Case study: Condition-based maintenance using cyclostationary analysis and numerical modeling with innovative indicators

Abstract

Early detection and prompt intervention by maintenance engineers to mitigate the impact of breakdowns while enhancing overall operational efficiency remain critical challenges. This study proposes an innovative approach aiming at improving the diagnosis of gear faults. The objective is to assess the sensitivity and performance of traditional indicators in comparison to cyclostationarity, examining their impact on noise levels and vibrational signatures. The initial phase involves simulating gear signals under various conditions such as amplitude, rotation frequency, and meshing frequency, providing the foundation for a thorough analysis of indicator sensitivity and performance. In the second phase, both scalar and cyclostationary indicators were calculated. First, these indicators were compared against simulated signals, and second, their sensitivity and roughness were evaluated using signals measured on the bearings of 101 BJR reducers. This approach revealed that cyclostationary indicators are more sensitive than scalar indicators, suggesting an opportunity to improve the prediction of signal roughness throughout the production process. By introducing new possibilities to enhance the reliability of vibrational measurements, this method contributes to advancing the diagnosis of gear faults.