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An Improved Particle Filter Method for Accurate Remaining Useful Life Prediction

机译:精确的剩余使用寿命预测的改进粒子滤波方法

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The prognostics method that updates the parameters of degradation model using particle filter to predict the remaining useful life (RUL) of equipment is widely used in recent years. However, most of the traditional methods that use this strategy for prognostics do not establish the state transition equation and measurement equation of particle filter from the aspect of degradation trend, which makes the predicted curve may not conform to the degradation trend of the known data because of the loss of information. This paper proposes a prognostics method based on degeneration trajectory, which updates model parameters using particle filter and makes the predicted curve which depends on the updated parameters conform to the known degradation trend by establishing the measurement equation of particle filter different from the traditional method. The proposed method is verified by using the turbine engine degradation data published by NASA and the experiment shows that this method is superior to the traditional method in prediction accuracy and precision.
机译:近年来,使用粒子过滤器更新退化模型参数以预测设备的剩余使用寿命(RUL)的预测方法已被广泛使用。但是,使用这种策略进行预测的大多数传统方法都没有从退化趋势的角度建立粒子过滤器的状态转换方程和测量方程,这使得预测曲线可能不符合已知数据的退化趋势,因为信息丢失。本文提出了一种基于退化轨迹的预测方法,该方法通过建立粒子过滤器的测量方程,使粒子过滤器的测量方程与粒子群的测量方程相吻合,使预测的曲线依赖于已知的退化趋势。利用美国宇航局发布的涡轮发动机退化数据对该方法进行了验证,实验表明,该方法在预测精度和精度上均优于传统方法。

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