Anomaly Detection Methods for Industrial Applications: A Comparative Study

Abstract

Anomaly detection (AD) algorithms can be instrumental in industrial scenarios to enhance the detection of potentially serious problems at a very early stage. Of course, the “Industry 4.0” revolution is fostering the implementation of intelligent data-driven decisions in industry based on increasingly efficient machine learning (ML) algorithms. Most well-known AD methods use a supervised learning approach focusing on fault classification. They assume the availability of labeled data for both normal and anomalous classes. However, in many industrial environments, a labeled set of anomalous data instances is more challenging to obtain than a labeled set of normal data. Hence, this work implements an unsupervised approach based on two different methods using a typical benchmark bearing-fault dataset. The first method relies on the manual extraction of typical vibration metrics provided as input to an ML algorithm. The second one is based on a deep learning (DL) approach, automatically learning latent representation from raw data. The performance metrics demonstrate that both approaches can distinguish the state of a bearing from normal to faulty. DL methodology proves a higher accuracy rate in recognizing faults and a better ability to provide information about the fault size.