Ulrik Dam Nielsen

FAULT DETECTION FOR SHIPBOARD MONITORING AND DECISION SUPPORT SYSTEMS

In this paper a basic idea of a fault-tolerant monitoring and decision support system will be explained. Fault detection is an important part of the fault-tolerant design for in-service monitoring and decision support systems for ships. In the paper, a virtual example of fault detection will be presented for a containership with a real decision support system onboard. All possible faults can be simulated and detected using residuals and the generalized likelihood ratio (GLR) algorithm.Copyright

Towards fault-tolerant decision support systems for ship operator guidance

Fault detection and isolation are very important elements in the design of fault-tolerant decision support systems for ship operator guidance. This study outlines remedies that can be applied for fault diagnosis, when the ship responses are assumed to be linear in the wave excitation. A novel numerical procedure is described for the calculation of residuals using the ships transfer functions which correlate the wave excitation and the ship responses. As tests, multiplicative faults have artificially been imposed to full-scale motion measurements and it is shown that the developed model is able to detect and isolate all faults.

Fault Detection for Shipboard Monitoring – Volterra Kernel and Hammerstein Model Approaches

Abstract In this paper nonlinear fault detection for in-service monitoring and decision support systems for ships will be presented. The ship is described as a nonlinear system, and the stochastic wave elevation and the associated ship responses are conveniently modelled in frequency domain. The transformation from time domain to frequency domain has been conducted by use of Volterra theory. The paper takes as an example fault detection of a containership on which a decision support system has been installed.

P-band radar ice sounding in Antarctica

In February 2011, the Polarimetric Airborne Radar Ice Sounder (POLARIS) was flown in Antarctica in order to assess the feasibility of a potential space-based radar ice sounding mission. The campaign has demonstrated that the basal return is detectable in areas with up to 3 km thick cold ice, in areas with up to several hundred meters thick warm shelf ice, and in areas with up to 700 m thick crevassed glacier ice. However, major gaps in the basal return are observed, presumably due to excessive absorption, scattering from ice inclusions in the firn, low basal reflectivity, and the masking effect of the surface clutter. Internal layers are observed down to depths exceeding 2 km. The polarimetric data show that the internal layers are strongly anisotropic at a ridge, where the ice flow is supposed to be highly unidirectional. In case of space-based ice sounding, the antenna pattern cannot offer sufficient surface clutter suppression, but improved clutter suppression has been demonstrated with novel multi-phase-center techniques.

Fault Isolation and Quality Assessment for Shipboard Monitoring

In this paper a new approach for increasing the overall reliability of a monitoring and decision support system will be explained. The focus is on systems used for ship operator guidance with respect to, say, speed and heading. 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. Fault isolation is an important part of the fault tolerant design for in-service monitoring and decision support systems for ships. In the paper, a virtual example of fault isolation will be presented. Several possible faults will be simulated and isolated using residuals and the generalized likelihood ratio (GLR) algorithm. It will be demonstrated that the approach can be used to increase accuracy of sea state estimations employing sensor fusion quality test.Copyright

Sea state estimation using model-scale DP measurements

Complex marine operations are moving further from shore, into deeper waters, and harsher environments. The operating hours of a vessel are weather dependent, and good knowledge of the prevailing weather conditions may ensure cost-efficient and safe operations. This paper considers the estimation of the peak wave frequency of the on-site sea state based on the vessels motion in waves. A sea state can be described by significant wave height, peak wave frequency, wave direction, and often wind speed and direction are added as well. The signal-based algorithm presented in this paper is based on Fourier transforms of the vessel response in heave, roll and pitch. The measurements are used directly to obtain an estimate of the peak frequency of the waves. Experimental results from model-scale offshore ship runs at the Marine Cybernetics Laboratory (MCLab) at NTNU demonstrate the performance of the proposed sea state estimation algorithm.

Calculating Outcrossing Rates Used in Decision Support Systems for Ships

Onboard decision support systems (DSS) are used to increase the operational safety of ships. Ideally, DSS can estimate — in the statistical sense — future ship responses on a time scale of the order of 1–3 hours taking into account speed and course changes. The calculations depend on both operational and environmental parameters that are known only in the statistical sense. The present paper suggests a procedure to incorporate random variables and associated uncertainties in calculations of outcrossing rates, which are the basis for risk-based DSS. The procedure is based on parallel system analysis, and the paper derives and describes the main ideas. The concept is illustrated by an example, where the limit state of a non-linear ship response is considered. The results from the parallel system analysis are in agreement with corresponding Monte Carlo simulations. However, the computational speed of the parallel system analysis proved slower than expected. Moreover, it is important that the failure surface of the limit state is smooth, otherwise the parallel system analysis may not be applicable.Copyright

Fault Isolation for Shipboard Decision Support

Abstract Fault detection and fault isolation for in-service decision support systems for marine surface vehicles will be presented in this paper. The stochastic wave elevation and the associated ship responses are modeled in the frequency domain. The paper takes as an example fault isolation of a containership on which a decision support system has been installed and it will be demonstrated that all the faults can be isolated. The paper shows how a shipboard decision support system could become highly reliable and comprise built-in supervision of the quality of the sensor signals that are crucial to the quality of decisions given to navigators.

Numerical Simulations of the Rolling of a Ship in a Stochastic Sea: Evaluations by Use of MCS and FORM

The paper elaborates on the probabilistic assessment of a simplified model for the rolling of a ship in a stochastic seaway. The model can be easily integrated with a probabilistic tool which enables evaluations of numerical simulations by the first order reliability method (FORM) and by Monte Carlo simulation (MCS). Results are presented for synchronous roll as well as parametric roll, where e.g. mean outcrossing rates have been calculated. FORM offers an efficient approach for the computations, although the approach should be applied with care in cases of parametric roll. The paper also touches on issues such as ergodicity and transient versus stationary stages in the roll realisations.Copyright

Evaluation of shipboard wave estimation techniques through model-scale experiments

The paper continues a study on the wave buoy analogy that uses shipboard measurements to estimate sea states. In the present study, the wave buoy analogy is formulated directly in the time domain and relies only partly on wave-vessel response amplitude operators (RAOs), which is in contrast to all previous works that either are formulated in the frequency domain and/or depend entirely on RAOs. Specifically, the paper evaluates a novel concept for wave estimation based on combined techniques using a wave frequency estimator, not dependent on RAOs, to detect wave frequency and, respectively, nonlinear least squares fitting to estimate wave amplitude and phase. The concept has been previously tested with only numerical simulations but in this study the techniques are applied to model-scale experiments. It is shown that the techniques successfully can be used to estimate the wave parameters of a regular wave train.