In chemical processes

fault propagation pathways and root cause identification could be discovered though analysis of interactions and time delay relationships among different process variables. Because of its importance in improving process safety and operation profit

fault discovery has been a popular and challenging research topic. Common methods such as correlation and entropy transfer functions

which usually cannot get accurate time delay and interaction strength between variables by limited applicability for linear and weak nonlinear systems or high computation demand

have experienced many disadvantages in actual application. Recently

a new convergent cross mapping(CCM) algorithm in ecology has been considered suitable for causality analysis and time delay identification for nonlinear coupling process variables. However

CCM fails to find application in externally disturbed chemical processes because it cannot establish stable embedded flow from process data. An improved CCM

disturbance filtered cross mapping method(DFCM)

overcame many challenges of creating stable embedded flow by analyzing external disturbance

filtering disturbed process data

and applying filtrated data to CCM calculation. Case studies showed good results of time delays and causality analysis

thus DFCM could be applied to chemical processes under external disturbance.