There is a growing use of carbon fiber reinforced polymers (CFRPs) in modern airframes with still a limited understanding of the in-service behavioral characteristics of these structures.
Structural Health Monitoring (SHM) technologies that use surface-bonded piezoceramic (PZT) transducers to generate and measure guided waves within these structures have demonstrated promising damage detection and localization results and potential for data gathering in data-driven damage prognosis. This paper investigates the development of a data-driven SHM based damage prognosis system for estimating remaining useful life (RUL) of CFRP coupons following damage initiation. A robust and realistic laboratory data gathering methodology is introduced as a building block for evaluating the feasibility of data-driven damage prognosis for in-service aerospace structures. Data are gathered using a PZT-based SHM system. Using the gathered raw guided wave signals, a number of time and frequency domain features are first extracted which are derived from existing damage imaging and detection algorithms. Then, using various combinations of the feature sets as inputs to generic data mining algorithms, the paper presents estimates of the predicted RUL against actual damage diameter progression.
SHM, PZT, data-driven, damage prognosis, data mining and machine learning, drop-weight impacting, composite
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