Abstract:
The anticipation of potential failures and provision of early warning signals are enabled by predictive maintenance, playing a vital role in ensuring the optimal performa...Show MoreMetadata
Abstract:
The anticipation of potential failures and provision of early warning signals are enabled by predictive maintenance, playing a vital role in ensuring the optimal performance and reliability of electromechanical systems. In this context, the research presents an effective and efficient approach for predicting bearing faults, focusing on the analysis of vibration signals from rolling elements, particularly bearings. By applying linear regression analysis, the vibration signal from each bearing sample is transformed into the frequency domain, enabling the calculation of the area under the curve. To estimate the remaining useful life (RUL) of the bearing, the research utilizes linear regression analysis, where the slope of the regression line serves as a crucial indicator. A positive slope suggests accelerated wear or imminent failure, indicating a decrease in the RUL as the independent variable increases.By proactively detecting and resolving potential faults, industries can effectively minimize costs linked to unexpected downtime, urgent repairs, and component replacements. Notably, the study utilizes benchmark data sourced from the NASA prognostics data archive.
Published in: 2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)
Date of Conference: 29-30 June 2023
Date Added to IEEE Xplore: 02 August 2023
ISBN Information: