A Novel Blind Deconvolution De-Noising Scheme in Failure Prognosis

  • Bing Zhang
  • Taimoor Khawaja
  • Romano Patrick
  • George Vachtsevanos
  • Marcos Orchard
  • Abhinav Saxena
Part of the Intelligent Systems, Control, and Automation: Science and Engineering book series (ISCA, volume 39)

With increased system complexity, Condition-Based Maintenance (CBM) becomes a promising solution to system safety by detecting faults and scheduling maintenance procedures before faults become severe failures resulting in catastrophic events. For CBM of many mechanical systems, fault diagnosis and failure prognosis based on vibration signal analysis are essential techniques. Noise originating from various sources, however, often corrupts vibration signals and degrades the performance of diagnostic and prognostic routines, and consequently, the performance of CBM. In this paper, a new de-noising structure is proposed and applied to vibration signals collected from a testbed of the main gearbox of a helicopter subjected to a seeded fault. The proposed structure integrates a blind deconvolution algorithm, feature extraction, failure prognosis, and vibration modeling into a syn-ergistic system, in which the blind deconvolution algorithm attempts to arrive at the true vibration signal through an iterative optimization process. Performance indexes associated with quality of the extracted features and failure prognosis are addressed, before and after de-noising, for validation purposes.


Fault Diagnosis Vibration Signal Blind Source Separation Planetary Gear Blind Deconvolution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science + Business Media B.V. 2009

Authors and Affiliations

  • Bing Zhang
    • 1
  • Taimoor Khawaja
    • 1
  • Romano Patrick
    • 1
  • George Vachtsevanos
    • 1
  • Marcos Orchard
    • 1
    • 2
  • Abhinav Saxena
    • 1
  1. 1.School of Electrical & Computer EngineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Department of Electrical EngineeringUniversity of ChileSantiagoChile

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