Reference : Fault Diagnosis in Industrial Systems Based on Blind Source Separation Techniques Using ...
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Mechanical engineering
http://hdl.handle.net/2268/94776
Fault Diagnosis in Industrial Systems Based on Blind Source Separation Techniques Using One Single Vibration Sensor
English
Nguyen, Viet Ha [Université de Liège - ULg > > > Doct. sc. ingé. (aérosp. & méca. - Bologne)]
Rutten, Christophe mailto [Université de Liège - ULg > Département d'aérospatiale et mécanique > LTAS - Vibrations et identification des structures >]
Golinval, Jean-Claude mailto [Université de Liège - ULg > Département d'aérospatiale et mécanique > LTAS - Vibrations et identification des structures >]
2011
International Conference on Structural Engineering Dynamics (ICEDyn 2011) - Proceedings
Maia NNM, Neves MM mailto
No
No
International
978-989-96276-1-1
International Conference on Structural Engineering Dynamics (ICEDyn 2011)
du 20 juin 2011 au 22 juin 2011
Instituto Superior Técnico, Technical University of Lisbon
Tavira
Portugal
[en] Fault diagnosis ; Condition monitoring ; Blind Source Separation Techniques
[en] In the field of structural health monitoring or machine condition monitoring, most vibration based methods reported in the literature require to measure responses at several locations on the structure. In machine condition monitoring, the number of available vibration sensors is often small and it is not unusual that only one single sensor is used to monitor a machine. The aim of this paper is to propose an extension of fault detection techniques that may be used when a reduced set of sensors or even one single sensor is available. Fault detection techniques considered here are based on output-only methods coming from the Blind Source Separation (BSS) family, namely Principal Component Analysis (PCA) and Second Order Blind Identification (SOBI). The advantages of PCA or SOBI rely on their rapidity of use and their reliability. Based on these methods, subspace identification may be performed by using the concept of block Hankel matrices which make possible the use of only one single measurement signal. Thus, the problem of fault detection in mechanical systems can be solved by using subspaces built from active principal components or modal vectors. It consists in comparing subspace features between the reference (undamaged) state and a current state. The angular coherence between subspaces is a good indicator of a dynamic change in the system due to the occurrence of faults or damages. The robustness of the methods is illustrated on industrial examples.
Researchers ; Professionals
http://hdl.handle.net/2268/94776

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