Use of blind deconvolution de-noising scheme in failure prognosis

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