传统的盲解卷积算法在更换刻画指标时，需要重新计算梯度或重新设计滤波器系数的优化方式，这使得构建新的盲解卷积算法的过程缺乏自适应性。针对上述问题，提出了一种利用牛顿-拉夫逊优化器（Newton-Raphson optimizer, NRBO）搜索最优滤波器系数的盲解卷积算法。首先通过广义球面坐标变换确定滤波器系数的搜索范围，然后选择包络谱广义l_p/l_q范数作为刻画指标，最后将所构建的盲解卷积算法应用于滚动轴承的早期弱故障诊断。仿真和试验结果验证了所提算法的有效性，且所提算法收敛速度相比经典的粒子群算法（particle swarm optimization, PSO）更快。

In traditional blind deconvolution algorithms, recalculating the gradient or redesigning the optimization approach for filter coefficients becomes necessary when altering the characterization index. This requirement can render the development process of new blind deconvolution algorithms inflexible. To address these issues, a blind deconvolution algorithm that employs NRO(Newton-Raphson optimizer) to seek out the optimal filter coefficients was proposed. Initially, generalized spherical coordinate transformation was used to define the search range for the filter coefficients. Subsequently, the generalized l_p/l_q norm of the envelope spectrum was adopted as the characterization index. The proposed blind deconvolution algorithm is then utilized for the early detection of minor faults in rolling bearings. Both simulation and experimental results confirm the efficacy of the proposed algorithm, demonstrating its faster convergence rate compared to classical PSO(particle swarm optimization).