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

The 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.

A cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network.

The electric signals of cantilever energy harvesting devices with/without a crack were mainly obtained by external sensors, so detecting device damage on a large scale is difficult. To tackle the issue, a cantilever-structure freestanding triboelectric nanogenerator (CSF-TENG) device was proposed, which can scavenge ambient energy and act as a self-powered sensor. Firstly, the relation between the peak-to-peak voltage and amplitude of the CSF-TENG was established. Next, the output performance of the CSF-TENG was measured. Then, depending on electric signals output by the CSF-TENG, a cantilever defect identification model was built by using the wavelet packet and long short-term memory (LSTM) algorithms. The experimental results manifest that the accuracy of the model is about 98.6%. Thus, the CSF-TENG with a crack can be detected timely due to its self-monitoring ability, which is of great significance for the development of self-powered sensor networks.

Interface Defect Detection and Identification of Triboelectric Nanogenerators via Voltage Waveforms and Artificial Neural Network.

To provide a robust working environment for TENGs, most TENGs are designed as sealed structures that isolate TENGs from the external environment, and thus their operating conditions cannot be directly monitored. Here, for the first time, we propose an artificial neural network for interface defect detection and identification of triboelectric nanogenerators via training voltage waveforms. First, interface defects of TENGs are classified and their causes are discussed in detail. Then we build a lightweight artificial neural network model which shows high sensitivity to voltage waveforms and low time complexity. The model takes 2.1 s for training one epoch, and the recognition rate of defect detection is 98.9% after 100 epochs. Meanwhile, the model successfully demonstrates the learning ability for low-resolution samples (100 × 75 pixels), which can identify six types of TENG defects, such as edge fracture, adhesion, and abnormal vibration, with a high recognition rate of 93.6%. This work provides a new strategy for the fault diagnosis and intelligent application of TENGs.

Power Density Improvement of Piezoelectric Energy Harvesters via a Novel Hybridization Scheme with Electromagnetic Transduction.

A novel hybridization scheme is proposed with electromagnetic transduction to improve the power density of piezoelectric energy harvester (PEH) in this paper. Based on the basic cantilever piezoelectric energy harvester (BC-PEH) composed of a mass block, a piezoelectric patch, and a cantilever beam, we replaced the mass block by a magnet array and added a coil array to form the hybrid energy harvester. To enhance the output power of the electromagnetic energy harvester (EMEH), we utilized an alternating magnet array. Then, to compare the power density of the hybrid harvester and BC-PEH, the experiments of output power were conducted. According to the experimental results, the power densities of the hybrid harvester and BC-PEH are, respectively, 3.53 mW/cm3 and 5.14 µW/cm3 under the conditions of 18.6 Hz and 0.3 g. Therefore, the power density of the hybrid harvester is 686 times as high as that of the BC-PEH, which verified the power density improvement of PEH via a hybridization scheme with EMEH. Additionally, the hybrid harvester exhibits better performance for charging capacitors, such as charging a 2.2 mF capacitor to 8 V within 17 s. It is of great significance to further develop self-powered devices.