Design, Analysis, and Evaluation of a Compact Electromagnetic Energy Harvester from Water Flow for Remote Sensors

This paper develops an electromagnetic energy harvester, which can generate small-scale electricity from non-directional water flow in oceans or rivers for remote sensors. The energy harvester integrates a Tesla disk turbine, a miniature axial-flux permanent magnet generator, and a ring cover with symmetrical grooves which are utilized to rectify flow direction. A compact structure is achieved by mounting the permanent magnets of the generator directly on the end surfaces of the turbine rotor. Theoretical analysis is implemented to illustrate the energy conversion process between flow kinetic form and electrical form. Additionally, a mathematical model is developed to investigate the magnetic field distribution produced by the cubical permanent magnets as well as parametric effect. Plastic prototypes with a diameter of 65 mm and a height of 46 mm are fabricated by using a 3D printing technique. The effect of the groove angle is experimentally investigated and compared under a no-load condition. The prototype with the optimal groove angle can operate at flow velocity down to 0.61 m/s and can induce peak-to-peak electromotive force of 2.64–11.92 V at flow velocity of 0.61–1.87 m/s. It can be observed from the results that the analytical and the measured curves are in good accordance. Loaded experiments show that the output electrical power is 23.1 mW at flow velocity of 1.87 m/s when the load resistance is approximately equal to the coil resistance. The advantages and disadvantages of the proposed energy harvester are presented through comparison with existing similar devices

A Hybrid Gearbox Fault Diagnosis Method Based on GWO-VMD and DE-KELM

In this paper, a vibration signal-based hybrid diagnostic method, including vibration signal adaptive decomposition, vibration signal reconstruction, fault feature extraction, and gearbox fault classification, is proposed to realize fault diagnosis of general gearboxes. The main contribution of the proposed method is the combining of signal processing, machine learning, and optimization techniques to effectively eliminate noise contained in vibration signals and to achieve high diagnostic accuracy. Firstly, in the study of vibration signal preprocessing and fault feature extraction, to reduce the impact of noise and mode mixing problems on the accuracy of fault classification, Variational Mode Decomposition (VMD) was adopted to realize adaptive signal decomposition and Wolf Grey Optimizer (GWO) was applied to optimize parameters of VMD. The correlation coefficient was subsequently used to select highly correlated Intrinsic Mode Functions (IMFs) to reconstruct the vibration signals. With these re-constructed signals, fault features were extracted by calculating their time domain parameters, energies, and permutation entropies. Secondly, in the study of fault classification, Kernel Extreme Learning Machine (KELM) was adopted and Differential Evolutionary (DE) was applied to search its regularization coefficient and kernel parameter to further improve classification accuracy. Finally, gearbox vibration signals in healthy and faulty conditions were obtained and contrast experiences were conducted to validate the effectiveness of the proposed hybrid fault diagnosis method