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基于LGA深度神经网络的伺服电机电液伺服控制系统的健康评估

Fei Wang^1,2, Gexin Chen^1,2, Keyi Liu²

¹School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China.

|March 12, 2024

概括

一个新的深度神经网络,LGA,准确地评估了电液伺服控制系统的健康状况. 该方法改进了现有模型,为关键的工业应用提供了更高的准确性.

科学领域:

工程工程师工程师工程师
人工智能的人工智能
控制系统控制系统

背景情况:

电液伺服控制系统 (EHSPCS) 由于其效率和功率,在工业中至关重要.
这些系统表现出复杂的非线性和时间变化的特征,使他们容易出现性能下降和潜在的故障.
系统故障可能导致严重事故,导致大量的人员和经济损失.

研究的目的:

为EHSPCS.开发一个先进的系统健康评估方法.
为应对系统非线性,时间变化的特征和复杂的故障模式所带来的挑战.
提高工业EHSPCS操作的可靠性和安全性.

主要方法:

建立了一个健康评估性能指数系统,使用油量气体含量,伺服电机空隙流量密度和系统泄漏系数.
开发了一个新的LSTM-GRNN-ANN (LGA) 深度神经网络模型用于EHSPCS健康评估.
在EHSPCS平台上进行的系统功能包括参数提取,算法设计,参数调试和实验验证.

主要成果:

拟议的LGA深度神经网络模型在系统健康评估中实现了96.37%的平均准确性.
这种精度明显超过了单个LSTM (89.84%) 和GRNN (87.99%) 模型的精度.
对典型系统故障的实验模拟证实了该模型的有效性.

结论:

LGA深度神经网络为EHSPCS健康评估提供了一个高度准确和可靠的方法.
这种方法有效地减轻了关键工业应用中与系统故障相关的风险.
该研究证实了集成LGA模型在复杂系统监控的传统方法上的优越性.

关键词:

在 EHSPCS 工作中.特性参数提取功能参数提取卫生评估健康评估在LGA深度神经网络中.伺服电机的发动机服务器

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