# Anomaly Detection of Metallurgical Energy Data Based on iForest-AE
## 1 Introduction  
The metallurgical industry is an essential component of the manufacturing industry and an essential support for maintaining world economic growth [1]. However, since almost all equipment in metallurgical enterprises usually lie in harsh environments [2], data collection is susceptible to external interference [3]. Sensor failures, network interruptions, and electromagnetic interference can also cause abnormalities in the collected data, thus affecting the modeling and analysis of the data. This also leads to incorrect decisions and guidance for the field dispatchers of metallurgical enterprises, causing serious safety accidents and irreparable economic losses. Data anomaly detection plays a pivotal role in data management. The detection and diagnosis of key energy data in metallurgical enterprises are necessary for the enterprise's safe production and scientific dispatch. Therefore, developing a set of anomaly detection algorithms for data collected by a metallurgical enterprise information system is of great significance in ensuring the safe production of metallurgical enterprises and improving the economic benefits of enterprises. As one of the important research directions of machine learning [4], anomaly detection has become a hotspot and been applied in many important areas. According to the different ideas of anomaly detection, it can be broadly categorized into: statistical-based [5], nearest neighbor-based [6], clustering-based [7], and iForest-based [8] methods. [PERSON] [9] proposed an improved KNN algorithm for log data anomaly detection. He used an existing mean-shift clustering algorithm to select a training set from log data, and assigned various weights to samples with various distances, which lessened the negative effect of an imbalanced distribution of the log samples. [PERSON] [10] came up with a clustering-based anomaly detection method. The idea of the method was to identify clusters in sensor data undernormal operating situations and determine if new observations belong to any of these clusters. Experimental results showed that cluster-based methods performed better than other methods. [PERSON] [11] developed an GA-iForest method for numerical data anomaly detection. This method improved the accuracy of anomaly detection by removing some duplicate, similar, and poor detection isolation trees. Experimental results indicated that the proposed method could not only increase the accuracy of anomaly detection, but also decrease the number of isolation trees. However, the above methods have randomness and a lack of robustness, and it is easy to cause a dimensional disaster in high-dimensional data space. In recent years, with the wide application of machine learning in speech recognition [12], machine translation [13], and data analysis [14], some machine learning models have also been gradually applied to the research of data anomaly detection [15]. Among various anomaly detection models based on machine learning, the autoencoder shows excellent performance due to its strong generalization and no dimensional disaster. [PERSON] et al. [16] proposed an autoencoder-based approach for high performance computing systems anomaly detection and it can be used to distinguish abnormal conditions from normal conditions by learning the normal state of the supercomputer nodes. [PERSON] et al. [17] built a model that projects the training data into a lower-dimensional subspace during the training phase, then optimized weights to minimize reconstruction error. It also used the reconstruction error of test data to determine whether the wireless transmission of data is abnormal. Although many attempts have been made, the performance of anomaly detection remains low, especially when the training data are contaminated with anomalies. It is difficult to distinguish abnormal data from normal data because the reconstruction error between the two is not very large. To solve this problem, a data anomaly detection method by combining the iForest and autoencoder algorithm is proposed in this paper.