A Systematic Review of Fuzzy Formalisms for Bearing Fault Diagnosis

Chuan Li, Jose Valente De Oliveira, Mariela Cerrada, Diego Cabrera, Rene Vinicio Sanchez, Grover Zurita

Research output: Contribution to journalArticlepeer-review

79 Scopus citations


Bearings are fundamental mechanical components in rotary machines (engines, gearboxes, generators, radars, turbines, etc.) that have been identified as one of the primary causes of failure in these machines. This makes bearing fault diagnosis (detection, classification, and prognosis) an economic very relevant topic, as well as a technically challenging one as evaluated by the extensive research literature on the subject. This paper employs a systematic methodology to identify, summarize, analyze, and interpret the primary literature on fuzzy formalisms for bearing fault diagnosis from 2000 to 2017 (March). The main contribution is an updated, unbiased, and (to a higher extend) repeatable search, review, and analysis (summary, classification, and critique) of the available approaches resorting to fuzzy formalisms in this trendy topic. A discussion on a new promising future research direction is provided. A comprehensive list of references is also included.

Original languageEnglish
Article number8510832
Pages (from-to)1362-1382
Number of pages21
JournalIEEE Transactions on Fuzzy Systems
Issue number7
StatePublished - 1 Jul 2019


  • Bearing
  • fault classification
  • fault detection
  • fault diagnosis
  • fault prognosis
  • fuzzy clustering
  • fuzzy entropy
  • fuzzy rules


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