1649 results about "Rotary machine" patented technology

The invention belongs to the field of equipment coupling monitoring, and particularly relates to a dynamic coupling compensation method for thermal expansion and axial displacement, which comprises the following steps of: calculating a first characteristic value for representing reference dynamic offset of a sensor based on a three-dimensional thermal-structure coupling model by acquiring a thermal expansion parameter and axial displacement parameter sequence of a casing; calculating a second characteristic value representing the real position change of the rotor in combination with a rotor-casing axial thermodynamic model, establishing a piecewise coupling function considering a nonlinear effect, working condition self-adaption and cross interference, and eliminating the mechanical thermal inertia and measurement system lag influence through a dynamic delay compensation mechanism; an accurate axial displacement measurement total error compensation index is generated, and grading compensation actions are intelligently triggered according to error grades; the problem of measurement distortion caused by dynamic coupling of thermal expansion and axial displacement is effectively solved, and the accuracy and reliability of state monitoring of the rotating machine are remarkably improved.

The invention discloses a rotating machine fault diagnosis method, which comprises the following steps of: acquiring vibration, temperature, acoustic emission and current signals at key parts of a rotating machine, and extracting characteristic parameters such as time domain and frequency domain after preprocessing such as filtering and noise reduction; and inputting the characteristic parameters into machine learning models such as a support vector machine, combining deep learning models such as a convolutional neural network and a long-short-term memory network, performing comparative analysis by using a digital twin model, and fusing diagnosis results to output fault types, positions, severity and maintenance suggestions. The method overcomes single diagnosis limitation, multi-source signal complementation, multi-model collaboration, accurate fault diagnosis and diagnosis reliability improvement, provides a scientific basis for equipment maintenance, and is of great significance for guaranteeing safe operation of rotating machinery, reducing maintenance cost and promoting industrial intelligent development.

The invention requests to protect a rotating machine noise robust fault diagnosis method based on a mixed domain graph neural network. Comprising the following steps: collecting vibration data, and dividing an original vibration signal into a plurality of sections of subsamples with the length of 1024 by using non-overlapping sampling; converting the constructed subsample signal vibration signal into a frequency domain signal by using fast Fourier transform (FFT); taking the subsample frequency domain signals as nodes, the frequency spectrums as node features and the fault types as node labels, and then constructing the nodes into a graph structure; dividing the generated graph structure data set into a training set and a test set; and performing fault diagnosis on a test set by using the trained model, realizing self-adaptive weight adjustment by using a gating mechanism to fuse mechanical features, outputting a diagnosis result, calculating a performance index and performing result visualization analysis. The result shows that compared with a single-domain graph neural network, the method has higher accuracy and noise immunity, and end-to-end rotating machine fault diagnosis under the strong noise condition can be effectively achieved.

The invention provides a rotating machine fault diagnosis method and system based on an improved deep adversarial migration network. The method and system are suitable for cross-working-condition intelligent diagnosis of rotating machines such as motors, fans and bearings. According to the method, based on frequency domain feature extraction of vibration signals, high-robustness image input is generated through GAF conversion and AUGMIX enhancement; constructing a feature extractor fusing multi-scale convolution and an attention mechanism, and realizing feature migration between a source domain and a target domain in combination with an improved domain adversarial neural network (DANN); and an entropy minimization classifier is introduced to improve the classification confidence of the target domain. And feature extraction and classification performance synchronous optimization are realized through end-to-end joint training, and the classification accuracy and generalization ability are significantly improved under the conditions of sample imbalance and no label target domain. Experimental results show that the method still keeps high diagnosis precision under the conditions of unbalanced samples and cross working conditions, and is suitable for an intelligent maintenance system in an industrial scene.

The invention provides a rotary mechanical equipment fault identification method and device based on vibration data, and belongs to the field of vibration monitoring. The method comprises the following steps: acquiring a component information library of to-be-monitored rotary mechanical equipment; fault information corresponding to the component information base is determined, the fault characteristic frequency of each fault is calculated, and a fault characteristic frequency model corresponding to the component information base is established; collecting a vibration signal characteristic value of each component in the component information base in real time; extracting a vibration signal characteristic value, and matching the frequency domain characteristic value with the fault characteristic frequency based on a component information base and a fault characteristic frequency model to obtain a matching result; positioning an abnormal feature in the signal based on the time domain feature value and the geometric feature value; and identifying the fault type and degree of the mechanical equipment according to the matching result and the abnormal features. According to the mechanical equipment vibration monitoring data processing method and device, potential faults of the equipment can be found as soon as possible, and the equipment can be maintained in time.

The invention relates to a rotating machine fault diagnosis method based on a self-supervised time-frequency comparison fusion network, and belongs to the technical field of fault diagnosis. The method comprises the following steps: converting a time sequence signal into a frequency signal by using fast Fourier transform, and respectively performing data enhancement on the time frequency signal; initializing model parameters, adopting a multi-stage contrast learning strategy, and combining instance-stage, clustering-stage and fusion-stage contrast loss to obtain a pre-training target loss function; variables in the target loss function are selected and updated until convergence or the maximum iteration number is reached, and the pre-training model is stored; pre-training model parameters are loaded, a cross entropy loss function is adopted, and a fine tuning target loss function is obtained through label guidance; selecting and updating the target loss function of the fine tuning model until convergence or reaching the maximum iteration number, and storing the fine tuning model; and loading the trained fine-tuning model parameters, and inputting diagnosis data to obtain a prediction label, namely a diagnosis result. According to the invention, the precision and efficiency of fault diagnosis of the rotating machinery are obviously improved.

A method for monitoring a rotating machine in order to detect a fault in a bearing, the method including acquiring, from the rotating machine, a vibration signal measured by a vibration sensor; determining a first-order spectrogram by first-order cyclostationary analysis of the vibration signal using a delta transform and spectral standardisation; determining a second-order spectrogram by second-order cyclostationary analysis of the vibration signal using averaged cyclic coherence, a delta transform and spectral standardisation; and detecting a vibration signature of the fault in the bearing on the basis of the first-order spectrogram and the second-order spectrogram.

The invention provides an intelligent rotating machine fault diagnosis method, and belongs to the technical field of mechanical fault diagnosis, and the method comprises the steps: firstly, obtaining a vibration signal to be subjected to fault diagnosis under a source domain working condition and a target domain working condition, and carrying out the noise reduction processing; secondly, converting the denoised signal into a two-dimensional time-frequency image through short-time Fourier transform, and respectively making a source domain data set and a target domain data set; thirdly, constructing an attention enhancement network (SE-CNN-BiLSTM-MSMHA), and performing pre-training on the model based on the source domain data set to obtain a source domain working condition model; then, freezing part of parameters of the source domain working condition model, and finely adjusting the parameters of the source domain working condition model by using the target domain data to obtain a target domain working condition model; and finally, inputting target domain data to realize fault diagnosis of target domain working conditions. The method can provide a scientific basis for improving the cross-working-condition fault diagnosis accuracy under the strong noise background.

The invention discloses a rotating machine health state evaluation method based on an LSTM and Transform fusion network, and relates to the field of intelligent monitoring, and the method comprises the following steps: collecting and preprocessing fault data of a rotating machine, and dividing the preprocessed fault data into a training set and a test set; a fusion network model based on LSTM and Transform is constructed, and the fusion network model is trained; using the trained fusion network model to predict the health state of the rotating machine, and outputting the health degree score of the rotating machine; by comparing and analyzing the health degree score prediction value of the rotating machine and the actually collected health state data of the rotating machine, the fusion network model is retrained and subjected to parameter adjustment, and the prediction accuracy and stability are improved. According to the method, the advantages of the LSTM and Transform networks are combined, the comprehensiveness of feature extraction and the reliability of prediction results are ensured, the high prediction precision and robustness of equipment health assessment under complex working conditions are ensured, and the efficiency and stability of operation monitoring of industrial equipment can be effectively improved.

The invention provides a rotating machine fault diagnosis method and a related system. The rotating machine fault diagnosis method comprises the following steps: S1, constructing a rotor vibration equation according to a vibration behavior of a rotor system under an operation condition; s2, acquiring real-time information of a to-be-diagnosed rotating machine, performing modal analysis based on a rotor vibration equation according to the real-time information of the to-be-diagnosed rotating machine to determine a sensitive frequency band and key features, and forming multi-dimensional feature data; s3, carrying out denoising processing on the multi-dimensional feature data and carrying out signal reconstruction to obtain a reconstructed signal; s4, inputting the reconstruction signal into a pre-trained rotating machine fault model to obtain a fault diagnosis result; and the rotating machine fault model is fused by adopting a neural network ordinary differential equation and a convolutional neural network-gating cycle unit. The method can accurately simulate and predict the continuous dynamic change of the system, improves the diagnosis precision, and enhances the real-time performance and robustness of the model.

The invention relates to the technical field of mechanical fault diagnosis, in particular to a rotating machine fault diagnosis method and system based on a time sequence large model, and the method comprises the following steps: extracting a time sequence value of a vibration signal, measuring the frequency and amplitude fluctuation of a time period, and recognizing an extreme point based on the operation data of a rotating machine; and calculating the fluctuation amplitude difference between the extreme point and the adjacent segment, measuring the time interval between the amplitude peak values, and generating the frequency change segment amplitude. According to the method, the time sequence value of the vibration signal is extracted, frequency and amplitude fluctuation extreme point recognition and adjacent segment amplitude difference calculation are combined, the fine-grained analysis capability of the dynamic characteristics is improved, key frequency point time distribution and amplification value change are detected, and amplitude fluctuation time interval and frequency amplification correlation analysis is combined; deep analysis of frequency and amplitude interaction characteristics, dynamic change characteristic positioning of an interaction ratio and rate quantification are realized, and diagnosis accuracy and evaluation depth are comprehensively improved.

The present invention discloses a method and device for simultaneously measuring the bending vibration and torsional vibration of a rotating machinery shaft. The gear disk is installed on the shaft to be measured, and the gear disk is driven to rotate by the shaft to be measured. An alternating voltage signal is output by the eddy current sensor and converted into a digital signal by the software part, so as to obtain the original vibration signal. The maximum value finding method is used twice to extract the bending vibration and torsional vibration of the shaft. Through the above method, the simultaneous measurement of the torsional vibration and bending vibration of the shaft can be realized. The same set of sensors and instruments are used for the two types of vibration measurements, and the cost is relatively low.

There is provided a sliding component that supplies a sealed fluid to a leakage side in a gap between sliding surfaces to exhibit high lubricity and has a small leakage of the sealed fluid. A sliding component that has an annular shape and is disposed in a place where relative rotation is performed in a rotary machine, includes a plurality of dynamic pressure generating mechanisms provided in a sliding surface of the sliding component, each of the dynamic pressure generating mechanisms including a deep groove portion that communicates with a leakage side, and a plurality of shallow groove portions that communicate with the deep groove portion and are arranged in a circumferential direction in parallel relationship to each other.

The invention discloses a rotating machine vibration protection method and system of an adaptive resonance neural network, and the method comprises the steps: collecting a millisecond vibration acceleration signal of a rotating machine through a distributed piezoelectric sensor array, obtaining the real-time working condition parameter of equipment, and generating a time-work synchronous vibration signal flow; inputting the time-work synchronous vibration signal flow into a resonance neural network, and dynamically generating a frequency domain-time varying adaptive threshold group in combination with the working condition parameters; extracting a weak impact resonance characteristic tensor by using a multi-scale resonance kernel of the resonance neural network; projecting a weak impact resonance characteristic tensor to an orthogonal fault subspace through a tensor decomposition algorithm, and separating out a decoupling fault characteristic matrix; and generating a real-time protection decision instruction set based on a comparison result of the decoupling fault feature matrix and the equipment operation historical database. According to the embodiment of the invention, the method can improve the detection sensitivity and positioning precision of a weak fault under an unsteady working condition, and achieves the real-time adaptive optimization of the vibration protection of the rotating machine.

The invention discloses a time-frequency analysis method for impact signal positioning based on transient scale extraction transformation, and belongs to the technical field of mechanical vibration signal processing. The method comprises the following steps: collecting a rotating machine fault vibration signal, and reconstructing a signal model through Hilbert transform and a Dirac function; a transition matrix is generated by using a Gaussian window function traversal model, and matching and Fourier transform are carried out in combination with a discretized scale basis function; solving a frequency partial derivative of a transformation result to generate a time redistribution operator, and redefining by a Dirac function to obtain a transient scale extraction operator; based on the sub-time-frequency representation of scale-based rotation discretization, screening optimal matching results of each time center through a maximum kurtosis value, and integrating the optimal matching results into a complete time-frequency representation; and finally, redistributing a time-frequency coefficient by using a transient extraction operator to realize accurate positioning of the impact component. According to the method, the problems of serious impact energy diffusion and insufficient positioning precision in existing time-frequency analysis are solved, and the time-frequency representation readability and the impact positioning reliability are remarkably improved.

The invention provides a vibration-temperature correlation fault early warning method based on multi-sensor fusion, and relates to the technical field of rotating machine fault diagnosis, and the method achieves the efficient diagnosis of the rotating machine fault through the three-field coupling analysis of vibration, temperature and torque. Multi-dimensional health indexes are introduced, sensor data of different dimensions are normalized into a monitorable numerical value, and the health state of equipment is reflected in real time through a dynamically optimized weight coefficient. A core depth time sequence neural network model is combined with an attention mechanism and a Transform coding layer, the problem of long-term dependence is effectively solved, and the fault recognition precision is improved. According to the method, future innovation directions such as cross-device collaborative diagnosis, quantum computing acceleration and digital twinborn visualization are also exhibited, and the method has important industrial application value.

The invention discloses a rotating machine life prediction method and device based on a multi-sensor spatio-temporal information fusion network, and relates to the technical field of rotating machine health state assessment, and the method comprises the steps: obtaining multi-source monitoring signal data of a to-be-predicted rotating machine in a set time period; performing normalization processing on the multi-source monitoring signal data; constructing a monitoring data sample based on a set sliding step length and the normalized multi-source monitoring signal data by adopting a set sliding window; constructing a time information mining module, a spatial relationship modeling module, a residual connection module and a full connection network, and performing program optimization processing to obtain a life prediction model; and inputting the monitoring data sample into the life prediction model to obtain the residual service life value of the to-be-predicted rotating machine. According to the invention, the monitoring signals collected by the multi-source sensor can be fused, the time information and the spatial relationship in the multi-source monitoring signals are extracted and fused, and the prediction precision and robustness of the life prediction model are improved.

The invention relates to the technical field of fault diagnosis, in particular to a rotating machine fault diagnosis method, device and equipment under variable working conditions, and the method comprises the steps: collecting the operation parameters of a target rotating machine under a plurality of preset working conditions, and carrying out the feature normalization processing of the operation parameters, thereby obtaining a sample data set; the sample data set considers a plurality of preset working conditions and minimizes signal characteristic changes caused by working condition changes; a deep multi-scale conditional adversarial network model is constructed, a test data set is input into the deep multi-scale conditional adversarial network model trained by a training data set to output a fault diagnosis result, and domain self-adaption of an output space is achieved on different feature levels by using time sequence features. The accuracy of mechanical fault diagnosis in an actual production scene is improved; meanwhile, as the constructed multi-scale conditional adversarial network model belongs to an unsupervised transfer learning category, the dependency on training label samples is relatively weak, and the time and resources invested into label marking are greatly reduced.

The invention discloses a sliding bearing-rotor system stability test system and method under a complex alternating load, and relates to the field of rotating machinery test in mechanical engineering. According to the technical key points, a multi-direction and multi-frequency dynamic load is applied through a six-degree-of-freedom platform, and a complex vibration and stress environment in an actual working condition is simulated. In the testing process, a high-precision sensor and a multi-channel acquisition board card are used for monitoring the vibration characteristic and the rotating speed of the system in real time, it is guaranteed that signals of different positions and types can be synchronously acquired, and comprehensive system state information is provided. Then, in combination with time domain and frequency domain analysis technologies, key features are extracted from mass data by applying an advanced Fourier transform algorithm, the resonant frequency, modal characteristics and potential fault modes of the system are identified, and accurate evaluation of the stability of the system is realized. Experimental results show that the real-time monitoring and analyzing method can effectively improve the stability testing precision of the sliding bearing-rotor system under the complex alternating load, and powerful technical support is provided for engineering application. The method is used for evaluating the stability of the sliding bearing-rotor system under the complex alternating load.

The invention provides a novel intelligent rotating machine fault diagnosis method, and belongs to the technical field of rotating machine fault diagnosis, and the method comprises the steps: obtaining vibration signals of a rotating machine under normal and different fault types; a variation mode decomposition method is adopted to pre-process and decompose the vibration signal of the rotating machine, noise is removed, and then a new signal is reconstructed and generated; the new vibration signal is converted into a two-dimensional time-frequency image through short-time Fourier transform; optimizing hyper-parameters of a convolutional neural network (CNN)-long and short term memory neural network (BiLSTM) by adopting a Sharla silver ant optimization algorithm (SSAO), wherein the hyper-parameters comprise the number of neurons of a convolutional layer, the size of a convolution kernel and the number of neurons of a BiLSTM layer; and inputting the two-dimensional time-frequency image into the CNN-BiLSTM model after the hyper-parameter optimization to realize fault diagnosis of the rotating machine. The method has very important practical significance for improving the rotating machine fault diagnosis accuracy and guiding equipment maintenance and repair.

A dynamo-electric rotary machine includes a rotor defining an axis, a stator, and a can designed for arrangement in an air gap between the rotor and the stator in order to seal the stator and the rotor in relation to each other. The can has at least one section of average electrical conductivity in a range from 1 S / m to 10000 S / m in an axial and / or a tangential direction. The can is electrically conductively incorporated in an earthing system of the dynamo-electric machine.

The invention discloses a small sample fault diagnosis method based on two-way convolution and closed set domain self-adaption, vibration signals are preprocessed through maximum and minimum normalization and FFT, preprocessed frequency domain data are used as input by DPFC-FFABNet, and useful features can be fully extracted through a two-way full convolution structure. And group normalization and layer normalization are introduced, so that the robustness of the model is enhanced. A global-local feature fusion attention mechanism is utilized to consider extraction of local and global information of a feature sequence. In order to solve the problem of small sample fault diagnosis generalization, the method provided by the invention has excellent performance under different rotating mechanical parts, different rotating speed conditions and different noise interference conditions, and has good application value. And finally, on the basis of the method provided by the invention, multi-core maximum mean difference MKMMD is combined with class confusion minimization MCC, so that the fault diagnosis generalization capability of variable rotating speed and variable load is further enhanced.

The invention provides a rotating mechanical system performance evaluation and optimization method and system based on system negentropy conversion rate, and the method comprises the following steps: carrying out the numerical simulation of a flow field of a rotating mechanical system through a computational fluid mechanics method, and obtaining the parameters of the flow field; calculating a viscous dissipation entropy yield, a turbulent dissipation entropy yield and a wall entropy yield in the fluid domain based on the flow field parameters; the total entropy yield of the system is obtained through the volume fraction and the wall area fraction of the whole fluid domain; converting the input power of the rotating machinery into an equivalent negentropy flow, and calculating the negentropy conversion rate of the system for evaluating the effective utilization degree of the input power; and evaluating the performance of the rotating machinery system based on the negentropy conversion rate, and identifying a key area of energy loss. According to the method, the negentropy conversion rate result is applied to structural design optimization, running state diagnosis and energy efficiency adjustment of the rotating machinery, and online calculation and dynamic visualization can be realized in combination with real-time monitoring data.

The invention discloses a bearing fault dynamic ridge tracking and feature matching diagnosis method based on high-resolution time-frequency analysis, which is applied to the technical field of fault diagnosis and signal processing of rotating machinery of a wind turbine generator, and comprises the following steps: acquiring an acceleration vibration signal of a bearing, and utilizing an optimized variational mode decomposition algorithm to obtain an acceleration vibration signal of the bearing; decomposing the acceleration vibration signal of the bearing to obtain an optimal intrinsic mode function; transforming the optimal intrinsic mode function component by using a synchronous compression transformation method to obtain a high-resolution time-frequency graph, and extracting a time-frequency ridge line in the high-resolution time-frequency graph by using a dynamic programming algorithm; constructing a fault characteristic curve based on the time-frequency ridge line, and calculating a fault actual characteristic coefficient of the fault characteristic curve; and according to a comparison result of the actual fault characteristic coefficient and the fault characteristic coefficient corresponding to the theoretical fault type, identifying the fault type of the bearing. The problem that the rotation frequency of the bearing is unstable under variable working conditions is solved, and the fault type of the bearing can be accurately recognized.

The utility model discloses a double-track rotary manipulator cigarette packet mixed loading device. The device comprises a cigarette packet conveying module (1), a rotary transmission module (4) and a manipulator module (5), wherein the cigarette packet conveying module (1) comprises a first track (A) and a second track (B) which are vertically arranged on the same horizontal plane; the rotary transmission module (4) is arranged at the vertical intersection of the second track (B) and the first track (A); the number of the mechanical arm modules (5) is two, and the two mechanical arm modules (5) are arranged at the two ends of the rotary transmission module (4) respectively.

The invention relates to the technical field of rotating machine fault diagnosis, and discloses a rotating machine fault diagnosis method in an open-set cross-domain scene, a terminal and a medium. The method comprises the following steps: firstly, acquiring a one-dimensional vibration signal of the rotating machine, and converting the one-dimensional vibration signal into a two-dimensional time-frequency image after preprocessing; constructing a fault diagnosis model which comprises a feature extractor module, a classifier module, a domain discriminator module and a weight calculation module; the weight calculation module can dynamically calculate the alignment weight according to the distance between the target domain sample and the center of the source domain category. Through cross-domain adversarial training, in combination with classification loss, domain discrimination loss and weight alignment loss, feature alignment of a source domain and a target domain is realized, so that training of a fault diagnosis model is completed; and finally, inputting the two-dimensional time-frequency image into a trained fault diagnosis model, and outputting a fault type and a fault probability. According to the method, negative migration caused by alignment of unknown class features and source domain features is reduced, and the process of manually rejecting target domain unknown samples for training is omitted.

The invention discloses a fine gap-adjustable tilting pad bearing and a control method, and belongs to the technical field of bearings, and the fine gap-adjustable tilting pad bearing comprises a bearing body, pads, pre-tightening screws, set screws, a cover plate, a displacement amplification structure, pre-tightening bolts, elastic gaskets and piezoelectric ceramic stacks. A plurality of tilting pads are arranged on the inner arc surface of the bearing body and are matched with the bearing body through a spherical support; four radial grooves are formed in the outer arc face in the circumferential direction and are opposite to the centers of the four tile blocks respectively. A notch of the radial groove is connected with a cover plate, is internally provided with a piezoelectric ceramic displacement amplification mechanism and is connected with the spherical support through a positioning pin; a piezoelectric ceramic stack is arranged between a left boss and a right boss of the mechanism and is pre-tightened by a pre-tightening bolt and an elastic gasket. According to the invention, the inverse piezoelectric effect of piezoelectric ceramics is utilized, a vibration and non-linear suppression method is provided, an advanced control algorithm is matched, the piezoelectric ceramics stack is electrified and contracted, a displacement amplification effect is generated through a displacement amplification mechanism, the tile gap is finely adjusted, and the high-performance requirement of modern rotating machinery is met.

An aerodynamic noise determining method and apparatus, relating to the technical field of noise, and used for implementing rapid aerodynamic noise prediction. For the problems of poor precision and low efficiency of existing aerodynamic noise prediction, an aerodynamic noise determining method is provided. Aerodynamic sound field computation implemented based on an unsteady-state flow field is converted into noise index computation under a steady-state flow field and linear transformation between a noise index and a noise sound power level, thereby achieving the objective of sound field-free computation. Thus, the problems of a large computation amount, low efficiency, and unsuitability for noise prediction for devices having complex working conditions such as a motor caused by sound field computation are avoided, and the requirement for analyzing and controlling aerodynamic noise of rotating machines in actual engineering can be better satisfied.

The invention discloses a fault diagnosis method and system for a rotating machine bearing, and belongs to the technical field of fault diagnosis. The method comprises the following steps: acquiring a to-be-diagnosed vibration signal of the rotary mechanical bearing; inputting the to-be-diagnosed vibration signal into a pre-constructed bearing fault diagnosis model, and outputting a fault diagnosis result of the rotating machine bearing; the bearing fault diagnosis model comprises a feature extraction network, a GMM feature enhancement module, a projection distillation module and a dynamic extensible classifier which are connected in sequence. According to the method, catastrophic forgetting can be effectively relieved, continuous diagnosis of newly-occurring accidental faults can be achieved, in the initial task, the model learns and identifies different fault types through a fault bearing data set collected in advance, a basis is provided for the subsequent increment task, the increment task is composed of a plurality of stages, and in each stage, the fault bearing data set is subjected to fault detection. The model enhances playback of old knowledge through historical category pseudo features generated by a Gaussian mixture model, and aligns feature representations of new and old models by using a projection distillation module.

The invention discloses a key phase pulse signal synchronous acquisition and multi-axis centering analysis method, which belongs to the technical field of mechanical monitoring and diagnosis, and comprises the following steps of: configuring a synchronous acquisition system, acquiring a conditioned key phase pulse signal and a conditioned vibration signal, and extracting an axis speed and a load; constructing a time drift monitoring model to compensate the key phase pulse signal in real time, and ensuring the time synchronization precision; the compensation signal is input into a shaft centering correction model based on dynamics and finite element analysis, and radial deviation and axial deviation under the dynamic load are calculated; establishing a nonlinear tolerance threshold model by adopting a nonlinear regression model, and dynamically adjusting an allowable centering deviation threshold in combination with the real-time shaft speed and the load; through closed-loop comparison of the shaft centering deviation and a threshold value, a centering qualified conclusion or a correction suggestion is automatically output, and triggering signal resampling is supported to adapt to working condition changes; the real-time and self-adaptive analysis of the centering deviation of the multi-axis system is realized, and the operation stability and the maintenance efficiency of the rotating machinery are improved.