Application of Blind Deconvolution Denoising in Failure Prognosis

Fault diagnosis and failure prognosis are essential techniques in improving the safety of many mechanical systems. However, vibration signals are often corrupted by noise; therefore, the performance of diagnostic and prognostic algorithms is degraded. In this paper, a novel denoising structure is proposed and applied to vibration signals collected from a testbed of the helicopter main gearbox subjected to a seeded fault. The proposed structure integrates a denoising algorithm, feature extraction, failure prognosis, and vibration modeling into a synergistic system. Performance indexes associated with the quality of the extracted features and failure prognosis are addressed, before and after denoising, for validation purposes.

[1]  P D McFadden,et al.  An Explanation for the Asymmetry of the Modulation Sidebands about the Tooth Meshing Frequency in Epicyclic Gear Vibration , 1985 .

[2]  G. Vachtsevanos,et al.  Use of blind deconvolution de-noising scheme in failure prognosis , 2007, 2007 IEEE Autotestcon.

[3]  George Vachtsevanos,et al.  A Particle Filtering Framework for Failure Prognosis , 2005 .

[4]  A. Szczepanik,et al.  Time synchronous averaging of ball mill vibrations , 1989 .

[5]  Romano Patrick-Aldaco,et al.  A model based framework for fault diagnosis and prognosis of dynamical systems with an application to helicopter transmissions , 2007 .

[6]  Bin Zhang,et al.  An integrated approach to helicopter planetary gear fault diagnosis and failure prognosis , 2007, 2007 IEEE Autotestcon.

[7]  Marcos Eduardo Orchard,et al.  A Particle Filtering-based Framework for On-line Fault Diagnosis and Failure Prognosis , 2007 .

[8]  Frank L. Lewis,et al.  Intelligent Fault Diagnosis and Prognosis for Engineering Systems , 2006 .

[9]  Abhinav Saxena,et al.  An approach to fault diagnosis of helicopter planetary gears , 2004, Proceedings AUTOTESTCON 2004..

[10]  J. Antoni.  Blind separation of vibration components: Principles and demonstrations , 2005 .

[11]  Paul,et al.  Vibration Monitoring of UH-60A Main Transmission Planetary Carrier Fault , 2003 .

[12]  Deepa Kundur,et al.  A novel blind deconvolution scheme for image restoration using recursive filtering , 1998, IEEE Trans. Signal Process..

[13]  Katherine T. McClintic,et al.  OF VIBRATION ANALYSIS METHODS FOR GEARBOX DIAGNOSTICS AND PROGNOSTICS , 2001 .

[14]  S. Boll,et al.  Suppression of acoustic noise in speech using spectral subtraction , 1979 .

[15]  Barney E. Klamecki,et al.  Use of stochastic resonance for enhancement of low-level vibration signal components , 2005 .

[16]  Robert B. Randall,et al.  Unsupervised noise cancellation for vibration signals: part I—evaluation of adaptive algorithms , 2004 .