Identifying Patterns and Relationships within Noisy Acoustic Data Sets

Acoustic emissions analysis can provide key information for monitoring the structural integrity of a system, such as the behavior of bone under various loading conditions and other complex biomechanical applications. However, when analyzing acoustic emissions data from complex systems, including systems that experience high-rate (103 s–1) loading, complex bending modes, unique shape effects, and multiple failure mechanisms, it is difficult to extract meaningful information and relationships because of an abundance of confounding factors. This article presents a methodology developed at the Johns Hopkins Applied Physics Laboratory (APL) for understanding fracture and characterizing acoustic signatures with distinct failure modes, leveraging techniques such as independent component analysis, self-organizing maps, and K-means clustering algorithms.