Hashim Shahis, Shakya Piyush
Engineering Asset Management Group, Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai, 600036, India.
Engineering Asset Management Group, Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai, 600036, India.
ISA Trans. 2023 Nov;142:492-500. doi: 10.1016/j.isatra.2023.07.035. Epub 2023 Jul 26.
Unanticipated background noises often convolute fault information in the gearboxes' vibration response. The Blind Deconvolution strategy has been extensively applied for fault-impulse enhancement to aid gear fault detection. The existing deconvolution strategies involve designing an optimum filter applied in the time domain. Gear tooth wear leads to the excitation of Gear Mesh Frequency harmonics. Hence, spectral analysis is typically used for gearbox fault detection. As such, feature enhancement in the order domain is more practical than existing blind deconvolution approaches. This study proposes a Spectral Kurtosis-based blind deconvolution strategy with filtering done in the order domain, to aid gear fault detection. Experimental analyses show 109.76% and 64.48% better performance for constant and real-world speed operation, respectively, for the proposed method to aid spectral analysis-based fault detection.
意外的背景噪声常常使齿轮箱振动响应中的故障信息变得复杂。盲反卷积策略已被广泛应用于故障脉冲增强，以辅助齿轮故障检测。现有的反卷积策略涉及设计一种应用于时域的最优滤波器。齿轮齿磨损会导致齿轮啮合频率谐波的激发。因此，频谱分析通常用于齿轮箱故障检测。如此一来，阶次域中的特征增强比现有的盲反卷积方法更具实用性。本研究提出一种基于谱峭度的盲反卷积策略，在阶次域中进行滤波，以辅助齿轮故障检测。实验分析表明，对于基于频谱分析的故障检测，所提出的方法在恒速和实际速度运行下的性能分别提高了109.76%和64.48%。
