Title :
Linear model identification for gear fault detection using higher order statistics and inverse filter criteria
Author_Institution :
Aeronaut. & Maritime Res. Lab., Defence Sci. & Technol. Organ., Melbourne, Vic., Australia
Abstract :
Our study in the past showed that the autoregressive (AR) modelling method could be effectively used in the detection of gear tooth cracking. In the search for further improvement, a technique of identifying linear parametric models for gear signals using higher order statistics and inverse filter criteria has been evaluated and was applied to some seeded fault gear test data. The results indicate that this approach is more effective than the AR modelling method and the conventional residual signal technique
Keywords :
aircraft testing; autoregressive processes; crack detection; fault diagnosis; filtering theory; helicopters; higher order statistics; identification; inverse problems; vibration measurement; AR modelling method; autoregressive modelling method; gear fault detection; gear signals; gear tooth cracking detection; helicopters; higher order statistics; inverse filter criteria; linear model identification; linear parametric models; residual signal technique; seeded fault gear test data; transmission gearboxes; vibration signals; Fault detection; Fault diagnosis; Filters; Gears; Higher order statistics; Predictive models; Shafts; Signal processing; Teeth; Vibration measurement;
Conference_Titel :
Signal Processing and its Applications, Sixth International, Symposium on. 2001
Conference_Location :
Kuala Lumpur
Print_ISBN :
0-7803-6703-0
DOI :
10.1109/ISSPA.2001.949855
