Research on Methods of Fault Symptoms Extraction Based on Autoregressive-Moving Average Model Identification

Paper #:
  • 2017-01-2129

Published:
  • 2017-09-19
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Abstract:
In order to ensure flight safety and reduce maintenance and operating costs, the research on typical aircraft systems fault diagnosis and prediction technology has became a hot research topic. For a simple linear time invariant system, the extraction of fault feature can be realized through the way of monitoring system operating parameters and data mining. However, as the flight control system is a high complexity system and it can dynamic feedback correction in closed-loop control process, and its failures also have the characteristics of sudden, latent, and hard to reproduce off-plain. This paper presents an extract technology of the weak fault symptom based on ARMA model identification, which use improved least-square method for system parameters identification online, track and detect system recession trend, solve the problem of early fault diagnosis under the mechanism of multi flight mission profiles and dynamic feedback fault-tolerant control, so as to reduce accidents, ensure the safety and reliability of flight operation task.
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