The vibration and friction triggered by the long-term operation of stacker inevitably lead to the damage problem of stacker’s track. In order to ensure the stable operation of the stacker, a novel scheme is proposed to address an accurate damage localization problem of the stacker’s track in industrial environment based on ensemble empirical mode decomposition-empirical mode decomposition (EEMD-EMD) and density-based spatial clustering of applications with noise (DBSCAN). First, the original electric current signal with unbalanced distribution is processed by fixed-interval smoothing and cubic Hermite interpolation algorithms. Second, the EEMD-EMD method, which solves the mode-mixing problem, is adopted to decompose the preprocessed signal into well-defined intrinsic mode functions (IMFs). Third, based on Hilbert–Huang transform (HHT) of the IMFs, the preprocessed signal and the difference of average instantaneous amplitudes in two adjacent time points are considered as the 2-D feature vector input of the DBSCAN algorithm. As such, damage locations of the stacker’s track are detected by means of the outliers that are obtained by the DBSCAN algorithm. The efficiency of the proposed localization scheme and its superiority over the existing cooperative damage localization methods, namely, the box-plot method, the pulse coupling neural network (PCNN) and wavelet transform (WT) theory method, and the box-plot and WT method, are verified through experiments using the data provided by State Grid Measuring Center of China.

中文翻译：

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基于EEMD-EMD和DBSCAN聚类算法的堆垛机轨道损伤定位

堆垛机长期运行引起的振动和摩擦不可避免地导致堆垛机履带损坏问题。为保证堆垛机的稳定运行，提出了一种基于集成经验模态分解-经验模态分解（EEMD-EMD）和基于密度空间的堆垛机轨道在工业环境中精确定位的新方案。具有噪声的应用程序聚类 (DBSCAN)。首先，对不平衡分布的原始电流信号进行固定间隔平滑和三次Hermite插值算法处理。其次，采用解决模式混合问题的 EEMD-EMD 方法将预处理信号分解为明确定义的本征模式函数 (IMF)。第三，基于 IMF 的 Hilbert-Huang 变换 (HHT)，将预处理后的信号和相邻两个时间点平均瞬时幅度的差值作为DBSCAN算法的二维特征向量输入。因此，堆垛机轨道的损坏位置是通过 DBSCAN 算法获得的异常值来检测的。所提出的定位方案的效率及其优于现有的协同损伤定位方法，即箱线图方法、脉冲耦合神经网络 (PCNN) 和小波变换 (WT) 理论方法，以及箱线图和 WT方法，利用国家电网测绘中心提供的数据进行实验验证。因此，堆垛机轨道的损坏位置是通过 DBSCAN 算法获得的异常值来检测的。所提出的定位方案的效率及其优于现有的协同损伤定位方法，即箱线图方法、脉冲耦合神经网络 (PCNN) 和小波变换 (WT) 理论方法，以及箱线图和 WT方法，利用国家电网测绘中心提供的数据进行实验验证。因此，堆垛机轨道的损坏位置是通过 DBSCAN 算法获得的异常值来检测的。所提出的定位方案的效率及其优于现有的协同损伤定位方法，即箱线图方法、脉冲耦合神经网络 (PCNN) 和小波变换 (WT) 理论方法，以及箱线图和 WT方法，利用国家电网测绘中心提供的数据进行实验验证。