Multimodal recognition and prognostics based on features extracted via multisensor degradation modeling

Remaining useful life (RUL) prediction is an important issue in prognostics and health management (PHM). Numerous studies have been conducted to construct degradation models for RUL prediction. However, their models fail to handle the scenarios where multiple failure modes exist, especially when the failure modes are unknown (unlabeled) beforehand and need to be recognized. This paper develops a multimodal recognition and prognostic method based on features extracted <i>via</i> multisensor degradation modeling. Specifically, we assume the failure mode of a unit follows a multinomial distribution. Given the failure mode distribution, we characterize the degradation status of the unit <i>via</i> degradation models based on each sensor signal and a constructed health index (HI). Our innovative idea is to extract features as the derivatives of the degradation status to comprehensively utilize information from multiple sensors for more effective failure mode recognition and RUL prediction. We develop a fusion coefficient-integrated expectation-maximization (FCIEM) algorithm to estimate model parameters by using data from historical units. Finally, we recognize the failure mode and predict the RUL of in-service units based on their extracted features and degradation status. Numerical experiments and a case study of aircraft engines were conducted to evaluate the performance of our proposed method.

剩余使用寿命（Remaining Useful Life, RUL）预测是预测与健康管理（Prognostics and Health Management, PHM）领域的重要研究议题。学界已开展大量研究，构建用于RUL预测的退化模型。然而，现有模型难以处理存在多种失效模式的场景，尤其是在失效模式事先未知（未标注）且需进行识别的情形下。本文提出一种基于多传感器退化建模提取特征的多模态识别与预测方法。具体而言，我们假设单个单元的失效模式服从多项分布。在给定失效模式分布的前提下，我们通过基于各传感器信号与构建的健康指标（Health Index, HI）的退化模型，对单元的退化状态进行表征。本文的创新思路在于，将特征提取为退化状态的导数形式，以综合利用多传感器信息，实现更高效的失效模式识别与RUL预测。我们提出一种融合系数集成期望最大化（FCIEM）算法，借助历史单元的观测数据估计模型参数。最终，我们基于在役单元的提取特征与退化状态，完成其失效模式识别与RUL预测。为验证所提方法的性能，本文开展了数值仿真实验与航空发动机案例研究。