Framework for discovering unknown abnormal condition patterns in gearboxes using a semi-supervised approach

© 2017 IEEE. Fault diagnosis plays a crucial role to maintain healthy conditions in rotating machinery. This paper proposes a framework to detect new patterns of abnormal conditions in gearboxes, that would be associated to new faults. This is achieved through a Hybrid Heuristic Algorithm for Evolving Models in scenarios of Classification and Clustering (HHA-EMCC), which is a machine learning algorithm that can be adapted to solve problems related to classification and clustering both combined. The design aims at creating clusters and classes inspired by the main principles of the nearest neighbour (1-NN) strategy and Kmeans. HHA-EMCC has the particularity of detecting new clusters after being trained, this characteristic defines some guidelines that determine whether a cluster represents new knowledge or not. The framework is able to discover abnormal conditions from unlabelled data through cluster constructions. This analysis can lead to labelling these clusters as new classes. Once a new pattern is identified, the associated data feeds the current classifier for a new training phase. The proposed framework is tested on a fault dataset for gearboxes and experimental results show that valuable new knowledge is obtained.