Machine Learning Method Based Industrial Risk Analysis and Prediction

Abstract

IoT-based technologies growing all over the world. After the industrial revolution, machines and robots gradually replaced human effort. In the absence of the human brain-machine and robots makes an error. In this paper, a plan was developed to get out of this situation that works not only efficiently but also thinks like humans. In this system, the machine will learn based on the situation that has been made by any occurrence. In this work Raspberry Pi-based system helps to make a proper analysis of the machines. Voltage, current, gas value, and temperate values are taken as input parameters. Machine learning matches/compares these real-time sensor data with training data (which is used to train the system). As a result, The machine learning module provides some statistics graphs of sensor data. Machine performance can analyze by observing these graphs. Also, determine the efficiency and predict the possibility of upcoming threats or risks.