Traditional engine health management development has focused on major gas turbine engine components (i.e., disks, blades, bearings, etc.) due to the fact that these components are expensive to maintain and their failures frequently have safety implications. However, the majority of events that lead to standing down of aircraft arise from gas turbine accessory components such as pumps, generators, auxiliary power units, and motors. Common vibration diagnostics, which are based on frequency domain analysis that assumes the monitored signal is “stationary” during the analysis period, are not effective for these components. This is true because operating conditions are often non-stationary and evolving, which leads to spectral smearing and erroneous analysis that can cause missed detections and false alarms. Traditionally, this is avoided by defining steady state operating conditions in which to perform the analysis. Although this may be acceptable for major engine components, which are typically highly loaded during normal steady operation, many engine accessories are only high loaded during transients, especially startup. For example, an engine starter or fuel pump may be more highly loaded and therefore susceptible to damage during engine start up, typically avoided by traditional vibration analysis methods. More importantly, certain component faults and their progression can also lead to non-stationary vibration signals that, because of the smearing they induced, would be missed by traditional techniques. As a result, the authors have developed a novel engine accessory health monitoring methodology that is applicable during non-stationary operation through application of joint time-frequency analysis (JTFA). These JTFA approaches have been proven in other disciplines, such as speech analysis, radar processing, telecommunications, and structural analysis, but not yet readily applied to engine accessory component diagnostics. This paper will highlight the results obtained from applying JTFA techniques, including Short-Time Fourier Transform, Choi-Williams Distribution, Continuous Wavelet Transform, and Time-Frequency Domain Averaging, to very high frequency (VHF) vibration data collected from healthy and damaged turbine engine accessory components. The resulting accuracy of the various approaches were then evaluated and compared with conventional signal processing techniques. As expected, the JTFA approaches significantly outperformed the conventional methods. On-board application of these techniques will increase prognostics and health management (PHM) coverage and effectiveness by allowing accessory health monitoring during the most life influencing regimes regardless of operating speed and reducing inspection and replacement costs resulting in minimizing the vehicle down time.

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