Research on motor fault prediction model based on machine learning and energy storage optimization

ZHANG Yuwei; JIANG Chaoli; LI Xinqiang; ZHAO Yawei

doi:10.19799/j.cnki.2095-4239.2026.0022

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Research on motor fault prediction model based on machine learning and energy storage optimization

Energy Storage System and Engineering | 更新时间：2026-05-28

- Research on motor fault prediction model based on machine learning and energy storage optimization
- Energy Storage Science and Technology Vol. 15, Issue 2, Pages: 552-554(2026)
- 作者机构：
  
  1.石家庄信息工程职业学院传媒艺术系，河北石家庄 050000
  2.河北地质大学管理科学与工程学院，河北石家庄 050000
  3.石家庄信息工程职业学院机电工程系，河北石家庄 050000
  4.河北工业大学建筑与艺术设计学院，天津 300401
- 作者简介：
- 基金信息：
- DOI：10.19799/j.cnki.2095-4239.2026.0022
  CLC： TM 764
- Received：09 January 2026，
  
  Revised：2026-01-20，
  
  Published：28 February 2026
- 稿件说明：
移动端阅览
张玉伟, 江朝力, 李新强, 等. 基于机器学习与储能优化的电机故障预测模型研究[J]. 储能科学与技术, 2026, 15(2): 552-554.

ZHANG Yuwei, JIANG Chaoli, LI Xinqiang, et al. Research on motor fault prediction model based on machine learning and energy storage optimization[J]. Energy Storage Science and Technology, 2026, 15(2): 552-554.
张玉伟, 江朝力, 李新强, 等. 基于机器学习与储能优化的电机故障预测模型研究[J]. 储能科学与技术, 2026, 15(2): 552-554. DOI： 10.19799/j.cnki.2095-4239.2026.0022.

ZHANG Yuwei, JIANG Chaoli, LI Xinqiang, et al. Research on motor fault prediction model based on machine learning and energy storage optimization[J]. Energy Storage Science and Technology, 2026, 15(2): 552-554. DOI： 10.19799/j.cnki.2095-4239.2026.0022.

摘要

随着机器人技术的飞速发展，机器学习应用于越来越多的领域。本文针对电机在工业生产中的重要性及故障带来的能源损耗与经济损失，提出基于机器学习与储能优化的电机故障预测模型研究，通过概括机器学习算法在故障预测中的应用、储能优化策略对能源利用效率及模型性能的影响，在理论层面构建融合多种特征的预测模型，并分析其优势，该模型的构建有助于提高工业生产中电机运行的能源利用效率与稳定性从而减少故障的发生。

Abstract

With the rapid development of robotics technology

machine learning has been applied to an increasing number of fields. Aiming at the importance of motors in industrial production and the energy loss and economic losses caused by faults

this paper proposes a study on a motor fault prediction model based on machine learning and energy storage optimization. By summarizing the application of machine learning algorithms in fault prediction and the impact of energy storage optimization strategies on energy utilization efficiency and model performance

a prediction model integrating multiple features is theoretically constructed

and its advantages are analyzed. The construction of this model helps improve the energy utilization efficiency and operational stability of motors in industrial production

thereby reducing the occurrence of faults.

关键词

Keywords

references

任红涛, 杨小波. 状态监测技术在电动机故障诊断中的应用[C]//中国机电一体化技术应用协会. 第八届全国石油和化工电气设计与应用论文大赛入选论文集. 中国石油兰州石化机电仪运维中心电气四部, 2025: 763-767. DOI:10.26914/c.cnkihy.2025.004621.

陆飞. 多源数据融合与机器学习算法的电气柜故障诊断[J]. 电工技术, 2025(3): 164-166. DOI:10.19768/j.cnki.dgjs.2025.03.040.

雷亚国, 贾峰, 孔德同, 等. 大数据下机械智能故障诊断的机遇与挑战[J]. 机械工程学报, 2018, 54(5): 94-104. DOI:10.3901/JME. 2018.05.094.

郑子萱, 倪扶瑶, 汪颖, 等. 基于模型预测控制混合储能系统的直流微电网韧性提升策略[J]. 电力自动化设备, 2021, 41(5): 152-159. DOI:10.16081/j.epae.202105043.

张宽阔. 基于机器学习模型的异步电机定子故障诊断方法研究[D]. 北京: 华北电力大学, 2020.

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