News & Press

SEAMLESS to be presented at IFAC SAFEPROCESS with 2 papers!

  • 28/05/2024

IFAC SAFEPROCESS is a major international gathering of leading experts in academia and industry. It aims at strengthening contacts between academia and industry to build up new networks and cultivate existing relations. High-level speakers will present the global spectrum of fault diagnosis, process supervision and safety monitoring, state-of-the-art applications, and emerging research directions. Fault diagnosis, Fault Detection and Isolation (FDI) and Fault-Tolerant Control (FTC) build a major area of research at the intersection of systems and control engineering, artificial intelligence, applied mathematics and statistics, and application fields like chemical, electrical, mechanical, aerospace engineering and transportation systems.

SEAMLESS will be presented by TUDELF with 2 papers aiming to diagnose faults ensuring the safety of autonomous vessels.

Paper 1: Active Thruster Fault Diagnosis for an Overactuated Autonomous Surface Vessel 

As Autonomous Surface Vessels (ASVs) become increasingly prevalent in marine applications, ensuring their safe operation, in the presence of faults, is crucial to human safety. This paper presents a scheme that encompasses the detection and isolation of actuator faults within ASVs to ensure uninterrupted and safe operation. The method primarily addresses the loss of thruster effectiveness as a specific actuator fault. For fault detection, the proposed method leverages residuals generated by nonlinear observers, coupled with adaptive thresholds, enhancing fault detection accuracy. The active fault isolation strategy employs actuator redundancy to insulate specific system states from faults by dynamically reconfiguring the actuation configuration in response to detected faults. Comprehensive simulation results demonstrate the effectiveness of this methodology across diverse marine traffic scenarios where the ASV needs to perform a collision avoidance maneuver.

Paper 2: A Multiple Sensor Fault Diagnosis Scheme for Autonomous Surface Vessels

Autonomous surface vessels (ASVs) have started to operate in many safety-critical scenarios where rich sensor information is required for situational awareness, environmental perception, motion planning, collision avoidance and navigational control. A timely diagnosis of faulty onboard sensors is therefore essential for ensuring maritime safety and reliability. In this paper, a model-based fault diagnosis scheme is presented for ASVs affected by multiple sensor faults. Various monitoring modules comprising nonlinear observers are employed for the detection of faults occurring in the vessel’s navigational sensors. Further, multiple fault isolation is performed based on a combinatorial decision logic, achieved by grouping the available sensors into multiple sensor sets. The efficacy of the proposed scheme is demonstrated through a simulation example of a vessel trajectory tracking scenario.