Metallurgy, Vol. 65 No. 3, 2026.
Original scientific paper
https://doi.org/10.64486/m.65.3.8
Printed PZT Sensors and Machine Learning for Intelligent Fault Diagnosis in EMS Trays
Xiaolong Wu
; Ningbo Cigarette Factory, Zhejiang China Tobacco Industry Co., LTD., 2001 Jiapu West Road, Fenghua Economic Development Zone, Ningbo 315040, China
Dongjiong Xu
; Ningbo Cigarette Factory, Zhejiang China Tobacco Industry Co., LTD., 2001 Jiapu West Road, Fenghua Economic Development Zone, Ningbo 315040, China
Anjie Zheng
; Ningbo Cigarette Factory, Zhejiang China Tobacco Industry Co., LTD., 2001 Jiapu West Road, Fenghua Economic Development Zone, Ningbo 315040, China
Min Xu
; Ningbo Cigarette Factory, Zhejiang China Tobacco Industry Co., LTD., 2001 Jiapu West Road, Fenghua Economic Development Zone, Ningbo 315040, China
Leilei Zhu
; Ningbo Cigarette Factory, Zhejiang China Tobacco Industry Co., LTD., 2001 Jiapu West Road, Fenghua Economic Development Zone, Ningbo 315040, China
*
Bin Lin
; Ningbo Cigarette Factory, Zhejiang China Tobacco Industry Co., LTD., 2001 Jiapu West Road, Fenghua Economic Development Zone, Ningbo 315040, China
Chao Zheng
; Ningbo Cigarette Factory, Zhejiang China Tobacco Industry Co., LTD., 2001 Jiapu West Road, Fenghua Economic Development Zone, Ningbo 315040, China
Kelei Sun
; Ningbo Cigarette Factory, Zhejiang China Tobacco Industry Co., LTD., 2001 Jiapu West Road, Fenghua Economic Development Zone, Ningbo 315040, China
Yexin Wang
; Key Laboratory of Impact and Safety Engineering, Ministry of Education, Ningbo University, No.818 Fenghua Road, Jiangbei District, Ningbo, Zhejiang, 315211, China; School of Mechanical Engineering and Mechanics, Ningbo University, No.818 Fenghua Road, Jiangbei District, Ningbo, Zhejiang, 315211, China
Kai Li
; Key Laboratory of Impact and Safety Engineering, Ministry of Education, Ningbo University, No.818 Fenghua Road, Jiangbei District, Ningbo, Zhejiang, 315211, China; School of Mechanical Engineering and Mechanics, Ningbo University, No.818 Fenghua Road, Jiangbei District, Ningbo, Zhejiang, 315211, China
* Corresponding author.
Abstract
Fault diagnosis in highly automated and sealed EMS trays presents significant challenges. This study proposes a novel method utilizing printed lead zirconate titanate (PZT) strain sensors combined with machine learning. Micro/nanoscale PZT structures were fabricated via printing, and the impact of annealing on their crystallization, microstructure, and resultant piezoelectric/dielectric properties and impedance characteristics (for both thick films and 3D structures) was investigated. A neural network model was developed, with its hyperparameters (weights, thresholds, and topology) optimized using a Bayesian approach. Comparative analysis of model performance demonstrated the method's effectiveness in achieving accurate fault diagnosis for EMS electric vehicle systems, providing valuable theoretical and technical support for their detection, operation, and maintenance.
Keywords
3D printing; PZT; EMS trolley; fault diagnosis; machine learning
Hrčak ID:
344963
URI
Publication date:
1.7.2026.
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