Structural health monitoring using empirical mode decomposition and the Hilbert phase

This paper discusses a new signal processing tool involving the use of empirical mode decomposition and its application to health monitoring of structures. Empirical mode decomposition is a time-series analysis method that extracts a custom set of basis functions to describe the vibratory response of a system. In conjunction with the Hilbert Transform, the empirical mode decomposition method provides some unique information about the nature of the vibratory response. In this paper, the method is used to process time-series data from a variety of 1-D structures with and without structural damage. Empirically derived basis functions are processed through the Hilbert–Huang Transform to obtain magnitude, phase, and damping information. This magnitude, phase, and damping information is later processed to extract the underlying incident energy propagating through the structure. This incident energy is also referred to as the dereverberated response of a structure. Using simple physics-based models of 1-D structures, it is possible to determine the location and extent of damage by tracking phase properties between successive degrees of freedom. This paper also presents experimental validation of this approach using a civil building model. Results illustrate that this new time-series method is a powerful signal processing tool that tracks unique features in the vibratory response of structures.