防止叉车前端故障:预测重物体的重量中心,在异常条件下预测剩余使用寿命,并根据警报规则进行故障诊断
Jeong-Geun Lee1,2, Yun-Sang Kim2, Jang Hyun Lee3
1Department of Smart Digital Engineering, INHA University, Incheon 22212, Republic of Korea.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
这项研究通过使用加速数据预测故障,提高了叉车的可靠性. 机器学习模型准确地预测剩余的使用寿命 (RUL) 和诊断故障,提高运营安全.
科学领域:
- 机械工程 机械工程
- 可靠性工程可靠性工程
- 机器学习应用 机器学习应用
背景情况:
- 叉车前端故障带来了重大运营风险.
- 预测叉车故障和剩余使用寿命 (RUL) 的现有方法需要改进.
- 准确的故障诊断对于保持运行可靠性至关重要.
研究的目的:
- 开发一种全面的方法来防止叉车前端故障.
- 准确预测叉车的剩余使用寿命 (RUL).
- 使用警报规则实施高效的故障诊断.
主要方法:
- 从叉车前端收集和处理加速度信号.
- 提取的时间域统计特征和应用的降噪技术.
- 使用数据增强,随机森林,光GBM和后勤回归来进行分类和RUL预测.
- 采用了SHAP算法来确定特征的重要性,并建立了故障诊断的报警规则.
主要成果:
- 随机森林和轻GBM模型在分类重心方面取得了高精度 (0.9563,0.9566).
- 物流回归和随机森林模型在分类故障阶段方面表现强 (F1分数为0.9790,0.9220).
- 开发了降解曲线和RUL预测模型,确定了故障诊断的重要特征.
结论:
- 提出的方法有效地提高了叉车的运行可靠性.
- 通过加速信号分析和机器学习,可以实现准确的RUL预测和故障诊断.
- 该研究为叉车主动维护和故障预防提供了一个强大的框架.
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