Center for Risk and Reliability, University of Maryland, College Park, MD 20742, USA.
Department of Mechanical Engineering, University of Chile, Santiago 8320000, Chile.
Sensors (Basel). 2019 Dec 27;20(1):176. doi: 10.3390/s20010176.
Multi-sensor systems are proliferating in the asset management industry. Industry 4.0, combined with the Internet of Things (IoT), has ushered in the requirements of prognostics and health management systems to predict the system's reliability and assess maintenance decisions. State of the art systems now generate big machinery data and require multi-sensor fusion for integrated remaining useful life prognostic capabilities. When dealing with these data sets, traditional prediction methods are not equipped to handle the multiple sensor signals in unison. To address this challenge, this paper proposes a new, deep, adversarial approach to any remaining useful life prediction in which a novel, non-Markovian, variational, inference-based model, incorporating an adversarial methodology, is derived. To evaluate the proposed approach, two public multi-sensor data sets are used for the remaining useful life prediction applications: (1) CMAPSS turbofan engine dataset, and (2) FEMTO Pronostia rolling element bearing data set. The proposed approach obtains favorable results when against similar deep learning models.
多传感器系统在资产管理行业中迅速普及。工业 4.0 与物联网相结合，对预测和健康管理系统提出了要求，以预测系统的可靠性并评估维护决策。目前最先进的系统会生成大量机器数据，并且需要多传感器融合来实现综合剩余使用寿命预测能力。在处理这些数据集时，传统的预测方法无法统一处理多个传感器信号。为了解决这个挑战，本文提出了一种新的深度对抗方法，用于任何剩余使用寿命预测，其中推导出了一种新颖的、非马尔可夫的、基于变分推理的、包含对抗方法的模型。为了评估所提出的方法，使用两个公共的多传感器数据集进行剩余使用寿命预测应用：（1）CMAPSS 涡轮风扇发动机数据集，以及（2）FEMTO Pronostia 滚动轴承数据集。当与类似的深度学习模型相比时，所提出的方法获得了有利的结果。
