Accurately estimating a Health Index (HI) from condition monitoring data (CM) is essential for reliable and interpretable prognostics and health management (PHM) in complex systems. In most scenarios, complex systems operate under varying operating conditions and can exhibit different fault modes, making unsupervised inference of an HI from CM data a significant challenge. Hybrid models combining prior knowledge about degradation with deep learning models have been proposed to overcome this challenge. However, previously suggested hybrid models for HI estimation usually rely heavily on system-specific information, limiting their transferability to other systems. In this work, we propose an unsupervised hybrid method for HI estimation that integrates general knowledge about degradation into the convolutional autoencoder's model architecture and learning algorithm, enhancing its applicability across various systems. The effectiveness of the proposed method is demonstrated in two case studies from different domains: turbofan engines and lithium batteries. The results show that the proposed method outperforms other competitive alternatives, including residual-based methods, in terms of HI quality and their utility for Remaining Useful Life (RUL) predictions. The case studies also highlight the comparable performance of our proposed method with a supervised model trained with HI labels.

翻译：准确从状态监测数据中估计健康指数（HI）对于复杂系统可靠且可解释的预测与健康管理（PHM）至关重要。在大多数场景中，复杂系统通常运行于变化的工作条件下并可能呈现多种故障模式，这使得从监测数据中无监督推断HI成为一项重大挑战。结合退化先验知识与深度学习模型的混合方法已被提出以应对这一挑战。然而，现有用于HI估计的混合模型通常严重依赖系统特定信息，限制了其向其他系统的迁移能力。本研究提出一种无监督混合方法，通过将退化通用知识融入卷积自编码器的模型架构与学习算法中，增强其跨系统适用性。通过涡扇发动机和锂电池两个跨领域案例研究验证了所提方法的有效性。结果表明，在HI质量及其对剩余使用寿命（RUL）预测的效用方面，所提方法优于包括残差基方法在内的其他竞争方案。案例研究还表明，所提方法取得了与使用HI标签训练的监督模型相当的性能。