Mining ship deficiency correlations from historical port state control (PSC) inspection data.

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.

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.

Port state control (PSC) inspection is an important measure to improve ship safety and reduce ship accident rate. To improve the effectiveness of PSC inspections, some MoUs have begun to implement new inspection regime (NIR). However, the effectiveness of NIR remains to be studied. Then this study aims to verify the effectiveness of NIR and improve shipping safety. In this study, Bayesian Network model is used to establish the relationship between NIR, ship deficiencies, detention and ship accident to explore the impact of NIR on maritime safety. The data in this study are from PSC inspection data in the Tokyo Memorandum of Understanding (MoU) and accident data in the International Maritime Organization (IMO). By analyzing the changes of the number of ship deficiency, detention rate and accident rate before and after NIR implementation, the effectiveness of NIR can be verified. The results show that the implementation of NIR does not effectively reduce the number of substandard ships, but the number of ships with serious deficiencies is significantly reduced. However, ship accident rate has not declined. Therefore, it is believed that Tokyo MoU needs to further improve the effectiveness of NIR and strengthen supervision of defective ships.

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.

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

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 paper evaluates effectiveness of fire drills for emergency and responding to Port State Control (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 Memorandum of Understanding (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

Is port state control influenced by the COVID-19? Evidence from inspection data

This article uses data generated by Port State Control (PSC) inspections of ships in national ports (Paris MoU) to assess their compliance with radio-communications safety regulations. By mainly applying binary logistic regression methods, the aim is to examine and understand the relationship between the severity of deficiencies in maritime communications and some characteristics of inspected ships. The raw data from the PSC detention database from 2005 to 2022 undergoes post-processing before being analyzed to explore patterns and coincidences with the rest of the potential risk areas. To do so, 23,725 PSC inspections were used. Several classification criteria have been proposed that can better gauge the risk related to distress communications at sea from the dataset. The results connect the probability of detention with the ship age at the inspection date, the flag of the registry, the type of ship, and the location of the port within the countries adhering to the Paris MoU. Another achievement is that the number of PSC inspections of maritime communications in a given period is a better indicator of the risk to safety than the total number of deficiencies detected in these inspections during the same period. This study also explores inspection deficiencies related to competency gaps identified in the Global Maritime Distress Safety System (GMDSS) operators, and precisely using the number of PSC inspections as a criterion of risk for safety is consistent with the recommendations of the Maritime Safety Committee Circular (2006), MSC.1/Circ.1208. Another finding from the time series is that a greater rate of decrease is identified for GMDSS equipment-related deficiencies compared to GMDSS training-related deficiencies. This alone poses a review of the refreshing courses and methods to maintain the General Operator Certificate (GOC) qualification to operate maritime radio communications facilities belonging to the (current and future) GMDSS.

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.

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.

This study analyzes the results of Port State Control (PSC) inspections carried out under the Paris Memorandum of Understanding between 2018 and 2022. Through a correspondence analysis, the most frequent deficiencies were identified according to the type of ship being inspected. The study sample included 186,255 inspections obtained from the THETIS platform. The results revealed significant heterogeneity in deficiency profiles across ship types, highlighting specific patterns associated with each category. Container ships, oil tankers and bulk carriers, for instance, exhibited distinctive deficiency profiles. The study emphasizes the necessity for a tailored approach to PSC inspections, with the objective of optimizing resources through the utilization of risk zone indicators for the inspector. The identification of specific risk indicators would not only facilitate the work of inspectors but also enable the earlier detection of potential problems and more effective intervention. The study provides a solid foundation for future research and decision-making on PSC inspections, with the aim of enhancing maritime safety and pollution prevention.

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.

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.