在风力轮机中通过人工智能技术进行听觉噪声评估
Mathaus Ferreira da Silva1, Juliano Emir Nunes Masson1, Murillo Ferreira Dos Santos2
1Robotictech Technology Services, Juiz de Fora 36036-230, Brazil.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
本研究介绍了一种人工智能模型,用于使用声音分析检测风力轮机故障. 该系统识别了部件声音中的异常,使预测性维护成为可能,并提高了运行可靠性.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 声学 声学 在声学方面
背景情况:
- 风力发电是重要的可再生能源,需要可靠的运行.
- 预测性维护对于风力轮机的可用性和寿命至关重要.
- 部件故障可能导致昂贵的停机时间和安全风险.
研究的目的:
- 开发一种人工智能驱动的方法,使用声学排放来检测风力轮机故障.
- 通过早期故障识别,提高风电系统的可靠性和可用性.
- 创建一个强大的系统,用于风能基础设施的预防性维护.
主要方法:
- 一个人工智能 (AI) 模型,采用无监督学习和图像处理.
- 分析来自风力轮机组件的声谱图,以确定基线"健康"条件.
- 重建当前的声学数据以检测表明潜在故障的偏差.
- 一个监督学习专家网络,用于从异常数据中识别特定的故障事件.
主要成果:
- 人工智能模型成功地识别了在五个测试的风力轮机中已知的故障.
- 重建和输入数据之间的低相似性表明特定组件故障.
- 该系统在捕捉潜在问题的标志性异常方面表现出有效性.
- 在通过声学特征分析检测故障方面取得了令人满意的结果.
结论:
- 拟议的AI方法为风力轮机的基于声学故障检测提供了可行的解决方案.
- 这种方法支持有效的预防性维护策略,提高运营效率.
- 该技术有可能在监控其他具有旋转部件的工业机械方面得到更广泛的应用.
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