Introduction [with Video]

Manufacturing factories are getting stateof the art as technology are getting improved everyday. Image a hidden part of a critical assembly macgine starts getting overheated without anyone’s notice. This will lead to a breakdown in a few days and will be out of action for several dayas or weeks for repair. This will cause loss of revenue for the business and other issues. Now suppose that a temperature sensor is attached to the machine and this is detected early enough due to the data collection from the sensor, you will be able to prevent the breakdown on time by appropriete corrective actions. In any realistic scenarios today, there are hundreds of sensors attached to machines that measure a whole host of parameters like temperature, vibration, etc. When you have so many parameters, it becomes a very complex process to “correctly predict” whether a machine requires preventive maintenance. This is where Machine Learning comes into play in analysing the Sensor data using statistical methods and predicting whether your machine requires maintenance, and this is what is called “Predictive Maintenance”.  

The objective of this project is to develop a predictive maintenance model for industrial machines using historical sensor data. By accurately predicting when a machine is likely to fail, maintenance teams can proactively intervene, conduct timely repairs, and prevent unplanned breakdowns, ensuring a smoother workflow and improved productivity.