Energy Harvesting Microelectromechanical System for Condition Monitoring Based on Piezoelectric Transducer Ring

For complex mechanical transmission equipment, shaft bearings are usually enclosed together with the shaft in the internal space of the housing to maintain good sealing and reliability. However, it is difficult to monitor the status of the shaft bearing through external sensors on the housing, while internal sensors face challenges in energy supply and data transmission. Therefore, a piezoelectric transducer ring-based energy harvesting microelectromechanical system (PTR-EH-MEMS) is proposed for the condition monitoring of shaft bearings. Specifically, the piezoelectric transducer ring is designed to convert mechanical vibrations into electrical energy, which simultaneously acts as a self-powered monitoring sensor through energy harvesting. In addition, the MEMS is embedded for piezoelectric data processing and condition monitoring of the shaft bearings. To verify the proposed PTR-EH-MEMS, an experimental investigation is implemented under different conditions. The experimental results demonstrate that the system can achieve the maximum DC output of 0.8 V and the root mean square power of 43.979 μW within 128 s, which can effectively identify early-stage bearing faults frequency through a self-powered mode. By combining energy harvesting with condition monitoring capability, the PTR-EH-MEMS offers a compact and sustainable approach for predictive maintenance in rotating machinery, reducing the reliance on external power sources and enhancing the reliability of industrial systems.

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Publication Details

Journal
Micromachines
Published
2025-05-22
DOI
https://doi.org/10.3390/mi16060602
Citations
3
Primary Topic
Advanced machining processes and optimization
Type
article
Field-Weighted Citation Impact
2.14

Funders

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article

Energy Harvesting Microelectromechanical System for Condition Monitoring Based on Piezoelectric Transducer Ring

Hao Long, Kaixuan Wang, Di Song, Hasan Shariar
3 citations
Micromachines
Advanced machining processes and optimization
2.14
article

Energy Harvesting Microelectromechanical System for Condition Monitoring Based on Piezoelectric Transducer Ring

Hao Long, Kaixuan Wang, Di Song, Hasan Shariar
article en
3 citations

Abstract

For complex mechanical transmission equipment, shaft bearings are usually enclosed together with the shaft in the internal space of the housing to maintain good sealing and reliability. However, it is difficult to monitor the status of the shaft bearing through external sensors on the housing, while internal sensors face challenges in energy supply and data transmission. Therefore, a piezoelectric transducer ring-based energy harvesting microelectromechanical system (PTR-EH-MEMS) is proposed for the condition monitoring of shaft bearings. Specifically, the piezoelectric transducer ring is designed to convert mechanical vibrations into electrical energy, which simultaneously acts as a self-powered monitoring sensor through energy harvesting. In addition, the MEMS is embedded for piezoelectric data processing and condition monitoring of the shaft bearings. To verify the proposed PTR-EH-MEMS, an experimental investigation is implemented under different conditions. The experimental results demonstrate that the system can achieve the maximum DC output of 0.8 V and the root mean square power of 43.979 μW within 128 s, which can effectively identify early-stage bearing faults frequency through a self-powered mode. By combining energy harvesting with condition monitoring capability, the PTR-EH-MEMS offers a compact and sustainable approach for predictive maintenance in rotating machinery, reducing the reliance on external power sources and enhancing the reliability of industrial systems.

MicromachinesVol. 16(6)
Jiangsu Normal University (CN), Xuzhou University of Technology (CN), Xuzhou College of Industrial Technology, University of Manitoba (CA), Southeast University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 14%
Advanced machining processes and optimization
2.14
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