A Novel Reinforcement Learning-based Unsupervised Fault Detection for Industrial Manufacturing Systems

A. Acernese, A. Yerudkar, C. D. Vecchio
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引用次数: 1

Abstract

With the advent of industry 4.0, machine learning (ML) methods have mainly been applied to design condition-based maintenance strategies to improve the detection of failure precursors and forecast degradation. However, in real-world scenarios, relevant features unraveling the actual machine conditions are often unknown, posing new challenges in addressing fault diagnosis problems. Moreover, ML approaches generally need ad-hoc feature extractions, involving the development of customized models for each case study. Finally, the early substitution of key mechanical components to avoid costly breakdowns challenge the collection of sizable significant data sets to train fault detection (FD) systems. To address these issues, this paper proposes a new unsupervised FD method based on double deep-Q network (DDQN) with prioritized experience replay (PER). We validate the effectiveness of the proposed algorithm on real steel plant data. Lastly, we compare the performance of our method with other FD methods showing its viability.
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基于强化学习的工业制造系统无监督故障检测方法
随着工业4.0的到来,机器学习(ML)方法主要应用于设计基于状态的维护策略,以改进故障前兆的检测和预测退化。然而,在现实场景中,揭示实际机器状态的相关特征往往是未知的,这给解决故障诊断问题带来了新的挑战。此外,机器学习方法通常需要特别的特征提取,涉及为每个案例研究开发定制模型。最后,为了避免代价高昂的故障,关键机械部件的早期替换对收集大量重要数据集来训练故障检测(FD)系统提出了挑战。为了解决这些问题,本文提出了一种基于优先体验重放(PER)的双深度q网络(DDQN)的无监督FD方法。在实际钢厂数据上验证了该算法的有效性。最后,我们将我们的方法与其他FD方法的性能进行了比较,显示了其可行性。
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