The purpose of this research project is to improve current onboard decision support systems. Special focus is on the onboard prediction of the instantaneous sea state. In this project a new approach to increasing the overall reliability of a monitoring and decision support system has been established. The basic idea is to convert the given system into a fault-tolerant system and to improve multi-sensor data fusion for the particular system. The background of the project is the SeaSense system, which has been installed on several container ships and navy vessels. The SeaSense system provides a crude and simple estimation of the actual sea state (Hs and Tz), information about the longitudinal hull girder loading, seakeeping performance of the ship, and decision support on how to operate the ship within acceptable limits. The system is able to identify critical forthcoming events and to give advice regarding speed and course changes to decrease the wave-induced loads. The SeaSense system is based on the combined use of a mathematical model and measurements from a set of sensors. The overall dependability of a shipboard monitoring and decision support system such as the SeaSense system can be improved using fault-tolerant techniques (Fault Diagnosis and System Re-design) and a Sensor Fusion Quality (SFQ) test. Fault diagnosis means to detect the presence of faults in the system. In case sea state estimation is conducted by a ship-wave buoy analogy the best solution is achieved when a set of three different ship responses are used. Faulty signals should be discarded from the procedure for sea state estimation if it is possible, if not the fault should be estimated. The fault diagnosis can be divided into three steps: Fault detection, fault isolation and fault estimation. Fault detection means to decide whether or not a fault has occurred. This step determines the time at which the system is subjected to the given fault. Fault isolation will find in which component a fault has occurred. This step determines the location of the fault. Fault estimation provides an estimate of magnitude of a fault. A supervisory function determines the severity of the fault once its origin has been isolated and its magnitude estimated. Fault-tolerant Sensor Fusion means that the monitoring and decision support system can accommodate faults so that the overall system continues to satisfy its goal and on the other hand in the absence of a fault, the system should be able to provide the most accurate information using the SFQ test.
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