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首页> 外文会议>ISSAT international conference on reliability quality in design >Fusion of Wavelet Transform Features for Reliable Fault Detection within an Ocean Turbine MCM System
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Fusion of Wavelet Transform Features for Reliable Fault Detection within an Ocean Turbine MCM System

机译：小波变换特征的融合，用于海洋涡轮机MCM系统中的可靠故障检测

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Through a case study involving experimental data, we demonstrate how feature level fusion can enable more reliable fault detection in a condition monitoring system for an ocean turbine. This study revolves around analyzing and interpreting vibration signals gathered from multiple accelerometers installed on various components of a dynamometer designed to test the drive train and generator of the turbine. We applied feature level fusion to combine these vibration readings, and then assessed the abilities of six well known machine learners to detect changes in state from the raw accelerometer data and from the fused data. Analysis of the performance of these classifiers showed more stable performances for the six classifiers in detecting the state of the machine from the fused data versus from the data from the individual sensor channels.
机译：通过涉及实验数据的案例研究，我们演示了特征级融合如何在海洋涡轮机状态监测系统中实现更可靠的故障检测。这项研究围绕分析和解释从安装在测力计的各个组件上的多个加速度计收集的振动信号进行测试，该测力计旨在测试涡轮机的传动系统和发电机。我们应用特征级融合来结合这些振动读数，然后评估了六个著名机器学习者从原始加速度计数据和融合数据中检测状态变化的能力。对这些分类器性能的分析表明，六个分类器从融合数据中检测机器状态的性能要比从单个传感器通道的数据检测出机器状态的性能更稳定。

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