N. H. W. Eklund, H. Qiu, General Electric Global Research, Niskayuna, NY; E. Hindle, M. Hirz, G. Van Der Merwe, General Electric Aviation, Cincinnati, OH
This paper introduces a sensor fusion approach for rolling element bearing prognostics in an aircraft engine application. Bearings are a critical component of aircraft engines, so detecting a defect as early as possible and the ability to assess damage state in real time has a profound effect on both operational safety and mission success, particularly in single-engine aircraft. The bearing prognostics problem can be divided into three sub problems, a) defect detection – the rapid and robust detection of spall, b) defect assessment – the quantitative assessment of current spall length, and c) life prediction – the estimation of spall propagation rate under a future operational scenario. The accuracy of life prediction relies on accurate defect detection and assessment, and a good understand of fault propagation physics. This paper focuses on a sensor fusion technique for defect detection and assessment. Remaining useful life prediction is covered in a separate report.
Summary: This paper introduces a sensor fusion approach for rolling element bearing prognostics in an aircraft engine application.
