School of Aerospace Science and Technology, Xidian University, Xi'an 710071, PR China.
School of Aeronautics and Astronautics, Xiamen University, Xiamen 361005, PR China.
ISA Trans. 2018 Aug;79:147-160. doi: 10.1016/j.isatra.2018.05.009. Epub 2018 May 26.
Early detection of faults developed in gearboxes is of great importance to prevent catastrophic accidents. In this paper, a sparsity-based feature extraction method using the tunable Q-factor wavelet transform with dual Q-factors is proposed for gearbox fault detection. Specifically, the proposed method addresses the problem of simultaneously extracting periodic transients and high-resonance component from noisy data for the gearboxes fault detection purpose. Firstly, a sparse optimization problem is formulated to jointly estimate the useful components from the noisy observation. In order to promote wavelet sparsity, non-convex regularizations are employed in the cost function of the optimization problem. Then, a fast converging, computationally efficient iterative algorithm which termed SpaEdualQA (the sparsity-based signal extraction algorithm using dual Q-factors) is developed to solve the formulated optimization problem. The derivation of the proposed fast algorithm combines the split augmented Lagrangian shrinkage algorithm (SALSA) with majorization-minimization (MM). Finally, the effectiveness of the proposed SpaEdualQA is validated by analyzing numerical signals and real data collected from engineering fields. The results demonstrated that the proposed SpaEdualQA can effectively extract periodic transients and high-resonance component from noisy vibration signals.
早期检测齿轮箱中的故障对于防止灾难性事故非常重要。本文提出了一种基于稀疏表示的特征提取方法，该方法使用具有双 Q 因子的可调 Q 因子小波变换。具体来说，该方法解决了从噪声数据中同时提取周期性瞬变和高共振分量的问题，以实现齿轮箱故障检测。首先，通过稀疏优化问题联合从噪声观测中估计有用分量。为了促进小波稀疏性，在优化问题的代价函数中采用了非凸正则化。然后，开发了一种快速收敛、计算效率高的迭代算法，称为 SpaEdualQA（基于双 Q 因子的稀疏信号提取算法），用于解决所提出的优化问题。所提出的快速算法的推导结合了分裂增广拉格朗日收缩算法（SALSA）和最大化-最小化（MM）。最后，通过分析数值信号和工程领域采集的实际数据验证了所提出的 SpaEdualQA 的有效性。结果表明，所提出的 SpaEdualQA 可以有效地从噪声振动信号中提取周期性瞬变和高共振分量。
