Liu Yang, Xu Bihe, Geng Yangli-Ao
Key Laboratory of Big Data & Artificial Intelligence in Transportation (Ministry of Education), Beijing Jiaotong University, Beijing 100044, China.
China Energy Railway Equipment Co., Ltd., Beijing 100011, China.
Entropy (Basel). 2025 Jan 17;27(1):79. doi: 10.3390/e27010079.
Accurate Remaining Useful Life (RUL) prediction is vital for effective prognostics in and the health management of industrial equipment, particularly under varying operational conditions. Existing approaches to multi-condition RUL prediction often treat each working condition independently, failing to effectively exploit cross-condition knowledge. To address this limitation, this paper introduces MoEFormer, a novel framework that combines a Mixture of Encoders (MoE) with a Transformer-based architecture to achieve precise multi-condition RUL prediction. The core innovation lies in the MoE architecture, where each encoder is designed to specialize in feature extraction for a specific operational condition. These features are then dynamically integrated through a gated mixture module, enabling the model to effectively leverage cross-condition knowledge. A Transformer layer is subsequently employed to capture temporal dependencies within the input sequence, followed by a fully connected layer to produce the final prediction. Additionally, we provide a theoretical performance guarantee for MoEFormer by deriving a lower bound for its error rate. Extensive experiments on the widely used C-MAPSS dataset demonstrate that MoEFormer outperforms several state-of-the-art methods for multi-condition RUL prediction.
准确的剩余使用寿命（RUL）预测对于工业设备的有效预后和健康管理至关重要，尤其是在不同的运行条件下。现有的多工况RUL预测方法通常独立处理每个工作条件，未能有效利用跨工况知识。为了解决这一局限性，本文介绍了MoEFormer，这是一种新颖的框架，它将编码器混合（MoE）与基于Transformer的架构相结合，以实现精确的多工况RUL预测。核心创新在于MoE架构，其中每个编码器被设计用于专门针对特定运行条件进行特征提取。然后通过门控混合模块动态集成这些特征，使模型能够有效利用跨工况知识。随后采用Transformer层来捕获输入序列中的时间依赖性，接着是一个全连接层以产生最终预测。此外，我们通过推导MoEFormer错误率的下限为其提供了理论性能保证。在广泛使用的C-MAPSS数据集上进行的大量实验表明，MoEFormer在多工况RUL预测方面优于几种先进方法。
