IEEE Trans Neural Netw Learn Syst. 2021 Jan;32(1):117-127. doi: 10.1109/TNNLS.2020.2977132. Epub 2021 Jan 4.
With the rapid development of sensor and information technology, now multisensor data relating to the system degradation process are readily available for condition monitoring and remaining useful life (RUL) prediction. The traditional data fusion and RUL prediction methods are either not flexible enough to capture the highly nonlinear relationship between the health condition and the multisensor data or have not fully utilized the past observations to capture the degradation trajectory. In this article, we propose a joint prognostic model (JPM), where Bayesian linear models are developed for multisensor data, and an artificial neural network is proposed to model the nonlinear relationship between the residual life, the model parameters of each sensor data, and the observation epoch. A Bayesian updating scheme is developed to calculate the posterior distributions of the model parameters of each sensor data, which are further used to estimate the posterior predictive distributions of the residual life. The effectiveness and advantages of the proposed JPM are demonstrated using the commercial modular aero-propulsion system simulation data set.
随着传感器和信息技术的快速发展，现在与系统退化过程相关的多传感器数据可用于状态监测和剩余使用寿命（RUL）预测。传统的数据融合和 RUL 预测方法要么不够灵活，无法捕捉健康状况和多传感器数据之间的高度非线性关系，要么没有充分利用过去的观测结果来捕捉退化轨迹。在本文中，我们提出了一种联合预测模型（JPM），其中为多传感器数据开发了贝叶斯线性模型，并提出了人工神经网络来建模剩余寿命、每个传感器数据的模型参数和观测时间之间的非线性关系。开发了一种贝叶斯更新方案来计算每个传感器数据的模型参数的后验分布，进一步用于估计剩余寿命的后验预测分布。使用商业模块化航空推进系统仿真数据集证明了所提出的 JPM 的有效性和优势。
