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An Empirical Application of Linear Regression Method and FIR Network for Fault Diagnosis in Nonlinear Time Series

Muhammad Shafique Shaikh, and Dote, Yasuhiko, (1999) An Empirical Application of Linear Regression Method and FIR Network for Fault Diagnosis in Nonlinear Time Series. Malaysian Journal of Computer Science, 12 (2). pp. 57-63. ISSN 0127-9084

Full text not available from this repository.

Official URL: http://mjcs.fsktm.um.edu.my/detail.asp?AID=78

Affiliations

Muroran Institute of Technology, Japan
Muroran Institute of Technology, Japan

Abstract

A fault diagnosis scheme for nonlinear time series recorded in normal and abnormal conditions is described. The fault is first detected from regression lines of the raw time series. Model for the normal condition time series is estimated using a Finite Impulse Response (FIR) neural network. The trained network is then used for filtering of abnormal condition time series. The fault is further confirmed/ analyzed using the regression lines of the predicted normal and inverse-filtered abnormal conditions time series.
The described scheme is applied to two fault diagnosis problems using acoustic and vibration data obtained from rotating parts of an automobile and a boring tool, respectively.

Item Type:Journal
Keywords:Signal Processing, Filtering, Fault Diagnosis, Linear Regression, Neural Network
Subjects:Q Science, Computer Science
ID Code:207

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