Addressing data scarcity in industrial reliability assessment with Physically Informed Echo State Networks
提出一种利用物理信息回声状态网络增强传感器数据的方法，在数据稀缺时生成替代信号，通过30千瓦射流风机实验验证，能显著提升新设备状态监测系统的运行能力。
This paper introduces a method for augmenting sensor data using Physically Informed Echo State Networks (ESNs), which facilitates system identification in scenarios with limited sensor data. The approach integrates domain-specific physical knowledge into the learning process of ESNs to generate surrogate time-amplitude signals from the Power Spectral Density (PSD) of the data and a predefined list of system excitation frequencies. This integration proves particularly beneficial during the initial design phases of condition monitoring systems, where empirical data is often sparse. We demonstrate the effectiveness of this method through experiments on a 30 kW jet fan in a road tunnel ventilation system. Results indicate significant improvements in the operational capabilities of condition monitoring systems for newly developed equipment. This method is versatile and applicable across various industrial contexts with insufficient historical operational data.