Search Results (2)

Lifeboats are life-saving equipment used when it is necessary to abandon a ship or, in some ships, for man-overboard situations (to collect persons from water). Every seafarer onboard a ship has a task related to lifeboat operation in an emergency. In order to master and practise the assigned tasks, be ready to react at any moment, and efficiently use life-saving equipment and appliances, seafarers on ships perform drills at prescribed intervals. Effective drill performance is of paramount importance, as it improves safety and enables crew members to practise lifeboat operations. However, although their primary role is life-saving, lifeboat drills have resulted in numerous accidents, causing injuries and fatalities, besides equipment damage. Therefore, it is necessary to prevent such unwanted events and discover their root causes. As the human factor is considered a significant cause of marine accidents, this paper aims to quantify human error probability (HEP) during lifeboat drills. In addition, because lifeboat drill accident data are scarce, this study adopted the Success Likelihood Index Method (SLIM) for human reliability analysis (HRA). Based on expert judgments, the tasks with the highest probability of human error and factors significantly influencing human performance during lifeboat drills are identified. According to the study results, the recovery of the lifeboat is the most hazardous phase with the highest HEP. In addition, the BN-SLIM is adopted to estimate the probability of human error during the recovery of the lifeboat. The task with the largest HEP is confirming the release lever is properly rested and hooks locked (HEP = 4.5%). Furthermore, the design and condition of equipment and Crew Competence are identified as the most important Performance-Shaping Factors (PSFs) that affect crew members’ performance. The BN-SLIM model was verified utilising a sensitivity analysis and validated by analysing real-life lifeboat drill accidents that occurred during lifeboat recovery. The results confirmed that the model could be used to analyse lifeboat accidents and for proactive preventive measures because most influencing factors are recognised, and acting on them can significantly reduce the HEP of the overall task, improve lifeboat safety, and save lives at sea. Full article

The efficient detection of crew overboard behavior has become an important element in enhancing the ability to respond to marine disasters. It remains challenging due to (1) the lack of effective features making feature extraction difficult and recognition accuracy low and (2) the insufficient computing power resulting in the poor real-time performance of existing algorithms. In this paper, we propose a Channel-Time Dense Residual Network (CTDR-Net) for detecting crew overboard behavior, including a Dense Residual Network (DR-Net) and a Channel-Time Attention Mechanism (CTAM). The DR-Net is proposed to extract features, which employs the convolutional splitting method to improve the extraction ability of sparse features and reduce the number of network parameters. The CTAM is used to enhance the expression ability of channel feature information, and can increase the accuracy of behavior detection more effectively. We use the LeakyReLU activation function to improve the nonlinear modeling ability of the network, which can further enhance the network’s generalization ability. The experiments show that our method has an accuracy of 96.9%, striking a good balance between accuracy and real-time performance. Full article