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Design Performance Analysis of a Self-Organizing Map for Statistical Monitoring of Distribution-free Data Streams

机译：用于无分布数据流统计监视的自组织映射的设计性能分析

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In industrial applications, the continuously growing development of multi-sensor approaches, together with the trend of creating data-rich environments, are straining the effectiveness of the traditional Statistical Process Control (SPC) tools. Industrial data streams frequently violate the statistical assumptions on which SPC tools are based, presenting non-normal or even mixture distributions, strong autocorrelation and complex noise patterns. To tackle these challenges, novel nonparametric approaches are required. Machine learning techniques are suitable to deal with distributional assumption violations and to cope with complex data patterns. Recent studies showed that those methods can be used in quality control problems by exploiting only in-control data for training (such a learning paradigm is also known as “one-class-classification”). In recent studies, the use of distribution-free multivariate SPC methods was proposed, based on unsupervised statistical learning tools, pointing out the difficulty of defining suitable control regions for non-normal data. In this paper, a Self-Organizing Map (SOM) based monitoring approach is presented. The SOM is an automatic data-analysis method, widely applied in recent works to clustering and data exploration problems. A very interesting feature of this method consists of its capability of providing a computationally efficient way to estimate a data-adaptive control region, even in the presence of high dimensional problems. Nevertheless, very few authors adopted the SOM in an SPC monitoring strategy. The aim of this work is to exploit the SOM network architecture, and proposing a network design approach that suites the SPC needs. A comparison study is presented, in which the process monitoring performances are compared against literature benchmark methods. The comparison framework is based on both simulated data and real data from a roll grinding application.
机译：在工业应用中，多传感器方法的不断发展，以及创建数据丰富的环境的趋势，都使传统的统计过程控制（SPC）工具的有效性受到压力。工业数据流经常违反SPC工具所基于的统计假设，呈现出非正态甚至均匀的分布，强烈的自相关和复杂的噪声模式。为了应对这些挑战，需要新颖的非参数方法。机器学习技术适合处理分布假设违规和复杂数据模式。最近的研究表明，这些方法可以通过仅利用控制中的数据进行训练来用于质量控制问题（这种学习范例也称为“一类分类”）。在最近的研究中，提出了基于无监督统计学习工具的无分布多元SPC方法的使用，指出了为非正态数据定义合适的控制区域的困难。本文提出了一种基于自组织图（SOM）的监视方法。 SOM是一种自动数据分析方法，在最近的工作中广泛应用于聚类和数据探索问题。该方法的一个非常有趣的特征在于，即使在存在高维问题的情况下，它也能够提供一种计算有效的方式来估计数据自适应控制区域。但是，很少有作者在SPC监视策略中采用SOM。这项工作的目的是利用SOM网络体系结构，并提出适合SPC需求的网络设计方法。提出了一个比较研究，其中将过程监控性能与文献基准方法进行了比较。比较框架基于辊磨应用程序的模拟数据和真实数据。

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