Abstract
Although they are crucial parts of heavy machinery, bearings are also prone to failure; between 50 and 60 percent of rotating machinery failures are caused by bearings. Data is gathered for the diagnosis and prognosis of bearing faults to help remedy this. Diagnosis relates to identifying a fault situation using recognized symptoms, whereas prognosis focuses on forecasting how the fault will turn out. To determine the states of machines, machine learning techniques are utilized. Deep learning is a branch of machine learning that focuses on training neural networks with several layers. Traditional (shallow) learning, which uses pre-defined features in data to identify machine conditions, and deep learning, which employs complex data types, are two methods of machine learning. In recent years, data fusion and transfer learning have become interesting and important subfields of deep learning. Transfer learning uses previously trained models as a starting point for new tasks by applying learned knowledge from the previous task on the related new task, whereas data fusion mixes many data types to improve comprehension. In this study, the need for large quantities of data fusion and transfer learning data is used to design a test rig for roller bearing fault identification and prognosis. Historically, academic researchers have concentrated on using the data they have gathered with their test rigs to undertake data analysis, rather than concentrating on the design of the test rig itself. Yet, there is a need for a test rig design that appropriately gathers clean multi-sensor data from a variety of bearing sizes to enhance deep machine learning algorithm research for bearing health diagnosis and prognosis via transfer learning. By offering useful methods that can be utilized to perform machine health diagnosis and prognosis on bearings, this work aims to close the gap between the needs of industry and the research community. This can aid in the early detection of faults, offer time frames for repair or replacement, and prevent catastrophic failures.
