ISA-PHM - a standardized format for storing and utilizing meta-data of diagnostic and prognostic tests

The development and implementation of diagnostic and prognostic algorithms for smart maintenance purposes is hindered by the fundamental lack of relevant, complete and properly labeled data from fielded systems. This issue is partly tackled by the generation of well-defined datasets using numerical simulations or experimental set-ups in a laboratory environment. However, the widely varying formats of (the description of) these datasets make that data scientists need to invest heavily in interpreting the data and transforming it to a format that fit the model requirements. To reduce that effort and ensure a robust and consistent processing, this work proposes a standardized way of documenting such datasets. This ISA-PHM standard is based on the existing ISA metadata standard originating from life and biomedical sciences, that has been translated to the prognostics and health management (PHM) context. This is achieved by firstly structuring and generalizing the information required to document both diagnostic and prognostic (numerical or physical) experiments. This information is then carefully mapped to the ISA ontology, ensuring a complete and unambiguous documentation of any PHM-related test. The concept is demonstrated by application of ISA-PHM to three well-known public datasets (NLN-EMP, NASA milling data, CMAPSS) and a real failure. Finally, for the implementation, some practical software tools are presented (available on linked website) as well as the planned future extension towards collective data generation through distributed testing.

• View at Publisher

Free Full Text ( Final Version , 1mb )