Fault diagnosis and prediction with industrial internet of things on bearing and gear assembly

In the era of automation, mechanical components such as bearings and gears are widely used in industrial machinery to transmit power and motion. Failure in these components directly affects the functioning of the machinery and causes the loss of money and time. Therefore, fault diagnosis and prediction of these components in advance are necessary to avoid catastrophic consequences. In this research, an experimental set-up is developed to predict the fault for various cases such as proper configuration, defective bearing, and defective gear configuration. An IIoT and conventional time and frequency domain-based techniques are used for condition-based monitoring of bearing-gear assembly. IIoT-based systems can perform three major tasks; measuring and displaying the real-time vibrational responses of bearing-gear assembly, comparing it with the prescribed threshold value, and sending a warning message to the end-user using the Blynk application, if the acquired acceleration values are greater than the prescribed threshold value.