[Event at CIG] PhD student opportunity

[Joxe Inaxio Aizpurua]

*Prognostics and Health Management solutions for*

*Reliable Autonomous Systems*

*Description*

The revolution in robotics and autonomous systems (RAS) is unstoppable. The
advance of autonomous system applications, such as autonomous transport [1,
2] and autonomous inspections [3], generate multiple benefits for the
industry and society, including the improved driving security in autonomous
transport, and improved reliability of critical and remote infrastructure
through specialized robots and drones.

However, the reliability assurance of RAS is a complex challenge, as it
requires incorporating advanced intelligence that should evolve according
to run-time operation [4]. The challenging yet exciting, operation context
of RAS, hampers the reliability assurance of RAS, which decelerates the
acceptance and everyday use of RAS.

Different technological solutions have emerged to improve the design and
reliability of RAS [5]. Most of the technological configurations include a
combination of mechanical and electrical components, along with onboard
software intelligence to adopt decisions without direct human intervention.
In this context, using the ever-increasing prognostics and health
management solutions, it is possible to develop a prognostics modelling
approach for RAS health monitoring using reliability, machine learning,
uncertainty modelling and optimization methods [6].

The project’s objective is to develop novel prognostics methods for RAS,
which can accurately inform about the model’s confidence in the decisions in
real-time and the way to mitigate the existing problem via optimization
methods, and always, provide a worst-case estimate on its predictions by
using a proper modelling and update of the different sources of
uncertainty. In particular, the work will focus on the integration of
uncertainties to make prognostic predictions robust, using concepts such as
adversarial learning, combined with statistical learning and artificial
intelligence methods.

The models developed in this project will be validated with the data
collected from industry partners that work with autonomous robots focusing
on (i) autonomous remote inspections for renewable energy and (ii)
automotive industry.

The project will be developed in <https://www.mondragon.edu/en/home>Mondragon
Unibertsitatea within the Electronics and Computer Science Department, in
collaboration between the Data Analytics and Signal Processing and
Communications groups. Throughout the thesis, the student will engage
continuously with industry and stays at different universities and/or
research centers will be pursued.

Interested applicants, send your CV and a short motivation letter to:
jiaizpurua at mondragon.edu and ezugasti at mondragon.edu

Application deadline. Review of applications will begin July 1st and
continue until the position is filled.

*Requirements*


   - M. Sc. degree in telecommunications, electronics, computer science,
   mathematics, embedded systems or a related area.
   - Programming skills: Matlab, Python, R, or C++ (samples from prior
   projects or a GitHub         repository are preferred)
   - Statistics/mathematics, data science/AI/ML
   - Knowledge/experience with autonomous systems is a plus.
   - Knowledge/experience with reliability and/or diagnostics/health
   management methods is a plus.
   - Experience with artificial intelligence / optimization methods is a
   plus.

*References*



[1] Feng, S., Yan, X., Sun, H. et al. Intelligent driving intelligence test
for autonomous vehicles with naturalistic and adversarial environment. *Nature
Communications* 12, 748 (2021). <https://doi.org/10.1038/s41467-021-21007-8>
https://doi.org/10.1038/s41467-021-21007-8

[2] Ellefsen, A. L., Æsøy, V., Ushakov, S., & Zhang, H. (2019). A
comprehensive survey of prognostics and health management based on deep
learning for autonomous ships. IEEE Transactions on Reliability, 68(2),
720-740

[3] Floreano, D., & Wood, R. J. (2015). Science, technology and the future
of small autonomous drones. *nature*, *521*(7553), 460-466.

[4] Aslansefat, K., Kabir, S., Abdullatif, A., Vasudevan, V., &
Papadopoulos, Y. (2021). Toward Improving Confidence in Autonomous Vehicle
Software: A Study on Traffic Sign Recognition Systems. *Computer*, *54*(8),
66-76.

[5] Elghazel, W., Bahi, J., Guyeux, C., Hakem, M., Medjaher, K., &
Zerhouni, N. (2015). Dependability of wireless sensor networks for
industrial prognostics and health management. Computers in Industry, 68,
1-15.

[6] Aizpurua, J. I., Catterson, V. M., Papadopoulos, Y., Chiacchio, F., &
Manno, G. (2017). Improved dynamic dependability assessment through
integration with prognostics. *IEEE Transactions on Reliability*, *66*(3),
893-913.

-- 
*Joxe Aizpurua.*