Temporal Convolutional Memory Networks for Remaining Useful Life Estimation of Industrial Machinery

TL;DR

This paper presents a novel neural network model combining temporal convolutions to capture both short and long-term dependencies for more accurate remaining useful life estimation of industrial machinery.

Contribution

It introduces a system model that integrates temporal convolutions with data augmentation, improving RUL prediction accuracy over existing methods.

Findings

Outperforms state-of-the-art algorithms on benchmark datasets

Effectively captures complex temporal variations in sensor data

Demonstrates robustness in complex environment datasets

Abstract

Accurately estimating the remaining useful life (RUL) of industrial machinery is beneficial in many real-world applications. Estimation techniques have mainly utilized linear models or neural network based approaches with a focus on short term time dependencies. This paper, introduces a system model that incorporates temporal convolutions with both long term and short term time dependencies. The proposed network learns salient features and complex temporal variations in sensor values, and predicts the RUL. A data augmentation method is used for increased accuracy. The proposed method is compared with several state-of-the-art algorithms on publicly available datasets. It demonstrates promising results, with superior results for datasets obtained from complex environments.