预测性维护效率的评估与机器学习模型在制动行业的加工生产过程中的比较
1Department of Management Information System, Dokuz Eylül University, İzmir, İzmir Province, Turkey.
PeerJ. Computer science
|September 24, 2025
概括
这项研究通过机器学习 (ML) 和用于工业生产的数据分析来增强机器故障预测. 先进的技术提高了模型的准确性,减少了停机时间和维护成本.
科学领域:
- 工业工程 工业工程 工业工程
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 工业部门越来越依赖人工智能 (AI) 和机器学习 (ML) 来提高生产效率和稳定性.
- 预测性维护,集成传感器和数据分析,是预测机器故障和防止停机时间的关键.
- 这种方法优化了生产并降低了维护费用.
研究的目的:
- 预测机器故障使用数据分析,用于制动元件制造商.
- 通过先进的数据分析技术,提高故障预测模型的准确性.
- 为了应对数据挑战,如类不平衡和异常值,以实现强大的预测维护.
主要方法:
- 经过全面的数据检查后,采用ML分类方法来预测故障.
- 利用数据分割和k倍交叉验证来管理数据集不平衡.
- 应用低采样,异常值检测和规范化,以提高数据质量和模型性能.
主要成果:
- 评估模型性能使用准确度,精度,回忆,F1分数,AUC和MCC.
- 通过超参数优化实现了显著的性能改进.
- 证明了数据预处理和先进建模在预测性维护中的有效性.
结论:
- 数据预处理和先进的ML技术对于有效的预测性维护至关重要.
- 解决不平衡和异常值等数据挑战显著提高了ML模型的可靠性.
- 该研究为预测性维护应用程序和数据分区策略提供了宝贵的见解.
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