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基于多余神经网络和证据推理规则的驱动电机轴承的微故障诊断
Aoxiang Zhang1, Lihong Tang1, Guanyu Hu1
1Key Laboratory of the Ministry of Education, Guilin University of Electronic Technology, Guilin 541004, China.
Entropy (Basel, Switzerland)
|January 28, 2026
概括
本研究引入了一种使用多余神经网络 (multi-ResNets) 和证据推理 (ER规则) 的新车辆电机轴承故障诊断模型. 它在不断变化的操作条件下提高了微故障检测的准确性和可靠性.
科学领域:
- 汽车工程 汽车工程
- 人工智能的人工智能是人工智能.
- 机械诊断仪器 机械诊断仪器
背景情况:
- 车辆发动机轴承中的微缺陷造成安全风险和经济损失.
- 在变化条件下诊断这些故障是具有挑战性的,因为信号较弱.
研究的目的:
- 为汽车发动机轴承开发一个准确可靠的故障诊断模型.
- 为了应对在可变的操作条件下检测微故障的挑战.
主要方法:
- 提出了一种结合多余神经网络 (多ResNets) 和证据推理规则 (ER规则) 的新型故障诊断模型.
- 实施了使用多重负载条件作为点的基准条件概括机制.
- 通过ER规则集成了用于诊断条件和类别评估的子模型可信度评估.
主要成果:
- 与传统的机器学习算法相比,提出的基于多个ResNets-ER规则的模型显示出更高的诊断准确性.
- 在可变的操作条件下识别微故障的可靠性提高.
结论:
- 基于多重ResNets-ER规则的模型为车辆电机轴承微故障诊断提供了有效的解决方案.
- 这种方法提高了安全性,并通过早期和可靠的故障检测来延长设备的寿命.
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