Model Based Fault Identification in Rotor Systemsby Least Squares Fitting

Richard Markert,Malte Seidler,Roland Platz

doi:10.1155/s1023621x01000264

Richard Markert, Malte Seidler + Show 1 more

Open Access

https://doi.org/10.1155/s1023621x01000264

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Abstract

In the present paper a model based method for the on‐line identification of malfunctions in rotor systems is proposed. The fault‐induced change of the rotor system is taken into account by equivalent loads which are virtual forces and moments acting on the linear undamaged system model to generate a dynamic behaviour identical to the measured one of the damaged system.By comparing the equivalent loads reconstructed from current measurements to the pre‐calculated equivalent loads resulting from fault models, the type, amount and location of the current fault can be estimated. The identification method is based on least squares fitting algorithms in the time domain. The quality of the fit is used to find the probability that the identified fault is present.The effect of measurement noise, measurement locations, number of mode shapes taken into account etc., on the identification result and quality is studied by means of numerical experiments. Finally, the method has also been tested successfully on a real test rig for some typical faults.

Highlights

Rotating machinery installed in power plants are normally equipped with vibration sensors which continuously provide large amounts of vibration data during operation, (Bruel and Kjaer, 1986)
Model based monitoring systems give more accurate and faster information than conventional signal based systems, since a-priori information about the rotor system is systematically included in the identification process
For reliable identification results as much information as possible should be available about the rotor-stator system

Summary

Introduction

Rotating machinery installed in power plants are normally equipped with vibration sensors which continuously provide large amounts of vibration data during operation, (Bruel and Kjaer, 1986). For diagnosing the state of a machine, usually signal based monitoring systems are used as a good tool, they do not fully utilise the information contained within the vibration data, (Bruel and Kjaer, 1986); (Glendenning et al, 1997). These approaches to machinery diagnostics are generic rather than machine specific and the interpretation of the data is based on qualitative rather than quantitative information. The type, position and severity of a fault can be estimated with more reliability and in most cases during operation without stopping the machine

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Results

Conclusion