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This dissertation was written as a part of the MSc in Data Science at the International Hellenic University. Machine learning is getting more and more developed in the industrial field to improve its performance. During the last years, machine learning was used to address the problems that arose in a time series classification process. The main problem identified was the management of large volumes of data at any given time. Deep learning is a part of the machine learning methods that have been very successful in many fields of pattern recognition. Recently, researchers have succeeded remarkable results in time series classification using Deep Learning techniques. The goal of the thesis is to present a fault diagnosis and anomaly detection in the benchmark Tennessee Eastman process by using Convolutional Neural Network techniques. The Inception Module and Residual Network are developed methods of the conventional Convolutional Neural Network that use fewer parameters but maintain the accuracy of the model. Furthermore, the Statistical method of Principal Components Analysis was used in the context of preprocessing to reduce the dimensionality of the data. These methods are going to be tested for their performance but the accuracy metric is not sufficient to evaluate the models. For this reason, Fault Detection Rate and Fault Positive Rate are the metrics that determine the performance of the models.