Abstract:

Fault diagnosis of an aero-engine spindle bearing is an important part of engine prognostics and health management.As is known,the diagnosis of the compound fault of an aero-engine spindle bearing is very difficult and easily affected by other vibration interference signals.We present a compound fault diagnosis method of an aero-engine spindle bearing based on blind signal extraction of canonical correlation analysis (CCA) and cyclic Wiener filtering.First,an adaptive conjugate gradient algorithm is proposed for extracting the blind signal by optimizing CCA criterion.Then,combined with the fault feature frequency,the blind signal extraction algorithm is used to extract the fault feature signal from the observed signal.The extracted fault fe12ature signal is regarded as the expected response of the cyclic Wiener filter.Finally,the cyclic Wiener filter is designed to recover the fault signal,and the envelope spectrum of the filtered signal is analyzed to complete the diagnosis of the bearing composite fault.The proposed algorithm overcomes the problem that the existing methods rely on the bearing parameters too much,and that the expected signal obtained from the fixed mathematical model is too ideal to be applied to practical engineering.Both simulated data and experimental data are used to verify the effectiveness of the algorithm in compound fault diagnosis.

Key words: cyclic wiener filter, rolling bearing, fault diagnosis, compound faults, envelop spectrum