AI-enabled rolling triboelectric nanogenerator for bearing wear diagnosis aiming at digital twin application

• Published in: Nano energy 2025-02, Vol.134, p.110550, Article 110550
• Main Authors: Dong, Fangyang, Zhu, Meixian, Wang, Yulian, Chen, Zhixiang, Dai, Yingwei, Xi, Ziyue, Du, Taili, Xu, Minyi
• Format: Article
• Language: English
• Subjects: Bearings; Deep learning; Fault diagnosis; Triboelectric nanogenerators; Waveform analysis
• ISSN: 2211-2855
• DOI: 10.1016/j.nanoen.2024.110550

In the era of artificial intelligence (AI) and digitization, developing self-monitoring and smart-diagnosis bearings has become a meaningful yet challenging problem. This study investigates an AI-enabled bearing-structural rolling triboelectric nanogenerator (B-TENG), which can achieve condition monitoring and fault diagnosis for bearing wear. The geometrical structure of B-TENG is designed to directly use rolling balls as the freestanding layer. Besides, the sensing principle of triboelectric signal waveforms and the mapping mechanism of wear faults are firstly revealed through a signal decomposition method. Furthermore, a deep learning algorithm can classify different wear types, degrees and positions on rolling balls, with higher accuracies of 95.20∼98.40 % for the feature components. The detection of wear degree related to bearing health and failure evolution is realized for the first time. The proposed B-TENG has the potential for digital twin application via interaction with professional simulation software according to the real-time diagnosis classified by AI. [Display omitted] •Integration of AI and triboelectric technology in bearings is established.•Mapping mechanism between the triboelectric signal and bearing wear is revealed.•Detected rate for the minor defects that simulate early failures reaches 96 %.•A bearing digital twin system by smart fault diagnosis is demonstrated.