Fault diagnosis of bladed disc using wavelet transform and ensemble empirical mode decomposition

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ABSTRACT

Blade faults are considered as the most common cause of failure in turbomachines. Any fault occurs in impeller’s blades gives the breakdown in these machines and creates undesired vibration. This paper presents a new method which combines wavelet transform (WT) with ensemble empirical mode decomposition (EEMD) method for early identification of blade state. The vibration signals measured from a blade rotor are filtered using the WT, and then the obtained signals are decomposed into intrinsic mode functions (IMFs) by EEMD method to obtain multichannel signals. The correlation coefficient is used as an index to select the effective IMFs. The selected IMFs are then reconstituted and its spectrum is generated. Experimental results validate the usefulness of the proposed method for detecting the blade faults.

KEYWORDS:

• Turbomachines
• blade faults
• vibration analysis
• WT
• EEMD

Additional information

Notes on contributors

Rima Bouhali

Rima Bouhali was born in Skikda, Algeria, in 1987. In 2008, she received the licence degree from the 20 Aout 1955 university, Skikda, Algeria. In 2010, she obtained his Master degree in mecatronic engineering from the Badji Mokhtar university, Annaba, Algeria. In 2011, she joined the Industrial mechanic Laboratory of Annaba University. She is currently a Ph.D student. His research interests include fault diagnostics of rotating machine.

Kamel Tadjine

Kamel Tadjine is a professor of industrial engineering and mechanics of materials at the University Center of Tamanrasset, Algeria. He received the Engineer degree from the Algerian Institute of Petroleum, Boumerdes, Algeria, in 1977. He obtained his Magister degree in Materials Engineering and Ph.D degree in material mechanics from the University of Badji Mokhtar, Annaba, Algeria in 1995 and 2007 respectively. His research interests include the reliability of machines and structures, industrial hazards, the behavior of composite materials, vibration and waves, non-destructive testing, and statistical physics.

Hocine Bendjama

Hocine Bendjama received the Engineer degree and the Magister degree in electronics from the University of Science and Technology, Mentouri, Constantine, Algeria in 2000 and 2003 respectively. In 2012, he obtained the Ph.D degree in automatic engineering from the Polytechnic National School Algiers, Algeria. He is a senior researcher in the field of quality and process monitoring in the Research Center in Industrial Technologies (CRTI). His research interests include system modelling and optimization, process fault diagnostics, and neural networks

Mohamed Nacer Saadi

Mohamed Nacer Saadi received the B.Sc. degree in electrical engineering from the National Institute Polytechnic of Grenoble (INPG), France, in 1980. He obtained the M.S. degree in automatic from the University of Baji Mokhtar Annaba, Algeria in 1988. In 2011, he received his Ph.D. degree in automatic from the University of Baji Mokhtar Annaba, Algeria. His current research interests are focused on condition monitoring, fault detection and diagnostics of rotating machine.