Health Monitoring Framework for Weather Radar Based on Long Short-Term Memory Network with a Real Case Study. (7th January 2022)

• Record Type: Journal Article
• Authors: Li, Wei; Wang, Dalin; Zhou, Wei; Wang, Yimeng; Shen, Chao
• Other Names: Wang Chao Academic Editor.
• Is Part Of: Journal of sensors. Volume 2022(2022)
• Publication Date: 2022-01-07
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2022/5386341
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : The health management of weather radar plays a key role in achieving timely and accurate weather forecasting. The current practice mainly exploits a fixed threshold prespecified for some monitoring parameters for fault detection. This causes abundant false alarms due to the evolving working environments, increasing complexity of the modern weather radar, and the ignorance of the dependencies among monitoring parameters. To address the above issues, we propose a deep learning-based health monitoring framework for weather radar. First, we develop a two-stage approach for problem formulation that address issues of fault scarcity and abundant false fault alarms in processing the databases of monitoring data, fault alarm record, and maintenance records. The temporal evolution of weather radar under healthy conditions is represented by a long short-term memory network (LSTM) model. As such, any anomaly can be identified according to the deviation between the LSTM-based prediction and the actual measurement. Then, construct a health indicator based on the portion of the occurrence of deviation beyond a user-specified threshold within a time window. The proposed framework is demonstrated by a real case study for the Chinese S-band weather radar (CINRAD-SA). The results validate the effectiveness of the proposed framework in providing early fault warnings.