Rotating machinery diagnostics based on NARX models
Abstract
Rotating machines are often described using linear methods with acceptable accuracy. Some malfunctions, however, are of non-linear nature. Accurate detection and identification of such malfunctions requires more accurate methods. One of such methods can be NARX - Non-linear AutoRegressive model with eXogenous input. The paper presents how NARX models can be applied for modeling rotating machinery malfunctions. Idea of the diagnostic algorithm based on such modeling is presented. Proposed algorithm was verified during research on a specialized test rig, which can generate vibration signals. The paper presents results of application of NARX models for detection of typical rotating machinery failures and the variations of NARX model parameters due to propagation of damage. In the paper authors present also a blade crack detection using the NARX models. The last chapter of the paper discusses the applicability of this method for damage detection in real machines.
Keywords
rotating machinery diagnostics, blade crack detection, neural networks, NARX models,References
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