This work aims to predict the remaining useful life of various components in the analog circuit under non-linear operating conditions. However, the failure process is described by sensed information that can be used to predict the failure time and then the remaining useful life of the system components. This work proposes an Improved Unscented Particle Filter (IUPF) with relative entropy. At first, the mathematical model of second order Sallen-key filter is developed to quantify the relationship between the input and predicted fault. Then, the relative entropy is introduced in unscented particle filtering to reduce the computation time and also to maintain the accuracy in the prediction. The IUPF uses relative entropy to measure the distance between actual and observed particles. The least value of entropy is considered to reduce the error in the proposal distribution and also to adjust the particles in the next step. The performance of improved unscented particle filter is validated for Sallen-key band pass filter and low pass filter. The proposed prognosis method is highly efficient in remaining useful life prediction.

中文翻译：

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使用改进的无味粒子滤波器预测模拟电路的剩余使用寿命

这项工作旨在预测非线性工作条件下模拟电路中各种组件的剩余使用寿命。然而，故障过程是通过可用于预测故障时间和系统组件剩余使用寿命的感知信息来描述的。这项工作提出了一种具有相对熵的改进无味粒子过滤器 (IUPF)。首先，建立二阶 Sallen-key 滤波器的数学模型来量化输入和预测故障之间的关系。然后，在无味粒子滤波中引入相对熵以减少计算时间并保持预测的准确性。IUPF 使用相对熵来测量实际粒子和观察粒子之间的距离。熵的最小值被认为是为了减少提议分布中的误差，也可以在下一步中调整粒子。改进的无味粒子滤波器的性能在 Sallen-key 带通滤波器和低通滤波器中得到验证。所提出的预测方法在剩余使用寿命预测中非常有效。