An Anomaly Detection Algorithm for Cathode Voltage of Aluminum Electrolytic Cell


Danyang Cao, Yanhong Ma, Lina Duan, Journal of Information Processing Systems Vol. 15, No. 6, pp. 1392-1405, Dec. 2019  

https://doi.org/10.3745/JIPS.04.0150
Keywords: Abnormal Pattern, Cathode Voltage, k-nearest neighbor, sliding window
Fulltext:

Abstract

The cathode voltage of aluminum electrolytic cell is relatively stable under normal conditions and fluctuates greatly when it has an anomaly. In order to detect the abnormal range of cathode voltage, an anomaly detection algorithm based on sliding window was proposed. The algorithm combines the time series segmentation linear representation method and the k-nearest neighbor local anomaly detection algorithm, which is more efficient than the direct detection of the original sequence. The algorithm first segments the cathode voltage time series, then calculates the length, the slope, and the mean of each line segment pattern, and maps them into a set of spatial objects. And then the local anomaly detection algorithm is used to detect abnormal patterns according to the local anomaly factor and the pattern length. The experimental results showed that the algorithm can effectively detect the abnormal range of cathode voltage.


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Cite this article
[APA Style]
Cao, D., Ma, Y., & Duan, L. (2019). An Anomaly Detection Algorithm for Cathode Voltage of Aluminum Electrolytic Cell. Journal of Information Processing Systems, 15(6), 1392-1405. DOI: 10.3745/JIPS.04.0150.

[IEEE Style]
D. Cao, Y. Ma, L. Duan, "An Anomaly Detection Algorithm for Cathode Voltage of Aluminum Electrolytic Cell," Journal of Information Processing Systems, vol. 15, no. 6, pp. 1392-1405, 2019. DOI: 10.3745/JIPS.04.0150.

[ACM Style]
Danyang Cao, Yanhong Ma, and Lina Duan. 2019. An Anomaly Detection Algorithm for Cathode Voltage of Aluminum Electrolytic Cell. Journal of Information Processing Systems, 15, 6, (2019), 1392-1405. DOI: 10.3745/JIPS.04.0150.