Deconvolution methods have been proven to be effective tools to extract excitation sources from the noisy measured signal. However, its application is confined by the extraction of incomplete information. To tackle this problem, a new deconvolution method, named period-oriented multi-hierarchy deconvolution (POMHD) is proposed in this paper. Various filters are designed adaptively by the iterative algorithm to update the filter coefficient using the harmonic-to-noise ratio as the deconvolution orientation. Additionally, a novel index, called normalized proportion of harmonics, is proposed as the evaluation criteria for the fault feature. Based on upon, a harmonics proportion diagram is constructed for the diagnostic decisions. The new deconvolution method overcomes the disadvantages of the traditional methods. More importantly, without an accurate fault period as the prior knowledge, the proposed POMHD can simultaneously extract multiple latent fault components by using the adaptive filter and intuitively present different fault information in one diagram. Finally, the simulated and experimental data which includes the signals collected from bearings with both single faults and compound faults is used to evaluate the new method. The results validate the feasibility and robustness of the proposed POMHD.

去卷积方法已被证明是从嘈杂的测量信号中提取激励源的有效工具。然而，它的应用受限于不完整信息的提取。为了解决这个问题，本文提出了一种新的反卷积方法，称为面向周期的多层次反卷积（POMHD）。通过迭代算法自适应设计各种滤波器，以谐波噪声比为解卷积方向更新滤波器系数。此外，提出了一种称为归一化谐波比例的新指标作为故障特征的评估标准。在此基础上，构建了用于诊断决策的谐波比例图。新的反卷积方法克服了传统方法的缺点。更重要的是，在没有准确的故障周期作为先验知识的情况下，所提出的 POMHD 可以通过使用自适应滤波器同时提取多个潜在故障分量，并在一张图中直观地呈现不同的故障信息。最后，包括从具有单一故障和复合故障的轴承收集的信号的模拟和实验数据用于评估新方法。结果验证了所提出的 POMHD 的可行性和稳健性。包括从具有单一故障和复合故障的轴承收集的信号的模拟和实验数据用于评估新方法。结果验证了所提出的 POMHD 的可行性和稳健性。包括从具有单一故障和复合故障的轴承收集的信号的模拟和实验数据用于评估新方法。结果验证了所提出的 POMHD 的可行性和稳健性。