Generalised statistical process control (GSPC) in stress monitoring

The early detection of fluctuations in operating conditions and fault detection is done with similar methods. The feature extraction uses statistical analysis based on generalised norms and moments. Intelligent stress indices are calculated from these features by nonlinear scaling. The scaling appro...

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Bibliographic Details
Published in:IFAC-PapersOnLine 2015-07, Vol.48 (17), p.207-212
Main Author: Juuso, Esko K.
Format: Article
Language:English
Subjects:
cavitation
fatigue
feature extraction
roller mill
Statisttical process control
vibration analysis
water turbine
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Summary:The early detection of fluctuations in operating conditions and fault detection is done with similar methods. The feature extraction uses statistical analysis based on generalised norms and moments. Intelligent stress indices are calculated from these features by nonlinear scaling. The scaling approach uses the norms and moments to produce indices, which are consistent with the vibration severity criteria. Nonlinear scaling can be used for finding suitable control limits for the features and indices. Harmful high levels of stress are efficiently detected with control limits adjusted to the process requirements. The limits can be explained by fuzzy set systems and categorical information is included through knowledge-based analysis. The statistical process control (SPC) can be extended to nonlinear and non-Gaussian data: the new generalised SPC is suitable for a large set of statistical distributions. It operates without interruptions in short run cases and adapts to the changing process requirements. The approach is tested in two application cases: a rolling mill and an underground load haul dump (LHD) machine.
ISSN:2405-8963
2405-8963
DOI:10.1016/j.ifacol.2015.10.104