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轴承的剩余使用寿命预测方法基于精确的精确线性时间状态分割和时间频率图
Xu Wei1, Jingjing Fan1,2, Huahua Wang2
1School of Electtrical and Control Engineering, North China University of Technology, Beijing 100144, China.
本研究介绍了一种先进的方法,用于预测轴承的剩余使用寿命 (RUL),使用状态细分和时间频率分析与Informer模型. 该方法提高了准确性和稳定性,以便更好地进行健康评估.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 准确预测轴承的剩余使用寿命 (RUL) 对于工业机械维护至关重要.
- 传统方法往往在复杂的工作条件和对振动数据的长期依赖性方面扎.
- 现有的RUL预测模型需要提高准确性,稳定性和计算效率.
研究的目的:
- 开发一种新的轴承RUL预测方法,提高准确性和稳定性.
- 准确地细分轴承的降解状态并提取全面的时间频率特征.
- 在RUL预测中利用Informer模型进行高效的长期时间序列依赖性建模.
主要方法:
- 利用Puned精确线性时间 (PELT) 算法,在整个生命周期中精确细分轴承振动信号.
- 应用波波变换用于时间频率分析,生成光谱图以提取强大的特征.
- 采用Informer模型,一个高效的时间序列预测模型,用于使用提取特征进行RUL预测.
主要成果:
- 拟议的方法准确地识别了临界降解状态,并优化了阶段划分.
- 时间频率分析有效地从振动信号中提取全面的特征.
- 该Informer模型在捕捉RUL预测的长期依赖性方面表现出卓越的性能.
结论:
- PELT细分,时间频率分析和Informer模型的综合方法显著提高了轴承RUL预测的准确性和稳定性.
- 与传统方法相比,这种方法提供了更高的计算效率.
- 拟议的技术非常适合在苛刻的操作环境中进行轴承健康评估和RUL预测.
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