Open AccessMachine-learning algorithms for helicopter hydraulic faults detection: model based research
Author(s) -
А. М. Гареев,
E. Yu. Minaev,
Д. М. Стадник,
Nikita Davydov,
V. I. Protsenko,
И. А. Попельнюк,
Артем Никоноров,
Asgat Gimadiev
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1368/5/052027
Subject(s) - computer science , support vector machine , fault detection and isolation , reliability (semiconductor) , algorithm , gradient boosting , artificial intelligence , machine learning , boosting (machine learning) , hydraulic machinery , data mining , engineering , random forest , mechanical engineering , power (physics) , physics , quantum mechanics , actuator
The problem of automatic reliability monitoring and reliability-centered maintenance is increasingly important today. In this paper, we compare the accuracy of four machine learning approaches for fault detection in a hydraulic system. The first three approaches are based on SVM classifiers with linear, polynomial and RBF kernels and the last one is a gradient boosting on oblivious decision trees. We evaluate algorithms on the synthetic dataset generated by our simulation model of the helicopter hydraulic system and show that high accuracy fault detection can be achieved.