The laminar cooling process is an important procedure in hot steel strip rolling. The spatial distribution and the drop curve of the strip temperature are crucial for the production and the quality of the steel strip. Traditionally, lumped parameter methods are often used for the modeling of the laminar cooling process, making it difficult to consider the impact of the variation of state variables and related parameters on the system, which seriously affect the stability of the steel strip quality. In this paper, a modeling and monitoring method with a time–space nature for the laminar cooling process is proposed to monitor the spatial variation of the strip temperature. Firstly, the finite-dimensional model is obtained by time–space separation to describe the temperature variation of the steel strip. Next, a global model is constructed by using the multi-modeling integration method. Then, a residual generator is designed to monitor the strip temperature where the statistics and the threshold are calculated. Finally, the superiority and reliability of the proposed method are verified by the actual-process data of the laminar cooling process for hot steel strip rolling, and different types of faults are detected successfully.

Laminar cooling process is crucial for the production and the quality of the strip steel in the hot‐rolled strip steel. Abnormalities or faults influence the temperature distributions in the direction of the length and the thickness, which determine the mechanical and physical performance of the strip steel. Considering that the spatial distribution of the strip temperature is hardly measured, the distributed parameter model can be constructed to deal with the problem of the abnormality detection and location. In this paper, a process monitoring and fault spatial location method with spatio‐temporal integration is proposed for the laminar cooling. First, a spatio‐temporal model is constructed by multi‐modelling method to monitor the spatial distribution of the strip temperature, and the dimension of the local thermodynamic model is reduced by time‐space separation. Then, a residual generator is provided for fault detection in the data‐driven realization, and the statistic and the threshold are formed to evaluate it. Next, the spatial location of the fault can be identified by reconstruction based contribution method. Finally, an experiment is conducted to demonstrate the practical application effects by a real laminar cooling process data.