of the bearing at the sleeving position of the bearing, the workbench is provided with a vibration sensor for monitoring the vibration signal of the bearing, a noise sensor for monitoring the noise signal of the bearing, and a stick-slip signal sensor for monitoring the stick-slip signal of the bearing, and the sensors collect signals when the driving shaft drives the inner ring of the bearing to rotate. The collected signals are used to establish a mathematical model to provide theoretical support for the manufacturing process of the bearing and the selection of the grease, so as to ensure the mutual matching of the surface quality of the bearing and the grease, and further reduce the vibration and noise generated by the bearing under high-speed working condition.

The invention discloses a primary fan bearing vibration intelligent early warning method and system. A single vibration signal is easily interfered by working condition fluctuation (such as load change and inlet pressure sudden change) of equipment and environmental noise, so that the false alarm rate is high. The method comprises the following steps: multi-source data synchronous acquisition; working condition adaptive parameter adjustment; dynamic threshold generation; performing multi-source information fusion early warning, wherein the auxiliary parameters comprise at least one of bearing temperature, motor three-phase current unbalance degree and fan inlet pressure fluctuation amplitude; and when the main early warning criterion and the at least one auxiliary early warning criterion are met at the same time, triggering a graded early warning signal. The method is used for intelligent early warning of the primary fan bearing vibration.

The application discloses an aero-engine bearing fault diagnosis method and system, which carries out amplitude normalization preprocessing on the vibration signal of the aero-engine bearing, unifies the signal data magnitude, and eliminates the analysis interference caused by the signal amplitude difference; a convolution sparse coding optimization objective function is built relying on a non-separable learnable sparse regularizer, the limitation of traditional norm and other separable regularization methods is broken, the sparse coefficients are no longer regarded as independent individuals, the structural interaction relationship between the multi-channel sparse coefficients can be fully mined, the interference caused by strong background noise, multi-source vibration coupling and complex transmission path under actual working conditions can be effectively stripped, the weak impact characteristics of the early bearing damage submerged by noise can be accurately separated, the early fault signal extraction effect under complex operating conditions is greatly improved, and the local impact fault characteristics in the aero-engine bearing vibration signal are effectively enhanced, so that the fault diagnosis is completed without any label.

The application discloses a bearing fault noise resistance diagnosis method based on IPDSCS and a Swin Transformer, and comprises the following steps: collecting vibration signals of a bearing under different working conditions to obtain multiple groups of original vibration signal data; performing wavelet transform on the original vibration signal data to convert the original vibration signal data into time-frequency images; constructing an improved inverted-pyramid deep separable convolution sequence feature extraction model to extract the time-frequency images to obtain multi-dimensional feature vectors; taking the feature vectors as input data, performing model training based on a Swin Transformer deep learning network, and establishing a bearing fault diagnosis model; and inputting bearing vibration signals to be diagnosed into the bearing fault diagnosis model to output corresponding fault categories. The method improves the noise resistance of signal processing through wavelet transform denoising and an IPDSCS feature extraction method; multi-scale modeling is performed on multi-dimensional features by using a Swin Transformer network, and the precision and robustness of fault diagnosis are significantly improved; and high-precision diagnosis of bearing faults is realized.

The invention relates to the technical field of steam turbine generator sets, and particularly discloses a steam turbine bearing vibration high-frequency interference removal optimization method and system, and the method comprises the steps: carrying out the wavelet transformation of a bearing vibration signal of a steam turbine, and obtaining a high-frequency part of the bearing vibration signal; segmenting the bearing vibration signal of the steam turbine according to a preset segment, calculating the waveform complexity of each vibration signal segment, and calculating a characteristic distance value according to the waveform complexity; determining a fault characteristic value according to the waveform complexity and the characteristic distance value of the vibration signal segment, and judging a high-frequency part type corresponding to the wavelet coefficient of the vibration signal segment according to the fault characteristic value, the high-frequency part type comprising a noise part and a fault part; and filtering a noise part in the bearing vibration signal, and performing fault evaluation on the turbine bearing according to a fault part of the bearing vibration signal after noise filtering. According to the method, high-frequency noise in the vibration signals can be effectively filtered out, fault features are reserved, and the noise removal capacity of the steam turbine bearing is optimized.