Title :
Machine health diagnosis based on approximate entropy
Author :
Yan, Ruqiang ; Gao, Robert X.
Author_Institution :
Dept. of Mech. & Ind. Eng., Massachusetts Univ., Amherst, MA, USA
Abstract :
As a statistical measure for time domain signals, Approximate Entropy (ApEn) quantifies the regularity of a data sequence, thus can serve as an indicator for the severity of structural defects in a machine system, such as an electrical drive or a rolling bearing. This paper investigates the utility of ApEn for machine health diagnosis, using a realistic-spindle-bearing test bed as the platform. Experimental results were consistent with the theoretical analysis, and confirmed that ApEn provides an effective measure for the severity of structural defect, with good computational efficiency and high robustness.
Keywords :
ball bearings; condition monitoring; electric machine analysis computing; entropy; machine testing; machine tool spindles; mechanical engineering computing; rolling bearings; state-space methods; time series; time-domain analysis; approximate entropy; ball bearings; computational efficiency; data sequence regularity; electrical drive; feature extraction; high robustness; machine health diagnosis; periodic time series; rolling bearing; spindle-bearing test bed; statistical measure; structural defects; time domain signals; Auditory system; Condition monitoring; Electroencephalography; Entropy; Feature extraction; Industrial engineering; Patient monitoring; Robustness; State-space methods; Testing;
Conference_Titel :
Instrumentation and Measurement Technology Conference, 2004. IMTC 04. Proceedings of the 21st IEEE
Print_ISBN :
0-7803-8248-X
DOI :
10.1109/IMTC.2004.1351493
