Fuzzy clustering and AR models for damage detection in CFRP coupons considering loading effect - Cano, Wagner Francisco Rezende; da Silva, Samuel [UNESP] (Springer Nature, 2020)
This paper proposes a strategy to avoid false alarms by distinguishing operation effects from damages effects in composite laminates. This strategy is based on active and sensing piezoelectric patches receiving Lamb waves that can be profoundly affected by operational factors such as load leading to false diagnostics. In order to overcome this drawback, this paper proposes an approach analyzing the use of prediction errors obtained by auto-regressive (AR) models. This index is computed using only the output signal received from sensors and combined with other traditional sensitive-damage indices. The fuzzy clustering technique is then applied for distinguishing the load effects from the effects of the damage. The method is evaluated using a carbon fiber-reinforced polymer coupons subject to tension–tension fatigue and with layers of piezoelectric sensors and actuators bonded on this surface. The results revealed that fuzzy clustering using a fuzzy c-means (FCM) algorithm could distinguish these effects using one-step-ahead AR errors combined with other standard indices extracted in time and frequency domains. This strategy may be easily implemented for signal processing, making possible its online application in a real-world structure.
Keywords: AR models; Composite materials; Damage detection; Fuzzy clustering; Lamb waves; Load variations; Smart Structures