Acoustic emission (AE) has been widely used to the nondestructive evaluation (NDE) and structural health monitoring (SHM) of hoisting machinery recently. Kernel entropy component analysis (KECA) is generally applied to extract the AE features based on its excellent nonlinear ability. However, traditional KECA specifically requires a considerable number of components (e.g. eigenvalues and eigenvectors) to excellently describe the original data, which leads to a reduction in the effect of approximate dimensionality reduction of high-dimensional data, thus causing readily unacceptable condition monitoring result. To overcome this weakness, a novel method named moving window-improved kernel entropy component analysis (MW-IKECA) is proposed in this study for structural condition monitoring of hoisting machinery, which is aimed at extracting more AE feature information and improving the condition identification accuracy. Firstly, a twiddle factor is introduced in the KECA model for the purpose of breaking the restriction that the projection axes originate only from the feature vectors and maximizing the independence between the components. Meanwhile, the moving window local strategy is incorporated into the proposed IKECA to extract more rich and effectiveness AE feature information at different scales. Finally, the Cauchy–Schwarz (CS) statistic is utilized to calculate the similarity between probability density functions and maintain the angular structure of the MW-IKECA feature space for the task of improving the monitoring accuracy and shortening the monitoring time-delay of MW-IKECA. Results of the experimental and practical engineering application validate the effectiveness and superiority of the proposed method in AE-based crane SHM under different working conditions compared with the traditional KECA and some combinatorial methods.

近年来，声发射（AE）已广泛应用于起重机械的无损评估（NDE）和结构健康监测（SHM）。核熵分量分析（KECA）基于其优异的非线性能力，通常用于提取AE特征。然而，传统的KECA特别需要相当数量的分量（例如特征值和特征向量）来很好地描述原始数据，这导致高维数据近似降维的效果降低，从而导致状态监测结果容易不可接受。为了克服这一弱点，本研究提出了一种名为移动窗口改进核熵分量分析（MW-IKECA）的新方法，用于起重机械结构状态监测，旨在提取更多AE特征信息，提高条件识别精度。首先，在KECA模型中引入了一个旋转因子，以打破投影轴仅来自特征向量的限制，最大化组件之间的独立性。同时，将移动窗口局部策略纳入所提出的IKECA，以在不同尺度上提取更丰富有效的AE特征信息。最后，利用Cauchy-Schwarz(CS)统计量计算概率密度函数之间的相似度，保持MW-IKECA特征空间的角结构，以提高MW-IKECA的监测精度和缩短监测时延。宜家。