Quantile index for gradual and abrupt change detection from CFB boiler sensor data in online settings
Alexandr V. Maslov, Mykola Pechenizkiy, Tommi Kärkkäinen, Matti Tähtinen · 2012
In this paper we consider the problem of online detection of gradual and abrupt changes in sensor data having high levels of noise and outliers. We propose a simple heuristic method based on the Quantile Index (QI) and study how robust this method is for detecting both gradual and abrupt changes with such data. We evaluate the performance of our method on the artificially generated and real datasets that represent different operational settings of a pilot circulating fluidized bed (CFB) reactor and CFB cold model. Our experiments suggest that QI can be used for designing very simple yet effective methods for gradual change detection in the noisy sensor data. It can be also used for detecting abrupt changes in the data unless they occur too often one after another.