This study focuses on the analysis of vibration-based signatures obtained from deep groove ball bearings with faults on the inner race. Various time-frequency-based methods are commonly used to diagnose faults in bearings. However, due to the nonself-adaptive nature of these methods and the nonlinear and nonstationary signals produced by the faults, mode decomposition techniques are seen as promising methods. This paper presents a novel approach based on Nonlinear Mode Decomposition (NMD), which decomposes the complex signal into nonlinear modes. The data is taken from an online database of deep groove ball bearing with inner race faults of different sizes. This data is then subjected to NMD to extract nonlinear modes. Statistical parameters are applied to select a subset of significant nonlinear modes from the complete set. Finally, the Fast Fourier Transform is applied to the Hilbert Transform (HT) of the selected modes to see fault frequency and its higher harmonics resulting from the nonlinearity. Additionally, the instantaneous frequency and instantaneous phase, two key parameters acquired from the HT are also plotted for normal and faulty bearings, and the results are discussed in the paper. The proposed method offers a valuable approach for accurately detecting and diagnosing deep groove ball-bearing faults.

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