LSTM-BASED-AUTO-BI-LSTM FOR REMAINING USEFUL LIFE (RUL) PREDICTION: THE FIRST ROUND OF TEST RESULTS

Abstract

The Remaining Useful Life (RUL) is one of the most critical indicators to detect a component's failure before it effectively occurs. It can be predicted by historical data or direct data extraction by adopting model-based, data-driven, or hybrid methodologies. Data-driven methods have mainly used Machine Learning (ML) approaches, despite several studies still pointing out different challenges in this sense. For instance, traditional ML methods cannot extract features directly from time series depending, in some cases, on the prior knowledge of the system. In this context, this work proposes a DL-based approach called LSTM-based-AUTO-Bi-LSTM. It ensembles an LSTM-based autoencoder to automatically perform feature engineering (instead of manually) with Bidirectional Long Short-Term Memory (Bi-LSTM) to predict RUL. We have tested the model using the Turbofan Engine Degradation Simulation Dataset (FD001), an open dataset. It was generated from the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) from the Prognostics Center of Excellence (PcoE), from the National Aeronautics and Space Administration (NASA). The objective is to release the first round of analytical results and statistical visualisations of the model application, which will guide us in future improvements.

1. INTRODUCTION

Cyber-Physical Systems (CPS), Internet of Things (IoT), Internet of Services (IoS), and Data Analytics have built Industry 4.0, which has improved manufacturing efficiency and helped industries face challenges such as economic, social, and environmental (Ruiz-Sarmiento et al., 2020) . Condition-Based Maintenance (CBM) performs machines and components' maintenance routines based on their needs, and Prognostics and Health Management (PHM) monitors components' wear evolution using indicators. PHM is a proactive way of implementing CBM by predicting the Remaining Useful Life (RUL), one of the most critical indicators to detect a component's failure before it effectively occurs (Wang et al., 2021; Huang et al., 2019; Wu et al., 2017; Kan et al., 2015) . RUL can be predicted by historical data or direct data extraction by adopting model-based, datadriven, or hybrid methodologies. Model-based methods are challenging, expensive, and timeconsuming to develop in complex equipment due to the need for prior system knowledge. Datadriven methods have mainly used Machine Learning (ML) approaches. They are less complex and expensive, more applicable and provide a suitable trade-off between complexity, cost, precision, and applicability (Cheng et al., 2021; Mrugalska, 2019; Li et al., 2019; Yang et al., 2016) , although they require large amounts of historical data for development (Liewald et al., 2022) Meanwhile, despite the increased use of ML to predict RUL, several studies have still pointed out different challenges in this sense (Huang et al., 2019) . For example, most ML methods' accuracy in predicting RUL largely depends on the feature extraction quality, and their performance is affected in the case of very complex systems with multiple components, multiple states, and a considerable amount of parameters (Zhao et al., 2021; Chen et al., 2019) . Moreover, the literature has also reported that most of these models do not consider operation conditions; the machines operate in different states, even on the same shop floor. It significantly impacts the degradation behaviour and raw sensor signals that may be non-stationary, nonlinear, and mixed with much noise (Liu et al., 

