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B. A. Paya, I. I. Esat and M. N. M. Badi, “Artificial Neural Network Based Fault Diagnos-tics of Rotating Machinery Using Wavelet Transforms as a Preprocessor,” Mechanical Systems and Signal Processing, Vol. 11, No. 5, 1997, pp. 751-765. doi:10.1006/mssp.1997.0090

has been cited by the following article:

• TITLE: Diagnosis on Surface Failure in Gear Equipment Using Time - Frequency Domain Analysis

  AUTHORS: Yuji Ohue, Toshiya Kounou, Katsuhiko Yazama

  KEYWORDS: Natural Asset, Financial Value, Neural Network

  JOURNAL NAME: Engineering, Vol.3 No.8, August 12, 2011

  ABSTRACT: In order to make more progress in the gear performance, it is important to evaluate the gear dynamics more precisely, since gear is a main machine element in motion and power transmissions. For preventing the unexpected failure in mechanical systems, a large amount of work has been carried out based on a statistical model developed by Lundberg and Palmgren [1] with reliability models using the classical fatigue theory. However, most of these works did not consider the operating conditions of machine during the fatigue process.