Data Quality Assessment

Collecting useful and informative data play an essential role in ensuring the performance of data-driven solutions for intelligent maintenance. However, there is still a lack of methodology to systematically assess the data usefulness (or data suitability) for modeling. This lack of data suitability assessment becomes a more pressing issue in the big data environment where a large volume of machine data is generated at a high velocity. Therefore, there are imperative needs for standardized procedures and systematic solutions that can scan through a large amount of data to quantify the data suitability and locate the useful datasets for model development.