Anomaly Detection of Metallurgical Energy Data Based on iForest-AE

Abstract

With the proliferation of the Internet of Things, a large amount of data is generated constantly by industrial systems, corresponding in many cases to critical tasks. It is particularly important to detect abnormal data to ensure the accuracy of data. Aiming at the problem that the training data are contaminated with anomalies in autoencoder-based anomaly detection, which makes it difficult to distinguish abnormal data from normal data, this paper proposes a data anomaly detection method that combines an isolated forest (iForest) and autoencoder algorithm. In this method (iForest-AE), the iForest algorithm was used to calculate the anomaly score of energy data, and the data with a lower anomaly score were selected for model training. After the test data passed through the autoencoder trained by normal data, the data whose reconstruction error was larger than the threshold were determined as an anomaly. Experiment results on the electricity consumption dataset showed that the iForest-AE method achieved an F1 score of 0.981, which outperformed other detection methods, and a significant advantage in anomaly detection.