轴承故障检测方法基于改进的灰狼算法,以优化多稳定随机共振的参数
Weichao Huang1,2, Ganggang Zhang2
1Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
本研究引入了用于轴承故障检测的自适应参数方法,增强了灰狼优化算法,以改善信号检测和准确诊断工业设备的故障. 与现有方法相比,新方法提供了更快的融合和更高的信号噪声比率.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 传统的随机共振系统缺乏适应性参数调整用于轴承故障检测.
- 有效的轴承故障诊断对于工业设备的可靠性至关重要.
研究的目的:
- 提出一个自适应参数轴承故障检测方法.
- 为了提高优化性能,以改善故障信号检测.
主要方法:
- 改进了灰狼优化算法,包括Sobol序列初始化,指数收因子,自适应位置更新和Cauchy-Gaussian混合变化.
- 使用增强算法优化多稳定性随机共振模型参数.
- 在开源轴承数据集和单晶炉起重装置轴承上的验证.
主要成果:
- 准确诊断轴承故障的识别故障频率 (例如,158 Hz内环,162 Hz外环,35 Hz提升装置).
- 实验结果与理论预测一致.
- 与其他改进的算法相比,证明了更快的融合速度和更高的输出信号噪声比率.
结论:
- 拟议的自适应参数方法有效检测轴承故障.
- 增强的灰狼优化算法显著提高了诊断性能.
- 该方法为工业应用中轴承故障诊断提供了可靠的解决方案.
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