Waterborne transport is very important for moving freight and passengers globally. To make this transport more efficient, vessel design must adapt to changing missions, regulations and the occurrence of malfunctions. This paper presents the design of an intelligent decision-support framework to assist marine engineers and vessel operators in updating the system and control architecture of marine vessels before and during a mission. The connection between the system architecture and control design perspectives is enabled using a semantics-based technique. To this end, the multi-level vessel control system is described by a semantic database, a knowledge graph used to connect the components automatically, and quantitative service criteria. Considering the system architecture, the optimal modification is deduced using modularity and complexity criteria, originating from the field of network theory. On the control side, an intelligent automation supervisor is designed to make offline and online decisions regarding the energy deficit to execute a new mission and the active automation configuration during operation. For offline decisions, system architecture modifications are requested by the vessel designers to cover the energy deficit. During operation, switching between hardware and virtual sensors as well as switching between energy management controllers is implemented to handle the effects of sensor faults. The framework is successfully applied to a case study of a tugboat used to adapt to missions with different power requirements, while simulation results are used to indicate its application in supporting the decisions of vessel designers and human vessel operators.

This article proposes a distributed model-based methodology for the diagnosis of faults affecting multiple sensors used for condition monitoring and control of marine internal combustion engines (ICEs). To handle the complexity of the ICE, we consider it as a set of interconnected physical subsystems that constitute the physical layer. For every subsystem, the detection of sensor faults relies on the design of cyber agents, where every agent monitors one subsystem. To handle the heterogeneous dynamics of each subsystem in the fault detection decision-making process, each agent uses differential and algebraic residuals alongside adaptive bounds. For isolation purposes, a combinatorial decision logic is employed, realized in two cyber levels: the local and the global decision logic. The first aims at the recognition of all sensor fault patterns that might have affected the engine based on the local agent fault signatures and certain binary decision matrices. The latter is used to capture the propagation of sensor faults between the different monitoring agents. Simulation results are used to showcase the proposed methodology's efficiency in tackling the problem and its applicability.

This paper proposes a greedy stochastic optimization algorithm for the sensor set decomposition used in the sensor fault monitoring of marine propulsion systems, based on fault isolability criteria. These criteria are expressed mathematically in terms of the number of unique columns in the theoretical fault signature matrices (FSMs) used during the sensor fault isolation process. Due to the large scale and complexity of marine propulsion plants, the diagnostic layer follows a distributed architecture with a combinatorial logic used for fault isolation in two cyber levels; the local and global decision logic. As a result, the FSMs of both levels are formulated as an integrated optimization problem. Each solution regarding the sensor set decomposition is then used to generate the respective distributed monitoring architecture, using semantic (qualitative) knowledge for the propulsion plant. Thus, the need for an analytical model of the plant is removed. Moreover, based on the design of the distributed monitoring architecture, the respective theoretical FSMs (quantitative) are Automatically generated and used for the evaluation of the objective function. Finally, simulation results are used to illustrate the application of the greedy stochastic optimization algorithm and its efficiency.

To integrate and assist the system and automation design phases of complex marine vessels, this paper proposes a two-level semantically enhanced scheme. At the design level, the system components are described and automatically connected by a developed graph-making tool using semantic 'knowledge'. Decisions regarding the system selection are made based on certain Quality of Service Criteria (QoS) and enforced in the final semantic database using a dedicated cognitive agent. The automation level leverages the selected systems semantic information with that of the associated automation components and reuses the graph-making tool to update the connection graph. The resulting knowledge-graph is then used to 'reason' for the creation of feasible closed-loop control architectures while a cognitive agent determines which closed-loop architecture to use based on various QoS criteria. The chosen closed-loop architecture can then change in an online manner during the vessel operation in case that system reconfiguration is required either due to malfunctioning components, or aiming to satisfy mission's goals. The applicability and efficiency of the proposed method are shown using a case study for marine propulsion.

Nowadays, marine vessels constitute safety-critical assets facilitating the transport of millions of passengers and tons of cargo worldwide. As such, they require a large number of heterogeneous sensors dispersed in the various on-board machinery for operational and condition monitoring of their vital systems, such as the propulsion system. Despite the vast availability of data from on-board sensors, there is hardly any collaboration between the spatially distributed sensor devices to boost vessel performance. Up to this day, physical redundancy has been mostly discussed in maritime literature and has also been required by certain ship system design regulations. The use of virtual sensors (software-based) has not been properly investigated yet for maritime applications, despite their successful application in other fields like aircraft control and process control. This paper proposes a novel switching mechanism to alternate between physical and virtual sensors used in the primary propulsion control layer of marine vessels aiming to compensate for the effects of sensor faults. The switching mechanism focuses on ensuring the safe performance of the propulsion grid after the sensor faults occur. The software sensors are constructed using mathematical models describing the nonlinear dynamics of the propulsion system and the input and sensor output data. Simulation results are used to illustrate the switching mechanism’s performance in the case of a hybrid propulsion system, where the different subsystems are controlled in a distributed configuration.

This paper proposes a virtual sensor scheme designed to compensate for sensor fault effects in marine fuel engines. The proposed scheme design follows a distributed approach, where the marine fuel engine is decomposed in several subsystems. Then, for each subsystem we design a monitoring agent that can actively compensate for the effects of sensor faults occurring in the specific subsystem. This is realized using virtual sensors that can estimate the sensor fault in order to reconstruct the faulty measurements. Due to the Differential-Algebraic mathematical description of marine fuel engine dynamics, we design three types of virtual sensors; using adaptive observers, Set Inversion via Interval Analysis (SIVIA) and static models. Simulation results are used to illustrate the efficiency of the method.