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

A. Maslov; M. Pechenizkiy; T. Kärkkäinen; M. Tähtinen

doi:10.1145/2350182.2350185

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

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

Data Mining

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

2 Citations (Scopus)

3 Downloads (Pure)

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 language	English
Title of host publication	Proceedings of the Sixth International Workshop on Knowledge Discovery from Sensor Data (SensorKDD'12, Beijing, China, August 12, 2012)
Place of Publication	New York
Publisher	Association for Computing Machinery, Inc
Pages	25-33
ISBN (Print)	978-1-4503-1554-8
DOIs	https://doi.org/10.1145/2350182.2350185
Publication status	Published - 2012
Event	conference; Sixth International Workshop on Knowledge Discovery from Sensor Data; 2012-08-12; 2012-08-12 - Duration: 12 Aug 2012 → 12 Aug 2012

Conference

Conference	conference; Sixth International Workshop on Knowledge Discovery from Sensor Data; 2012-08-12; 2012-08-12
Period	12/08/12 → 12/08/12
Other	Sixth International Workshop on Knowledge Discovery from Sensor Data

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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'12, Beijing, China, August 12, 2012) (pp. 25-33). Association for Computing Machinery, Inc. https://doi.org/10.1145/2350182.2350185

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

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.",

author = "A. Maslov and M. Pechenizkiy and T. K{\"a}rkk{\"a}inen and M. T{\"a}htinen",

year = "2012",

doi = "10.1145/2350182.2350185",

language = "English",

isbn = "978-1-4503-1554-8",

pages = "25--33",

booktitle = "Proceedings of the Sixth International Workshop on Knowledge Discovery from Sensor Data (SensorKDD'12, Beijing, China, August 12, 2012)",

publisher = "Association for Computing Machinery, Inc",

address = "United States",

note = "conference; Sixth International Workshop on Knowledge Discovery from Sensor Data; 2012-08-12; 2012-08-12 ; Conference date: 12-08-2012 Through 12-08-2012",

}

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'12, Beijing, China, August 12, 2012). Association for Computing Machinery, Inc, New York, pp. 25-33, conference; Sixth International Workshop on Knowledge Discovery from Sensor Data; 2012-08-12; 2012-08-12, 12/08/12. https://doi.org/10.1145/2350182.2350185

Quantile index for gradual and abrupt change detection from CFB boiler sensor data in online settings. / Maslov, A.; Pechenizkiy, M.; Kärkkäinen, T. et al.
Proceedings of the Sixth International Workshop on Knowledge Discovery from Sensor Data (SensorKDD'12, Beijing, China, August 12, 2012). New York: Association for Computing Machinery, Inc, 2012. p. 25-33.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Quantile index for gradual and abrupt change detection from CFB boiler sensor data in online settings

AU - Maslov, A.

AU - Pechenizkiy, M.

AU - Kärkkäinen, T.

AU - Tähtinen, M.

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

U2 - 10.1145/2350182.2350185

DO - 10.1145/2350182.2350185

M3 - Conference contribution

SN - 978-1-4503-1554-8

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BT - Proceedings of the Sixth International Workshop on Knowledge Discovery from Sensor Data (SensorKDD'12, Beijing, China, August 12, 2012)

PB - Association for Computing Machinery, Inc

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T2 - conference; Sixth International Workshop on Knowledge Discovery from Sensor Data; 2012-08-12; 2012-08-12

Y2 - 12 August 2012 through 12 August 2012

ER -

Maslov A, Pechenizkiy M, Kärkkäinen T, Tähtinen M. 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'12, Beijing, China, August 12, 2012). New York: Association for Computing Machinery, Inc. 2012. p. 25-33 doi: 10.1145/2350182.2350185

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

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