Local recurrence based performance prediction and prognostics in the nonlinear and nonstationary systems

Authors:
Hui Yang;Satish T. S. Bukkapatnam;Leandro G. Barajas
Affiliations:
Department of Industrial & Management Systems Engineering, University of South Florida, Tampa, FL 33620, USA;Department of Industrial Engineering & Management, Oklahoma State University, Stillwater, OK 74078, USA;Manufacturing Systems Research Laboratory, General Motors R&D Center, Warren, MI 48090, USA
Venue:
Pattern Recognition
Year:
2011

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Abstract

This paper presents a local recurrence modeling approach for state and performance predictions in complex nonlinear and nonstationary systems. Nonstationarity is treated as the switching force between different stationary systems, which is shown as a series of finite time detours of system dynamics from the vicinity of a nonlinear attractor. Recurrence patterns are used to partition the system trajectory into multiple near-stationary segments. Consequently, piecewise eigen analysis of ensembles in each near-stationary segment can capture both nonlinear stochastic dynamics and nonstationarity. The experimental studies using simulated and real-world datasets demonstrate significant prediction performance improvements in comparison with other alternative methods.