Quantile Index for Gradual and Abrupt Change Detection from CFB Boiler Sensor Data in Online Settings

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Provided by: Association for Computing Machinery
Topic: Data Management
Format: PDF
In this paper, the authors consider the problem of online detection of gradual and abrupt changes in sensor data having high levels of noise and outliers. They 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. They evaluate the performance of their 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.
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