CRAN - Campus Sciences
BP 70239 - 54506 VANDOEUVRE Cedex
Tél : +33 (0)3 72 74 52 90
Ph. D. Project : Fault Diagnosis and prognosis & health management (PHM) for autonomous unmanned Vehicle and a fleet of autonomous unmanned Vehicles
Dates : 2017/05/29 - 2019/12/31
Manager(s) CRAN: Jean-Christophe PONSART
Other Manager(s): Dr GARCIA BELTRAN Carlos Daniel ( , Dr ASTORGA ZARAGOZA Carlos Manuel (
Full reference: Objective:
To develop a diagnosis and prognosis architecture to enable a safety system for autonomous unmanned vehicle and a fleet of autonomous unmanned vehicles.

Autonomous vehicles present unique challenges in the diagnosis, prognosis and health monitoring of engines and drive systems, sensors (electro-optical/infrared (EO/IR), Radar, etc), electro-mechanical actuator (EHA), and communications, during endurance missions. Wherein the pilot debrief is often used to identify changes in engine performance or aircraft handling characteristics for maintenance purposes, without a pilot in the loop, the PHM system on autonomous vehicles must be relied upon to a greater extent to report propulsion faults and drive maintenance actions. Propulsion faults such as compressor surge/stall, vibrations, and screech can often be identified by auditory or vibration changes in a manned platform. This detection is obviously not available on unmanned platforms, making it more difficult to detect, diagnose, and repair propulsion system problems.

The first step is to define and determine the feasibility of providing a dependable and robust fault diagnosis and prognosis system for autonomous unmanned vehicles using model-based approach and residuals. A residual is a signal (constructed from sensor measurements) that reacts to a chosen fault or subset of the considered faults. And by generating a suitable set of such residuals, fault detection and isolation can be achieved.

The second step is to design and develop an architecture able to detect faults or incipient failures and predict the remaining useful life of failing components for one autonomous vehicle and then, for a fleet of autonomous vehicles considering some cooperative control strategies or mission.

Novel/Additive Information:
The novelty of this thesis proposal as well as the expected results is the fusion of two research disciplines in a very important application: the diagnosis and the prognosis for detecting and isolating present and future faults. There are some results of both disciplines separately for autonomous UAV but just a few for a fleet of UAV. The reason of this work is also to establish a unified theory

[1] Xinwei Li, Wenjin Zhang, Design of Prognostic and Health Management Structure for UAV System, 21st International Conference on Systems Engineering (ICSEng), 2011
[2] Edward Balaban and Juan J. Alonso, A Modeling Framework for Prognostic Decision Making and its Application to UAV Mission Planning, Annual Conference of the Prognostics and Health Management Society 2013
[3] I. Paixao de Medeiros, L. Ramos Rodrigues, R. Santos, E. Hideiti Shiguemori, C.L. Nascimento Junior, PHM-based Multi-UAV task assignment, 8th Annual IEEE on Systems Conference (SysCon), 2014
[4] Handbook of Unmanned Aerial Vehicles, Kimon P. Valavanis & George J. Vachtsevanos Editors, Springer, 2014 (ISBN 978-90-481-9706-4)
[5] F. R. López-Estrada, J. C. Ponsart, D. Theilliol, C. Astorga-Zaragoza, Y. Zhang, Robust sensor fault diagnosis and tracking controller for a uav modelled as LPV system, International Conference on Unmanned Aircraft Systems (ICUAS’2104), 2014
Automatic Control-Identification Diagnosis