Estimation on the Underwater Explosion Equivalent Based on the Threshold Monitoring Technique

Underwater nuclear explosions can be monitored in near real-time by the hydroacoustic network of the International Monitoring System (IMS) established by the Comprehensive Nuclear-Test-Ban Treaty (CTBT), which could also be used to monitor underground and atmospheric nuclear explosions. The equivalent is an important parameter for the nuclear explosions’ monitoring. The traditional equivalent estimation method is to calculate the bubble pulsation period, which is difficult to obtain satisfactory results under the current conditions. In this paper, based on the passive sonar equation and the conversion process of acoustic energy parameters in the hydroacoustic station, the threshold monitoring technique used for underwater explosion equivalent estimation was studied, which was not limited to the measurement conditions and calculation results of the bubble pulsation period. Through the analysis of practical monitoring data, estimation on the underwater explosion equivalent based on the threshold monitoring technique was verified to be able to reach the accuracy upper boundary of current methods and expand the measurement range to further ocean space, along with the real-time monitoring capability of IMS hydroacoustic stations which could be estimated by this method.

Monitoring Wind Turbine Gearbox with Echo State Network Modeling and Dynamic Threshold Using SCADA Vibration Data

Health monitoring of wind turbine gearboxes has gained considerable attention as wind turbines become larger in size and move to more inaccessible locations. To improve the reliability, extend the lifetime of the turbines, and reduce the operation and maintenance cost caused by the gearbox faults, data-driven condition motoring techniques have been widely investigated, where various sensor monitoring data (such as power, temperature, and pressure, etc.) have been modeled and analyzed. However, wind turbines often work in complex and dynamic operating conditions, such as variable speeds and loads, thus the traditional static monitoring method relying on a certain fixed threshold will lead to unsatisfactory monitoring performance, typically high false alarms and missed detections. To address this issue, this paper proposes a reliable monitoring model for wind turbine gearboxes based on echo state network (ESN) modeling and the dynamic threshold scheme, with a focus on supervisory control and data acquisition (SCADA) vibration data. The aim of the proposed approach is to build the turbine normal behavior model only using normal SCADA vibration data, and then to analyze the unseen SCADA vibration data to detect potential faults based on the model residual evaluation and the dynamic threshold setting. To better capture temporal information inherent in monitored sensor data, the echo state network (ESN) is used to model the complex vibration data due to its simple and fast training ability and powerful learning capability. Additionally, a dynamic threshold monitoring scheme with a sliding window technique is designed to determine dynamic control limits to address the issue of the low detection accuracy and poor adaptability caused by the traditional static monitoring methods. The effectiveness of the proposed monitoring method is verified using the collected SCADA vibration data from a wind farm located at Inner Mongolia in China. The results demonstrated that the proposed method can achieve improved detection accuracy and reliability compared with the traditional static threshold monitoring method.