Zheng Fang , Lingji Kong , Jiangfan Chen , Hongyu Chen , Xinyi Zhao , Dabing Luo , Zutao Zhang
{"title":"用于智能交通的三电-电磁纳米传感器多节点自供电故障检测系统","authors":"Zheng Fang , Lingji Kong , Jiangfan Chen , Hongyu Chen , Xinyi Zhao , Dabing Luo , Zutao Zhang","doi":"10.1016/j.nanoen.2024.109882","DOIUrl":null,"url":null,"abstract":"<div><p>The harnessing of vibrational energy is becoming increasingly pivotal in the development of intelligent rail transit systems. The integration of emerging technologies such as triboelectric nanogenerators (TENGs), electromagnetic generators (EMGs), or hybrid generators has become crucial for fault detection and energy harvesting in rail transit. This paper introduces a self-powered fault detection system (SPFDS). SPFDS combines multiple compact rotating Triboelectric-Electromagnetic Nanosensor (TENS) nodes with a deep learning-based diagnostic module to transform vibrational energy generated during train operations into electrical power and accurately identifies five distinct train bogie fault conditions. Simulations and experiments have shown that the TENS nodes, with a root mean square power of 0.21 W and a power density of 1595.7 W/m³, can efficiently detect various bogie faults. Additionally, their power output is adequate to support commercial sensors and Bluetooth modules. Through hyperparameter optimization, the diagnostic module utilizing multi-TENS nodes achieves an average diagnostic accuracy of 99.38 % for the five fault modes of freight train bogies. Implementing multiple TENS nodes in SPFDS enhances fault detection accuracy by an average of 32 % compared to a single TENS node, with a peak increase of 128 %. The multi-node TENS configuration and SPFDS's self-powered detection capabilities represent an innovative approach to complex fault detection, significantly contributing to the advancement of vibration energy harvesting and the development of distributed self-powered sensor network technologies for smart transportation.</p></div>","PeriodicalId":394,"journal":{"name":"Nano Energy","volume":null,"pages":null},"PeriodicalIF":16.8000,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A multi-node self-powered fault detection system by triboelectric-electromagnetic nanosensors for smart transportation\",\"authors\":\"Zheng Fang , Lingji Kong , Jiangfan Chen , Hongyu Chen , Xinyi Zhao , Dabing Luo , Zutao Zhang\",\"doi\":\"10.1016/j.nanoen.2024.109882\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The harnessing of vibrational energy is becoming increasingly pivotal in the development of intelligent rail transit systems. The integration of emerging technologies such as triboelectric nanogenerators (TENGs), electromagnetic generators (EMGs), or hybrid generators has become crucial for fault detection and energy harvesting in rail transit. This paper introduces a self-powered fault detection system (SPFDS). SPFDS combines multiple compact rotating Triboelectric-Electromagnetic Nanosensor (TENS) nodes with a deep learning-based diagnostic module to transform vibrational energy generated during train operations into electrical power and accurately identifies five distinct train bogie fault conditions. Simulations and experiments have shown that the TENS nodes, with a root mean square power of 0.21 W and a power density of 1595.7 W/m³, can efficiently detect various bogie faults. Additionally, their power output is adequate to support commercial sensors and Bluetooth modules. Through hyperparameter optimization, the diagnostic module utilizing multi-TENS nodes achieves an average diagnostic accuracy of 99.38 % for the five fault modes of freight train bogies. Implementing multiple TENS nodes in SPFDS enhances fault detection accuracy by an average of 32 % compared to a single TENS node, with a peak increase of 128 %. The multi-node TENS configuration and SPFDS's self-powered detection capabilities represent an innovative approach to complex fault detection, significantly contributing to the advancement of vibration energy harvesting and the development of distributed self-powered sensor network technologies for smart transportation.</p></div>\",\"PeriodicalId\":394,\"journal\":{\"name\":\"Nano Energy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":16.8000,\"publicationDate\":\"2024-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nano Energy\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S221128552400630X\",\"RegionNum\":1,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nano Energy","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S221128552400630X","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
A multi-node self-powered fault detection system by triboelectric-electromagnetic nanosensors for smart transportation
The harnessing of vibrational energy is becoming increasingly pivotal in the development of intelligent rail transit systems. The integration of emerging technologies such as triboelectric nanogenerators (TENGs), electromagnetic generators (EMGs), or hybrid generators has become crucial for fault detection and energy harvesting in rail transit. This paper introduces a self-powered fault detection system (SPFDS). SPFDS combines multiple compact rotating Triboelectric-Electromagnetic Nanosensor (TENS) nodes with a deep learning-based diagnostic module to transform vibrational energy generated during train operations into electrical power and accurately identifies five distinct train bogie fault conditions. Simulations and experiments have shown that the TENS nodes, with a root mean square power of 0.21 W and a power density of 1595.7 W/m³, can efficiently detect various bogie faults. Additionally, their power output is adequate to support commercial sensors and Bluetooth modules. Through hyperparameter optimization, the diagnostic module utilizing multi-TENS nodes achieves an average diagnostic accuracy of 99.38 % for the five fault modes of freight train bogies. Implementing multiple TENS nodes in SPFDS enhances fault detection accuracy by an average of 32 % compared to a single TENS node, with a peak increase of 128 %. The multi-node TENS configuration and SPFDS's self-powered detection capabilities represent an innovative approach to complex fault detection, significantly contributing to the advancement of vibration energy harvesting and the development of distributed self-powered sensor network technologies for smart transportation.
期刊介绍:
Nano Energy is a multidisciplinary, rapid-publication forum of original peer-reviewed contributions on the science and engineering of nanomaterials and nanodevices used in all forms of energy harvesting, conversion, storage, utilization and policy. Through its mixture of articles, reviews, communications, research news, and information on key developments, Nano Energy provides a comprehensive coverage of this exciting and dynamic field which joins nanoscience and nanotechnology with energy science. The journal is relevant to all those who are interested in nanomaterials solutions to the energy problem.
Nano Energy publishes original experimental and theoretical research on all aspects of energy-related research which utilizes nanomaterials and nanotechnology. Manuscripts of four types are considered: review articles which inform readers of the latest research and advances in energy science; rapid communications which feature exciting research breakthroughs in the field; full-length articles which report comprehensive research developments; and news and opinions which comment on topical issues or express views on the developments in related fields.