Sensor fault diagnosis of autonomous underwater vehicle based on extreme learning machine
Xun Li, Yan Song, Jia Qi Guo, Chen Feng, Guangliang Li, Tianhong Yan, Bo He · 2017
Autonomous underwater vehicles (AUVs) work in complex marine environments, and sensors play an important role in AUV systems. Therefore, research on sensor failure diagnosis technology is important for improving the reliability of AUV systems. In this paper, a new method combining phase space reconstruction and extreme learning machine (ELM) is proposed. This method is applied to predict sensor output to achieve sensor fault diagnosis for AUVs. The results of the simulation experiments based on sea trial data shown that the proposed method can diagnose sensor faults and recover the signal after faults occur in a period of time.