6 results about "Relevance vector machine" patented technology

The application relates to a rotating machine residual life prediction method and system, and belongs to the field of mechanical life prediction. The method comprises the following steps: collecting signals capable of reflecting mechanical life of a rotating machine to be measured at least at two positions, extracting one-dimensional time characteristic data from the signals capable of reflecting mechanical life by using principal component analysis pooling, training a related vector machine by using the one-dimensional time characteristic data, so that the full life cycle of the rotating machine is divided into a period without obvious failure trend and a failure tendency period; training a deep separable convolution gate recurrent unit network by using data of the failure tendency period, and predicting the residual life of the rotating machine in the failure tendency period. The application can improve the prediction accuracy when the residual service life of the rotating machine is predicted.

The invention discloses a bearing residual service life prediction method based on a relevance vector machine and an NNHTS model, and the method comprises the steps: firstly extracting the time domain features of a bearing vibration signal, and constructing a comprehensive health index through filtering noise reduction and principal component analysis; secondly, optimizing variational mode decomposition parameters by using a genetic algorithm, and decomposing the health index sequence into high-frequency and low-frequency components; then, training and predicting the high-frequency component and the low-frequency component by adopting an NNHTS model and a correlation vector machine respectively; and finally, superposing the predicted values of the components, and reconstructing a health index decline curve to predict the residual life. According to the method, through decomposition, hierarchical modeling and integration strategies, the fitting ability of deep learning to complex fluctuation and the sparse probability modeling advantage of a relevance vector machine to trend are combined, and the prediction precision and working condition adaptability are remarkably improved.

This invention discloses an online prediction method for battery SOC based on an incremental correlation vector machine (RVM) strategy using a multi-kernel ensemble approach. The method includes the following steps: Step 1, data preprocessing; Step 2, training set sampling; Step 3, kernel function selection; Step 4, model training; Step 5, model validation; Step 6, adaptive kernel parameters; Step 7, RVM model ensemble; Step 8, model prediction; Step 9, incremental learning strategy; and Step 10, online incremental prediction. From a practical perspective, this invention addresses the complexities and diverse needs of various applications. Drawing on the ideas of incremental learning and ensemble learning, it generates highly differentiated RVM individual learning models containing multiple kernel functions through dual perturbation of training samples and kernel functions. Combined with a novel incremental ensemble strategy, it avoids the problem of model overlearning, improves the model's generalization ability and robustness, and expands its application scope.

The invention provides a titanium alloy milling surface roughness prediction method based on Fe3O4 nano-particle reinforced cutting fluid. The titanium alloy milling surface roughness prediction method comprises the following steps: preparing the cutting fluid containing Fe3O4 nano-particles; establishing a milling force, milling noise and surface roughness acquisition system, and carrying out TC4 titanium alloy wet milling tests under different process parameters; drawing a change curve of milling force, noise and surface roughness test data, and comparing the performance difference before and after addition of the Fe3O4 nanoparticles; and constructing a prediction model based on a convolutional neural network and a relevance vector machine to realize accurate prediction of the surface roughness. The cutting fluid has the advantages that the Fe3O4 nanoparticles are added into the cutting fluid, so that the milling force and noise are remarkably reduced, the surface roughness is improved, and intelligent prediction of the surface quality can be effectively realized by combining experimental testing with a machine learning model.

The invention provides an unmanned aerial vehicle inspection terminal data anomaly detection method and system based on behavior coding cooperating with Transform-RVM, and relates to the technical field of artificial intelligence, the method comprises the following steps: preprocessing sensor original data of an unmanned aerial vehicle inspection terminal, and generating a time sequence sample set; the time sequence samples are converted into behavior coding vectors, and space-time fusion features are generated by fusing equipment space topology information through a graph convolutional network; inputting the space-time fusion features into a Transform editor, and extracting deep features through a multi-head self-attention mechanism, residual connection, layer normalization and a feedforward network; constructing a probability classification model by adopting a relevance vector machine RVM, modeling a parameter optimization process by utilizing a neural differential equation, and performing efficient training in combination with a dynamic sparsity control and adjoint sensitivity method to obtain a highly sparse RVM model; and performing anomaly detection on the unmanned aerial vehicle inspection terminal data acquired in real time by using the highly sparse RVM model. According to the scheme, the anomaly detection precision of the unmanned aerial vehicle inspection terminal data can be improved.

The application relates to a rolling bearing fault diagnosis method and system. Vibration signals of a rolling bearing to be detected are acquired, a plurality of decomposition algorithms are used for signal decomposition, a plurality of feature vectors under the plurality of decomposition methods are respectively obtained, the plurality of feature vectors are respectively input into a trained correlation vector machine model for fault diagnosis, a plurality of fault diagnosis outputs are obtained, the plurality of fault diagnosis outputs are fused, and a final diagnosis result is determined. The application adopts a plurality of decomposition methods to decompose the vibration signals, and fuses a plurality of fault diagnosis probability outputs obtained, so that fault features can be more effectively extracted, and the accuracy of fault diagnosis is improved.