By Topic

The study of the helicopter rotor prognostic method and health management system based-on FMA

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Hongzheng Fang ; Beijing Aerosp. Meas. & Control Corp., Beijing, China ; Yi Xiong ; Kai Luo ; Liming Han

Helicopter rotor system consists of a large number of dynamic components, and works in a single-channel and complex environment, which is a critical system that ensures the helicopter reliability and security. Because the data on the rotating parts is not easy to measure, and the noise environment is complex, the rotor fault detection capacity is limited, thus seriously affecting the reliability and safety of the helicopter as a whole. This paper firstly presents the simulation of neural network prognostic method based on the rotor system failure mechanism and failure mode analysis (FMA), which can effectively use the failure mode for the modeling of the prediction method combined with the test and simulation data of key components, and greatly improved the effectiveness of fault detection. Besides, a general, open architecture of an advanced health usage and management system (A-HUMS) is proposed, and the overall structure, hierarchy and functions are elaborated. Finally, taking the helicopter rotor system components as the research object, the validation system of the helicopter HUMS system has been planned and designed. The analysis results show that the proposed method can effectively predict to preset a variety of fault conditions and solve the low predictive ability of key components of the rotor system failure problems.

Published in:

Prognostics and System Health Management (PHM), 2012 IEEE Conference on

Date of Conference:

23-25 May 2012