The development of automated pattern recognition and statistical feature isolation techniques for the diagnosis of reciprocating machinery faults using acoustic emission

M H El-Ghamry, Robert Lewis Reuben, John Alexander Steel

Research output: Contribution to journalArticle

45 Citations (Scopus)

Abstract

This work presents techniques which can be used for the diagnosis of faults in reciprocating machines. The techniques are generic in nature but the applications considered here concentrate on the interrogation of acoustic emission (AE) signals acquired from three types of reciprocating machine under normal running and with various fault conditions. Analysis of the signals in the time domain enables identification of machine timing, which can then be used to isolate automatically those parts of the signal associated with specific mechanical events. Various statistical feature isolation and pattern recognition techniques are then used on a selected time window of the signal to identify machine fault conditions. The applications considered demonstrate the generic nature of this approach, which could be applied to the monitoring of many types of machines and faults using a variety of sensor data. The ultimate aim of the work, of which this paper is a part, is to provide a means of on-line automatic monitoring of reciprocating machines using AE without recourse to any additional sensors. (C) 2003 Published by Elsevier Science Ltd.

Original languageEnglish
Pages (from-to)805-823
Number of pages19
JournalMechanical Systems and Signal Processing
Volume17
Issue number4
DOIs
Publication statusPublished - Jul 2003

Keywords

  • KNOCK DETECTION
  • CLASSIFICATION

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