High-dimensional control charts with application to surveillance of grease damage in bearings of wind turbines

Abstract

High-dimensional data, characterized by having more attributes or variables than observations, presents unique challenges in industrial operations surveillance. Traditional multivariate control charts, like Hotelling’s [Formula: see text] chart, perform adequately with lower-dimensional data. However, they often fail to detect variations in process means as data dimensionality increases. This research proposes new control charts designed to enhance the detection of mean variations in both high and low-dimensional data. Specifically, Srivastava-Du (SD), Bai-Saranadasa (BS) and Dempster (DS) statistic-based charts are introduced, and their effectiveness is evaluated through simulations and real-life data applications. The performance of these charts is compared under various multivariate normal and non-normal distributions. Results indicate that DS and BS charts perform similarly, with the DS chart outperforming in low-dimensional normal distribution. Conversely, the SD chart outperformed in high-dimensional non-normal distributions. Additionally, the practical application of these proposed charts is illustrated through the monitoring of grease degradation in wind turbine bearings.

Keywords

• Control chart
• high-dimensional data
• low-dimensional data
• memoryless chart
• statistical process control