Prediction Analysis of Engineering Fault Detection and Segregation based on RNN

Abstract

Deep Learning Methods enables the automatic extraction of features from the Industrial Fault Detection Data, because it has the high learning ability that enhances the efficiency of the input data for the feature mining. It determines fault in the dataset of industrial processes by mimicking the way the brain of a human detects the flaw in the industrial processes. The present system identifies defects and insulation automatically relying on Deep Learning. The current system makes use of the cycle component to eliminate and track industrial process failures. The existing system is NOT feasible and impractical to introduce on a large scale, owing to the operation size and quality of the equipment. Parameter optimization techniques such as genetic algorithm (GA) and particle-swarm optimization (PSO) are the foundation of the proposed system. Such methods are used to increase the ability of the trained model. So as the trained model is capable of handling the complicated co-relations between the data set. The proposed system uses the recurrent neural-networks (RNN) to boost predictive performance. The proposed algorithm therefore forecasts the future state of manufacturing processes based on the past activity thus taking into account the significant outliers in the results.