一种基于深度卷积随机森林自编码的泵站机组状态评价方法 (2023)
本发明公开了一种基于深度卷积随机森林自编码的泵站机组状态评价方法，包括收集泵站机组全寿命周期的上导震动X向波形、上导振动Y向振动波形、水导振动X向振动波形、水导振动Y向振动波形、上机架X向振动波形、上机架Y向振动波形、上机架垂直振动波形、叶轮外壳X向振动、叶轮外壳Y向振动、大轴摆度波形以及上述各个波形数据的键相信号，以得到历史波形健康样本库；从历史波形健康样本库中，选取泵站机组健康状态时期的各个测点信息作为历史正常数据样本；等步骤。优点是：基于在线监测数据，无须进行停机和人工布置测点，增加了电厂的经济效益，利用机组多源数据进行建模，自动挖掘隐含特征，适应于各个不同形式、运行时间的机组。 The invention discloses a state evaluation method for a pump station unit based on deep convolution random forest self-coding. The method comprises the following steps: collecting an upper guide vibration X-direction waveform, an upper guide vibration Y-direction vibration waveform, a water guide vibration X-direction vibration waveform, a water guide vibration Y-direction vibration waveform, an upper rack X-direction vibration waveform, an upper rack Y-direction vibration waveform, an upper rack vertical vibration waveform, impeller shell X-direction vibration and impeller shell Y-direction vibration of the whole life cycle of a pump station unit; obtaining a historical waveform health sample library according to lar