一个基于自回归功率光谱密度的多维健康指标,用于机器状态监测
Roberto Diversi1, Nicolò Speciale1
1Department of Electrical, Electronic and Information Engineering, University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy.
Sensors (Basel, Switzerland)
|August 10, 2024
概括
本研究介绍了一种数据驱动的状态监测 (CM) 方法,使用自回归建模和频率子带用于工业设备健康. 该方法通过分析信号频谱变化来有效检测故障,提高可靠性和安全性.
科学领域:
- 工程 工程师 工程师 工程师
- 数据科学数据科学数据科学
- 信号处理 信号处理
背景情况:
- 条件监测 (CM) 对于工业环境中的预后和健康管理 (PHM) 是至关重要的.
- 有效的CM提高了设备的可靠性,安全性和运营效率.
- 现有的CM方法往往需要大量的人类干预和系统特定的调整.
研究的目的:
- 提出一种以数据为导向的条件监测方法,最小的人类干预.
- 开发一个强大的健康指标,用于检测设备故障.
- 为了使在线状态监测和故障诊断适用于各种系统.
主要方法:
- 自动回归 (AR) 在自动确定频率子频段内对传感器数据进行建模.
- 利用同步挤压变换来增强时间频率信号能量分布.
- 使用AR功率光谱密度和Itakura-Saito光谱距离构建一个多维健康指标.
主要成果:
- 拟议的健康指标有效地检测出表明故障的光谱变化.
- 该方法在初始频段定义和名义频谱计算后不需要进一步干预.
- 使用CWRU轴承数据中心基准数据集进行了成功的验证.
结论:
- 开发的数据驱动的CM方法为工业设备监控提供了自动化和有效的解决方案.
- 该方法的系统不可知性使其在不同的工业应用中具有广泛的应用性.
- 这种技术通过先进的故障检测,大大提高了工业运行可靠性和安全性.
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