Analyzing Port State Control Data to Explore Future Improvements to GMDSS Training

Flag choice and Port State Control inspections—Empirical evidence using a simultaneous model

Port state control (PSC) inspections, considered a crucial means of maritime safety supervision, are viewed by the industry as a critical line of defense ensuring the stability of the international supply chain. Due to the high level of globalization and strong regional characteristics of PSC inspections, improving the accuracy of these inspections and efficiently utilizing inspection resources have become urgent issues. The construction of a PSC inspection ontology model from top to bottom, coupled with the integration of multisource data from bottom to top, is proposed in this paper. The RoBERTa-wwm-ext model is adopted as the entity recognition model, while the XGBoost4 model serves as the knowledge fusion model to establish the PSC inspection knowledge graph. Building upon an evolutionary game model of the PSC inspection knowledge graph, this study introduces an evolutionary game method to analyze the internal evolutionary dynamics of ship populations from a microscopic perspective. Through numerical simulations and standardization diffusion evolution simulations for ship support, the evolutionary impact of each parameter on the subgraph is examined. Subsequently, based on the results of the evolutionary game analysis, recommendations for PSC inspection auxiliary decision-making and related strategic suggestions are presented. The experimental results show that the RoBERTa-wwm-ext model and the XGBoost4 model used in the PSC inspection knowledge graph achieve superior performance in both entity recognition and knowledge fusion tasks, with the model accuracies surpassing those of other compared models. In the knowledge graph-based PSC inspection evolutionary game, the reward and punishment conditions (n, f) can reduce the burden of the standardization cost for safeguarding the ship. A ship is more sensitive to changes in the detention rate β than to changes in the inspection rate α. To a certain extent, the detention cost CDC plays a role similar to that of the detention rate β. In small-scale networks, relevant parameters in the ship’s standardization game have a more pronounced effect, with detention cost CDC having a greater impact than standardization cost CS on ship strategy choice and scale-free network evolution. Based on the experimental results, PSC inspection strategies are suggested. These strategies provide port state authorities with auxiliary decision-making tools for PSC inspections, promote the informatization of maritime regulation, and offer new insights for the study of maritime traffic safety management and PSC inspections.

Early warning on the ship deficiency is crucial for enhancing maritime safety, improving maritime traffic efficiency, reducing ship fuel consumption, etc. Previous studies focused on the ship deficiency exploration by mining the relationships between the ship physical deficiencies and the port state control (PSC) inspection results with statistical models. Less attention was paid to discovering the correlation rules among various parent ship deficiencies and subcategories. To address the issue, we proposed an improved Apriori model to explore the intrinsic mutual correlations among the ship deficiencies from the PSC inspection dataset. Four typical ship property indicators (i.e., ship type, age, deadweight and gross tonnage) were introduced to analyze the correlations for the ship parent deficiency categories and subcategories. The findings of our research can provide basic guidelines for PSC inspections to improve the ship inspection efficiency and maritime safety.

Ship detention serves as an obligatory but efficient manner in port state control (PSC) inspection, and accurate ship detention prediction provides early warning information for maritime traffic participants. Previous studies mainly focused on exploiting the relationship between ship factors (i.e., ship age and ship type) and PSC inspection reports. Less attention was paid to identify and predict the correlation between ship fatal deficiency and ship detention event. To address the issue, we propose a novel framework to identify crucial ship deficiency types with an optimized analytic hierarchy process (AHP) model. Then, the Naïve Bayes model is introduced to predict the ship detention probability considering weights of the identified crucial ship deficiency types. Finally, we evaluate our proposed model performance on the empirical PSC inspection dataset. The research findings can help PSC officials easily determine main ship deficiencies, and thus, less time cost is required for implementing the PSC inspection procedure. In that manner, the PSC officials can quickly make ship detention decision and thus enhance maritime traffic safety.

ABSTRACTThe inspection of foreign ships in national ports is a critical measure in port state control (PSC), preventing substandard ships from entering national ports. Multifarious inspection items, limited inspection time and inspector manpower are challenging PSC inspection. This research applies data mining to analyze historical PSC inspection records in Taiwan’s major ports to extract potential valuable information for PSC onboard inspections. Using the Apriori Algorithm, the analysis identifies many useful association rules among PSC deficiencies in terms of specific ship characteristics, such as ship types, societies, and flags. The general rules identified show that the items ‘Water/Weathertight conditions’ and ‘Fire safety’ are significantly related. Besides, in the analysis of the various ship types, several different rules are found. After comparing the analysis of ship types and ship societies, it can be observed that the association rules for specific ship types, such as oil tankers, have a better effect than those for individual ship societies do. These identified rules can not only help inspectors effectively spot the associated deficiencies, but also improve the efficiency of PSC inspection. The ports other than Taiwan’s ports can apply a similar analysis method to identify corresponding association rules suitable for their own inspections.

Port state control (PSC) inspection is the most important mechanism to ensure world marine safe. Recently, some SVM-based risk assessment systems have been presented in the world. They estimate the risk of each candidate ship based on its generic factors and history inspection factors to select high-risk one before conducting on-board PSC inspection. However, how to improve the performance of the PSC inspection under the situation of noisy data when applying SVM is still a challenging problem. In this paper, we propose a new approach for PSC inspection, which uses a novel support vector machine and k-nearest neighbor (KNN-SVM) to remove noisy training examples and Bag of Words (BW) to extract some new target factors for the PSC inspection database. The experimental results show that the generalization performance and the accuracy of risk assessment are improved significantly compared to that of the traditional SVM classifier, and adapt to engineering applications.

The paper evaluates effectiveness of fire drills for emergency and responding to PSC inspections on board. A brief background about the PSC inspection on fire drills on board is introduced in the beginning. Then the significance of effectiveness evaluation on fire drills is presented. Next, legal ground is discussed on International Conventions, including regulation of related regional group, national maritime laws and rules and Concentrated Inspection Campaign (CIC). Furthermore, PSC New Inspection Regime (NIR) for Paris MOU and Tokyo MOU are also discussed, and many deficiencies related to fire safety measures found in the PSC inspection are statistically analyzed. More importantly, the paper introduces System Engineering Theory, presents the principle and method of effectiveness evaluation, focuses on the preparation, performance and rehabilitation of fire drill and develops the Criterion of Effectiveness Evaluation. Finally, some suggestions are raised to carry out effectiveness evaluation for emergency and responding to the PSC inspection.

A knowledge graph-based inspection items recommendation method for port state control inspection of LNG carriers

The Port State Control (PSC) inspection of liquefied natural gas (LNG) carriers is crucial in maritime transportation. PSC inspection requires rapid and accurate identification of defects with limited resources, necessitating professional knowledge and efficient technical methods. Knowledge distillation, as a model lightweighting approach in the field of artificial intelligence, offers the possibility of enhancing the responsiveness of LNG carrier PSC inspections. In this study, a knowledge distillation method is introduced, namely, the multilayer dynamic multi-teacher weighted knowledge distillation (MDMD) model. This model fuses multilayer soft labels from multi-teacher models by extracting intermediate feature soft labels and minimizing intermediate feature knowledge fusion. It also employs a comprehensive dynamic weight allocation scheme that combines global loss weight allocation with label weight allocation based on the inner product, enabling dynamic weight allocation across multiple teachers. The experimental results show that the MDMD model achieves a 90.6% accuracy rate in named entity recognition, which is 6.3% greater than that of the direct training method. In addition, under the same experimental conditions, the proposed model achieves a prediction speed that is approximately 64% faster than that of traditional models while reducing the number of model parameters by approximately 55%. To efficiently assist in PSC inspections, an LNG carrier PSC inspection knowledge graph is constructed on the basis of the recognition results to quickly and effectively support knowledge queries and assist PSC personnel in making decisions at inspection sites.

Port state control (PSC) inspection is the most important mechanism to ensure world marine safe. This paper presents a risk assessment system, which estimates the risk of each candidate ship based on its generic factors and history inspection factors to select high-risk one before conducting on-board PSC inspection. The target factors adopted in Paris MOU PSC inspection and Tokyo MOU PSC inspection are considered in this system as well as the new factors discovered in the PSC inspection database. A risk assessment system based on support vector machine (SVM) is developed to classify candidate ships to high risk or low risk, respectively, based on the target factors. Experiment results show that the proposed system enhances the risk assessment accuracy effectively.

Port state control (PSC) inspection contributes a lot to improving maritime safety and protecting the marine environment. After selecting the ships coming to a port for inspection, one critical challenge faced by the PSC authorities is deciding what deficiency items should be inspected and what the inspection sequence of these items is. To address this problem, two innovative and high-efficient PSC inspection schemes describing specific PSC inspection items and sequence are proposed for the inspectors’ reference when time and resources are limited, especially when there are difficulties in estimating the possible deficiencies in advance. Both schemes take the occurrence probability, inspection cost, and ignoring loss of each deficiency item into account. More specifically, the first inspection scheme is based on the occurrence probabilities of the deficiency items in the whole data set, while the second scheme further considers the correlations among the deficiency items extracted by association rules. The results of numerical experiments show that the efficiency of the two proposed inspection schemes is 1.5 times higher than that of the currently used inspection scheme. In addition, the second inspection scheme performs better than the first inspection scheme, especially with inspecting ships with no less than five deficiency items and limited inspection resources.

Port state control (PSC) is an international regime to inspect the foreign ships coming to the port state in order to ensure that they are compliant with various international conventions. In this study, we conduct a review of the literature related to the PSC inspection in four areas: factors influencing the PSC inspection results, inspected ship selection scheme, effect of PSC inspection, and suggestions to improve PSC inspection. We found that both ship factors and non-ship factors would influence the PSC inspection outcomes, and the PSC inspection could improve the safety level of maritime industry and protect marine environment. Meanwhile, there is still room to improve the PSC inspection, including adopting more efficient methods to select high-risk ships, constructing combined databases and harmonization the PSC inspection authorities. It is expected that more research in improving and summarizing the PSC inspection will emerge in the coming years. We believe that this review can shed light on the development of PSC-related studies.

Port State Control (PSC) inspections conducted under the Paris Memorandum of Understanding (MoU) agreement have become a crucial tool for maritime administrations in European Union countries to ensure compliance with international maritime safety standards by ships entering their ports. This paper analyses all PSC inspections conducted in 10 major European ports belonging to the Paris MoU between 2012 and 2019. For its study, a multivariate HJ-Biplot statistical analysis is carried out, which facilitates the interpretation and understanding of the underlying relationships in a multivariate data set by representing a synthesis of the data on a factorial plane, with an interpretation that is very intuitive and accessible for readers from various fields. Applying this method with ship characteristics as explanatory variables, several classifications were derived. These classifications align with the annual performance lists published by the Paris MoU and the International Association of Classification Societies list, suggesting that this method could serve as a reliable classification approach. It provides maritime authorities with an additional indicator of a ship’s risk profile, aiding in the prioritising of inspections. The method also effectively categorises ports and types of ships used for cargo transport, offering insights into the specific maritime traffic each port experiences. Furthermore, this study identifies characteristics associated with substandard ships, which is a primary objective of PSC inspections. Beyond revealing these traits, this research underscores the existence of several readily applicable techniques to enhance maritime safety and reduce the risk of ocean pollution.

The factors to consider in selecting which vessels to board for port state control (PSC) inspections are crucial. This paper tries to identify these factors using 4080 reported PSC inspections from the Swedish Maritime Administration for the period 1996–2001. It relies on count data models and compares results from the Poisson, negative binomial, random effect and random parameters models. The results suggest that three factors are the main determinants of the number of reported deficiencies: the age at inspection, the flag of registry and the type of ships. Conversely, the year when the inspection occurs does not seem to be a significant factor. Estimations also stress that the relationship between the age at inspection and the number of deficiencies detected is not similar for different vessel types. For instance, the number of deficiencies detected decreases for chemical carriers and Ro–Ro passenger vessels older than 25 and 22 years old, while for instance the effect is rather small for tanker and bulk carriers and only occurs when vessels are older than 35 years.

Port State Control (PSC) is a critical inspection mechanism used to regulate and remove substandard foreign ships in national ports, with the aim of ensuring compliance with safety and pollution regulations to prevent threats to the environment. With the heavy and concentrated traffic volumes at ports, executing efficient and effective PSC inspections has become increasingly challenging. This study investigates the risk factors of ship detention and identifies the most critical factor for detention to strengthen maritime safety and environmental protection towards cleaner environment. Using six years dataset with a total inspection of 178,153 from 2010 to 2015, a Bayesian network model was developed to analyze the influencing factors of inspection that lead to detention viz. The flag State, ship type, recognized organization, inspection authority and ship age. The results indicate that the flag State has the greatest influence, followed by ship type, recognized organization, inspection authority and ship age in descending order of importance. These findings guide PSC officers and ship owners in identifying critical areas to enhance maritime safety, promote environmental sustainability and achieve a cleaner environment. A similar approach can be applied to PSC inspection records from other years for further analysis.