Assessment of Data Suitability for Machine Prognosis Using Maximum Mean Discrepancy

As more and more data become available for machine prognostic analysis in the big data environment, effective data suitability assessment methods become highly desired to help locate data with sufficient quality for analysis. Driven by this purpose, this paper proposes a novel and systematic methodology for data suitability assessment based on the needs of prognostics and health management (PHM). In this study, the data suitability for PHM is assessed from the aspects of detectability, diagnosability, and trendability, which correspond to the three major tasks of PHM: fault detection, fault diagnosis, and degradation assessment. The proposed methodology is mainly built upon the recent research studies on maximum mean discrepancy in the field of machine learning, which include a family of test statistics that are used to test the difference between two data distributions. The effectiveness of the proposed methodology is demonstrated in diverse industrial applications, which include semiconductors, boring tool degradation, and sensorless drive diagnosis. The results in the case studies indicate that the proposed methodology can be a promising tool to evaluate whether the data under study or the extracted feature set is suitable for PHM tasks.