PREDCARE: BEARINGS FAULT PREDICTION

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.