Intelligent Predictive Maintenance for Industrial Internet of Things (IIoT) Using Machine Learning Approach

The Industrial Internet of Things is a intricate area which comprises feature like information and operation technology, statistics, and engineering. The industrial data management system uses five basic layers like things layer, edge layer, fog Layer, communication layer, and cloud services to build a system for industrial operation. The cloud assisting in fetching and acquiring vast industrial data generated by several devices in the industry on the shop floor and retrieve necessary information based on context aware approach to create a smart enterprise based on industrial scenario. The paper presented a new solution for industry using IoT for predictive and remote maintenance provision for various industrial environmental parameters and assisting in increasing the work carried out by hand as well as productivity in industry using a machine learning approach. More specifically, this IIoT solution captures air quality, outdoor temperature humidity, boiling temperature from one sensor node and object detection, indoor temperature, humidity, smoke and light intensity data sensors from other sensor node in the system base on controllers and analyses them in the fog layer to provide a timely evaluation of intelligence require to operate the system which is helpful in increasing the productivity in the production line. The proposed experimentation illustrated the design of the IIoT solution, described the prototype industrial plant in normal and abnormal operation, analyzed with supervised machine learning approach and presented the sensor data analysis to create a smart enterprise.