سیستم پشتیبانی تصمیم دانش محور برای کنترل آسیب های دریایی

The operational complexity of modern ships requires the use of advanced applications, called damage control systems (DCSs), able to assist crew members in the effective handling of dangerous events and accidents. In this article we describe the development of a knowledge-based decision support system (KDSS) integrated within a DCS designed for a national navy. The KDSS uses a hybrid design and runtime knowledge model to assist damage control operators through a kill card function which supports damage identification, action scheduling and system reconfiguration. We report a fire fighting scenario as illustrative application and discuss a preliminary evaluation of benefits allowed by the system in terms of critical performance measures. Our work can support further research aimed to apply expert systems to improve shipboard security and suggest similar applications in other contexts where situational awareness and damage management are crucial.

Navy ships have been traditionally manned with a large crew involved in the manual control of onboard systems. The reduction of through-life costs of vessels is today a priority and there is an interest towards reducing crew without affecting damage control capabilities or jeopardizing the ability of ships to complete their missions (Cosby & Lamontagne, 2006). Beside efficiency pressures, factors like the increased complexity of modern vessels, the requirements for easy maintainability and more sophisticated operational demands generate a need for intelligent functionalities and leading-edge technologies (Bøgh & Severinsen, 2009) which can assist human decisions and actions onboard. In this endeavor, one relevant area is related to the management of events which may lead to shipboard damage and crew danger. These events require rapid actions, also without on-site human intervention, to prevent serious injuries to personnel or damages to vital ship systems. Whereas damage control has been traditionally a manual and manpower-intensive function, the automation of emergency management operations is today driven by complex technology architectures called damage control systems (DCSs) and related progresses in human-system integration, which gets increasing attention in ship design (Runnerstrom, 2003). A DCS is an information-retrieval and equipment-control system that gives ship personnel the ability to detect, analyze, and handle various types of damage situations, based on the collection and processing of vast quantities of shipboard information. In the navy context, a damage control system (DCS) is aimed to assure the timely and informed application of men and equipment in scenarios such as fire or flooding, violation of the ship closure state, threats to essential equipments, ventilation close down, and atomic/biological/chemical issues. DCSs are also relevant for emergency training and damage instructor assistance purposes (Bulitko and Wilkins, 1999 and Peters, 2004). The noteworthiness of these systems is proven by the number of leading market players (e.g. ABB, L3, Northrop Grumman, Rockwell, and Siemens) involved in the design and development of innovative solutions for damage control. The assistance to damage control operators, with recommendations for counteractions and reconfigurations, requires a highly structured approach to problem identification and action planning. The field of expert and decision support systems can thus provide a relevant contribution to design more performing DCSs. However, the study of expert systems and DSS in navy contexts has mostly focused on the design process whereas a very limited number of contributions have addressed the implementation of integrated systems to ensure the safety and operational stability of modern ships. In this paper, we show the development of a knowledge-based decision support system (KDSS) which has been integrated within the DCS designed for the operating needs of a national navy. We start from the analysis of the typical damage control process and identify a model of knowledge acquisition and reuse in damage management scenarios. The model is implemented through the development of a kill card function providing an interactive interface and a shared decision and action platform for damage control personnel onboard. The tool provides a graphical information-retrieval and equipment-control dashboard that gives damage crew the ability to handle various types of damage control situations. The remainder of the paper is structured as follows: Section 2 reviews existing literature on damage control and DSS applications in the navy context; Section 3 introduces the research requirements and describes the overall architectural design; Section 4 illustrates the KDSS developed; Section 5 presents an illustrative application related to a fire-fighting scenario and proposes a preliminary evaluation of quali-quantitative benefits; Section 6 concludes the paper and draws avenues for future research.

The identification and management of events that may lead to shipboard damage and crew danger are interesting areas of application for expert and decision support methods and tools. National navies around the world are in fact turning to enhanced and distributed damage control systems to achieve higher level of security and operational efficiency through effective information sharing, fast problem identification and action planning and automation. In this paper, we have presented a knowledge based DSS which uses design time and run time knowledge sources to streamline the decision making process and sequence of actions required to the damage control operator in case of emergency. The system reduces situational awareness time, action time, crew need and overall action cost. The KDSS also allows full navigability of damage control information, the use of multimedia tools for damage monitoring, the interoperability with other DCS applications, and a more effective information sharing. The dedicated displays onboard enable the operators to immediately identify the emergency and initiate corrective actions. The ship-wide data network allows several dispersed damage stations to retrieve coherent information and thereby effectuate a coordinated and effective action, resulting in reduced damage control response time, enhanced consistency of actions, and reduced manning. The use of the application developed could be enlarged to other contexts (e.g. building sites, nuclear and other energy production sites) where the monitoring of risky events in different compartments or operation areas requires advanced control and decision/action support technologies. In such cases, the KDSS can be of value to increase the situational awareness of damage crew members and enhance data consistency through the use of automatized control devices for the remote identification of risks. Next research will be addressed to extend the application of the DSS for training purposes, and in particular for on-the-job training of damage crew members. The onboard training system of the ship could be indeed used to simulate events in normal ship operation as well as in degraded conditions using the same interface. A second area of development is represented by the adoption of enhanced reality and 3D technologies and functionalities within the system. This could further enhance the situational awareness of operators and their ability to promptly identify and assess the problem, resulting in faster and more effective actions.