Machine health condition prediction via online dynamic fuzzy neural networks

Abstract

Machine health condition (MHC) prediction is useful for preventing unexpected failures and minimizing overall maintenance costs in condition-based maintenance. The neural network (NN)-based data-driven method has been considered to be promising for MHC prediction due to the adaptability, nonlinearity and universal approximation capability of NNs. This paper presents an online MHC prediction approach using online dynamic fuzzy NNs (OD-FNNs) with structure and parameters learning. To meet the requirement of real-time application, the original OD-FNN is simplified based on an extreme learning machine technique as follows: (1) initial fuzzy rules are randomly generated without the knowledge of training data; (2) fuzzy rules are added and pruned uniformly by fired strength-based criteria; (3) antecedent parameters are fixed after generation so that only consequent parameters are updated online. The modified OD-FNN is particularly suitable for MHC prediction since: (1) fuzzy rules can evolve as new training datum arrives, which enables us to cope with non-stationary processes in MHC; (2) learning mechanisms applied are simple and efficient for real-time implementation. The validity and superiority of the proposed MHC prediction approach has been evaluated by real-world monitoring data from the accelerated bearing life.