一种基于层次矩阵分解的方法,用于智能工业故障诊断
Yanxia Li1, Han Zhou2, Jiajia Liu1
1School of Automation, Chengdu University of Information Technology, Chengdu 610225, China.
Sensors (Basel, Switzerland)
|August 29, 2024
概括
本研究介绍了在工业环境中数据驱动故障诊断的层次矩阵因数分解 (HMF). 高功率机能高效地表示复杂的工业数据,改善安全管理和超越传统模型.
科学领域:
- 工业安全管理 工业安全管理
- 机器学习 机器学习
- 数据挖掘 数据挖掘
背景情况:
- 数据驱动的故障诊断对于智能工业安全至关重要.
- 工业数据通常涉及混合的物理属性,挑战传统的浅层模型.
- 现有的模型很难利用连贯的信息来提高诊断性能.
研究的目的:
- 介绍一种新的层次矩阵因子化 (HMF) 模型,用于高效的工业数据表示.
- 通过学习隐性特征和高级特征来增强故障诊断能力.
- 解决非线性延伸 (NHMF) 的工业过程中的非线性问题.
主要方法:
- 层次矩阵因子化 (HMF) 通过连续的矩阵因子化将数据分解为多个层次结构.
- 中间层次结构作为分析操作员,学习隐含的数据特征.
- 非线性扩展 (NHMF) 包含激活功能来处理过程非线性.
主要成果:
- 与浅层和深层模型相比,HMF和NHMF在故障诊断方面表现出竞争力.
- 与深度学习方法相比,拟议的模型在减少计算时间的情况下实现了这种性能.
- 在多相流程过程中的实验评估验证了HMF和NHMF的有效性.
结论:
- HMF提供了高效的数据表示,以改善故障诊断.
- NHMF有效地处理非线性,扩大了该方法的适用性.
- 拟议的模型为智能工业安全提供了计算效率高和高性能解决方案.
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