A review on data-driven fault severity assessment in rolling bearings

Mariela Cerrada, René Vinicio Sánchez, Chuan Li, Fannia Pacheco, Diego Cabrera, José Valente de Oliveira, Rafael E. Vásquez

Research output: Contribution to journalReview articlepeer-review

481 Scopus citations


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 component. 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 degradation, 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 contributions in this field.

Original languageEnglish
Pages (from-to)169-196
Number of pages28
JournalMechanical Systems and Signal Processing
StatePublished - 15 Jan 2018

Bibliographical note

Publisher Copyright:
© 2017 Elsevier Ltd


  • Fault assessment
  • Fault severity
  • Fault size
  • Quantitative diagnosis
  • Rolling bearings


Dive into the research topics of 'A review on data-driven fault severity assessment in rolling bearings'. Together they form a unique fingerprint.

Cite this