Abstract
In the industrial environment, the health status of critical machinery is constantly monitored, consequently generating a large amount of data that needs to be analyzed by experts. However, it becomes unfeasible to a human to verify and correlate all real time data, especially in annotating and classifying the interesting patterns present in the data - such as normal or abnormal/failure - which is valuable in researches that involve the development of predictive and classification models using Artificial Intelligence. This paper presents a comparative study between methods of detecting interesting patterns and anomalies based on unsupervised machine learning, aiming to automate the data annotation process between normal or abnormal classes (or failures), in order to further detect the failures in industrial machinery. Multivariate real data acquired from 21 sensors coupled to a gearbox of a turbo generator were used. The results revealed that unsupervised learning methods effectively detected normal and anomalous behaviors without the need of prior labeling or classification by experts, with emphasis on the C-AMDATS algorithm. In fact, the use of real data proves that the proposed approach is suitable for unsupervised anomaly detection. Therefore, it is possible to conclude that unsupervised machine learning algorithms are able to assist experts and managers in decision making and preparing labeled data for later use in supervised machine learning algorithms for prediction and classification purposes, providing greater reliability in maintenance.