使用传感器数据对工业制造系统预测性维护的深度学习模型进行比较
1School of Artificial Intelligence, Suzhou Vocational Institute of Industrial Technology, Suzhou, 215000, Jiangsu, China. liwenjun_sc@163.com.
Scientific reports
|July 2, 2025
概括
包括CNN-LSTM混合体在内的深度学习模型显著改善了工业制造业的预测性维护 (PdM). 这些先进的技术提供了准确的设备故障预测和使用传感器数据的剩余使用寿命估计.
科学领域:
- 工业工程 工业工程 工业工程
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 预测性维护 (PdM) 对于优化工业制造业务至关重要.
- 传统的方法往往难以应对传感器数据的复杂性和数量.
- 深度学习为分析复杂的工业数据提供了先进的功能.
研究的目的:
- 为了比较深度学习模型用于工业制造业的预测性维护.
- 评估卷积神经网络 (CNN),长期短期记忆 (LSTM) 网络和混合变体的性能.
- 确定最佳的深度学习架构,用于设备故障预测和剩余使用寿命估计.
主要方法:
- 开发了一个数据采集,预处理和模型构建的框架.
- 实现了多个深度学习架构,包括CNN,LSTM和CNN-LSTM混合体.
- 在三个不同的工业数据集上进行了实验.
主要成果:
- 该CNN-LSTM混合型实现了最高的性能,精度为96.1%,F1得分为95.2%.
- 混合模型在预测设备故障方面优于独立的CNN和LSTM架构.
- 废弃性研究确定了影响模型性能的关键组件.
结论:
- 深度学习,特别是CNN-LSTM混合型,显示了在工业制造业中彻底改变PdM的巨大潜力.
- 使用数据驱动策略可以实现准确的故障预测和剩余使用寿命估计.
- 这些发现为在现实世界的工业应用中实施先进的PdM提供了宝贵的见解.
相关概念视频
01:14Mechanical Efficiency of Real Machines
879
The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
However, in reality, no machine can be truly ideal, and all of them experience some...
However, in reality, no machine can be truly ideal, and all of them experience some...
879
01:26Classification of Systems-I
319
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
319