The quality indicator monitoring has received widely attention and research in recent years, however, the detection of indicator-related incipient fault is still a challenging topic. In this paper, a supervised probability density analysis algorithm is proposed to detect the incipient fault in quality indicator. Firstly, the core process variable filter is introduced, and the regression model is constructed to extract the indicator-related information from process variable. Secondly, the data distribution extension and subsegment division strategy are presented, and a probability density estimation method is put forward for the indicator-related latent variable. Through the proposed symmetric divergence index, the distribution discrepancy between the online sample and the reference sample set is evaluated, which can be used for the incipient fault detection. Finally, a numerical example and the Tennessee Eastman process are used to demonstrate the effectiveness of the proposed method.

质量指标监测近年来受到了广泛的关注和研究，但与指标相关的初期故障的检测仍然是一个具有挑战性的课题。本文提出了一种有监督的概率密度分析算法来检测质量指标中的初期故障。首先，引入核心过程变量过滤器，构建回归模型，从过程变量中提取指标相关信息。其次，提出了数据分布扩展和子段划分策略，提出了指标相关潜变量的概率密度估计方法。通过提出的对称散度指数，评估在线样本和参考样本集之间的分布差异，可用于早期故障检测。最后，用一个数值例子和田纳西伊士曼过程证明了所提方法的有效性。