预测性维护中的故障分析:使用专家系统和塔古奇方法对非常规的振动特征进行带驱动诊断
Ahmed Adnan Shandookh1, Ahmed Ali Farhan Ogaili2, Luttfi A Al-Haddad3
1Mechanical Engineering Department, University of Technology- Iraq, Baghdad, Iraq.
Heliyon
|July 29, 2024
概括
这项研究使用振动数据和人工智能来检测皮带驱动系统的故障,提高预测性维护和机器可靠性. 随机森林模型在识别系统健康方面取得了很高的准确性.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 预测性维护对于通过防止疲劳和故障来提高机械系统可靠性至关重要.
- 振动时间域数据分析为机械故障诊断提供了一个有前途的方法.
- 带驱动系统在工业应用中很常见,容易发生各种故障.
研究的目的:
- 探索使用振动时间域数据用于故障诊断在带驱动系统的预测性维护.
- 评估先进的统计特征和基于人工智能的故障分类专家系统的有效性.
- 使用随机森林模型优化故障检测的准确性.
主要方法:
- 从带驱动系统在各种操作条件 (速度,电压) 和故障状态 (健康,故障,不平衡) 下收集的振动信号.
- 提取了五种先进的统计特征:变量,平均绝对偏差 (MAD),零交叉率 (ZCR),自相关系数和能量.
- 利用塔古奇方法根据信号与噪声 (S/N) 比率进行特征选择,并使用随机森林 (RF) 模型进行分类.
主要成果:
- 随机森林模型在分类带驱动系统的运行状态时实现了高精度 (0.990) 和曲线下的面积 (AUC) (0.999).
- 选择的高级统计特征在集成到专家系统中时显示出显著的潜力.
- 该方法在推进预测性维护故障诊断方面被证明是有效的.
结论:
- 从振动数据中集成先进的统计特征与基于AI的随机森林模型的集成显著提高了皮带驱动系统的故障诊断.
- 这种方法为预测性维护提供了可靠和准确的方法,提高了整体系统的可靠性.
- 该研究强调了人工智能驱动的信号处理在优化工业机械维护策略方面的潜力.
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