Vibration Energy Conversion Power Supply Based on the Piezoelectric Thin Film Planar Array.

Vibration energy harvesting has received much attention as a new type of power solution for low-power micro/nano-devices. However, VEH (vibration energy harvester) based on PVDF (polyvinylidene fluoride) piezoelectric materials have a low output power and energy conversation efficiency due to the relatively low piezoelectric constant, coupling coefficient, and dielectric constant. For this reason, we design a vibration energy conversion power supply, which consists of a VEH with a PVDF piezoelectric thin film planar array vibration structure and an energy harvesting circuit for regulating the electric energy of multiple sources. Furthermore, our solution was validated by simulations of structural dynamics in COMSOL and equivalent circuits model in Multisim. From the circuitry simulation results, the output current and the charging period increase and decrease, doubling, respectively, for each doubling of the number of array groups of films. Moreover, the solid mechanics simulation results show that the planar array structure makes the phase and amplitude of the input vibration waves as consistent as possible so that the same theoretical enhancement effect of the circuitry model is achieved. An identical experimental test was implemented with vibration conditions of 75 Hz-2.198 g. The fabricated harvester quickly charged the 22 V-0.022 F ultracapacitor bank to 5 V in 24 min. The maximum open circuit voltage and output power, respectively, were 10.4 V and 0.304 mW. This maximum charging power was 11.69 times higher than that of a single film. This special power supply can replace batteries to power low-power electronics deployed in vibrating environments, thus reducing the maintenance costs of equipment and environmental pollution rates.

Robust Achromatic All-Dielectric Metalens for Infrared Detection in Intelligent Inspection.

Metalens has the advantages of high design freedom, light weight and easy integration, thus provides a powerful platform for infrared detection. Here, we numerically demonstrated a broadband achromatic infrared all-dielectric metalens over a continuous 800 nm bandwidth, with strong environmental adaptability in air, water and oil. By building a database with multiple 2π phase coverage and anomalous dispersions, optimizing the corrected required phase profiles and designing the sizes and spatial distributions of silicon nanopillars, we numerically realized the design of broadband achromatic metalens. The simulation results of the designed metalens show nearly constant focal lengths and diffraction-limited focal spots over the continuous range of wavelengths from 4.0 to 4.8 µm, indicating the ability of the designed metalens to detect thermal signals over a temperature range from various fault points. Further simulation results show that the metalens maintains good focusing performance under the environment of water or oil. This work may facilitate the application of metalens in ultra-compact infrared detectors for power grid faults detection.

Doublet Metalens with Simultaneous Chromatic and Monochromatic Correction in the Mid-Infrared.

Metalenses provide a powerful paradigm for mid-infrared (MIR) imaging and detection while keeping the optical system compact. However, the design of MIR metalenses simultaneously correcting chromatic aberration and off-axis monochromatic aberration remains challenging. Here, we propose an MIR doublet metalens composed of a silicon aperture metalens and a silicon focusing metalens separated by a fused silica substrate. By performing ray-tracing optimization and particle-swarm optimization, we optimized the required phase profiles as well as the sizes and spatial distributions of silicon nanopillars of the doublet metalens. Simulation results showed that the MIR doublet metalens simultaneously achieved chromatic and off-axis monochromatic aberration reduction, realizing a continuous 400 nm bandwidth and 20° field-of-view (FOV). Thanks to its planar configuration, this metalens is suitable for integration with CMOS image sensor to achieve MIR imaging and detection, which has potential application in troubleshooting and intelligent inspection of power grids. This work may facilitate the practical application of metalens-integrated micro/nanosensors in intelligent energy.

Hybrid condition monitoring of nonlinear mechatronic system using biogeography-based optimization particle filter and optimized extreme learning machine.

This paper proposes a hybrid condition monitoring approach, which integrates bond graph model-based diagnostic technique and data-driven remaining useful life (RUL) prediction, for a nonlinear mechatronic system. In this approach, various degrading faults can be considered and the physical degradation model is not required for RUL prediction. Firstly, an integrated fault signature matrix is proposed by the causal path of bicausal-bond graph model to improve fault isolation performance. After that, a biogeography-based optimization (BBO)-particle filter is developed for fault identification. For prognosis, an optimized extreme learning machine (OELM) is proposed where the hidden layer biases and input weights are optimized by BBO. The fault identification results provide data set to train the OELM for prognosis. Finally, the effectiveness of the approach is verified by simulation and experiment results.