It is widely accepted that structural defects in rotating machinery components (e.g. bearings and gears) can be detected through monitoring of vibration and/or sound emissions. Traditional diagnostic vibration analysis attempts to match spectral lines with a priori-known defect frequencies that are characteristic of the affected machinery components. Emphasis herein is on use of bispectral-based statistical change detection algorithms for machinery health monitoring. The bispectrum, a third-order statistic, helps identify pairs of phase-related spectral components, which is useful for fault detection and isolation. In particular, the bispectrum helps sort through the clutter of usual (second-order) vibration spectra to extract useful information associated with the health of particular components. Seeded and non-seeded helicopter gearbox fault results (CH-46E and CH-47D, respectively) show that bispectral algorithms can detect faults at the level of an individual component (i.e. bearings or gears). Fault isolation is implicit with detection based on characteristic a priori-known defect frequencies. Important attributes of the bispectral SCD approach include: (1) it does not require a priori training data as is needed for traditional pattern-classifier-based approaches (and thereby avoids the significant time and cost investments necessary to obtain such data); (2) being based on higher-order moment-based energy detection, it makes no assumptions about the statistical model of the bispectral sequences that are generated; (3) it is operating-regime independent (i.e. works across different operating conditions, flight regimes, torque levels, etc., without knowledge of same); (4) it can be used to isolate faults to the level of specific machinery components (e.g. bearings and gears); and (5) it can be implemented using relatively inexpensive computer hardware, since only low-frequency vibrations need to be processed. The bispectral SCD algorithm thus represents a general methodology for the automated analysis of rotating machinery.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Signal Processing
- Civil and Structural Engineering
- Aerospace Engineering
- Mechanical Engineering
- Computer Science Applications