CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation

TL;DR

This paper introduces a hybrid CNN-LSTM deep learning model for more accurate Remaining Useful Life (RUL) estimation, effectively capturing sequential data patterns in prognostics, outperforming traditional methods.

Contribution

It is the first to apply a CNN-LSTM hybrid model specifically for RUL estimation, combining feature extraction and sequence learning for improved accuracy.

Findings

CNN-LSTM outperforms existing methods in RUL prediction accuracy

The model effectively handles multiple operating and fault conditions

It uncovers hidden patterns in sensor data for prognostics

Abstract

Remaining Useful Life (RUL) of a component or a system is defined as the length from the current time to the end of the useful life. Accurate RUL estimation plays a crucial role in Predictive Maintenance applications. Traditional regression methods, both linear and non-linear, have struggled to achieve high accuracy in this domain. While Convolutional Neural Networks (CNNs) have shown improved accuracy, they often overlook the sequential nature of the data, relying instead on features derived from sliding windows. Since RUL prediction inherently involves multivariate time series analysis, robust sequence learning is essential. In this work, we propose a hybrid approach combining Convolutional Neural Networks with Long Short-Term Memory (LSTM) networks for RUL estimation. Although CNN-based LSTM models have been applied to sequence prediction tasks in financial forecasting, this is the first attempt to adopt this approach for RUL estimation in prognostics. In this approach, CNN is first employed to efficiently extract features from the data, followed by LSTM, which uses these extracted features to predict RUL. This method effectively leverages sensor sequence information, uncovering hidden patterns within the data, even under multiple operating conditions and fault scenarios. Our results demonstrate that the hybrid CNN-LSTM model achieves the highest accuracy, offering a superior score compared to the other methods.