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Intelligent Real-Time Risk Analysis for Machines and Process Devices

  • Esko K. JuusoEmail author
  • Diego Galar
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

Automatic fault detection with condition and stress indices enables reliable condition monitoring to be combined with process control. Useful information on different faults can be obtained by selecting suitable features. Generalised norms can be defined by the order of derivation, the order of the moment and sample time. These norms have the same dimensions as the corresponding signals. The nonlinear scaling used in the linguistic equation approach extends the idea of dimensionless indices to nonlinear systems. The Wöhler curve is represented by a linguistic equation (LE) model. The contribution of the stress is calculated in each sample time, which is taken as a fraction of the cycle time. The cumulative sum of the contributions indicates the degrading of condition and the simulated sums can be used to predict failure time. To avoid high stress situations, the statistical process control (SPC) is 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 scaling functions are updated recursively, which is triggered by a fast increase of the deviation indices. The higher levels, which are rough estimates in the beginning, are gradually refined.

Keywords

Underground Mine Stress Index Statistical Process Control Process Capability Index Intelligent Analyser 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The risk analysis approach has been developed within the research program “Measurement, Monitoring and Environmental Efficiency Assesment (MMEA)” on the basis of two projects “Production integrated condition-based maintenance model for mining industry (DEVICO)” and “Integrated condition-based control and maintenance (ICBCOM)”. The funding of the TEKES (the Finnish Funding Agency for Technology and Innovation) and several companies is acknowledged.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Control EngineeringUniversity of OuluOuluFinland
  2. 2.Division of Operation and Maintenance EngineeringLuleå University of TechnologyLuleåSweden

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