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ISO DIS 13379-2 2014 Edition, January 9, 2014 CONDITION MONITORING AND DIAGNOSTICS OF MACHINES - DATA INTERPRETATION AND DIAGNOSTICS TECHNIQUES - PART 2: DATA-DRIVEN APPLICATIONS
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Description / Abstract:
This part of ISO 13379 gives procedures to implement data-driven
monitoring and diagnostic methods to facilitate the work of
analysis carried out by specialist staff typically located in a
monitoring centre.
Although some of the steps are embedded in existing tools, it is
essential to be aware of the following steps for optimum use:
— selection of the asset, the critical failures and the
available process parameters;
— data cleaning and resampling;
— model development;
— model initialization and tuning;
— model performance evaluation;
— diagnostics process.
The implementation of these steps does not require a thorough
knowledge of the statistical methods. It does require the
competence first to build the training models and then to carry out
monitoring and diagnostics processes.
The training in data-driven monitoring is carried out on
equipment that is exhibiting normal behaviour. In that case, the
principle of fault detection is to compare observed data to
estimated data. A difference (called residuals) between an observed
and expected values of the parameters reveals the presence of an
anomaly, which can be related either to equipment or
instrument.
The training in data-driven diagnosis is carried out both on
equipment that is exhibiting normal behaviour and failures. The
principle of the method is not to detect the deviation of a
parameter but to identify a fault by comparison of the observed
situation to the faults learnt during the training phase. The
technique usually applied is pattern recognition followed by
pattern classification.
Data can be available from the data historian of the distributed
control system (DCS) or from specialized monitoring systems.