Abstract:
Bearing fault prediction is a critical task in ensuring the reliability and performance of
rotating machinery. The aim is to identify any abnormalities in bearing behavior before
serious damage occurs. In recent years, machine learning techniques, including artificial neural
networks and support vector machines, have been utilized to develop precise bearing fault
prediction models. These models use different sensor signals, such as vibration and acoustic
signals, to detect and categorize various types of bearing faults. Accurate and dependable
bearing fault prediction models are necessary to avoid unexpected machinery breakdowns,
reduce downtime, and minimize maintenance expenses.
One of the key ideas in bearing fault prediction is to use various sensor signals, such as vibration
and speed signals, to detect and predict faults in the bearings of rotating machinery. Vibration
signals are widely used because they provide valuable information about the condition of
the bearings. They can be used to measure the magnitude, frequency, and waveform of the
vibrations caused by the bearings’ movement. Speed signals, on the other hand, provide
information about the rotational speed of the machinery, which can also be used to identify the
existence of bearing faults. By analyzing these signals, machine learning models can identify
patterns and trends that indicate the presence of faults, such as cracks, wear, and misalignment,
and predict when the bearings may fail. The use of these signals in bearing fault prediction
can help prevent unexpected downtime, reduce maintenance costs, and improve the overall
efficiency of rotating machinery.
