The increasing scale of industrial processes has significantly motivated the development of data-driven fault detection and diagnosis techniques. The selection of representative fault-free modeling data from operation history is an important prerequisite to establishing a long-term effective process monitoring model. However, industrial data are characterized by a high dimension and multimode, and are also contaminated with both outliers and frequent random disturbances, making automatic modeling data selection a great challenge in industrial applications. In this work, an information entropy-based automatic selection strategy for modeling data is proposed, based on which a general real-time process monitoring framework is developed for a large-scale industrial methanol to olefin unit with multiple operating conditions. Modeling data representing normal operating conditions are automatically selected with only a few manually defined normal samples. A long-term effective process monitoring model is then established based on a multi-layer autoencoder, through which unexpected disturbances in real-time operation can be detected early and the root cause can be preliminarily diagnosed by contribution plots. The adjustment of operating conditions has also been considered through a model update strategy. Details of the proposed data selection strategy and modeling process have been provided to facilitate the industrial application of process monitoring systems by other researchers or companies.