IoT based Predictive Maintenance of Electrical Machines in Aircraft

Airways are the fast-growing mode of transportation; millions of people travel in airplanes around the world. Airways are elevating its progress steeply, where safety is the main concern. There are a lot of safety measures implemented to detect the fault and use alternatives to ensure the safety of passengers. But what if it could be predicted the fault before it occurs and rectify the same. In this paper, a model for IoT based preventive maintenance of the alternators and motors inside the aircraft to ensure more safety before every take off. The research work aims to predict the fault before occurrence and protect the machines from damage. The machines are continuously monitored using sensors such as vibration sensors, current, temperature sensors, and the data is stored in the cloud for further analysis. The data stored in the cloud is analyzed using the required algorithms and the health condition of the machine is examined for maintenance. The entire system is deployed in Node-red (flow-based simulation); data is stored in Thing speak (cloud storage) and analyzed using MATLAB. The visualization charts are displayed for better understanding.