Periodicity Estimation in Mechanical Acoustic Time-Series Data

Zhu, Yanjun; Hong, Zhipan; Lu, Guoliang

doi:10.1051/matecconf/20153402002

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…For the data with longer periodicity and instantaneous spikes, anomaly detection is easily affected if these disturbance segments are not removed. For the data with shorter periodicity and oscillations, if the peaks and valleys are not handled well, they can easily cause false alarms of the anomaly detection system (Wen et al, 2021;Y. Zhu et al, 2015;Zhu, 2006).…”

Section: Data Periodicitiesmentioning

confidence: 99%

A Novel Multi-Domain Adaptation-Based Method for Blast Furnace Anomaly Detection

Xiao

Zhou²,

Xia

et al. 2023

International Journal of Web Services Research

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In the steelmaking process, ensuring stable and reliable furnace plays a vital role for guaranteeing production quality of steel products. Traditional methods for detecting furnace anomalies in blast furnaces rely on operator judgment models built upon expert knowledge that can be limited by human experience. Moreover, data generated in blast furnace ironmaking process can be multidimensional, non-Gaussian distributed, and periodical, which can be easily affected by environmental and human factors and thus resulting in low accuracy of anomaly detection. Therefore, an online intelligent framework for detecting furnace anomalies is in high need. In this paper, the authors propose a novel anomaly detection method based on a furnace condition parameter-characterization model, a mining model of periodic patterns in the ironmaking process, and a multi-domain adaptive anomaly detection algorithm. They conduct extensive numerical analysis based on real-world production datasets as well to evaluate the effectiveness and accuracy of the method.

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Section: Data Periodicitiesmentioning

confidence: 99%