Adaptive phase control impact device (APCID) was developed for performing in-service modal analysis using impact synchronous modal analysis. However, this device is large and heavy, making it unsuitable for real world applications. This automated impact device can be replaced with human hand but the randomness in human behavior can reduce the accuracy of APCID control scheme. To replace APCID with a smart semi-automated device while still using APCID control scheme, machine learning models are presented in this paper to recognize human behavior by classifying 13 different impact types and predicting impact time using the impact classification. The impact classification model gave classification accuracy of over 96% with 130 real time impacts. With successful classification of different impact types, randomness in human behavior can be reduced by two to three times by associating a range of impact time with each impact type. However, the impact time ranges may differ person to person. To address this issue and to further reduce variations in impact time, a time prediction machine learning model was developed to make compensations in the control scheme of APCID by predicting impact time. The model gave reasonable accuracy with mean prediction errors of 5.2% in real time testing compared to measured time for 100 impacts.
In recent years, much research has been carried out to enhance the efficiency of the piezoelectric energy harvester (PEH). This study focuses on the performance of the compressive Hull PEH under impact forces, which simulates real-world scenarios, such as foot strikes or vehicular wheel excitations, more accurately compared to harmonic forces. The experimental results prove the performance of the Hull PEH with less than 5.2% of deviation compared to finite element analysis outcomes under impact forces between 10 N and 1 kN. The Hull PEH more substantially amplified the input force and compressed the piezoelectric material, which was Lead Zirconate Titanate (PZT). Consequently, it amplified the voltage output of a standalone PZT up to 16.9 times under a similar boundary condition. A maximum peak power output of 7.16 W was produced across 50 kΩ of optimum load resistance under 1 kN of impact force, which surpassed the benchmark Cymbal PEH by 37.68 times. Furthermore, it demonstrated a higher energy conversion efficiency of 84.38% under the impact force compared to the harmonic force. This research conclusively proves that the Hull PEH has superior performance in terms of voltage output, power output, loading capacity, and efficiency, making it a promising technology for impact loading applications to generate green energy.
Abstract In recent years, harnessing electrical energy from mechanical vibration by using a piezoelectric energy harvester (PEH) has attracted much attention from researchers. This sustainable energy harvester is useful for wireless sensor network, where a replacement or replenishment of an energy source such as a battery is impractical. From previous studies, the amount of energy generated by the PEH is very limited even in a high force environment. To solve this issue, mechanical amplifier structure such as Cymbal structure is implemented to amplify the tensile loading force towards the PEH. In terms of the material strength perspective, this performance can be further enhanced by using a compressive-type mechanical amplifier structure, as the compressive yield strength of piezoelectric material is much higher than its tensile yield strength. In this study, a compressive structural design which is named as Hull structure is proposed. Several techniques included analytical model analysis, finite element analysis (FEA), and experimental testing have been used to evaluate its performance. It shows a force amplification factor of 9.72 at 6° through the analytical model. From the FEA result, the proposed Hull structure shows great potential in enhancing the power output of 11.34 mW, which is 3.08 times larger than the benchmarking Tensile Cymbal structure. It also shows 5.28 times greater output voltage than the benchmark case in the experiment. Besides, it has a great advantage of providing a wider area for excitation loading force which increases the PEH’s load capacity and suitable for the vehicular excitation application.
Efficient walking or running requires symmetrical gait. Gait symmetry is one of the key factors in efficient human dynamics, kinematics and kinetics. The desire of individuals with a lower-limb amputation to participate in sports has resulted in the development of energy-storing-and-returning (ESR) feet. This paper analyses a case study to show the effect of symmetry and asymmetry as well as energy transfer efficiency during periodic jumping between simulated bilateral and unilateral runners. A custom gait analysis system is developed as part of this project to track the motion of the body of a physically active subject during a set of predefined motions. Stance and aerial times are accurately measured using a high speed camera. Gait frequency, the level of symmetry and the non-uniform displacement between left and right foot and their effects on the position of the Centre of Mass (CM) were used as criteria to calculate both peak energies and transformation efficiency. Gait asymmetry and discrepancy of energy transfer efficiency between the intact foot and the ESR are observed. It is concluded that unilateral runners require excessive effort to compensate for lack of symmetry as well as asymmetry in energy transfer, causing fatigue which could be a reason why bilateral amputee runners using ESR feet have a superior advantage over unilateral amputees.
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