An Anomaly Detection Algorithm for Cathode Voltage of Aluminum Electrolytic Cell
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The cathode voltage of aluminum electrolytic cell is relatively stable under normal conditions and fluctuatesgreatly when it has an anomaly. In order to detect the abnormal range of cathode voltage, an anomaly detectionalgorithm based on sliding window was proposed. The algorithm combines the time series segmentation linearrepresentation method and the k-nearest neighbor local anomaly detection algorithm, which is more efficientthan 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 ofspatial objects. And then the local anomaly detection algorithm is used to detect abnormal patterns accordingto the local anomaly factor and the pattern length. The experimental results showed that the algorithm caneffectively detect the abnormal range of cathode voltage.
Abstract
The cathode voltage of aluminum electrolytic cell is relatively stable under normal conditions and fluctuatesgreatly when it has an anomaly. In order to detect the abnormal range of cathode voltage, an anomaly detectionalgorithm based on sliding window was proposed. The algorithm combines the time series segmentation linearrepresentation method and the k-nearest neighbor local anomaly detection algorithm, which is more efficientthan 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 ofspatial objects. And then the local anomaly detection algorithm is used to detect abnormal patterns accordingto the local anomaly factor and the pattern length. The experimental results showed that the algorithm caneffectively detect the abnormal range of cathode voltage.
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