بخشی از مقاله (انگلیسی)
Numerous methods have been developed in the context of expert and intelligent systems for structural health monitoring (SHM) with wireless sensor networks (WSNs). However, these techniques have been proven to be efficient when dealing with continuous signals, and the applicability of such expert systems with discrete noisy signals has not yet been explored. This study presents an intelligent data mining methodology as part of an expert system developed for SHM with noisy and delayed signals, which are generated by a through-substrate self-powered sensor network. The noted sensor network has been demonstrated as an effective means for minimizing energy consumption in WSNs for SHM. Experimental vibration tests were conducted on a cantilever plate to evaluate the developed expert system for SHM. The proposed data mining method is based on the integration of pattern recognition, an innovative probabilistic approach, and machine learning. The novelty of the proposed system for SHM with data interpretation methodology lies in the integration of the noted intelligent techniques on discrete, binary, noisy, and delayed patterns of signals collected from self-powered sensing technology in the application to a practical engineering problem, i.e., data-driven energy-efficient SHM. Results confirm that the proposed data mining method employing a probabilistic approach can be effectively used to reconstruct delayed and missing signals, thereby addressing the important issue of energy availability for intelligent SHM systems being used for damage identification in civil and aerospace structures. The applicability and effectiveness of the expert system with the data mining approach in detecting damage with noisy signals was demonstrated for plate-like structures with an accuracy of 97%. The present study successfully contributes to advance data mining and signal processing techniques in the SHM domain, indicating a practical application of expert and intelligent systems applied to damage detection in SHM platforms. Findings from this research pave a way for development of the data analysis techniques that can be employed for interpreting noisy and incomplete signals collected from various expert systems such as those being used in intelligent infrastructure monitoring systems and smart cities.
Energy-efficient wireless sensor networks (WSNs) for structural health monitoring (SHM) have emerged due to progress in self-powered sensors and low-power data communication protocols. One such network is the through-substrate ultrasonic selfpowered sensor network (Das et al., 2017), which employs ultra-sonic pulses to communicate binary signals (from self-powered sensors) through the material substrate. However, the noted network creates time-delay on the generated signals due to the power budgeting required for sensing and data transmission. This study presents an expert system with a data mining approach for SHM in order to deal with such discrete noisy and delayed signals. Signal time-delay estimation/reconstruction is a problem that has attracted considerable attention in the SHM community. Numerous techniques have been developed for signal delay estimation for SHM and damage identification (Giurgiutiu, 2005; Giurgiutiu & Cuc, 2005; Nichols, 2003; Sun, Chaudhry, Rogers, Majmundar, & Liang, 1995; Yan, Royer, & Rose, 2010). Ultrasonic techniques (e.g., lamb wave methods) have been used for time-delay estimation for SHM (C. H. Dib & Udpa, 2016; Park, Farrar, di Scalea, & Coccia, 2006; Petculescu, Krishnaswamy, & Achenbach, 2007; Wang, Rose, & Chang, 2004). Aranguren, Monje, Cokonaj, Barrera, and Ruiz (2013) proposed a piezoelectric-based SHM system for damage detection using lamb waves and delayed signals. Kudela, Radzienski, Ostachowicz, and Yang (2018) developed an SHM approach based on lamb waves to increase damage imaging resolution, where the effectiveness of the approach for damage detection with time-delayed signals was demonstrated on a structural plate.