Structure‐Crack Detection and Digital Twin Demonstration Based on Triboelectric Nanogenerator for Intelligent Maintenance

Abstract

AbstractThe accomplishment of condition monitoring and intelligent maintenance for cantilever structure‐based energy harvesting devices remains a challenge. Here, to tackle the problems, a novel cantilever‐structure freestanding triboelectric nanogenerator (CSF‐TENG) is proposed, which can capture ambient energy or transmit sensory information. First, with and without a crack in cantilevers, the simulations are carried out. According to simulation results, the maximum change ratios of natural frequency and amplitude are 1.1% and 2.2%, causing difficulties in identifying defects by these variations. Thus, based on Gramian angular field and convolutional neural network, a defect detection model is established to achieve the condition monitoring of the CSF‐TENG, and the experimental result manifests that the accuracy of the model is 99.2%. Besides, the relation between the deflection of cantilevers and the output voltages of the CSF‐TENG is first built, and then the defect identification digital twin system is successfully created. Consequently, the system is capable of duplicating the operation of the CSF‐TENG in a real environment, and displaying defect recognition results, so the intelligent maintenance of the CSF‐TENG can be realized.