Learning representations with end-to-end models for improved remaining useful life prognostics

TL;DR

This paper introduces a simple yet effective end-to-end deep learning model combining MLP and LSTM layers for accurate remaining useful life prediction, outperforming existing methods on a standard aerospace dataset.

Contribution

The paper presents a novel end-to-end deep learning architecture that integrates feature learning and temporal modeling for RUL prognostics, demonstrating superior performance.

Findings

Outperforms existing models on NASA C-MAPSS dataset

Achieves lower root mean square error in RUL prediction

Reduces competition score significantly

Abstract

The remaining Useful Life (RUL) of equipment is defined as the duration between the current time and its failure. An accurate and reliable prognostic of the remaining useful life provides decision-makers with valuable information to adopt an appropriate maintenance strategy to maximize equipment utilization and avoid costly breakdowns. In this work, we propose an end-to-end deep learning model based on multi-layer perceptron and long short-term memory layers (LSTM) to predict the RUL. After normalization of all data, inputs are fed directly to an MLP layers for feature learning, then to an LSTM layer to capture temporal dependencies, and finally to other MLP layers for RUL prognostic. The proposed architecture is tested on the NASA commercial modular aero-propulsion system simulation (C-MAPSS) dataset. Despite its simplicity with respect to other recently proposed models, the model developed outperforms them with a significant decrease in the competition score and in the root mean square error score between the predicted and the gold value of the RUL. In this paper, we will discuss how the proposed end-to-end model is able to achieve such good results and compare it to other deep learning and state-of-the-art methods.