Evaluation of the Correlation Coefficient as a Prognostic Indicator for Electromechanical Servomechanism Failures

Matteo D. L. Dalla Vedova, Paolo Maggiore, Lorenzo Pace, and Alessio Desando
Publication Target: 
IJPHM
Publication Issue: 
1
Submission Type: 
Full Paper
AttachmentSizeTimestamp
ijphm_15_006.pdf1.13 MBJune 19, 2015 - 11:56pm

In order to detect incipient failures due to a progressive wear of a primary flight command electromechanical actuator, prognostics could employ several approaches; the choice of the best ones is driven by the efficacy shown in failure detection, since not all the algorithms might be useful for the proposed purpose. In other words, some of them could be suitable only for certain applications while they could not give useful results for others.
Developing a fault detection algorithm able to identify the precursors of the above mentioned electromechanical actuator (EMA) failure and its degradation pattern is thus beneficial for anticipating the incoming failure and alerting the maintenance crew such to properly schedule the servomechanism replacement.
The research presented in the paper was focused to develop a prognostic technique, able to identify symptoms alerting that an EMA component is degrading and will eventually exhibit an anomalous behavior; in particular four kinds of failure are considered: friction, backlash, coil short circuit, rotor static eccentricity. To this purpose, an innovative model based fault detection technique has been developed merging together several information achieved by means of FFT analysis and proper "failure precursors" (calculated by comparing the actual EMA responses with the expected ones). To assess the robustness of the proposed technique, an appropriate simulation test environment was developed.
The results showed an adequate robustness and confidence was gained in the ability to early identify an eventual EMA malfunctioning with low risk of false alarms or missed failures.

Publication Year: 
2015
Publication Volume: 
6
Publication Control Number: 
006
Page Count: 
13
Submission Keywords: 
EMA
fault detection
servomechanism
multiple failures
failure maps
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
Modeling and simulation
  
 
 
 

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