Health Assessment And Remaining Useful Life Prediction Of Mechanical Equipment Based On Deep Learning

In the background of the current big data era,with the increasing precision,automation and integration of machinery and equipment,the correlation and coupling between the components inside the equipment are becoming more and more complex.Traditional strategies are difficult to analyze more and more complex physical principles of machinery and equipment,build accurate physical assessment models and meet actual maintenance needs.To ensure the safe,reliable,and continuous operation of mechanical equipment,in response to the application scenarios of equipment systems under complex and variable working conditions,based on status monitoring data,realtime evaluation of the health status of equipment is conducted,and the remaining service life is predicted to implement effective maintenance strategies.There are many factors affecting the operation data collection of mechanical equipment,among which interference noise has a significant negative impact on its condition monitoring and fault prediction.Existing methods can effectively extract health status features and predict remaining service life to a certain extent,but there is also a problem of insufficient maintenance.In terms of fault feature extraction,a single feature index cannot accurately characterize the operating status of mechanical equipment,and cannot fully capture the deep features of data;However,the state of mechanical equipment represented by multiple feature indicators is uneven,and the ability to extract fault features is limited,unable to achieve generalization.In terms of remaining service life prediction,traditional methods have insufficient capacity to handle complex and long time series,requiring a large amount of expert experience support,and model design is time-consuming and poorly versatile,making it difficult to adapt to increasingly complex and cumbersome equipment data.Therefore,in order to address the problem of low accuracy in predicting equipment remaining life under complex operating conditions that are difficult to fully capture equipment fault characteristics and high noise real operating conditions,this paper deeply studies equipment health assessment based on parallel multiscale features and equipment remaining life prediction methods based on variational coding timing networks.The specific contents are as follows:(1)To solve the problem of effectively extracting data features that are highly correlated with device fault status,a parallel multiscale convolutional neural network(MHA-PSCNN)based on multi head attention mechanism was proposed to achieve accurate estimation of device health status.Fully extract different scale degradation features of operational data through multi-scale convolution models,and fully learn the deep characterization of equipment degradation;The multi head attention mechanism is used to strengthen the weight of feature indicators that have strong correlation with the degradation state,so that the model has adaptive feature selection ability.Finally,through an example study of CWRU datasets,the MHA-PSCNN method proposed in this paper can achieve a fault recognition rate of over 92% under different loads or different noise environments,and even achieve a fault diagnosis effect of 100% on some experimental datasets.(2)Aiming at the problem of low accuracy in predicting residual life of equipment under high noise and real operating conditions,a temporal prediction model(MAVBLSTM)combining multiple attention mechanisms and variational coding was proposed.By embedding a multi attention mechanism,different weights of all features in spatial and channel dimensions are obtained to improve the extraction ability of degraded features;Using a variational encoder to encode degraded information and learn deeply hidden information between data to enhance the robustness of the model;Utilize the bidirectional processing capabilities of the bidirectional long-term and shortterm memory network to strengthen the time dependence of data.The experimental results on the C-MAPSS public dataset show that the proposed MA-VBLSTM algorithm reduces the RMSE and Score values by 5.27% and 10.70%,1.37% and 1.68%,6.37% and 26.94%,3.02% and 2.06%,respectively,on the FD001,FD002,FD003,and FD004 sub datasets compared to the existing methods.