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The University of Southampton
Engineering

Research project: Crack detection of rotating machinery by order tracking analysis using particle filtering

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Condition monitoring of rotating machinery such as turbines, pumps and motors, aims to identify when repairs are necessary and to avoid shutdown and disassembly of machine in an industrial plant.

Project Overview

Condition monitoring of rotating machinery such as turbines, pumps and motors, aims to identify when repairs are necessary and to avoid shutdown and disassembly of machine in an industrial plant. Many existing diagnosis methods assume that the machine is running at steady state (i.e. the signals are stationary). But much information about a rotating machine condition can be obtained during nonstationary conditions, such as during run-up and run-down. Order tracking analysis (OTA) is a powerful tool which is widely used for condition monitoring of rotating machinery during these non-stationary periods. However OTA has traditionally employed a second sensor which directly measures the rotation speed of the machine, referred to as a tacho signal. Our work develops a new form of OTA which uses the sampling importance resampling (SIR) particle filter and the chirplet transform. This method does not need any tacho signal and has a high resolution compared to other OTAs. It can help to detect faults such as cracks automatically and the particle filter tracks the frequency and the number of order components. The new method was successfully applied to the diagnosis of a cracked rotor.

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