Industrial processes face new challenges with the advancement of Industry 4.0 and the increasing demand for improvements in fault detection. Fault detection is based on various techniques of statistical methods and machine learning. Although effective, they have some disadvantages, such as process simplification, low capacity to deal with noise, low capacity to deal with complex nonlinear systems, high computational demand, and risk of overfitting. In response to these limitations, this work introduces an innovative approach on the polymerization field that employs siamese neural networks (SNNs) and long short-term memory (LSTM) cells for early detection of faults in styrene polymerization. The modeling of styrene polymerization in a CSTR reactor was carried out using the method of moments for mass and energy balance, and in this system, proportional-integral-derivative (PID) control was added to simulate a real process control situation in the context of an industrial process. From the model, it was possible to obtain thirteen simulations, of which five are non-fault processes and eight are processes with faults. These data were processed and used to train the siamese networks. With the ability to classify whether these input data are similar or dissimilar, it was possible to perform fault detection. The results found demonstrate a fault detection rate with an accuracy of up to 100 percent, demonstrating the capability of this model in detecting faults in complex, dynamic, and nonlinear chemical processes. This study represents a substantial advance in the field of fault detection and also offers valuable findings for future investigations and improvements in intelligent fault detection systems in the chemical industry.