The performance assessment of safety barriers is essential to find vulnerable elements in a safety barrier system. Traditional performance assessment approaches mainly focus on using several static indicators for quantifying the performance of safety barriers. However, with the increasing complexity of the system, emerging hazards are highly uncertain, making it challenging for the static indicators to assess the performance of safety barriers. This paper proposes a resilience−based performance assessment method for safety barriers to overcome this problem. Safety barriers are classified according to their functions first. The dynamic Bayesian network (DBN) is then introduced to calculate the availability function under normal and disruption conditions. The ratio of the system's availability, when affected by the disruption, to the initial availability, is used to determine the absorption capacity of the system. The ratio of the quantity of availability recovery to the total quantity of system represents the adaptation and restoration capacity of the system. The system's resilience is represented by the sum of absorption, adaptation, and restoration capacities. The wax oil hydrogenation process is used to demonstrate the applicability of the proposed methodology.@en

To maintain continuous production, chemical plant operators may ignore faults or handle faults online rather than shutting down process systems. However, interaction and interdependence links between components in a digitalized process system are substantial. Thus, faults will be propagated to downstream nodes, potentially leading to risk accumulation and major accidents. However, limited attention has been paid to this type of risk. To model the risk accumulation process, a dynamic risk assessment method is proposed by integrating the system-theoretic accident model and process approach (STAMP) and the cascading failure propagation model (CFPM). Firstly, STAMP is used to model and analyze the system safety of a process system. Two CFPMs are then proposed to measure risk accumulation under two different engineering situations. The proposed method is applied to the Chevron Richmond refinery crude unit and its associated upstream process. The results show that the proposed approach can effectively quantify the process of risk accumulation. This method can generate a real-time dynamic risk profile to support auxiliary decision-making.@en

Reliability-based and risk-based methods for directing maintenance activities play a critical role in ensuring system safety and reducing unnecessary downtime. Those methods focus on preventive maintenance to avoid component failures and are applicable before unexpected disruptions occur. However, when disruptions are unavoidable, more attention should be paid to systems’ recovery from unwanted changes. As a remedy of preventive maintenance, improving system restoration capacity of resilience through optimizing the system's maintenance asset and operational cost is an efficient way to help system restore from disruption conditions within an optimal cost. In this paper, a resilience-based approach is proposed to optimize maintenance asset and operational cost. A novel resilience metric is developed and utilized to quantify system resilience under various restoration capacities. The minimal acceptable resilience level (MARL) and maximal acceptable restoration time (MART) are proposed to determine the optimal maintenance cost. The proposed approach is applied to the Chevron Richmond refinery crude unit and its upstream process. The results show that it can help practitioners identify the optimal cost to ensure a system is resilient to respond to uncertain disruptions and provide a dynamic resilience profile to support decision-making.@en

Dynamic collision avoidance between multiple vessels is a task full of challenges for unmanned surface vehicle (USV) movement, which has high requirements on real-time performance and safety. The difficulty of multi-obstacle collision avoidance is that it is hard to formulate the optimal obstacle avoidance strategy when encountering more than one obstacle threat at the same time; a good strategy to avoid one obstacle sometimes leads to threats from other obstacles. This paper presents a dynamic collision avoidance algorithm for USVs based on rolling obstacle classification and fuzzy rules. Firstly, potential collision probabilities between a USV and obstacles are calculated based on the time to the closest point of approach (TCPA). All obstacles are given different priorities based on potential collision probability, and the most urgent and secondary urgent ones will then be dynamically determined. Based on the velocity obstacle algorithm, four possible actions are defined to determine the basic domain in the collision avoidance strategy. After that, the Safety of Avoidance Strategy and Feasibility of Strategy Adjustment are calculated to determine the additional domain based on fuzzy rules. Fuzzy rules are used here to comprehensively consider the situation composed of multiple motion obstacles and the USV. Within the limited range of the basic domain and the additional domain, the optimal collision avoidance parameters of the USV can be calculated by the particle swarm optimization (PSO) algorithm. The PSO algorithm utilizes both the characteristic of pursuance for the population optimal and the characteristic of exploration for the individual optimal to avoid falling into the local optimal solution. Finally, numerical simulations are performed to certify the validity of the proposed method in complex traffic scenarios. The results illustrated that the proposed method could provide efficient collision avoidance actions.@en

Dynamic collision avoidance between multiple vessels is a task full of challenges for unmanned surface vehicle (USV) movement, which has high requirements on real-time performance and safety. The difficulty of multi-obstacle collision avoidance is that it is hard to formulate the optimal obstacle avoidance strategy when encountering more than one obstacle threat at the same time; a good strategy to avoid one obstacle sometimes leads to threats from other obstacles. This paper presents a dynamic collision avoidance algorithm for USVs based on rolling obstacle classification and fuzzy rules. Firstly, potential collision probabilities between a USV and obstacles are calculated based on the time to the closest point of approach (TCPA). All obstacles are given different priorities based on potential collision probability, and the most urgent and secondary urgent ones will then be dynamically determined. Based on the velocity obstacle algorithm, four possible actions are defined to determine the basic domain in the collision avoidance strategy. After that, the Safety of Avoidance Strategy and Feasibility of Strategy Adjustment are calculated to determine the additional domain based on fuzzy rules. Fuzzy rules are used here to comprehensively consider the situation composed of multiple motion obstacles and the USV. Within the limited range of the basic domain and the additional domain, the optimal collision avoidance parameters of the USV can be calculated by the particle swarm optimization (PSO) algorithm. The PSO algorithm utilizes both the characteristic of pursuance for the population optimal and the characteristic of exploration for the individual optimal to avoid falling into the local optimal solution. Finally, numerical simulations are performed to certify the validity of the proposed method in complex traffic scenarios. The results illustrated that the proposed method could provide efficient collision avoidance actions.@en

Unmanned Surface Vessels (USV) may interfere with ships in the same lane during navigation and may be affected by ships that actively collide or intercept the USV. Evidently, the latter poses a greater threat than the former. This paper proposes the concept of active and inactive interference. It is necessary for the USV to accurately classify the vessels’ intentions to conduct a suitable collision avoidance strategy. However, the existing research on collision avoidance of USV focuses on avoiding moving obstacles and rarely considers the interference intention of dynamic obstacles for USV when choosing collision avoidance strategies. This paper proposes an algorithm to recognize the interference intention of ships by combining visual classification with a Support Vector Machine (SVM). First, a Convolutional Neural Network (CNN) is proposed to distinguish the merchant ship from a high-performance ship that actively interferes with the USV with high probability. A high-dimensional feature dataset was designed to illustrate the navigation characteristics of an obstacle, and Principal Component Analysis (PCA) was then employed to reduce the dimensionality of the dataset for obstacle classification. A modified SVM is presented to classify obstacles into active and inactive interference intention categories. Moreover, an escape algorithm based on an improved artificial potential field method is proposed for vessels with active interference. The simulation and experimental results clearly show that the USV can successfully identify the interference intention of obstacles and adopt a specific collision avoidance strategy for obstacles with active interference intentions. Using this algorithm, the USV can safely avoid obstacles with different interference intentions.@en

Unmanned Surface Vessels (USV) may interfere with ships in the same lane during navigation and may be affected by ships that actively collide or intercept the USV. Evidently, the latter poses a greater threat than the former. This paper proposes the concept of active and inactive interference. It is necessary for the USV to accurately classify the vessels’ intentions to conduct a suitable collision avoidance strategy. However, the existing research on collision avoidance of USV focuses on avoiding moving obstacles and rarely considers the interference intention of dynamic obstacles for USV when choosing collision avoidance strategies. This paper proposes an algorithm to recognize the interference intention of ships by combining visual classification with a Support Vector Machine (SVM). First, a Convolutional Neural Network (CNN) is proposed to distinguish the merchant ship from a high-performance ship that actively interferes with the USV with high probability. A high-dimensional feature dataset was designed to illustrate the navigation characteristics of an obstacle, and Principal Component Analysis (PCA) was then employed to reduce the dimensionality of the dataset for obstacle classification. A modified SVM is presented to classify obstacles into active and inactive interference intention categories. Moreover, an escape algorithm based on an improved artificial potential field method is proposed for vessels with active interference. The simulation and experimental results clearly show that the USV can successfully identify the interference intention of obstacles and adopt a specific collision avoidance strategy for obstacles with active interference intentions. Using this algorithm, the USV can safely avoid obstacles with different interference intentions.@en