Detection of Sensor Detachment in Aircraft Engines Using Vibration Signals

Sensor detachment are very common in aeronautic applications wherein equipment operate under very high power and severe environmental conditions. The effect of such defects is detrimental for health monitoring which requires an accurate vibration measure and, therefore, their detection is of high practical importance. This paper investigates the effect of accelerometer faults on the measured vibrations and proposes a two-step methodology to detect their presence using the signal itself. The first step consists of a prewhitening step which has for aim to remove the deterministic part of the signal. The second designs a relevant asymmetry indicator, namely the outlier counter indicator, able to detect the presence of an attachment fault. The efficiency of the proposed methodology is demonstrated on real vibration signals acquired from an aircraft engine gearbox in healthy and faulty sensor cases.

[1]  Michael Feldman,et al.  Hilbert Transform Applications in Mechanical Vibration: Feldman/Hilbert Transform Applications in Mechanical Vibration , 2011 .

[2]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[3]  B. B. Seth,et al.  Analysis of repetitive mechanism signatures , 1980 .

[4]  P. D. McFadden,et al.  Vibration monitoring of rolling element bearings by the high-frequency resonance technique — a review , 1984 .

[5]  Robert B. Randall,et al.  A comparison of methods for separation of deterministic and random signals , 2011 .

[6]  D. W. Thomas,et al.  Bearing Fault Detection Using Adaptive Noise Cancelling , 1982 .

[7]  Jérôme Antoni,et al.  Cyclostationary modelling of rotating machine vibration signals , 2004 .

[8]  Robert B. Randall,et al.  Application of cepstrum pre-whitening for the diagnosis of bearing faults under variable speed conditions , 2013 .

[9]  P. D. McFadden,et al.  Model for the vibration produced by a single point defect in a rolling element bearing , 1984 .

[10]  Komi Midzodzi Pekpe,et al.  Vibration-based fault detection of accelerometers in helicopters , 2012 .

[11]  Robert B. Randall,et al.  Unsupervised noise cancellation for vibration signals: part II—a novel frequency-domain algorithm , 2004 .

[12]  P. D. McFadden,et al.  The vibration produced by multiple point defects in a rolling element bearing , 1985 .

[13]  Simon Braun,et al.  The synchronous (time domain) average revisited , 2011 .