Improved anomaly detection using multi-scale PLS and generalized likelihood ratio test
In the process monitoring procedure, Data-driven (statistical) methods usually rely on the process measurements. In most industrial process this measurements has a multi-scale substance in time and frequency. Therefore the statistical methods which are proper for one scale may not be able to detect events at several scales. A Multi-Scale Partial Least Squares (MSPLS) algorithm consists of Wavelet Transforms for extracting multiscale nature of measurements and Partial Least Squares (PLS) as a popular technique of statistical monitoring methods. In this paper the MSPLS algorithm is applied for monitoring of the Tennessee Eastman Process as a benchmark. To show the advantages of MSPLS, its process monitoring performance is compared with the standard PLS and is proved that MSPLS can be a more efficient technique than standard PLS for fault detection in industrial processes.