Crack is one of the common types of defect in sensors that may cause system failure. In this paper, crack fault detection is considered in piezoelectric sensors. Piezoelectric sensors are assumed in micro-scale and cantilever-based MEMS sensors. Therefore, the usual methods used for macro-scale systems and FEMs to find natural frequencies cannot be used. To find the natural frequencies of the piezoelectric sensors, Modified Couple Stress Theory (MCST) and the Hamilton principle are used. Crack is modeled with a torsional spring whose stiffness depends on the depth and location of the cracks and the material length scale parameter. The PSO optimization algorithm is used to find the depth and location of the crack in the sensor. The results of optimization indicate the proper performance of the Particle Swarm Optimization (PSO) algorithm for detecting the crack in piezoelectric sensors. The results of the PSO algorithm are accurate for cracks near the fixed end of the sensor and are acceptable for cracks near the free end.
Rahi, A., & Yarmohammadi, R. (2020). Crack fault detection in piezoelectric sensors using particle swarm optimization. Challenges in Nano and Micro Scale Science and Technology, 8(2), 71-80. doi: 10.22111/tpnms.2020.34038.1189
MLA
Abbas Rahi; Reza Yarmohammadi. "Crack fault detection in piezoelectric sensors using particle swarm optimization". Challenges in Nano and Micro Scale Science and Technology, 8, 2, 2020, 71-80. doi: 10.22111/tpnms.2020.34038.1189
HARVARD
Rahi, A., Yarmohammadi, R. (2020). 'Crack fault detection in piezoelectric sensors using particle swarm optimization', Challenges in Nano and Micro Scale Science and Technology, 8(2), pp. 71-80. doi: 10.22111/tpnms.2020.34038.1189
VANCOUVER
Rahi, A., Yarmohammadi, R. Crack fault detection in piezoelectric sensors using particle swarm optimization. Challenges in Nano and Micro Scale Science and Technology, 2020; 8(2): 71-80. doi: 10.22111/tpnms.2020.34038.1189
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