用机器学习提前预测风机报警，减少故障发生。
Early wind turbine alarm prediction based on machine learning: AlarmForecasting
- 用LSTM模型预测报警时间序列，再分类标记报警类型。
- 10/20/30分钟预警准确率分别为82%/52%/41%。
- 适合风电运维团队用于主动干预，降低故障率。
报警数据在抑制风力发电机（WTs）故障行为中至关重要，是先进预测监控系统的核心。传统研究仅将报警数据用作诊断工具，仅反映设备异常状态。本文提出一种报警预测与分类（AFC）框架，包含两个模块：首先使用基于长短期记忆（LSTM）的回归模块进行时序报警预测，随后通过分类模块对预测出的报警进行标签标注。该方法可整体预测所有报警类别，而非仅限于特定几类。以14台运行5年的Senvion MM82风机为案例，10、20、30分钟的报警预测准确率分别达到82%、52%和41%。结果表明，提前预警并阻止报警触发，能显著降低报警频率，提升运行效率。
原文摘要 · Abstract (English)
Alarm data is pivotal in curbing fault behavior in Wind Turbines (WTs) and forms the backbone for advancedpredictive monitoring systems. Traditionally, research cohorts have been confined to utilizing alarm data solelyas a diagnostic tool, merely indicative of unhealthy status. However, this study aims to offer a transformativeleap towards preempting alarms, preventing alarms from triggering altogether, and consequently avertingimpending failures. Our proposed Alarm Forecasting and Classification (AFC) framework is designed on twosuccessive modules: first, the regression module based on long short-term memory (LSTM) for time-series alarmforecasting, and thereafter, the classification module to implement alarm tagging on the forecasted alarm. Thisway, the entire alarm taxonomy can be forecasted reliably rather than a few specific alarms. 14 Senvion MM82turbines with an operational period of 5 years are used as a case study; the results demonstrated 82%, 52%,and 41% accurate forecasts for 10, 20, and 30 min alarm forecasts, respectively. The results substantiateanticipating and averting alarms, which is significant in curbing alarm frequency and enhancing operationalefficiency through proactive intervention.
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