Bearing Degradation Evaluation Using Improved Cross Recurrence Quantification Analysis and Nonlinear Auto-Regressive Neural Network

This paper presents an improved cross recurrence quantitative analysis (CRQA) method for bearing degradation evaluation. It extracts CRQA variable from bearings' vibration signals to establish the degradation trend. Due to the fact that traditional process for calculating the hyperparameters of CRQA requires a large amount of time to reconstruct phase space, this step is replaced by calculating divergence rate of each signal sample to cut down the time consumption. After establishing the degradation trend based on the extracted variable, nonlinear auto-regressive neural network (NARNN) is developed to predict future degradation trend. Furthermore, a new failure detection method is investigated to indicate the first failure point during degradation tracking. The method applies temperature signals as auxiliary information to calculate the adaptive threshold and classify extracted features into different health status. The experiments on bearing vibration signals have verified that the improved CRQA method can reduce time consumption by more than 90%. In addition, defect characteristic frequencies extracted using wavelet analysis have validated the detection accuracy.