Port State Control Inspections Research Articles

Evaluation of Port State Control Inspections in Türkiye

With technological developments, transportation has become easier and maritime trade between countries has reached very important dimensions, but this situation has led to the emergence of various risks in terms of both environment and maritime safety due to accidents or other reasons. The most important factor threatening the environment and maritime safety is the continued operation of substandard ships. There are various inspection mechanisms to prevent the operation of these ships and one of these mechanisms is port state control inspections. In this context, in this study, Port State Control inspections carried out in Türkiye between 2018 and 2022 were statistically examined and frequency analysis was performed and Pearson Chi-Square Independence test was utilized to analyse the hypotheses and the degree of relationship between the hypotheses was analysed by Phi- Cramer’s V test. The study contributes to the literature in terms of statistical analysis of PSC Inspections in Turkish Ports within the scope of Black Sea Memorandum and Mediterranean Memorandum.

Port State Control Inspections under the Paris Memorandum of Understanding and Their Contribution to Maritime Safety: Additional Risk Classifications and Indicators Using Multivariate Techniques

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.

Prescriptive analytics models for vessel inspection planning in maritime transportation

Port state control (PSC) inspections are crucial for maritime safety and pollution reduction. The inspection process involves identifying high-risk vessels, allocating surveyors, and conducting onboard checks. This study aims to optimize the selection and assignment process through a two-stage framework, balancing the benefits of identifying deficiencies against the costs of inspection delays. Initially, we employ a predict-then-optimize approach, predicting the number of vessel deficiencies using a k-nearest neighbor (kNN) model, which informs the inspection decisions. However, due to the nonlinear nature of the optimization in relation to predicted values, we also explore an estimate-then-optimize framework that estimates distributions of potential deficiencies. We enhance two prescriptive analytics models and introduce an advanced global model with a pre-processing algorithm for better distribution estimation. A case study using data from the Hong Kong port demonstrates that the estimate-then-optimize models surpass the predict-then-optimize approach, offering solutions closer to the optimal policy. Furthermore, our improved model outperforms existing methods, proving more effective in practical applications.

Identification of Data Analysis Methods and Focus Trends in Port State Control Inspections: A Comprehensive Literature Review

Identification of Data Analysis Methods and Focus Trends in Port State Control Inspections: A Comprehensive Literature Review

Sailing Towards Sustainability: How Seafarers Embrace New Work Cultures for Energy Efficient Ship Operations in Maritime Industry

It is imperative to take proactive measures in light of the escalating concerns about greenhouse gas (GHG) emissions of international shipping. The GHG study conducted by the International Maritime Organization (IMO) indicates that the sector accounted for 2.2% and 2.89 % of emissions in 2012 and 2018, respectively. As a result, IMO has endorsed strategies to reduce ship-induced emissions to 50% by 2050 and net zero emissions by 2100. For reducing carbon emissions, regulations such as the Energy Efficiency Design Index (EEDI) and Ship Energy Efficiency Management Plan (SEEMP) were implemented a decade ago. We collected data from 104 seafarers in 24 countries using purposive sampling, exploring how the energy-efficient operation of ships transforms their work cultures onboard ships. The majority of seafarers in our study agreed that energy-efficient ship operation has changed seafarers' working cultures over the last decade. Seafarers have adopted a variety of energy efficiency (EE) practices, including meticulous record-keeping of fuel consumption and adherence to Planned Maintenance Systems. As well as preparing for Port State Control (PSC) inspections, they follow energy-saving measures prescribed by the SEEMP. In addition to engine maintenance, energy-saving practices, accurate reporting and recording of EE-related data, and efficient power usage, they provide just-in-time arrival, crew training, energy-efficient lighting, environmental awareness, and slow steaming to minimize fuel consumption. The majority of seafarers implemented SEEMP successfully and met the company's objectives, but they did not receive any form of recognition or financial incentives. Although SEEMP can help shipping companies gain financial benefits and preserve the environment, seafarers do not receive tangible rewards or appreciation for implementing it. Only one-fourth of seafarers were rewarded with incentives, bonuses, formal recognition, or trophies. This study holds significant implications for policymakers, offering insight into seafarers' adaptation to energy-efficient ship operations and advocating for integrating Industry 4.0 technologies for EE regulations compliance by reducing seafarers' workloads and improving data accuracy.

Classification and regression in prescriptive analytics: Development of hybrid models and an example of ship inspection by port state control

In prescriptive analytics, unknown quantities are involved in practical decision-making problems, and these unknown quantities need to be predicted using auxiliary data. A classic approach is to develop machine learning (ML) models to generate point estimates, which are then input to the decision making problem in a deterministic manner to prescribe the optimal decision. However, the limited quantity and inevitable errors in the auxiliary data lead to inaccurate predictions and thus sub-optimal decisions. One viable approach to addressing the above issue is to consider the uncertainties in data by inputting the conditional distributions of the unknown quantities on the auxiliary data to the optimization problem on hand, and the distributions are predicted by regression ML models. Meanwhile, it is observed that the quantitative target in some problems are discrete, and these properties are analogous to categorical targets in classification problems. Considering the fact that describing and estimating the distribution of categorical variables are much easier than quantitative variables, this study innovatively develops random forest (RF) models with regression and classification features to generate the distribution of quantitative targets that are discrete. Especially, nodes splitting criteria in the RF models is in a regression manner, while the outputs of individual decision trees and the whole RF model is in a classification manner. Numerical experiments using real port state control (PSC) inspection records and settings at the Hong Kong port are conducted to validate and compare the above prescriptive analytics approaches. The superiority of applying the newly proposed RF model into the development of prescriptive analytics approaches is also demonstrated.

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

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.

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

Port State Control (PSC) inspections are essential for port authorities to improve vessel quality and ensure maritime safety worldwide. However, the increasing frequency and duration of ship detentions indicate serious deficiencies of visiting vessels still largely exist, highlighting the urgent need for scientific solutions. This research aims to improve the efficiency of inspection policy and reduce the duration of detention by developing a data-driven Bayesian Network (BN) model using an improved machine-learning (ML) based methodology. New risk variables influencing the duration of ship detention, especially deficiency types, are identified based on the established database containing detention records within the jurisdiction of the Paris MoU from January 2015 to March 2022. Thorough analysis using the developed model allows the identification of deficiency types with a significant impact on the duration of detention, the discovery of interdependencies between these types and the clarification of the major and abnormal deficiency types in different port states. Policy implications and managerial recommendations for port authorities are presented. These include developing clear instructions on types of deficiencies that significantly impact detention time and proposing a selection strategy for vessels in different countries based on their specific circumstances. The proposed model utilizes big data analytics to support the development of inspection policies that are rational and effective. This research will provide good reference for effectively reducing the duration of ship detention, providing policy recommendations, improving ship standards, and ensuring maritime safety.

A machine learning-based Bayesian model for predicting the duration of ship detention in PSC inspection

Port state control (PSC) inspections are deemed as an effective way to detect substandard vessels and ensure maritime safety around the world. Despite great efforts on PSC in recent years, one challenge that still exists in today’s PSC inspection practice is that there lack relevant scheme or academic research focusing on the duration of vessel detention, which is of great importance to the inspection system. To assist port authorities in estimating detention duration and minimizing the existence of substandard vessels, this paper aims to develop a novel data-driven machine learning based model based on the inspection records collected within the jurisdiction of Paris MoU from January 2015 to March 2022. The model is trained via the incorporation of an Improved Tree Augmented Naïve (ITAN) learning approach and a maximum a posteriori probability (MAP) of Expectation Maximization (EM) approach for the first time within the context of PSC research, which could be used as a prediction tool to determine rational durations for detained vessels. Thorough analysis of the proposed model enables the identification of risk variables and deficiency types having significant effects leading to long duration of detention. Further, the research findings could reveal managerial suggestions and insights for port authorities to reduce the occurrence of substandard vessels via the inspection system, i.e., identify specific risk level of vessels and ensure a more-efficient vessel selection process; design specific instructions and rules to regulate risk variables and deficiencies with huge effect on a long duration of detention. This research will provide insightful reference for effectively improving vessel quality, inspection efficiency, and maritime safety.

Analiza nedostataka prema pariškom memorandumu o nadzoru država Luka

Substandard ships pose a significant threat to human life and the environment. Therefore, detecting deficiencies and removing such vessels from service is necessary, which is one of the port state control (PSC) inspection tasks. In addition, the role of PSC inspection is to improve safety at sea and in ports. To be up to the task, the States jointly created several memoranda to fight against substandard ships. The most frequent deficiencies detected on ships during Paris Memorandum of Understanding (MoU) PSC inspections are analyzed in this paper. The analysis was made according to factors such as type of vessel, flag and the category to which deficiency belongs. Results revealed that most frequent deficiencies detected on ships, regardless of type or flag, come from International Safety Management (ISM) and fire protection. Furthermore, it is found that among the ten most frequent deficiencies, three are from the navigation safety category. Therefore, corrective actions to improve safety at sea are needed and presented.

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

Vessel emissions are one of the major emissions sources affecting carbon neutralisation achievement in the Greater Bay Area (GBA). The vessel pollution prevention ability is effective in preventing vessels from discharging mass emissions. Thus, it is of great value to figure out the vessel's performance in pollution prevention in this area. This study develops a data-driven Bayesian network model focusing on vessel performance in pollution prevention in port state control (PSC) inspections in the GBA. The Tree Augmented Naïve and Expectation Maximisation approaches are applied to learn structures and parameters based on the inspection records obtained from January 2015 to September 2022. An analysis can identify key variables with significant effects on vessel performance in pollution prevention and clarify the positive influence and contributions of PSC inspections toward carbon neutralisation achievement. Practical implications are provided to foster port authorities and local governments in the GBA to minimize the vessels with poor pollution prevention performance sailing in this area. The study also identifies the characteristics of vessels with poor performance and proposes valuable suggestions to restrict the inattentive actions of ship owners and stimulate ship owners to put more effort into the improvement of pollution prevention ability. Further, the study reveals the significance of executing efficient and reliable PSC inspections in promoting carbon neutralisation progress.

A Decision-Focused Learning Framework for Vessel Selection Problem

Maritime transportation safety is pivotal in international trade, with port state control (PSC) inspections being crucial to vessel safety. However, port authorities need to identify substandard vessels effectively because of resource constraints and high costs. Therefore, we propose robust predictive models and optimization strategies for vessel selection, using the random forest (RF) algorithm. We first use a traditional RF model serving as a benchmark, denoted as model M0. Then, we construct model M1 by refining the RF algorithm with a batch-processing method, thereby providing a better measure of the relative relationship between the predicted deficiency counts within a batch of ships. Then, we propose model M2, incorporating a decision-focused learning (DFL) framework into the tree construction process, enhancing the decision performance of the algorithm. In addition, we propose a variant model of M2, denoted as M2-0, considering the worst-case scenario when designing the decision loss function. By conducting experiments with data from the port of Hong Kong, we demonstrate that models M1 and M2 offer superior decision-making performance compared to model M0, and model M2 outperforms model M2-0 in both decision performance and stability. We further verify the robustness of these models by testing them under various instance scales. Overall, our study enhances the PSC inspection efficiency, ultimately bolstering maritime transportation safety.

Application of Multivariate Statistical Techniques as an Indicator of Variability of the Effects of COVID-19 on the Paris Memorandum of Understanding on Port State Control

The first pandemic of the 21st Century was declared at the beginning of the year 2020 due to the spread of the COVID-19 virus. Its effects devastated the world economy and greatly affected maritime transport, one of the precursors of globalisation. This paper studies the effects of the pandemic on this type of transport, using data from 23,803 Paris Memorandum of Understanding Port State Control (PSC) inspections conducted in the top 10 major European ports. Comparisons have been made between Pre-COVID (2013–2019) and COVID (2020–2021) years, by way of multivariate methodologies: CO-X-STATIS, X-STATIS, and correspondence tables. The results were striking and indicate a clear change in the conduct of inspections during the COVID period, both quantitatively and qualitatively, showing a drastic reduction in the number of inspections and a change in type, with exhaustive inspections assuming a secondary role. Another notable result came from the use of the same methodology to study the different countries of registry and their evolution within PSC inspections during the Pre-COVID and COVID periods, where different behaviours were identified based on a ship’s flag. These results can help us to determine important supervisory objectives for each country’s maritime administration and their inspectors, to indicate weaknesses in the inspection routines caused by the pandemic, and to attempt corrections to improve maritime safety.

Knowledge graph for maritime pollution regulations based on deep learning methods

Pollution prevention in the shipping industry has attracted the attention of government authorities globally. A meticulous Port State Control (PSC) inspection is an effective way to prevent pollution from ships. PSC inspections involve a variety of regulations, but currently lack effective methods to utilize these regulations to support making swift and effective decisions for maritime pollution prevention management at the frontlines. The present study developed a deep learning knowledge extraction model, the Bidirectional Encoder Representation from Transformers -Multiple Convolutional Neural Network model (BERT-MCNN), to extract needed information for frontline management from the maritime pollution-prevention-related laws, regulations, rules, and conventions in China. The results revealed that the maritime pollution prevention regulation knowledge graph can be formed based on the deep learning methods, with a precision of 92.4% and 92.7% for multi-relation extraction and named entity recognition, respectively. The knowledge graph formed by the extraction results is applied based on the graph database Neo4j, which can effectively provide knowledge queries and assist PSC officers in decision-making at the inspection scene. The method proposed in the present study can be applied to analyze the regulations for pollution prevention in China and support pollution prevention inspection.

Optimization of the Concentrated Inspection Campaign Model to Strengthen Port State Control

The concentrated inspection campaign (CIC) is a derivative of the port state control (PSC) supplement, which is a fixed single series of deficiency inspections performed for three consecutive months at the end of each year. This study used grey relational analysis (GRA) and the technique for order preference by similarity to ideal solution (TOPSIS) to analyze the data of 71,376 deficiency records with 496 deficiency codes and 21 ship types in the Paris MoU for the last three years so as to improve the existing focus inspection pattern, which uses only the most accumulated number of deficiency series of the previous year’s PSC inspection. It also combines the three-sigma rule to find the inspection items most likely to be found as deficient by the port state control officer (PFSO) of the member country and creates a new rolling CIC scheme with deficiency inspection data for the last three years, which can filter out the significant deficiency codes with high numbers of deficiency inspections and use them as a modified CIC. It can not only solve the existing CIC’s lack of thoroughness, but also avoid the problems of missing important inspection codes, missing substandard ships, and failing to meet the inspection consensus. The new CIC inspection mechanism created in this paper can indeed identify potential substandard ships more effectively and fill the inspection gap of the existing port state control.

Safer Ships and Cleaner Seas: Reducing Vessel Risks through Targeted Inspections and Recognized Organization Oversight

Abstract This article explores the lessons that might be learned from port State con-trol (PSC) inspections for flag States, examining the scholarly literature on vessel risk targeting. It begins by identifying the major classification socie-ties (and recognized organizations or ROs) before discussing the obligations and responsibilities of governments in achieving a global ‘safer ships and cleaner seas’ objective. It then presents a framework for thinking about the relationship between owner/operators, ship classification societies, flag State authorities and PSC parties in order to improve safety, discussing where gaps remain in the execution of the objective. The author draws con-clusions about where the future focus by flag States might achieve addi-tional improvements in their oversight of classification societies when they act as ROs, carrying out ship survey and certification functions on behalf of flag States. Identified challenges include improving the quality of data used in vessel risk targeting, expanding the transparency of that data for RO oversight, and focusing efforts on those flag States and ROs who do not meet their obligations.

Analyzing the influencing factors of Port State Control for a cleaner environment via Bayesian network model

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.

A smart predict-then-optimize method for targeted and cost-effective maritime transportation

In maritime transportation, port state control (PSC) is the last line of defense against substandard ships. During a PSC inspection, PSC officers (PSCOs) identify ship deficiencies that lead to a ship's detention, which can cause severe economic and reputational losses to the ship operator. Therefore, this study innovatively uses PSC inspection data to design ship maintenance plans for ship operators to minimize overall operational costs. We identify three types of operational costs associated with each deficiency code: inspection cost, repair cost, and risk cost in the ship operators’ decision-making process. The risk cost of a deficiency code is strongly related to the detention contribution of the deficiency items under a deficiency code, as indicated by the feature importance of that code in the random forest (RF) model used to predict detention outcomes. To design ship maintenance plans, the sequential predict-then-optimize (PO) method typically solves the optimization problem using input parameters, including the predicted probabilities of having deficiency items under each code and the three types of operational costs. However, the loss function in this two-stage framework does not consider the effect of predictions on the downstream decisions. Hence, we use a smart predict-then-optimize (SPO) method using an ensemble of SPO trees (SPOTs). Each SPOT uses an SPO loss function that measures the sub-optimality of the decisions derived from the predicted parameters. By exploiting the structural properties of the optimization problem analyzed in this study, we demonstrate that training an SPOT for this problem can be simplified tremendously by using the relative class frequency of true labels within a leaf node to yield a minimum SPO loss. Computational experiments show that the SPO-based ship maintenance scheme is superior to other schemes and can reduce a ship's total operating expenses by approximately 1% over the PO-based scheme and by at least 3% over schemes that do not use machine learning methods. In the long run, SPO-based ship maintenance plans also improve the efficiency of port logistics by reducing the resources needed for formal PSC inspections and alleviating port congestion.

Differences in deficiency types causing ship detentions at the Black Sea Region during the Covid-19 pandemic and pre-pandemic

Port State Control (PSC) is a crucial process that controls the compatibility of ships with maritime standards and regulations. PSC’s duty is to provide a safe and secure maritime environment, as well as the protection of coastal regions, through inspections. After the outbreak of the Covid-19 pandemic, the PSC inspection process, and the number of inspections and detentions, has changed. This study aims to find the most influential deficiency types that cause ship detentions both before and during the pandemic with the Entropy-based Grey Relation Analysis (GRA) method and to study the hidden relationships of deficiency types by examining the frequency of co-occurrence of them with the Association Rule Mining (ARM) method, before and during the Covid-19 pandemic. Detention data was gathered from the Black Sea Memorandum of Understanding (MoU) Region covering the 1st of January 2018–31st December 2021 period. GRA analysis reveals that deficiencies related to Fire Safety and Emergency Systems are found as the most crucial deficiency types for ship detentions in both pre-pandemic and pandemic periods. ARM analysis indicates that there is a strong relationship between International Safety Management (ISM) and Certificate and Documentation (CD) during the pandemic. The findings of this study imply that changes in the PSC regime during the pandemic period led to sub-standard inspections. Based on these results and implications, this study gives recommendations to improve the efficiency of PSC inspections during the pandemic and similar conditions.

A novel FMEA approach for risk assessment of air pollution from ships

Port State Control (PSC) is the inspection mechanism that ensures commercial vessels comply with internationally binding rules and regulations. During the PSC inspections, non-compliances with international maritime conventions may result in deficiencies or even the detention of the vessel. On the other side, deficiencies identified during PSC inspections are significant for the safe, secure, clean seas and sustainable marine environment. In this line, International Maritime Organization (IMO) aims to reduce ship-related pollution within the scope of the International Convention for the Prevention of Pollution from Ships (MARPOL). Specifically, Annex-VI is the most recently ratified regulation of MARPOL covering air pollution. Although Annex-VI deficiencies pose significant risks to maritime stakeholders and the natural environment, this topic has been broadly neglected by researchers. For this reason, a detailed risk analysis for the MARPOL Annex-VI deficiencies was carried out to demonstrate a prioritization concerning the marine environment and maritime stakeholders in this study. An improved Failure Mode and Effects Analysis (FMEA) approach is employed in the computation of risk priority numbers (RPN) by taking O, S, and D inputs from two different expert groups and the Paris MoU database. According to the results, the riskiest MARPOL Annex-VI failure modes are identified as 14615-Fuel change-over procedure (RPN: 235.71), 14608-Incinerator operations and operating manual (RPN: 232.65), and 14601-Technical files and if applicable, monitoring manual (RPN: 219.86). Consequently, the presented risk analysis and findings are intended to be a reference for maritime stakeholders to prevent harmful scenarios involving ship-related air pollution.