Measurements quality is important for process systems engineering. In this paper, an estimation scheme is proposed in the state-space form to monitor the degree of accuracy of measurements within a predefined horizon. Under the assumption that all the sensors are uncorrelated with each other, the distribution of measurement noise covariance as well as the distribution of state vector are estimated simultaneously. The key technique is to approximate the true posterior distribution by two independent proposal distributions using the variational Bayesian inference. It is shown that the proposed algorithm provides not only a complete picture of the working status of each sensor, but also satisfied estimates of the hidden states in the presence of faulty signals. Numerical examples with a moving target tracking model and a quadrate water tank experiment are conducted to demonstrate that the proposed method exhibits better performance than the existing methods, and even a small fluctuation of sensors can be accurately captured by the proposed algorithm.

中文翻译：

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使用变分贝叶斯推理对状态空间中的传感器进行概率监测

测量质量对于过程系统工程很重要。在本文中，提出了一种状态空间形式的估计方案，以监测预定义范围内测量的准确度。在所有传感器不相关的假设下，同时估计测量噪声协方差的分布以及状态向量的分布。关键技术是使用变分贝叶斯推理通过两个独立的提议分布来近似真实的后验分布。结果表明，所提出的算法不仅提供了每个传感器工作状态的完整图片，而且在存在故障信号的情况下也能满足对隐藏状态的估计。