Building intelligent conveyor system using classification techniques in a logistics industry

Abstract

Machine learning techniques plays an important role in knowledge discovery and assists humans in decision making. They help to detect patterns and predict the actions/outcome. In a complex industrial environment mode of operations of a machine depends on various internal and external parameters which are often done using expert judgement method which is not accurate and results in machine breakdown thereby resulting in unplanned outage. In this paper, we discussed and demonstrated how machine learning algorithms can help to handle conveyor systems autonomously in an optimum way without any human intervention. A conveyor belt system operational data is used to select the appropriate classification technique for the selected dataset. The details of the dataset collected, algorithms used and the test results are discussed in this paper.