Inverse Normalized Energy Based Feature using Wavelet for Trend Prognosis on Mechanical Coupling for the Common Remotely Operated Weapon Station (CROWS)

Report No. ARL-TR-5327
Authors: Canh Ly, Andrew Bayba, and Derwin Washington
Date/Pages: September 2010; 28 pages
Abstract: In this report, we present an innovated method for diagnosing and predicting loosened bolts on the elevation control motor (ECM) of the Common Remote Operated Weapons Station (CROWS) system in order to prevent a disastrous fault or failure in the gear mechanism that loose bolts could cause. Our method uses the "symlet" wavelet to de-noise non-stationary vibration signals from a tri-axial accelerometer mounted on the ECM. We calculated the ratio of the normalized energy of the "baseline," in which all the bolts were tightened, to cases in which the bolts were loosened to different levels of torque. The normalized energy signals were calculated from the output of Fast Fourier Transform (FFT) spectral components at the frequency band of the residual signals-the difference between the "raw" experimental data and the de-noised data. We then conducted a series of controlled experiments where we deliberately loosened the ECM bolts to demonstrate the system's diagnostic and prognostic capability. Based on the experimental data and results from our method, we showed that we can detect a fault and the trend of the loosening bolts on the weapons station. If faults are detected early enough, appropriate measures can be to taken to enhance the reliability of the weapons station.
Distribution: Approved for public release
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Last Update / Reviewed: September 1, 2010