Remaining Useful Life Prediction of Rail Based on Improved Pulse Separable Convolution Enhanced Transformer Encoder

In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is difficult to capture the long-term dependency relationship of the time series in the modeling of the long time series of rail damage, due to the coupling relationship of multi-channel data from multiple sensors. Here, in this paper, a novel RUL prediction model with an enhanced pulse separable convolution is used to solve this issue. Firstly, a coding module based on the improved pulse separable convolutional network is established to effectively model the relationship between the data. To enhance the network, an alternate gradient back propagation method is implemented. And an efficient channel attention (ECA) mechanism is developed for better emphasizing the useful pulse characteristics. Secondly, an optimized Transformer encoder was designed to serve as the backbone of the model. It has the ability to efficiently understand relationship between the data itself and each other at each time step of long time series with a full life cycle. More importantly, the Transformer encoder is improved by integrating pulse maximum pooling to retain more pulse timing characteristics. Finally, based on the characteristics of the front layer, the final predicted RUL value was provided and served as the end-to-end solution. The empirical findings validate the efficacy of the suggested approach in forecasting the rail RUL, surpassing various existing data-driven prognostication techniques. Meanwhile, the proposed method also shows good generalization performance on PHM2012 bearing data set.

Numerical simulation of the pulsing air separation field based on CFD

The flow field of pulsing air separation is normally in an unsteady turbulence state.With the application of the basic principles of multiphase turbulent flows,we established the dynamical computational model,which shows a remarkable variation of the unstable pulsing air flow field.CFD(computational fluid dynamics) was used to conduct the numerical simulation of the actual geometric model of the classifier.The inside velocity of the flowing fields was analyzed later.The simulation results indicate that the designed structure of the active pulsing air classifier provided a favorable environment for the separation of the particles with different physical characters by density.We shot the movement behaviors of the typical tracer grains in the active pulsing flow field using a high speed dynamic camera.The displacement and velocity curves of the particles in the continuous impulse periods were then analyzed.The experimental results indicate that the effective separation by density of the particles with the same settling velocity and different ranges of the density and particle size can be achieved in the active pulsing airflow field.The experimental results provide an agreement with the simulation results.