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@article{165463,
        author = {Aman S. Chougule and Prof. Prasad Shinde},
        title = {Vibrational Fault Diagnosis of Bearing in Rotating  Machineries using Hilbert-Huang Transform (HHT)},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {1},
        pages = {2669-2680},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=165463},
        abstract = {Rotating machinery depends on fault diagnosis for smooth operations and avoiding severe breakdowns. This study delves into detecting bearing faults by analyzing vibration signals using the Hilbert-Huang Transform (HHT). HHT stands out in handling the complex signals typical in rotating machinery due to its ability to deal with non-linear and non-stationary characteristics. The analysis starts with Empirical Mode Decomposition (EMD), which breaks down vibration signals into intrinsic mode functions (IMFs). These IMFs offer a detailed view of the signal's time-frequency features. By applying the Hilbert Transform to these IMFs, the method uncovers the instantaneous frequency and amplitude, enabling accurate fault detection. The main goal here is to improve the precision and dependability of bearing fault diagnosis using the unique strengths of HHT. This, in turn, aims to enhance maintenance practices and minimize downtime in industrial environments. The effectiveness of this approach is proven through comprehensive experiments and comparative studies, highlighting its success in identifying and categorizing various bearing faults.},
        keywords = {},
        month = {December},
        }