Bearing condition prediction considering uncertainty: An interval type-2 fuzzy neural network approach

  • Authors:

  • Chaochao Chen;George Vachtsevanos

  • Affiliations:

  • School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA;School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA

  • Venue:
  • Robotics and Computer-Integrated Manufacturing
  • Year:
  • 2012

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Abstract

Rolling-element bearings are critical components of rotating machinery. It is important to accurately predict in real-time the health condition of bearings so that maintenance practices can be scheduled to avoid malfunctions or even catastrophic failures. In this paper, an Interval Type-2 Fuzzy Neural Network (IT2FNN) is proposed to perform multi-step-ahead condition prediction of faulty bearings. Since the IT2FNN defines an interval type-2 fuzzy logic system in the form of a multi-layer neural network, it can integrate the merits of each, such as fuzzy reasoning to handle uncertainties and neural networks to learn from data. The interval type-2 fuzzy linguistic process in the IT2FNN enables the system to handle prediction uncertainties, since the type-2 fuzzy sets are such sets whose membership grades are type-1 fuzzy sets that can be used in failure prediction due to the difficult determination of an exact membership function for a fuzzy set. Noisy data of faulty bearings are used to validate the proposed predictor, whose performance is compared with that of a prevalent type-1 condition predictor called Adaptive Neuro-Fuzzy Inference System (ANFIS). The results show that better prediction accuracy can be achieved via the IT2FNN.