A semi-supervised approach based on evolving clusters for discovering unknown abnormal condition patterns in gearboxes

Research output: Contribution to conferencePaper

3 Scopus citations

Abstract

© 2018 - IOS Press and the authors. All rights reserved. Fault diagnosis plays a crucial role to maintain healthy conditions in rotating machinery. In real industrial applications, a Machine Learning based Classifier (ML-C) analyses data from a current machinery condition to detect abnormal behaviours. Usually, this is achieved through a previous training of the ML-C model, under supervised learning; however, for new machinery conditions, the classifier is not able to correctly identify these new condition. This paper proposes a framework to detect new patterns of abnormal conditions in gearboxes, that could be associated to new faults. The framework relies on an algorithm to build evolving models in simultaneous scenarios of classification and clustering. The design is inspired by the main principles of the K-means and the One Nearest Neighbour (1-NN) algorithms. A heuristic metric is defined to analyse the new discovered clusters; as a result, these new clusters can be labelled as new classes corresponding to new faulty patterns. Once a new pattern is identified, the associated data feeds a dedicated supervised classifier which is updated through a new training phase. The proposed framework is tested on data collected from a gearbox test bed under realistic conditions of faults. Experimental results show that the algorithm is able to discover new valuable knowledge than can be identified as new faulty classes.
Original languageEnglish
Pages3581-3593
Number of pages13
DOIs
StatePublished - 1 Jan 2018
EventJournal of Intelligent and Fuzzy Systems - , Netherlands
Duration: 1 Jan 1996 → …

Conference

ConferenceJournal of Intelligent and Fuzzy Systems
Country/TerritoryNetherlands
Period1/01/96 → …

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