NARX MODEL IN ROTATING MACHINERY DIAGNOSTICS

Authors

  • Jarosław Bednarz AGH University of Science and Technology
  • Tomasz Barszcz AGH University of Science and Technology
  • Tadeusz Uhl AGH University of Science and Technology

Keywords:

NARX model, rotating machinery diagnostics, wind-turbine damage detection

Abstract

Rotating machines are often described using linear methods with acceptable accuracy. Some malfunctions, however, are of non-linear nature. The most common examples of those malfunctions are loose bearings and rotor rubs. Accurate detection and identification of such malfunctions requires more accurate methods. One of such methods can be NARX - nonlinear systems identification. This method is based on neural networks approach and is especially efficient in modeling and diagnostics of nonlinear systems. Application of this method leads to shorter and less costly tuning of the model to the object, which is the key requirement when practical application of a method is concerned. The paper presents how NARX can be applied for modeling of rotating machinery malfunctions. Idea of the diagnostic algorithm based on such modeling is presented. The proposed algorithm was verified during research on a specialized test rig, which can generate vibration signals. The paper also presents results of an application of the NARX method for data collected at a wind turbine.

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Published

2011-06-26

Issue

Vol. 30 No. 2 (2011)

Section

Articles

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