Comparison between overall and respective electrical rectifications in array of piezoelectric energy harvesting

Lien, I. C.; Lo, Yih‐Hsing; Chiu, S H; Shu, Y. C.

doi:10.1093/jom/ufac039

Cited by 6 publications

(4 citation statements)

References 103 publications

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“…6 This reliance on numerous sensors for extended monitoring poses challenges, such as unwanted wired connections, especially in severe environments like wind turbines on high towers. Thus, the utilization of wireless sensors powered by vibration-based piezoelectric energy harvesters [7][8][9][10][11][12] presents a promising solution. In addition, significant research efforts have been directed towards the development of self-powered sensors using piezoelectric transducers in recent years.…”

Section: Introductionmentioning

confidence: 99%

Multi-location fault detection with piezoelectric arrays and multi-task CNN learning

Chiu,

Lo,

Shu

2024

Active and Passive Smart Structures and Integrated Systems XVIII

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This article presents an innovative method for monitoring rotating instruments using piezoelectric array sensors and multi-task CNN (convolutional neural network) learning. The setup involves connecting piezoelectric patches, enabling spatial condition monitoring with a single voltage signal. The sensor array simultaneously tracks four bearing conditions and three gear conditions. However, a single-task CNN faces challenges, especially when trying to distinguish weak gear faults while simultaneously considering different bearing conditions. To address this, the article employs multi-task learning, simplifying the classification task by utilizing shared convolutional layers and two distinct fully connected layers dedicated to gear and bearing health states. This approach is also adaptable for extending fault detection to additional locations. Experiment demonstrates that multi-task learning achieved a 90% accuracy in identifying missing tooth defects in gears, outperforming the 78% accuracy of single-task learning. It confirms multi-task learning's effectiveness in detecting weak faults during multi-location defect monitoring using piezoelectric arrays.

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Section: Introductionmentioning

confidence: 99%

Multi-location fault detection with piezoelectric arrays and multi-task CNN learning

Chiu,

Lo,

Shu

2024

Active and Passive Smart Structures and Integrated Systems XVIII

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“…[20][21][22] Another powerful approach for enhancing power output and bandwidth is the development of piezoelectric harvester arrays, adjusting the resonance of each harvester to cover the frequency range of ambient excitations. [23][24][25][26][27] Moreover, the frequency up-conversion approach, driven by wrist-worn energy harvesting, 28,29 has gained recent adoption. This technique enables a resonant harvester to operate in a non-resonant manner, as successfully demonstrated in energy harvesting by rotary magnetic plucking.…”

Section: Introductionmentioning

confidence: 99%

Physics-informed neural network for parameter identification in a piezoelectric harvester

Bai,

Yeh,

Shu

2024

Active and Passive Smart Structures and Integrated Systems XVIII

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The article aims to develop a physics-informed neural network (PINN) for parameter identification in a piezoelectric harvester using experimental sampling data. The advantage of PINN lies in its efficient inverse calculation of parameters with minimal sampled signals. For instance, with a single piezoelectric oscillator, the data collection process requires only two sets of piezoelectric voltage waveforms acquired at different electric loads and excitation frequencies. The training process involves minimizing the loss function, which comprises the model-based differential equations and the sampled time-domain voltage signals. The results successfully achieve inverse parameter identification, covering mechanical damping ratio, capacitance, and voltage source (force magnitude divided by the piezoelectric constant). In addition, the voltage frequency response, based on the inverse parameters, agrees well with experimental observations, confirming the model's reliability.

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“…Specifically, a novel sensing technique is developed for building a multi-input-single-output (MISO) sensing system. It is based on the mixed parallel-series connection of piezoelectric array patches with resonant circuits 27,28 for health monitoring of bearings and gears. It is capable of defect location and classification using the same output layer in the CNN model.…”

Section: Introductionmentioning

confidence: 99%

Piezoelectric-array-based MISO diagnostic system for CNN-condition monitoring of bearing/gearbox instruments

Lo¹,

Chiu²,

Liu³

et al. 2023

Active and Passive Smart Structures and Integrated Systems XVII

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The article presents a novel MISO (multi-input-single-output) diagnostic system suitable for spatial condition monitoring of bearing/gearbox instruments with multi-location defects. The sensor array consists of three piezoelectric patches: one is attached to the surface of the bearing house and the other two connected in parallel are mounted on the wall of the planetary gear. These two sets of patches are electrically connected in series for sensing the fault signals whose sources of anomalies come from either the bearing or the gear. They offer an advantage of allowing a single voltage output from multiple inputs. In addition, two inductances are connected to the sensor array to form LC resonant circuits for filtering the irrelevant noise at high frequency. A convolutional neural network (CNN) classifier is trained by 12x150 FFT spectrums. The result from the testing data with 12x10 FFT spectrums shows that the average accuracy is achieved to be as high as 92:5%, confirming the soundness of the proposed model.

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Comparison between overall and respective electrical rectifications in array of piezoelectric energy harvesting

Cited by 6 publications

References 103 publications

Multi-location fault detection with piezoelectric arrays and multi-task CNN learning

Multi-location fault detection with piezoelectric arrays and multi-task CNN learning

Physics-informed neural network for parameter identification in a piezoelectric harvester

Piezoelectric-array-based MISO diagnostic system for CNN-condition monitoring of bearing/gearbox instruments

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