风力轮机的联合条件监测框架基于多任务学习,数据质量不佳
Jiawen Ding1, Lei Deng1, Qikang Li1
1The State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, China.
ISA transactions
|February 7, 2024
概括
尽管数据质量不佳,但新的联合修复特征金字塔网络 (JRFPN) 能够有效地监测轮状况. 该方法通过解决数据复杂性和增强工业设备的长期依赖性分析来提高可靠性.
科学领域:
- 工程 工程师 工程师 工程师
- 数据科学数据科学数据科学
背景情况:
- 有效的状态监控对于轮机的可靠性和减少停机时间至关重要.
- 操作条件往往引入质量差的数据,破坏传统的监测方法,影响长期数据依赖.
研究的目的:
- 提出一种新的方法,即联合修复参数化特征金字塔网络 (JRFPN),以应对因质量不佳的数据造成的轮状况监测方面的挑战.
- 提高状态监测系统对数据噪声的稳定性,提高故障检测的准确性.
主要方法:
- 开发了三种重制参数化技术来处理时间信息并减轻长期数据依赖的问题.
- 设计了一个联合状态监测框架,其中有一个辅助任务是提取退化趋势,一个主要任务是抑制特征混并定义故障值.
- 整合了一个频道权重机制,以考虑不同监控变量之间的质量差异.
主要成果:
- 拟议的JRFPN框架成功地抑制了质量差和错误数据之间的特征混.
- 综合推断退化趋势和故障值,相互纠正,导致更准确的状态评估.
- 实验验证表明,与现有方法相比,JRFPN的性能优越.
结论:
- 对于像轮机这样的系统来说,JRFPN在状态监测方面取得了重大进展,特别是在处理杂或不完整数据时.
- 该方法提高了系统可靠性,并通过提供更准确和更强大的状态评估来减少运行停机时间.
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