TITLE:
Remaining Useful Life Prediction of Rail Based on Improved Pulse Separable Convolution Enhanced Transformer Encoder
AUTHORS:
Zhongmei Wang, Min Li, Jing He, Jianhua Liu, Lin Jia
KEYWORDS:
Equipment Health Prognostics, Remaining Useful Life Prediction, Pulse Separable Convolution, Attention Mechanism, Transformer Encoder
JOURNAL NAME:
Journal of Transportation Technologies,
Vol.14 No.2,
March
22,
2024
ABSTRACT: 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.