大型造机的故障预测方法基于多个规模和多个模型的综合方法
Chao Yuan1,2, Tianmin Zhang3, Yuxi Tang4
1China Academy of Machinery Beijing Research Institute of Mechanical & Electrical Technology Co., LTD, Beijing, China.
Scientific reports
|August 20, 2025
概括
这项研究引入了一种新的多模型整体方法,用于预测大型造机的故障. 虽然不符合所有行业标准,但与现有的关键组件状态预测模型相比,拟议的方法表现优越.
科学领域:
- 机械工程 机械工程
- 制造业 制造技术 制造技术
- 工业中的人工智能
背景情况:
- 大型造机在汽车,造船和航空航天制造业中至关重要.
- 这些压力机中复杂的组件关系使得早期故障检测具有挑战性.
- 传统的单一模型预测方法与非线性,随机故障特征作斗争.
研究的目的:
- 为大型造机开发一个先进的故障预测模型.
- 在复杂的工业环境中克服传统单一模型方法的局限性.
- 为了提高故障信号预测的准确性和概括能力.
主要方法:
- 提出了一种多级自回归-支持向量回归-长短期记忆 (AR-SVR-LSTM) 集合预测方法.
- 集成的AR,SVR和LSTM模型具有混合预测的重量约束.
- 通过80MN电动螺杆压机的制动油压力数据验证了模型.
主要成果:
- 拟议的AR-SVR-LSTM组合模型显示了相对于比较模型的显著优势.
- 该模型的准确性,虽然不符合严格的行业标准 (MAE < 5%,RMSE < 1%),但证明了有效性.
- 对80MN电力螺丝压机的案例研究证实了该模型的实际应用.
结论:
- 多模组合方法为大型造机中的故障预测提供了一个优秀的替代方案.
- 尽管不符合所有行业基准,但该模型的增强性能需要进一步调查.
- 这项研究有助于推进重型制造业的预测性维护策略.
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