Mr. Takaharu Yamada Profile

Takaharu Yamada

Graduate student at Kanazawa univ

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Proceedings Article | 9 May 2024 Presentation + Paper

Wireless sensor node powered by piezoelectric vibration energy harvester for vibrational mode identification and structural health monitoring

T. Yamada, H. Asanuma, Y. Hara

Proceedings Volume 12946, 129460V (2024) https://doi.org/10.1117/12.3010821

KEYWORDS: Sensor networks, Vibration, Data transmission, Structural health monitoring, Continuous wavelet transforms, Image classification, Wireless sensors, Signal intensity, Capacitors, Sensors

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We applied our previously developed wireless sensor node (WSN), which is powered by a piezoelectric vibration energy harvester (VEH), to structural health monitoring and verified its classification performance. The self-powered WSN can measure structural vibrations and transmit the three-axis acceleration waveform data wirelessly. A normal intact beam and four beams that had a single notch cut at four different locations were prepared for classification testing. The WSN, which was connected to the piezoelectric VEH, was mounted on the tip of an aluminum alloy beam, and a random vibration was then applied to the clamped end of the beam. First, the power of the piezoelectric VEH under the random vibration condition was investigated by introducing the probability of harvesting. The beam’s vibrational modes were identified within the 0–1,600 Hz frequency range using the transmitted waveform data. Second, based on an analogy to 2D image classification, we created an input image by arranging the x, y, z-axis acceleration spectrum data vertically and then classifying the arranged data using 2D convolutional neural network (2D-CNN) layers. The Bayesian optimization technique was used to maximize classification accuracy by optimizing the hyperparameters. We confirmed that the self-powered WSN can provide high classification accuracy of 99.9%. We also revealed the basis of this high classification accuracy using gradient-weighted class activation mapping (Grad-CAM) and the continuous wavelet transform (CWT). Our self-powered WSN, which can provide additional features by transmitting the three-axis acceleration waveforms, represents a useful tool for structural health monitoring or predictive maintenance applications.

Proceedings Article | 28 April 2023 Presentation + Paper

Acceleration-waveform-sending wireless sensor node powered by piezoelectric vibration energy harvester

T. Yamada, H. Asanuma, Y. Hara, A. Erturk

Proceedings Volume 12483, 124830R (2023) https://doi.org/10.1117/12.2657742

KEYWORDS: Sensor networks, Vibration, Capacitors, Data transmission, Power consumption, Wireless sensors, Sensors, Structural health monitoring, Frequency response, Transceivers

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We have developed a wireless sensor node (WSN) powered by a piezoelectric vibration energy harvester that enables transmission of three-axis acceleration waveform data. Unlike a conventional WSN, which sends a single point representing the root mean square acceleration value, the proposed WSN allows the frequency, vibrational modes, and displacement of the target structure to be obtained. Therefore, this waveform-sending scenario is highly suitable for structural health monitoring applications. We used a power gating technique to reduce the standby energy consumption significantly and thus realize the waveform-sending concept. The overall dimensions and mass of the WSN are 3×3×3 cm³ and 26 g, respectively. The overall dimensions of the harvester are 5.6×2×2.1 cm3. The WSN measures the threeaxis acceleration of the structure’s vibration for 1.2 s at a sampling rate of 3,200 samples every 5 min, transmits the data, and then goes into standby mode. Because of the power gating technique, the energy consumption per cycle is as low as 108 mJ. We evaluated the WSN under both harmonic and random vibration conditions. For harmonic vibrations, the acceleration magnitude applied using a shaker was 1 m/s² at the harvester’s resonance. For random vibrations, a power spectral density (PSD) of 0.1 (m/s²)²/Hz and a frequency range of 10–100 Hz were set. The WSN operated successfully using only energy generated by the harvester and the transmitted waveforms matched the waveforms measured by a high-precision acceleration pick-up. Here, we report the WSN design methodology and the detailed charging characteristics of the energy storage capacitor.

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