Abstract
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.
Abstract (translated)
准确从病况监测数据（CM）估算健康指数（HI）对于复杂系统中的可靠且可解释的预后护理和健康管理（PHM）至关重要。在大多数情况下，复杂系统在不同的运行条件下运行，可能表现出不同的故障模式，因此从CM数据中无监督地推断HI是一个重要的挑战。为了克服这一挑战，已经提出了结合前知识 about degradation with deep learning models的混合模型。然而，以前提出的用于HI估计的混合模型通常依赖于系统特定信息，限制了它们在其他系统上的可迁移性。在本文中，我们提出了一种无监督的混合方法用于HI估计，将降解的一般知识融入到卷积自编码器的模型结构和求解算法中，增强了它在各种系统上的适用性。所提出方法的有效性在两个不同领域的案例研究中得到了证明：涡轮喷气发动机和锂离子电池。结果表明，与基于残余的方法的竞争对手相比，所提出的方法在HI质量和它们对剩余使用寿命（RUL）预测的实用性方面都表现优异。案例研究还强调了与使用HI标签进行有监督训练的监督模型具有可比较的性能。
URL
https://arxiv.org/abs/2405.04990
