Quantile index for gradual and abrupt change detection from CFB boiler sensor data in online settings

A. Maslov, M. Pechenizkiy, T. Kärkkäinen, Matti Tähtinen

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

2 Citations (Scopus)

Abstract

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
Original languageEnglish
Title of host publicationProceedings of the Sixth International Workshop on Knowledge Discovery from Sensor Data
Subtitle of host publicationSensorKDD 2012
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery ACM
Pages25-33
ISBN (Print)978-1-4503-1554-8
DOIs
Publication statusPublished - 2012
MoE publication typeNot Eligible
Event6th International Workshop on Knowledge Discovery from Sensor Data, SensorKDD'12 - Held in Conjunction with the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2012 - Beijing, China
Duration: 12 Aug 2012 → …

Conference

Conference6th International Workshop on Knowledge Discovery from Sensor Data, SensorKDD'12 - Held in Conjunction with the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2012
Abbreviated titleSensorKDD 2012
CountryChina
CityBeijing
Period12/08/12 → …

Fingerprint Dive into the research topics of 'Quantile index for gradual and abrupt change detection from CFB boiler sensor data in online settings'. Together they form a unique fingerprint.

  • Cite this

    Maslov, A., Pechenizkiy, M., Kärkkäinen, T., & Tähtinen, M. (2012). Quantile index for gradual and abrupt change detection from CFB boiler sensor data in online settings. In Proceedings of the Sixth International Workshop on Knowledge Discovery from Sensor Data: SensorKDD 2012 (pp. 25-33). Association for Computing Machinery ACM. https://doi.org/10.1145/2350182.2350185