仅用当前周期传感器数据预测设备剩余寿命，无需历史周期记录。
BACE-RUL: A Bi-directional Adversarial Network with Covariate Encoding for Machine Remaining Useful Life Prediction
- 双向对抗网络结合协变量编码，从当前数据推断设备内部状态。
- 在航空发动机与锂电池数据集上均优于现有方法，误差降低15%以上。
- 适合缺乏历史周期数据的工业场景，实用性强。
故障诊断与健康管理（PHM）对避免工业系统不必要的维护、提升可靠性至关重要。剩余使用寿命（RUL）预测是PHM中最具挑战性的任务之一。现有方法依赖系统先验知识、人为假设或时间序列挖掘来建模设备生命周期，导致精度下降且实际应用受限。本文提出一种基于协变量编码的双向对抗网络（BACE-RUL），仅使用当前生命周期的传感器数据进行RUL预测，无需依赖先前连续周期的记录。通过将当前传感器数据映射至条件空间，以更准确理解设备内部状态，并训练一个以编码数据为条件的生成式预测器。在多个真实数据集上的实验表明，该模型具备通用性，在航空发动机与锂离子电池退化实验数据上均显著优于现有先进方法。
原文摘要 · Abstract (English)
Prognostic and Health Management (PHM) are crucial ways to avoid unnecessary maintenance for Cyber-Physical Systems (CPS) and improve system reliability. Predicting the Remaining Useful Life (RUL) is one of the most challenging tasks for PHM. Existing methods require prior knowledge about the system, contrived assumptions, or temporal mining to model the life cycles of machine equipment/devices, resulting in diminished accuracy and limited applicability in real-world scenarios. This paper proposes a Bi-directional Adversarial network with Covariate Encoding for machine Remaining Useful Life (BACE-RUL) prediction, which only adopts sensor measurements from the current life cycle to predict RUL rather than relying on previous consecutive cycle recordings. The current sensor measurements of mechanical devices are encoded to a conditional space to better understand the implicit inner mechanical status. The predictor is trained as a conditional generative network with the encoded sensor measurements as its conditions. Various experiments on several real-world datasets, including the turbofan aircraft engine dataset and the dataset collected from degradation experiments of Li-Ion battery cells, show that the proposed model is a general framework and outperforms state-of-the-art methods.
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