Ship-Bridge Collision Real-Time Alarming Method Based on Cointegration Theory.

Abstract

Ship-bridge collisions in inland waterways pose a serious threat to bridge infrastructure, often resulting in structural damage and jeopardizing safety. Despite the widespread deployment of collision warning systems, these systems fail to function effectively due to factors such as weather conditions, equipment malfunctions, and human error. Current alarming technologies, such as wavelet-based methods, are limited by poor real-time performance, high sensitivity to noise, and low localization accuracy, which hinder their practical application. This paper proposes an innovative Kalman filter-cointegration alarming (KFCA) technology, combining cointegration theory with Kalman filtering to achieve precise and real-time collision detection. Through numerical simulation, KFCA is validated, with the results summarized as follows: (i) KFCA effectively recognizes ship-bridge collisions under an SNR of 60, 70, and 80 dB; and (ii) it accurately identifies impact locations on the bridge based on sensor arrangement indices. Compared to existing methods, KFCA offers significant advantages in real-time response, noise resistance, and localization accuracy. This technology provides an efficient solution for bridge management departments, enabling the timely and accurate detection of ship-bridge collisions, thereby enhancing bridge safety and reducing secondary disasters.