报告详情
  1. 活动首页
  2. 报告清单
  3. 报告详情
A Teaching Case for Vehicle Powertrain Fault Diagnosis Based on Deep Learning Method
编号：50 访问权限：仅限参会人 更新：2026-09-20 10:55:27 浏览：24次 张贴报告

报告开始：暂无开始时间（Asia/Shanghai）

报告时间：暂无持续时间

所在会场：[暂无会议] [暂无会议段]

暂无文件

摘要
The “Intelligent Instruments” course is designed to develop students’ ability to integrate sensing, data acquisition, signal processing, and intelligent decision-making techniques to solve engineering measurement problems. Core topics, including sensing principles, data acquisition and processing, and artificial intelligence, are incorporated into the curriculum. The course covers extensive foundational material and emphasizes system-level integration. However, instructional cases are typically presented in isolation, making it difficult to demonstrate the complete process from sensing physical quantities to making engineering decisions. In this study, an instructional case on switched reluctance motor (SRM) fault diagnosis is developed using a lightweight diagnostic framework, termed Orthogonal Channel Attention-based Lightweight Fault Diagnosis Network (OCA-LFNet), to deepen students’ understanding of sensing principles, data acquisition, and signal processing and to illustrate how these topics are integrated with artificial intelligence. Vibration and three-phase current signals are acquired under various conditions, including normal operation, high-resistance connection faults, bearing faults, and compound faults. Using data from 13 SRM operating conditions, students are guided through sensor configuration, synchronous data acquisition, signal processing, fault classification, and performance evaluation, enabling them to investigate the relationships among measurement information, computational cost, and diagnostic performance. Therefore, this case effectively links the “Intelligent Instruments” course with practical condition-monitoring tasks.
关键词
intelligent instruments course,switched reluctance motor,OCA-LFNet,fault diagnosis,laboratory instruction
报告人
Siliang Lu
Prof. Anhui University

稿件作者
Siliang Lu Anhui University
Mengxuan Tang Anhui University
Zhongping Zhai Anhui Zhihuan Technology Co. Ltd.
Wenzhe Deng Anhui University
Yibao Wu CETC Ecrieepower (Anhui) Co., Ltd.
Mingliang Shu Jiangsu Hongda Electric Co., Ltd.
Cheng Wang Hefei D2S Information Technology Co., Ltd.
Huangjun Hu Jiangsu Aolian Vehicle Manufacturing Co., Ltd.
Luning Fu Jiangsu Chaoneng Power Engineering Co.,Ltd.
发表评论
验证码 看不清楚，更换一张
全部评论
重要日期

  • 会议日期

    11月06日

    2026

    至

    11月08日

    2026

  • 10月15日 2026

    初稿截稿日期

  • 11月10日 2026

    注册截止日期

主办单位
IEEE Instrumentation and Measurement Society
承办单位
Sichuan University
联系方式
登录查看完整联系方式
历届会议
移动端
在手机上打开
小程序
打开微信小程序
客服
扫码或点此咨询