Incorporation of deficiency data into the analysis of the dependency and interdependency among the risk factors influencing port state control inspection

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.

Using Bayesian network-based TOPSIS to aid dynamic port state control detention risk control decision

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.

During the past decades the economic contribution of the maritime transportation to the shipping industry has been increased rapidly. This is a positive trend, but it entails many threats (e.g. human life loose, environmental threats, sea pollution etc.) for the shipping industry. Therefore, a wide range of maritime measures have been created with the aim to eliminate the safety threats. Among these measures are included the Port State Control (PSC) inspections. Since the signature of the Hague Memorandum in 1978, the Port State Control (PSC) constitutes a strategy, that is implemented worldwide, with the aim to foreflight the substandard ships. Considering the importance of the Port State Control (PSC) inspections, this is an in-depth literature review, which aims to identify the risk-based approaches that are proposed in the international literature for the improvement of the Port State Control (PSC) performance. In addition, this research aims to identify the factors that influence the (PSC) inspections based on the international literature. The sample of this literature review are 47 academic articles which have been published in high quality journals. A total of 28 papers identified which proposed risk-based approaches for the improvement of the Port State Control (PSC) inspections’ performance. Additionally, a total of 19 papers were identified which examined the factors that influence the Port State Control (PSC) performance. The research results, shown that most of the selected papers focus on the data driven Bayesian Networks (BN). Fewer studies have been published on other approaches such as on the Support Vector Machine (SVM) method, the Bayesian Networks (BN) and game mode and the K-nearest neighbor. As concerning the factors, which influence the Port State Control (PSC) inspections, the research results show that 17 out 19 researches, which examined in this Dissertation, focused on the following factors: the ship age, the ship flag, the inspection history and the classification society.

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.

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

A data-driven Bayesian model for evaluating the duration of detention of ships in 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.

Ship detention decision plays a key role in port state control (PSC) inspection process, which is compactly related to navigation safety and maritime environmental protection. Many focuses were paid to exploit intrinsic relationship among ship attributes (ship age, type, etc.), detention events and typical ship deficiencies. It is noted that many ship detention prediction frameworks were implemented considering single type of factors regardless of internal relationship between ship crucial deficiencies and ship attributes. To address the issue, we proposed a support vector machine (SVM) based framework to exploit crucial ship deficiencies, and thus forecast the probability of ship detention event. Firstly, we design a feature selection scheme to determine ship fatal deficiency types by exploring historical PSC inspection data. Secondly, we predict the ship detention event via conventional support vector machine (SVM) with support of ship feature selection outputs. Thirdly, we verify the proposed framework performance by predicting ship detention event from historical PSC data, which is quantified with the indicators of accuracy () and area under ROC curve (). The research findings help PSC officials easily identify fatal ship deficiencies, and thus make more reasonable ship detention decision in real-world PSC activity.

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.

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.

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.

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

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

Pollution prevention of vessels in the greater bay area: A practical contribution of port state control inspection system towards carbon neutralisation using a tree augmented naive bayes approach