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 (10(3) 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.