风力轮机变速箱的故障诊断方法基于集成精制复合材料多尺度基于波动的反分散透
1School of Energy and Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China.
Entropy (Basel, Switzerland)
|August 29, 2024
概括
本研究介绍了一种智能方法,用于诊断风力轮机变速箱故障,使用集成精制复合材料多尺度波动式反分散 (ERCMFRDE). 该方法有效地识别复杂的振动信号中的故障,确保可靠的风力发电.
科学领域:
- 可再生能源工程可再生能源工程
- 机械系统诊断 机械系统诊断
- 信号处理 信号处理
背景情况:
- 风力轮机变速箱故障诊断对于可靠的风能发电至关重要.
- 来自变速箱的振动信号通常是非静止的,非线性,并且受到噪声的影响.
- 现有的方法可能会因为这些信号的复杂性而扎.
研究的目的:
- 开发风力轮机变速箱的智能故障诊断方法.
- 为了应对非静止,非线性和杂的振动信号所带来的挑战.
- 为了提高变速箱故障检测的准确性和可靠性.
主要方法:
- 信号分解,降噪和重组使用改进的完整合体实证模式分解与自适应噪声 (ICEEMDAN) 和静止波形变换 (SWT).
- 通过多顺序时刻粗粒加工方法的特征提取,应用于精制复合材料的基于多尺度波动的反分散 (RCMFRDE).
- 使用最小平方支持向量机 (LSSVM) 分类器进行故障诊断.
主要成果:
- 拟议的ERCMFRDE方法有效处理复杂的变速箱振动信号.
- 混合故障特征集成功地从denoised信号中提取出来.
- 实验验证证证实了该方法的有效性和可靠性.
结论:
- 开发的智能故障诊断方法为风力轮机变速箱提供了高精度和强大的性能.
- 这种方法为在具有挑战性的信号条件下检测故障提供了可靠的解决方案.
- 该研究有助于提高风能系统的运行完整性.
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