Application of robust statistics in vibration time histories analysis

Statistical parameters of vibration data distributions provide a remarkable source of information in condition monitoring. The assumption of normality leads to comparatively simple description within the framework of classical statistics. However, due to the presence of outliers and heavy-tailed distributions, this approach is often unacceptable. In such cases, robust methods prove superior. The paper is focused on applying robust statistics in analyzing vibration data dispersion and correlation for the purpose of lifetime consumption estimation. This approach is suitable for large rotating machines operated in an industrial plant environment and characterized by significant influences of control parameters and interference. It is shown that robust meth¬ods yield results that are easier to interpret from both qualitative and quantitative viewpoints. Examples re¬ferring to large steam turbines operated by utility power plants are given; however, certain results can be generalized over a broader class of rotating machines or even diagnostic objects.

Vibration-based symptoms are extremely important in monitoring technical condition of virtually all types of rotating machinery, especially large and complex units, e.g. turbo-generators, large fans or compressors. This is justified by their high information content, non-intrusive nature and com­paratively well-developed data acquisition and processing procedures. The most straightforward approach consists in analyzing vibration patterns recorded at a certain moment on the basis of a purpose-developed diagnostic model. Information on the object condition is, however, also con­tained in time histories of certain vibration components. As pointed out in References, a vibra­tion component evolution type and relevant timescale (the latter varying within a broad range, from seconds to months) contain information on a fault type and thus are useful already at the qualitative diagnosis stage. More detailed analysis of vibration time histories becomes even more important when it comes to quantitative assessment and is mandatory for a prognosis.

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