Reliability improvement of the dredging perception system

Abstract

In the dredging industry, the automation and accuracy of the Dredging Perception System (DPS) are vital for operational efficiency and environmental safety. Current DPS implementations face challenges with sensor fault tolerance, leading to system unreliability and increased false alarm rates that can disrupt dredging operations. We propose a Hybrid Redundancy Sensor Fault Tolerance (HRSFT) strategy that integrates matching physical sensors (PS) with two distinct types of virtual sensors (VS) driven by multi-sensor association and time-series prediction models. The HRSFT employs a voting-cold storage strategy to address the false alarm issues commonly associated with single virtual sensor systems. Through experimental validation, the HRSFT strategy has demonstrated its capability to provide accurate replacement information during both single and multi-sensor failure scenarios, effectively managing abnormal sensor data and enhancing the operational reliability of the DPS. The implementation of the HRSFT strategy significantly improves the accuracy and stability of the DPS, suggesting a substantial advancement in sensor fault tolerance that could be applied to similar systems in various industries, leading to safer and more reliable operations.