Detection of seeded fault and growing fault in rotating machinery under different speed scenarios using adaptive resonance-based signal sparse decomposition

Abstract

Detection of seeded faults (SFs) in machinery elements such as gears and bearing has been widely explored, whereas there is limited research considering the growing fault (GF). The literature indexes separate techniques for diagnosing SFs and GFs, because of their nature. No technique yet reported is capable to detect both types of faults. This work proposes a novel adaptive approach and an uncertainty index (UI) to detect both SF and GF. In the proposed approach, the signal is pre-processed by adaptive resonance-based signal sparse decomposition to get low-resonance components (LRCs) and high-resonance components across different decomposition levels. Using the UI, the fault prone LRC is identified based on impulsiveness and complexity content. Afterwards the empirical mode decomposition (EMD) is applied to extract the intrinsic mode functions. The Fast Fourier Transform (FFT)s of the modes extracted from EMD reflected the characteristic frequencies and increased amplitude of sidebands. Lastly, the statistical analysis of the FFTs was performed using the sideband energy ratio to validate the state of faults. The proposed approach when examined for both SFs in gears and GFs in bearings at different conditions, yielded satisfactory results. The proposed approach performance was found to be at least 15% better for incipient fault detection and more than 50% for severe fault conditions.