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首页> 外文期刊>IEEE Transactions on Industrial Electronics >Discovering Prognostic Features Using Genetic Programming in Remaining Useful Life Prediction
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Discovering Prognostic Features Using Genetic Programming in Remaining Useful Life Prediction

机译：在剩余使用寿命预测中使用遗传编程发现预后特征

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In prognostics approaches, features (e.g., vibration level, root mean square or outputs from signal processing techniques) extracted from the measurement (e.g., vibration, current, and pressure, etc.) are often used or modeled as an indicator to the equipment's health condition. When faults are detected or when increasing/decreasing trends are shown in the health indicator, prediction algorithms are applied to extrapolate the future behavior and predict remaining useful life (RUL). However, it is difficult to make an accurate prediction if the trend of the health indicator is not obvious through the entire life cycle or if the trend is only shown right before a failure occurs. The challenge lies in whether an advanced feature (e.g., a mathematical combination of a group of the extracted features) can be found to clearly present/correlate with the fault progression. A genetic programming method is proposed to address the challenge of automatically discovering advanced feature(s), which can well capture the fault progression, from the measurement or extracted features in the purpose of RUL prediction.
机译：在预测方法中，通常将从测量中提取的特征（例如，振动水平，均方根或信号处理技术的输出）（例如，振动，电流和压力等）用作模型或指示设备健康的指标健康）状况。当检测到故障或运行状况指示器中显示上升/下降趋势时，将应用预测算法来推断未来行为并预测剩余使用寿命（RUL）。但是，如果在整个生命周期中健康指标的趋势不明显，或者仅在发生故障之前就显示趋势，则很难做出准确的预测。挑战在于是否可以发现高级特征（例如，一组提取的特征的数学组合）清楚地呈现/与故障进展相关。提出了一种遗传编程方法，以解决自动发现高级特征的挑战，这些特征可以很好地捕获来自测量或提取特征的故障进展，从而达到RUL预测的目的。

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