Feasibility study of Hierarchical Temporal Memories applied to welding diagnostics
Feasibility study of Hierarchical Temporal Memories applied to welding diagnostics
复制标题
DOI：
10.1016/j.sna.2013.09.021
复制
发表时间：
2013-12
影响因子：
4.6
通讯作者：
L. Rodríguez-Cobo;R. Ruiz-Lombera;O. Conde;J. López-Higuera;A. Cobo;J. Mirapeix
中科院分区：
文献类型：
--
作者：
L. Rodríguez-Cobo;R. Ruiz-Lombera;O. Conde;J. López-Higuera;A. Cobo;J. Mirapeix
焊接质量在线监测系统中的缺陷分类是一个活跃的研究领域，对于广泛采用焊接工艺的几个工业部门具有重要的意义。已经尝试了一些基于人工智能实现的方法，如人工神经网络或模糊逻辑，但它们在实际工业场景中的影响目前相当有限。本文通过多个弧焊试验，探索和分析了一种基于分层时间记忆和等离子体光谱采集的新方法。结果表明，所提出的解决方案能够在多个焊接扰动中执行适当的分类。还将讨论寻找算法的最佳配置以及模式的空间(谱)和时间识别的有用性，并将结果与基于特征选择和神经网络的解决方案所提供的结果进行比较，表明HTM模型在输入数据的性能和处理方面具有更好的性能。
Defect classification in on-line welding quality monitoring systems is an active area of research with a significant relevance to several industrial sectors where welding processes are extensively employed. Approaches based on some artificial intelligence implementations, like Artificial Neural Networks or Fuzzy Logic have been attempted, but their impact in real industrial scenarios is nowadays rather modest. In this paper a new approach based on Hierarchical Temporal Memories and the acquired plasma spectra is explored and analyzed by means of several arc-welding experimental tests. Results show the ability of the proposed solution to perform a suitable classification among several weld perturbations. The search for an optimal configuration of the algorithm and the usefulness of both spatial (spectral) and temporal identification of patterns will be also discussed, and the results will be compared with those provided by a solution based on feature selection and neural networks, exhibiting the better performance of the HTM model in terms of performance and handling of the input data.