-
UKAEA-RACE-PR(23)032022
Calibration data for condition monitoring (CM), often originates from equipment running in different environment conditions, and operational settings. Due to the uneven distribution of the data, the performance of traditional machine learning approaches for CM can easily be skewed in favour of operating conditions with larger data distributions.…
-
UKAEA-RACE-CP(21)032021
Driving energy consumption plays a major role in mobile robots navigating challenging environments, especially if they are left to operate unattended under limited on-board power. This paper reports on first results of an energy-aware path planner, which can provide estimates of the driving energy consumption and energy recovery of a robot traversi…
-
UKAEA-RACE-PR(20)012020
The DEMO remote maintenance system (RMS) will have a significant role in keeping the power plant operational, and therefore, would have a large impact on its economic feasibility. The identification of impending mechanical failures of the RMS equipment during active maintenance operations is crucial if unscheduled and or unplanned recovery and resc…
-
UKAEA-CCFE-CP(20)732019
An extensive knowledge of a system’s failures is crucial for identifying areas where the reliability of the system can benefit from improvements, as well as informing the design of new systems. Moreover, relationships between faults and failures can be used to enhance the maintenance of the system. In this paper we present a taxonomy of failure …
-
CLM P1051966
Showing 1 - 5 of 5 UKAEA Paper Results
Page 1 of 1