一种新型状态监测系统的算法设计及实例分析

The Algorithm Design and Case Studies for the New Generation Condition Monitoring Systems

随着振动分析技术的普及，越来越多的风场为了达到最大化降低维护成本的目的，积极地转变了风机群的维护理念。基于振动的状态监控技术可以提供部件潜在故障的早期预警。风场维护团队通过实时的各个部件的健康状况来积极地调整维护措施。TurbinePHD状态监测系统的算法是一种以时域同步平均为核心的算法体系。本文介绍了这种算法并通过实验平台及现场案例验证了算法的有效性。

As the wide spread of vibration analysis technologies in recent days, more and more windfarm sites have swiftly changed the maintenance strategy with the purpose of lowering the operational cost. Vibration based condition monitoring techniques can provide early warning to potential component failure. The wind farm maintenance team can actively schedule maintenance behavior based on the health of individual components of the turbine fleet TurbinePHD is a condition monitoring system that utilizes Tune Synchronous Averaging （TSA） as its core signal processing technique. This article described the algorithm in detail. The effectiveness of the proposed technique was validated via field case studies as well as the laboratory based testing platform.