Navigation Environment Detection in Ice Area Based on Vibration of Ship Main Engine

Sea ice seriously endangers the safe navigation of ships at sea. Therefore, it is the key to accurately judge the type of sea ice around the ship. However, remote sensing technology will be restricted by external weather changes. Therefore, this paper presents a method based on statistical feature extraction and machine learning of ship main engine vibration signal, so as to realize the recognition of sea ice types. Firstly, according to the sea ice video information captured by the camera, the acceleration and vibration data collected by the vibration sensor are divided, and the wavelet threshold denoising algorithm is adopted to enhance the features. Then, based on the statistical characteristics of vibration signals, time domain and frequency domain features are extracted. Finally, Random Forest(RF) and Gradient Boosting Decision Tree(GBDT) machine learning algorithms are used to classify the extracted eigenvalues. The experimental results show that the accuracy of sea ice identification using ship main engine vibration signals is 79%. This method provides a reference for identifying sea ice types by ship ice-induced vibration, and is helpful for further research on sea ice classification.

I. Introduction

Sea ice has become one of the main dangerous sources for ships to navigate at sea, so it is the key to accurately judge the type of sea ice for safe navigation. For this reason, many scholars have been trying to put forward effective methods to achieve accurate and timely sea ice monitoring. At present, remote sensing technology is the main means of sea ice monitoring [1] o