The constant and rapid rise in the field of Industrial Internet of Things has enabled the manufacturing and process industries to have access to large amounts of process data. This process data can be effectively analyzed to identify the faults in the system thereby facilitating in avoiding critical process breakdowns. Deep neural networks with their inherent ability to model complex non-linear representations, have been proven to fit well for contemporary fault detection. This work proposes a Time Series based approach to fault detection in the benchmark Tennessee Eastman process making use of the temporal dependencies within the process data. Since standard Feed Forward Neural Networks (FFNN) are not capable of learning these temporal dependencies, a novel approach using Convolutional Neural Networks (CNN) with its architectural and algorithmic variants is proposed. The experimental results show comparatively superior performance of the proposed CNN based models with the standard FFNN for fault detection. Also the different hyperpaprameters which effect the time series classification task are highlighted.