1983 results about "Rotary machine" patented technology

A method for diagnosing rotor rub-impact in a rotating machinery based on vibration signal deconstruction and frequency modulation characteristic anti-noise enhancement, including: converting, based on two-stage integration transformation and high-pass filtering, vibratory acceleration signals from a rotating machinery device into vibratory displacement signals; performing targeted extraction on a rotating frequency component therein based on an improved variational mode decomposition method; calculating and estimating instantaneous fluctuation characteristics of a fundamental frequency of the extracted rotating frequency component using a quadrature-derivative-based normalized Hilbert transform to obtain an instantaneous frequency; inputting a calculated instantaneous frequency sequence into an optimum stochastic resonance system for anti-noise enhancement of intrawave frequency modulation characteristics; and processing FFT on an instantaneous frequency of intrawave frequency modulation characteristics after anti-noise enhancement, and diagnosing and identifying a rotor rub-impact fault of the rotating machinery based on a distribution characteristic of harmonic amplitudes related to a rotor rotating frequency.

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 self-adaptive stage division and residual life prediction method for a multi-stage degraded bearing, and relates to the technical field of rotary machinery prediction and health management. Firstly, multi-domain degradation features are extracted from original vibration signals. And secondly, fusing a Chebyshev inequality and a Transform network to realize online health state automatic identification. A novel parallel residual life prediction model fusing an attention mechanism is provided, and the model automatically captures degradation characteristics of different dimensions through parallel construction of a time convolution network and a bidirectional gating circulation unit channel. And the prediction model of the corresponding stage is adaptively and dynamically matched according to the stage identification result so as to accurately predict the RUL of the stage. And finally, processing the correlation between the multi-stage identification and the residual life prediction by adopting smoothing operation so as to obtain a final residual life value. According to the method, the change point of the degradation stage can be automatically identified, the key feature extraction process is optimized, and the dependence on artificial experience is reduced, so that the precision and robustness of a prediction result are improved.

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 discloses a rotor blade dynamic field rapid reconstruction method based on a deep neural network model, and the method comprises the steps: firstly collecting a BTT signal containing blade vibration information, then designing a deep neural network model, and training the deep neural network model through a training data set; processing the actually measured BTT signal by using the trained deep neural network model, and reconstructing a blade vibration displacement signal; performing spectral analysis on the reconstructed blade vibration displacement signal, and identifying vibration frequency, amplitude and phase parameters; then establishing a finite element model of the blade, and determining the inherent frequency and modal shape of the blade through modal analysis; and finally, quickly calculating a displacement field, a strain field and a stress field of the blade in a vibration state based on a modal superposition theory by utilizing the identified vibration parameters and modal analysis results. According to the invention, the vibration displacement and stress condition of the blade can be known in real time, so that real-time decision is supported, and the safety, reliability and durability of a rotating machine are improved.

The invention belongs to the technical field of a computer system based on a specific calculation model, relates to a virtual and real dynamic interaction-based digital twin bearing fault diagnosis method, and aims to solve the problem that a bearing system in large rotary mechanical equipment is large in fault hidden danger and avoid serious consequences caused by faults. A generally used bearing is in a high-precision standard, so that system signal data under bearing fault operation is not easy to collect in a factory, and the unbalanced data sample distribution directly leads to finiteness of fault diagnosis in a real scene only based on a big data driving mode classification method, so that the system signal data cannot be easily collected in the factory. The method has good scene working condition self-adaption, can be applied to fault diagnosis of a bearing system in different industrial scenes, fully excavates limited industrial data information and rich physical priori knowledge, and ensures safe and reliable operation of an important rotating mechanical system through a digital twinning technology.

The invention provides a mechanical equipment fault diagnosis model construction method, a diagnosis method, a system and equipment, and relates to the technical field of mechanical fault diagnosis, and the fault diagnosis method comprises the steps: obtaining the vibration signal data of a rotating machine in real time; and inputting the vibration signal data into the fault diagnosis model to obtain the fault type of the rotating machine. According to the method, the specific fault type of the rotating machine can be accurately identified, so that measures can be taken in time for repair or maintenance, further deterioration of faults is effectively avoided, equipment safety is protected, production efficiency is improved, and maintenance cost is reduced.

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.

The invention belongs to the technical field of rotating machinery fault diagnosis, and provides a machine tool bearing fault feature extraction method and system, a storage medium and equipment, and the method comprises the steps: respectively building dictionaries for a fault impact component, a harmonic component and a modal component of a vibration signal of a machine tool bearing; establishing a physical information multi-dictionary sparse decomposition framework based on the original vibration signals and the constructed dictionaries, and solving the physical information multi-dictionary sparse decomposition framework until a global optimal solution is converged; and according to a solving result, removing a modal component and a harmonic component in the vibration signal, and extracting a fault impact component for fault diagnosis. According to the method, the problems of low feature extraction accuracy and more interference in the existing method are solved, the fault impact component, the separation mode and the harmonic component can be effectively extracted, and the noise is suppressed, so that the accuracy and the reliability of fault diagnosis are improved.

The invention discloses a sliding bearing-rotor system stability test system and method under an impact load, and relates to the field of rotating machinery test in mechanical engineering. According to the technical scheme, different types of impact loads are applied through a vibration acceleration platform, and starting, stopping or external impact force in actual working conditions 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 analysis method can effectively improve the stability test precision of the sliding bearing-rotor system under the impact load, and provides powerful technical support for engineering application. The method is used for evaluating the stability of the sliding bearing-rotor system under the impact load.

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 an electromagnetic bearing fault mode analysis method for fault diagnosis of a primary helium fan. The method comprises the following steps: establishing an electromagnetic bearing-rotor-rack high-fidelity coupling dynamic model; extracting fault feature frequency and vibration amplitude, preprocessing the monitoring signal, and extracting multi-source heterogeneous fault features; evaluating the sensitivity of each feature to a fault, designing an adaptive weighted D-S evidence theory fusion algorithm, and identifying a bearing fault mode by dynamically adjusting the weight of an information source; building a convolutional neural network model to automatically extract deep fault features corresponding to the working conditions, and performing diagnosis model adaptation among different working conditions by adopting a transfer learning strategy; the GRU network is used for training and learning time sequence monitoring data, the development trend of bearing faults is predicted, and a heuristic algorithm is used for optimizing a fan operation and maintenance strategy. According to the method, the limitation of the existing method can be overcome, accurate diagnosis and trend prediction of the bearing fault are realized, and key technical support is provided for intelligent operation and maintenance of the rotating machinery.

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.

The invention provides a rotating machine fault intelligent diagnosis method based on minimum average composite entropy, and belongs to the technical field of rotating machine fault diagnosis. The method comprises the following steps: inputting a rotary mechanical vibration signal; a minimum average composite entropy is used as a fitness function, and an improved dung beetle optimization algorithm is used to optimize two key parameter modal quantity k and a penalty factor alpha of variational modal decomposition (VMD). The composite entropy divergence is used as a characteristic parameter to extract an effective mode of variational mode decomposition, and a new vibration signal is reconstructed. And converting the new vibration signal into a two-dimensional time-frequency image through short-time Fourier transform, and inputting the two-dimensional time-frequency image into a CNN model integrated with a channel attention mechanism to realize fault diagnosis of the rotating machinery.

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 belongs to the technical field of signal processing. According to the signal mode decomposition method and system for early fault diagnosis of the rotating machinery, vibration signals are decomposed through an MCKD algorithm, a characteristic mode set of the signals is obtained, fusion is carried out according to correlation coefficients of a cross-correlation spectrum of the modes and a mode envelope power spectrum density, and finally a characteristic mode representing a fault is extracted. According to the weak fault feature signal extraction method, the feature modes are extracted by using the fault period, the feature modes are fused on the aspects of time domain and frequency domain features, the sensitivity to complex noise is low, the weak fault feature signal extraction capability is high, the precision is high, and more efficient and accurate weak fault feature signal extraction is achieved.

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 belongs to the technical field of rotating machinery fault detection, and discloses a spindle machining composite fault detection method based on a robust multi-working-condition process model, which comprises the following steps: collecting and preprocessing current signals of spindle machining equipment in a normal operation state; the preprocessed current signal passes through a wavelet kernel attention convolution layer and a convolution auto-encoder in sequence; constructing a Gaussian mixture hidden Markov model, obtaining the number of optimal Gaussian components through a Dirichlet process, and calculating the energy corresponding to the low-dimensional representation of the sample; and constructing a joint optimization framework loss function comprising a sample reconstruction error, energy corresponding to sample low-dimensional representation, a penalty term and an evidence lower bound of variational Bayesian inference, and updating network parameters to convergence. According to the invention, the interpretability of the model is improved, and the effectiveness of fault detection is enhanced.

A hand truck includes a frame that is supported by main wheels and a handle having two side members. Two handle brackets are pivotably mounted on opposite sides of the frame. The handle brackets are arranged for rotation in relation to the frame around a handle rotation axis. The handle brackets slidably receive the side members of the handle so that the handle is able to slide between a first retracted position and a second extended positon. The handle is also able to rotate with the handle brackets between first and second rotational positions. A lock mechanism that is mechanically associated with the handle brackets and the handle is able to releasably lock the degree of extension of the handle in at least one position between the retracted and extended positions and is able to lock the rotational position of the handle in at least one rotational position.

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.