ISO 13379-2:2015
Condition monitoring and diagnostics of machines — Data interpretation and diagnostics techniques — Part 2: Data-driven applications
ISO 13379-2:2015 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.
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Foreword
Introduction
1 Scope
2 Normative references
3 Terms and definitions
4 Procedure to implement data-driven monitoring
4.1 Principle of data-driven monitoring methods
4.2 Asset critical failures and process parameters selection
4.3 Data cleaning and resampling
4.3.1 General
4.3.2 Interpolation errors
4.3.3 Data quality issues
4.3.4 Data resampling
4.4 Model development
4.4.1 General
4.4.2 Definition of models and selection of relevant inputs
4.4.3 Selection of relevant operating conditions and data
4.4.4 Preparation of the model tests
4.5 Model performance evaluation
4.6 Alarm setting
5 Procedure to implement data-driven diagnosis
5.1 General
5.2 Automated pattern classification approach
5.3 Simplified automated signature classification approach
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
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Foreword
Introduction
1 Scope
2 Normative references
3 Terms and definitions
4 Procedure to implement data-driven monitoring
4.1 Principle of data-driven monitoring methods
4.2 Asset critical failures and process parameters selection
4.3 Data cleaning and resampling
4.3.1 General
4.3.2 Interpolation errors
4.3.3 Data quality issues
4.3.4 Data resampling
4.4 Model development
4.4.1 General
4.4.2 Definition of models and selection of relevant inputs
4.4.3 Selection of relevant operating conditions and data
4.4.4 Preparation of the model tests
4.5 Model performance evaluation
4.6 Alarm setting
5 Procedure to implement data-driven diagnosis
5.1 General
5.2 Automated pattern classification approach
5.3 Simplified automated signature classification approach
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