将通用新颖性检测和监督分类管道相结合,用于工业状态监测

Steffen Klein, Yannick Wilhelm, Andreas Schütze, T. Schneider
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引用次数: 0

摘要

为提高工业流程的效率和可靠性,工业状态监测领域的机器学习目前是一个快速发展的研究领域。许多使用的算法都是有监督的方法,可以学习和识别数据中隐藏的模式。然而,学习这些模式需要训练数据,而由于成本高昂,这些数据只能在有限的工业环境中生成。此外,在训练数据中不可能表示所有可能发生的事件。相比之下,无监督或半监督方法可用于检测新情况或新事件。不过,这些方法通常无法诊断或量化故障状况,因此对现代维护策略的作用有限。因此,稳健的状态监测系统应结合这两种方法的功能。本文介绍了一种将有监督分类和半监督新颖性检测相结合的方法,通过将重复出现的新颖性作为新条件转移到有监督分类中,从而建立一个可扩展、可适应的状态监测系统。本文提出了一种上位算法,可根据新颖性检测发现的新情况逐步扩展监督模型。采用这种方法,状态监测系统可以首先基于新机器或流程的 "正常 "数据,逐步增加故障或新情况。此外,通过分析监督分类所选择的特征,监督方法还可用于帮助相关人员识别未知状况。我们以焊枪气动驱动系统的状态监测为例,演示了一般工作流程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Combination of generic novelty detection and supervised classification pipelines for industrial condition monitoring
Machine learning in industrial condition monitoring is currently a rapidly developing field of research, to improve the efficiency and reliability of industrial processes. Many of the used algorithms are supervised methods, which can learn and recognize hidden patterns in the data. However, training data is required to learn these patterns, which can only be generated to a limited extent in an industrial environment due to the high costs involved. Furthermore, it is impossible to represent all possible events in the training data. In contrast, unsupervised or semi-supervised methods can be used to detect new conditions or events. However, these usually do not allow diagnosis or quantification of a fault condition, which is why their usefulness for modern maintenance strategies is limited. Consequently, a robust condition monitoring system should combine the functionality of both approaches. This paper presents a methodology for the combination of supervised classification and semi-supervised novelty detection to build an expandable and adaptable condition monitoring by transferring recurring novelties as new conditions to the supervised classification. A superordinate algorithm is proposed to achieve a stepwise extension of the supervised model based on new conditions detected by novelty detection. With this approach, a condition monitoring system can at first be based on “normal” data of a new machine or process by adding failures or novel conditions step-by-step. Furthermore, the supervised methods can be used to help the corresponding staff identify unknown conditions by analyzing the features selected by the supervised classification. The general workflow is demonstrated for condition monitoring of the pneumatic drive system of a welding gun.
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