Machine Fault Diagnostics and Condition Monitoring Using Augmented Reality and IoT

Machine monitoring has its importance in determining conditions of different machine parts for forecasting various mechanical failures. With the view of eliminating undesired maintenance costs and production loss, condition monitoring has been adopted in different sectors. For predicting the life of different machine components a method called “Machine Fault Diagnostics and Condition Monitoring using Augmented Reality and Internet of Things” is proposed here. The main objective of the method proposed here is to diagnose a machine and its parts by monitoring different parameters like temperature, pressure, speed, vibration noise etc., to predict overheating, wear and tear or any other kinds of defects. The values of these parameters will be taken to analyze the working conditions of different machine components and finally to predict their life. IoT has its importance in making the machines connected by employing sensors which are mounted onto the machine parts for collecting data from them. Sensors like temperature sensors, pressure sensors, vibration sensors, proximity sensors etc., are most commonly used for condition monitoring. Machine parts that can be taken for monitoring may include gearbox, engine parts or even any kinds of pumps can be used. Data collected will be deployed to cloud servers like Thingspeak or MQTT Broker for further analysis in the future. Thingspeak with the assistance of Mathlab, allows visualization of data. These data will be taken and visualized using augmented reality in any devices like hololens, tablets or smartphones by buiding an android application with the help of a developing environment called Unity. The users will be able to see a visual overlay of the collected data which gives the health information of the machines that help them to infer whether a component is to be replaced or not. Thus continuous evaluation is made possible and minute defects can be detected before the occurrence of a catastrophic breakdown. The benefits of this type of method include reducing downtimes of machinery along with production losses and an accurate maintenance scheduling is possible.