Underwater Robots - Gianluca Antonelli.pdf

4.3 Fault Detection Schemes 85
planar simulation is provided in case of low speed under wave action and a
stuck fin.
+
−
FIS
FD
FTC UUV
high level
Fig. 4.4. Fuzzy fault detection/tolerant control scheme proposed by A.J. Healey:
the FIS (Fuzzy Inference System) block is in charge of isolating faults observed by
the FD (Fault Detection) block between fin stroke, servo error, residual and wave
activity detectors
Amodel-free fault detection method isproposed in [43, 173]: this is based
on the Hotelling T 2 statistic and it is adata-driven approach. The validation
is based on a6-DOFs simulation affected by stern plane jams and rudder
jams.
Amodel-based, integrated heterogeneous knowledge approach is proposed
in [140]. Amulti-dimensional correlation analysis allows to increase the confidence
in the detected fault and to detect also indirectly sensed subsystems.
Some preliminary results with the vehicle RAUVER are also given.
In [1] amodel-based fault detection scheme for thrusters and sensors is
proposed. It has been designed based on the identified model of the 6-thruster
ROV Linotip and it is composed of abank ofsingle-output Luenberger observers.
Its effectiveness is verified bysimulations. Another model-based fault
detection scheme verified bysimulations is provided in [238]. Arobust approach
in[199].
Aneuro-symbolic hybrid system is used in[102] to perform fault diagnosis
on AUVs with learning capability. The method issimulated on the planar
motion ofthe VORTEX mathematical model. Another learning technique is
proposed in [113] and verified bymeans of 6-DOFs simulations. In [284], a
neural network mathematical model is used to set-up aself-diagnosis scheme
of the AUV. Asoftware forhealth monitoringofAUVs’ missions with learning
capabilities is also described in [151].
The work in [39] studies asystematic, quantitative approach inorder to
maximize the mission and return success probabilities. The failed sensor is