Review A review on data-driven fault severity assessment in rolling bearings Mariela Cerrada a,b,⇑ , René-Vinicio Sánchez a , Chuan Li c , Fannia Pacheco d , Diego Cabrera a , José Valente de Oliveira e , Rafael E. Vásquez f a GIDTEC, Universidad Politécnica Salesiana, Cuenca, Ecuador b CEMISID, Universidad de Los Andes, Mérida, Venezuela c National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing, China d Laboratoire d’Informatique LIUPPA, Université de Pau et des Pays de L’Adour, Anglet, France e Universidade do Algrave, Faro, Portugal f Department of Mechanical Engineering, Universidad Pontificia Bolivariana, Medellín, Colombia article info Article history: Received 24 January 2017 Received in revised form 9 June 2017 Accepted 10 June 2017 Keywords: Rolling bearings Fault severity Fault assessment Fault size Quantitative diagnosis abstract Health condition monitoring of rotating machinery is a crucial task to guarantee reliability in industrial processes. In particular, bearings are mechanical components used in most rotating devices and they represent the main source of faults in such equipments; reason for which research activities on detecting and diagnosing their faults have increased. Fault detection aims at identifying whether the device is or not in a fault condition, and diagnosis is commonly oriented towards identifying the fault mode of the device, after detection. An important step after fault detection and diagnosis is the analysis of the magnitude or the degradation level of the fault, because this represents a support to the decision-making process in condition based-maintenance. However, no extensive works are devoted to analyse this problem, or some works tackle it from the fault diagnosis point of view. In a rough manner, fault severity is associated with the magnitude of the fault. In bearings, fault severity can be related to the physical size of fault or a general degradation of the compo- nent. Due to literature regarding the severity assessment of bearing damages is limited, this paper aims at discussing the recent methods and techniques used to achieve the fault severity evaluation in the main components of the rolling bearings, such as inner race, outer race, and ball. The review is mainly focused on data-driven approaches such as signal processing for extracting the proper fault signatures associated with the damage degrada- tion, and learning approaches that are used to identify degradation patterns with regards to health conditions. Finally, new challenges are highlighted in order to develop new contri- butions in this field. Ó 2017 Elsevier Ltd. All rights reserved. Contents 1. Introduction ............................................................................................ 170 2. Fault severity in bearings.................................................................................. 172 http://dx.doi.org/10.1016/j.ymssp.2017.06.012 0888-3270/Ó 2017 Elsevier Ltd. All rights reserved. ⇑ Corresponding author at: GIDTEC, Universidad Politécnica Salesiana, Cuenca, Ecuador. E-mail addresses: mcerrada@ups.edu.ec (M. Cerrada), rsanchezl@ups.edu.ec (R.-V. Sánchez), chuanli@21cn.com (C. Li), f.pacheco@univ-pau.fr (F. Pacheco), dcabrera@ups.edu.ec (D. Cabrera), jvo@ualg.pt (J. Valente de Oliveira), rafael.vasquez@upb.edu.co (R.E. Vásquez). Mechanical Systems and Signal Processing 99 (2018) 169–196 Contents lists available at ScienceDirect Mechanical Systems and Signal Processing journal homepage: www.elsevier.com/locate/ymssp