Risk Assessment of Work Accident in Container Terminals Using the Fault Tree Analysis Method

Mathematically Improved XGBoost Algorithm for Truck Hoisting Detection in Container Unloading

Truck hoisting detection constitutes a key focus in port security, for which no optimal resolution has been identified. To address the issues of high costs, susceptibility to weather conditions, and low accuracy in conventional methods for truck hoisting detection, a non-intrusive detection approach is proposed in this paper. The proposed approach utilizes a mathematical model and an extreme gradient boosting (XGBoost) model. Electrical signals, including voltage and current, collected by Hall sensors are processed by the mathematical model, which augments their physical information. Subsequently, the dataset filtered by the mathematical model is used to train the XGBoost model, enabling the XGBoost model to effectively identify abnormal hoists. Improvements were observed in the performance of the XGBoost model as utilized in this paper. Finally, experiments were conducted at several stations. The overall false positive rate did not exceed 0.7% and no false negatives occurred in the experiments. The experimental results demonstrated the excellent performance of the proposed approach, which can reduce the costs and improve the accuracy of detection in container hoisting.

Faster RCNN mixed-integer optimization with weighted cost function for container detection in port automation

The development of port automation requires sensors to detect container movement. Vision sensors have recently received considerable attention and are being developed as AI advances, leading to various container motion detection methods. Faster-RCNN is a detection method that performs better precision and recall than other methods. Nonetheless, the detectors are set using the Faster-RCNN default parameters. It is of interest to optimized its parameters for producing more accurate detectors for container detection tasks. Faster RCNN requires mixed integer optimization for its continuous and integer parameters. Efficient Modified Particle Swarm Optimization (EMPSO) offers a method to optimize integer parameter by evolutionary updating the space of each candidate solution but has high possibility stuck in the local minima due to rapid growth of Gbest and Pbest space. This paper proposes two modifications to improve EMPSO that could adapt to the current global solution. Firstly, the non-Gbest and Pbest total position spaces are made adaptive to changes according to the Gbest and Pbest position spaces. Second, a weighted multiobjective optimization for Faster-RCNN is proposed based on minimum loss, average loss, and gradient of loss to give priority scale. The integer EMPSO with adaptive changes to Gbest and Pbest position space is first tested on nine non-linear standard test functions to validate its performance, the results show performance improvement in finding global minimum compared to EMPSO. This tested algorithm is then applied to optimize Faster-RCNN with the weighted cost function, which uses 1300 container images to train the model and then tested on four videos of moving containers at seaports. The results produce better performances regarding the speed and achieving the optimal solution. This technique causes better minimum losses, average losses, intersection over union, confidence score, precision, and accuracy than the results of the default parameters.

Detecting Shipping Container Impacts with Vertical Cell Guides inside Container Ships during Handling Operations

Due to the mechanical nature of container handling operations, as well as natural factors, container and handling infrastructure suffers various types of damage during use, especially within the tight and enclosed environments of a ship’s hull. In this operational environment, it is critical to detect any sort of physical impacts between the vertical cell guides of the ship’s hull and the container. Currently, an inspection of impacts and evaluation of any consequences is performed manually, via visual inspection processes. This process is time-consuming and relies on the technical expertise of the personnel involved. In this paper, we propose a five-step impact-detection methodology (IDM), intended to detect only the most significant impact events based on acceleration data. We conducted real measurements in a container terminal using a sensory device placed on the spreader of the quay crane. The proposed solution identified an average of 12.8 container impacts with the vertical cell guides during common handling operations. In addition, the results indicate that the presented IDM can be used to recognize repeated impacts in the same space of each bay of the ship, and can be used as a decision support tool for predictive maintenance systems.

A Three-Stage Dynamic Assessment Framework for Industrial Control System Security Based on a Method of W-HMM

Industrial control systems (ICS) are applied in many fields. Due to the development of cloud computing, artificial intelligence, and big data analysis inducing more cyberattacks, ICS always suffers from the risks. If the risks occur during system operations, corporate capital is endangered. It is crucial to assess the security of ICS dynamically. This paper proposes a dynamic assessment framework for industrial control system security (DAF-ICSS) based on machine learning and takes an industrial robot system as an example. The framework conducts security assessment from qualitative and quantitative perspectives, combining three assessment phases: static identification, dynamic monitoring, and security assessment. During the evaluation, we propose a weighted Hidden Markov Model (W-HMM) to dynamically establish the system's security model with the algorithm of Baum-Welch. To verify the effectiveness of DAF-ICSS, we have compared it with two assessment methods to assess industrial robot security. The comparison result shows that the proposed DAF-ICSS can provide a more accurate assessment. The assessment reflects the system's security state in a timely and intuitive manner. In addition, it can be used to analyze the security impact caused by the unknown types of ICS attacks since it infers the security state based on the explicit state of the system.

Causation analysis and governance strategy for hazardous cargo accidents at ports: Case study of Tianjin Port's hazardous cargo explosion accident

As an important part of the global shipping industry, hazardous cargo transportation at ports is concerned by countries around the world due to the great hazards and high risks during its operations. However, multiple hazardous cargo accidents have occurred at ports in recent years. The explosion accident of hazardous cargoes at Tianjin Port, China, in 2015 is a typical case. It is a topic worth in-depth study to figure out how to analyze the causation factors of such accident and propose effective governance strategies against them. This article takes the hazardous cargo explosion at Tianjin Port of China as the subject and systematically analyzes the causation factors of the accident based on the Fault Tree Analysis (FTA) method. It proposes a strategy for governing hazardous cargoes at the port. The analysis results show that the hazardous cargo explosion at the port has complicated causation factors, among which management and human factors play a predominant role in the overall accident causation structure. Other factors include environmental factors and cargo & facility factors. Finally, the corresponding safety governance strategy is proposed based on the structural relationship of various accident causation factors in the above analysis. This study can offer guidance for port enterprises to reduce hazardous cargo accidents at ports and provide an important basis for port authorities to formulate strategies on emergency management and emergency decision-making of hazardous cargo accidents at ports.

Machine Learning-Based Models for Accident Prediction at a Korean Container Port

The occurrence of accidents at container ports results in damages and economic losses in the terminal operation. Therefore, it is necessary to accurately predict accidents at container ports. Several machine learning models have been applied to predict accidents at a container port under various time intervals, and the optimal model was selected by comparing the results of different models in terms of their accuracy, precision, recall, and F1 score. The results show that a deep neural network model and gradient boosting model with an interval of 6 h exhibits the highest performance in terms of all the performance metrics. The applied methods can be used in the predicting of accidents at container ports in the future.

Fault Tree Analysis and Failure Diagnosis of Marine Diesel Engine Turbocharger System

The reliability of marine propulsion systems depends on the reliability of several sub-systems of a diesel engine. The scavenge air system is one of the crucial sub-systems of the marine engine with a turbocharger as an essential component. In this paper, the failures of a turbocharger are analyzed through the fault tree analysis (FTA) method to estimate the reliability of the system and to predict the cause of failures. The quantitative method is used for assessing the probability of faults occurring in the turbocharger system. The main failures of a scavenge air sub-system, such as air filter blockage, compressor fouling, turbine fouling (exhaust side), cooler tube blockage and cooler air side blockage, are simulated on a Wärtsilä-Transas engine simulator for a marine two-stroke diesel engine. The results obtained through the simulation can provide improvement in the maintenance plan, reliability of the propulsion system and optimization of turbocharger operation during exploitation time.