High-Dimensional Cumulative Sum Control Charts for Industrial Measurement Systems

High dimensional sensing in modern factories and energy assets produces thousands of correlated signals. Detecting small process shifts is challenging because many classical multivariate charts lose power or become unstable. We present two cumulative sum procedures for two sample monitoring that remain effective when the number of variables is large relative to observations. Each procedure forms a memory-based score from simple statistics, updates a recursive cumulative sum, and triggers an alarm when a data driven limit is crossed. The design uses only vector operations, avoids estimating large covariance matrices, and integrates with streaming data pipelines used in industrial software. Detection performance is studied with simulations spanning normal, heavy tailed and skewed data, a range of shift sizes, dimensions in the hundreds, and both balanced and unbalanced reference and test samples. Compared with related Shewhart charts, the procedures detect small and moderate shifts faster while preserving the desired in control behavior, with one method strongest under normal data and the other more stable with heavy tails. Two applications show practical value using wind turbine bearing measurements and daily asset prices. The approach scales linearly with variables, requires small memory, and supports real time deployment in manufacturing and condition monitoring platforms.