Bearing Fault Diagnosis Using Machine Learning Models

The bearing serves as a crucial element of any machinery with a gearbox. It is essential to diagnose bearing faults effectively to ensure the machinery’s safety and normal operation. Therefore, the identification and assessment of mechanical faults in bearings are extremely significant for ensuring reliable machinery operation. This comparative study shows the performance of fault diagnosis of bearings by utilizing various machine learning methodologies, including SVM, KNN, Linear Regression, Ridge Regression, XGB Regressor, AdaBoost Regressor, and Cat Boosting Regressor. Bearings are like the unsung heroes of the mechanical world, immensely supporting and guiding the smooth motion in everything, from your car’s wheel to the propeller in a ship. However, like other mechanical components, over the course of time, the constant use of bearings can lead to wear and tear, which may ultimately result in a fault. Bearing faults can manifest in several ways, including vibration, noise, heat, and changes in lubrication that reduce the efficiency of a machine. Therefore, it is essential to regularly monitor the bearings and inspect them to detect any issues early on. The aim of this present work is to use the various ML methodology, and their application on the bearing’s data to watch the condition of the machine’s bearing. The present work is carried out in four phases. In the first phase, the data of various loads is collected. In the second phase, the data undergoes an Exploratory Data Analysis (EDA). During the third phase, the data undergoes both training and testing processes to evaluate its effectiveness. In the fourth and final phase, the model that gives the highest accuracy among all is chosen. The present approach is based on the various machine learning algorithms and their application.