Multipath Temporal Convolutional Network for Remaining Useful Life Estimation

The remaining useful life (RUL) estimation of a component is a core aspect in the implementation of a predictive maintenance policy for mechanical systems. Several novel deep learning approaches have already been developed to forecast the failure of a particular system or element. Most of them focus only on the improvement of the accuracy and thus, very complex deep architectures are generated to solve the RUL prediction task. However, such complex models can hardly be implemented in an embedded system. Thus, they could not be suitable for applications such as the condition monitoring of vehicles or mobile robots. On the other hand, we propose a new architecture named Multipath Temporal Convolutional Network (MTCN), which is based on residual networks. This novel architecture enables an accurate prediction of the time to failure (TTF) of a system and simultaneously reduces the computation complexity. Moreover, the hyperparameters of the network are optimized with a genetic algorithm (GA). We validate the efficiency of our approach by using the C-MAPSS database and perform a direct comparison with the state of the art approaches for failure prognosis. The experimental study validates the high performance of MTCNs to solve the RUL prediction task with a minimal computational complexity in comparison to other published approaches. This will enable the deployment of MTCN for on-board monitoring applications of mobile systems in future research.