Article Fault detection in rolling element bearings using wavelet-based variance analysis and novelty detection Aleksandra Ziaja 1 , Ifigeneia Antoniadou 2 , Tomasz Barszcz 1 , Wieslaw J Staszewski 1 and Keith Worden 2 Abstract Fractal signal processing and novelty detection are used for fault detection in rolling element bearings. The former applies the concept of self-similarity based on wavelet variance, and the latter is based on machine learning and utilises artificial neural networks. The method is demonstrated using simulated and experimental vibration data. The work presented involves validation both on laboratory test rig data and industrial wind turbine data. The results show that the method can be used successfully for automated fault detection in ball bearings under real operational conditions. Keywords Fault detection, fractal theory, novelty detection, rolling element bearing, self-similarity, wavelet-based variance 1. Introduction Vibration-based methods are prevalent in industrial applications for machine condition monitoring. This is relevant particularly in the field of rotating machin- ery where a broad range of different fault types and various approaches in the time, frequency and combine time-frequency domains have been developed over the last 40 years, as discussed in the literature (Carden and Fanning, 2004; Randall, 2011). A wide range of these studies focuses on fault detection in rolling element bearings due to a common applicability of these com- ponents in the vast majority of rotating machinery. Also, bearing failures are one of the most common causes of breakdown in rotating machines. The very basic techniques – often used in industrial applications – assess the condition of monitored bear- ing elements using statistical parameters that are calcu- lated globally, i.e. from entire vibration characteristics and/or their power spectra. Commonly used param- eters include: root mean square amplitude, peak-to-peak amplitude, crest factor and kurtosis. An increase in the value of these parameters at constant operating conditions is frequently associated with fault development, hence they are used as fault indica- tors. Frequency-domain methods analyze bearing char- acteristic frequencies related to certain fault types. These frequencies can be estimated theoretically from bearing geometry (Randall and Antoni, 2011). However, typically any change of simple time- and fre- quency-domain features at an early stage of defect development is too small to be uniquely and reliably identified. This is particularly relevant when machines in non-stationary operations are monitored (Fakhfakh et al., 2012). Various theoretical models have been developed for rolling element bearing faults (McFadden and Smith, 1984a; Wang and Kootsookos, 1998). These models can be used for model-based approaches in bearing fault detection (Lou et al., 2004). More fruitful fault detection approaches are based on the analysis of amplitude modulations of high fre- quency resonances of bearings. These modulations are caused by periodic excitations caused by bearing 1 Department of Robotics and Mechatronics, AGH University of Science and Technology, Krakow, Poland 2 Department of Mechanical Engineering, University of Sheffield, Sheffield, UK Corresponding author: Wieslaw J Staszewski, Department of Robotics and Mechatronics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland. Email: w.j.staszewski@agh.edu.pl Received: 16 September 2013; accepted: 15 March 2014 Journal of Vibration and Control 1–16 ! The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1077546314532859 jvc.sagepub.com at University of Sheffield on June 12, 2015 jvc.sagepub.com Downloaded from