Fault Prediction and Diagnosis of Bearing Assembly

Vibration is one of the main causes responsible for the failure of any machinery, which makes it a prominent cause of failure in the detection and prediction of a machine’s working baseline. Prediction and maintenance at the right time play a very important role in curbing hazardous situations. It also provides the required data to construct a roadmap to proceed in case of a fault. It also helps in selecting a favorable maintenance procedure, approximation of time required, and the price of maintenance or substitution of the required components. The focus of this paper is to investigate the vibration signals in a “bearing rotation mechanism” for hardware fault prediction and condition-based maintenance. This paper is based on the study and simulation of vibration signals and the primary data is taken from a test setup rig of a rotating bearing mechanism. The data is acquired through the combination of a vibration sensor and a NI DAQ (data acquisition) card to make a suitable dataset and also simulate that data with the help of MATLAB software. This study covers various parameters to study the behavior of the bearing such as Kurtosis, RMS factor, and Skewness. After considering the lack of existing datasets for the parameters necessary to make predictions for the bearing status, a new dataset DBCM or “Dataset for Bearing Condition Monitoring” was designed, containing data in terms of voltage within an interval of 0.04 s. The simulation results verified the readings obtained by hardware setup.