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.

翻译：剩余使用寿命（RUL）指组件或系统从当前时刻至其使用寿命终止的时间长度。精确的RUL预测在预测性维护应用中具有关键作用。传统线性与非线性回归方法在该领域难以实现高精度预测。虽然卷积神经网络（CNN）已展现出更高的准确度，但这类方法通常忽略数据的时序特性，主要依赖滑动窗口提取的特征。由于RUL预测本质上是多元时间序列分析问题，强大的序列学习能力至关重要。本研究提出一种结合卷积神经网络与长短期记忆网络（LSTM）的混合方法用于RUL估计。尽管基于CNN的LSTM模型已在金融预测等序列预测任务中得到应用，但将其用于预测性维护中的RUL估计尚属首次。该方法首先利用CNN高效提取数据特征，随后通过LSTM基于这些特征预测RUL。该技术能有效利用传感器序列信息，揭示数据中隐藏的规律模式，即使在多工况与多故障场景下仍保持稳健。实验结果表明，混合CNN-LSTM模型实现了最优预测精度，其评估得分显著优于其他对比方法。