Abstract

This study introduces a novel multimode online monitoring approach that employs a covariance test for the analysis of multimode, high-dimensional, and non-normally distributed data from Internet of Things (IoT) devices. The methodology involves estimating the covariance matrix using a linear shrinkage estimation method, followed by the calculation of a sparse principal eigenvalue test statistic based on the estimated covariance matrix. Additionally, an Exponentially Weighted Moving Average (EWMA) control chart is designed, incorporating a sliding window and a mode transition constraint parameter, referred to as the MMEWMA chart. To evaluate the performance of the MMEWMA control chart, Monte Carlo simulations are conducted under various conditions, including dimensionality, data distribution, drift size, in-control sample size, and mode transition parameters. The results demonstrate that the proposed MMEWMA control chart significantly outperforms other covariance test-based control charts, particularly in scenarios characterized by substantial drift and non-normally distributed data. Furthermore, the method’s effectiveness is validated through the analysis of real IoT device data sourced from wind turbines.

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