A systematic review of diagnosis methods for rolling bearing compound faults: research status, challenges, and future prospects

Abstract Rolling bearing compound faults (RBCFs), which are one of the primary causes for unscheduled downtime of rotating machinery, are characterized by randomness, sequentiality, coupling, and concealment. Therefore, timely detecon of defects is essential to reduce downtime and ensure the safety of equipment. This paper provides a systematic review of the existing applications and developments of diagnosis methods for RBCFs since 2004. They are categorized as fault mechanism analysis methods based on analytical models, feature extraction methods based on signal processing, and pattern recognition methods based on artificial intelligence, and their diagnostic frameworks are summarized in detail. The advantages and disadvantages of the reviewed methods are concluded. The challenges and prospects for RBCF diagnosis methods are analyzed and discussed further. This work can offer valuable insights and research inspiration for academic scholars and industry engineers in diagnosing compound faults of rolling bearings.