PCA algorithm for detection, localisation and evolution of damages in gearbox bearings
| Autor: | Luigi Garibaldi, E Gandino, A Torri, J.M. Machorro-López, Miriam Pirra |
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| Rok vydání: | 2011 |
| Předmět: |
History
Engineering Bearing (mechanical) business.industry Pattern recognition Structural engineering Fault (power engineering) Computer Science Applications Education law.invention Set (abstract data type) Vibration Identification (information) law Feature (computer vision) Component (UML) Principal component analysis Artificial intelligence business |
| Popis: | A fundamental aspect when dealing with rolling element bearings, which often represent a key component in rotating machineries, consists in correctly identifying a degraded behaviour of a bearing with a reasonable level of confidence. This is one of the main requirements a health and usage monitoring system (HUMS) should have. This paper introduces a monitoring technique for the diagnosis of bearing faults based on Principal Component Analysis (PCA). This method overcomes the problem of acquiring data under different environmental conditions (hardly biasing the data) and allows accurate damage recognition, also assuring a rather low number of False Alarms (FA). In addition, a novel criterion is proposed in order to isolate the area in which the faulty bearing stands. Another useful feature of this PCA-based method concerns the capability to observe an increasing trend in the evolution of bearing degradation. The described technique is tested on an industrial rig (designed by Avio S.p.A.), consisting of a full size aeroengine gearbox. Healthy and variously damaged bearings, such as with an inner or rolling element fault, are set up and vibration signals are collected and processed in order to properly detect a fault. Finally, data collected from a test rig assembled by the Dynamics & Identification Research Group (DIRG) are used to demonstrate that the proposed method is able to correctly detect and to classify different levels of the same type of fault and also to localise it. |
| Databáze: | OpenAIRE |
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