Title :
Using EMD-AR spectrum for concurrent fault diagnosis of engine
Author :
Huimin Zhao ; Jide Jia ; Qingle Yang ; Chun Chang
Author_Institution :
Automobile Eng. Dept., Mil. Transp. Univ., Tianjin, China
fDate :
Aug. 31 2014-Sept. 3 2014
Abstract :
The problem of concurrent fault diagnosis is one of the difficulties in diesel fault diagnosis because of the complex mechanism of the composite fault. A method combining the empirical mode decomposition (EMD) and autoregressive model (AR) spectrum is proposed and applied to the concurrent fault diagnosis of crankshaft bearing and connecting rod bearing. This method not only can overcome the estimation errors and windowing effects in Hilbert separation algorithm by means of extensive properties of AR spectrum, but also can get the clear spectrum and be easy to extract the fault features. The test results confirm that.
Keywords :
autoregressive processes; condition monitoring; engines; fault diagnosis; rolling bearings; shafts; AR spectrum; EMD; EMD-AR Spectrum; Hilbert separation algorithm; autoregressive model spectrum; connecting rod bearing; crankshaft bearing; diesel fault diagnosis; empirical mode decomposition; engine concurrent fault diagnosis; estimation errors; fault features; windowing effects; Diesel engines; Fault diagnosis; Feature extraction; Frequency modulation; Frequency-domain analysis; Transforms; Vibrations; EMD-AR spectrum; concurrent fault diagnosis; connecting rod bearing; crankshaft bearing;
Conference_Titel :
Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), 2014 IEEE Conference and Expo
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-4240-4
DOI :
10.1109/ITEC-AP.2014.6940853