Abstract
In the fault diagnosis of UAVs, extremely imbalanced data distribution and vast differences in effects of fault modes can drastically affect the application effect of a data-driven fault diagnosis model under the limitation of computing resources. At present, there is still no credible approach to determine the cost of the misdiagnosis of different fault modes that accounts for the interference of data distribution. The performance of the original cost-insensitive flight data-driven fault diagnosis models also needs to be improved. In response to this requirement, this paper proposes a two-step ensemble cost-sensitive diagnosis method based on the operation and maintenance data of UAV. According to the fault criticality from FMECA information, we defined a misdiagnosis hazard value and calculated the misdiagnosis cost. By using the misdiagnosis cost, a static cost matrix could be set to modify the diagnosis model and to evaluate the performance of the diagnosis results. A two-step ensemble cost-sensitive method based on the MetaCost framework was proposed using stratified bootstrapping, choosing LightGBM as meta-classifiers, and adjusting the ensemble form to enhance the overall performance of the diagnosis model and reduce the occupation of the computing resources while optimizing the total misdiagnosis cost. The experimental results based on the KPG component data of a large fixed-wing UAV show that the proposed cost-sensitive model can effectively reduce the total cost incurred by misdiagnosis, without putting forward excessive requirements on the computing equipment under the condition of ensuring a certain overall level of diagnosis performance.
Highlights
Unmanned aerial vehicles (UAVs), as a typical complex electromechanical system, have been widely used in the military and commercial fields but have a high fault rate.Improving the competence of fault diagnosis and ground maintenance, so as to improve the functionality and reliability of UAVs has become an essential research area [1,2,3].With the development of Prognostics Health Management (PHM) technology, abundant onboard sensors and multisource analysis records have brought about the swift growth of operation and maintenance data of UAVs [4]
Based on preferable meta-classifiers and ensemble patterns, the MetaCost method can improve the diagnosis performance in accuracy, total misdiagnosis cost, and computational resource occupation, in response to the actual demand of UAV fault diagnosis
Thanks to the above improvements, this paper proposes a two-step ensemble costsensitive diagnosis model based on the MetaCost framework (MC–LGB), for a UAV fault diagnosis based on operation and maintenance data
Summary
Improving the competence of fault diagnosis and ground maintenance, so as to improve the functionality and reliability of UAVs has become an essential research area [1,2,3]. With the development of Prognostics Health Management (PHM) technology, abundant onboard sensors and multisource analysis records have brought about the swift growth of operation and maintenance data of UAVs [4]. These data-driven methods, thanks to the growth of data scales, are gradually replacing the traditional Physics of Failure (PoF). With the rapid development of artificial intelligence technology, datadriven fault diagnosis based on machine learning models have achieved considerable progress.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
