A novel forecasting model for compressor casing wall pressure based on phase space reconstruction and radial basis function network was established.Phase space for the experimental pressure data was reconstructed

and corresponding chaotic characteristics were analyzed.Number of network input variables was determined through the computed minimum embedding dimension of the reconstructed phase space.The network’s topology was optimized via K-means clustering method

and the maximum effective forecasting steps was determined by computing the largest Lyapunov exponent of the examined pressure time series.Taking advantage of the strong nonlinear mapping capability of the radial basis function neural network

nonlinear forecasting of the measured time series was realized.The forecasting results successfully validate the feasibility and effectiveness of the newly developed algorithm

which may serve as a promising trend monitoring method for axial flow compressors.