Benchmarking a Wide Range of Unsupervised Learning Methods for Detecting Anomaly in Blast Furnace

Steel plays important roles in our daily lives, as it surrounds us in the form of various products. Blast furnace, one of the main facility in steel production process, is traditionally monitored by skilled workers to prevent incidents. However, there is a growing demand to automate the monitoring process by leveraging machine learning. This paper focuses on investigating the suitability of unsupervised learning methods for detecting anomalies in blast furnaces. Extensive benchmarking is conducted using a dataset collected from blast furnaces, encompassing a wide range of unsupervised learning methods, including both traditional approaches and recent deep learning-based techniques. The computational experiments yield results that suggest the effectiveness of traditional methods over deep learning-based methods. To validate this observation, additional experiments are performed on publicly available non time series datasets and complex time series datasets. These experiments serve to confirm the superiority of traditional methods in handling non time series datasets, while deep learning methods exhibit better performance in dealing with complex time series datasets. We have also discovered that dimensionality reduction before anomaly detection is beneficial in eliminating outliers and effectively modeling the normal data points in the blast furnace dataset.