BS ISO 13379-2:2015

Foreword
Introduction
1 Scope
2 Normative references
3 Terms and definitions
4 Procedure to implement data-driven monitoring
5 Procedure to implement data-driven diagnosis
6 General recommendations to implement data-driven
  monitoring methods
Annex A (informative) - Example of data-driven monitoring
        application
Annex B (informative) - Example of data-driven diagnostic
        application
Bibliography

Provides 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.

This part of ISO13379 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.