Monitoring of dynamic process using hierarchical probability density decomposition

Monitoring of dynamic industrial process has been increasingly important due to more and more strict safety and reliability requirements. Popular methods like time lagged arrangement-based and subspace-based approaches exhibit good performance in fault detection, however, they suffer from difficulty in accurately isolating faulty variables and diagnosing fault types. To alleviate this difficulty, this article considers a state space model whose joint probability is decomposed hierarchically into the multiplication of several conditional densities and a low dimensional density. Two nonparametric kernel density estimation methods are used to estimate these decomposed densities. By analyzing which density exceeds the confidence limit, information on faulty variables and fault types can be obtained. Application study to simulation examples show that the proposed method is more efficient in isolating and diagnosing process fault than competitive methods.