强噪声下基于时间收缩可解释深度强化学习的齿轮箱故障诊断

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Zeqi Wei , Hui Wang , Zhibin Zhao , Zheng Zhou , Ruqiang Yan
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引用次数: 0

摘要

齿轮箱故障诊断对于机械系统的安全运行至关重要。虽然深度学习(DL)在这一领域取得了令人鼓舞的成果,但大多数现有方法都依赖于静态监督学习,缺乏类似于人类决策的动态、交互式学习能力。为解决这一问题,本研究提出了一种新方法,将深度强化学习(DRL)的优势与时态收缩可解释网络的可解释性相结合。DRL 将深度强化学习(DL)的感知能力与强化学习(RL)的决策能力相结合,为复杂的挑战提供了更全面的解决方案。在该方法中,变速箱故障诊断被表述为分类马尔可夫决策过程(CMDP)中的一个顺序决策问题。利用多尺度时间收缩模块来构建可解释网络,从而提高了模型的可解释性,并减少了恶劣工作条件下噪声数据的负面影响。诊断代理可自主学习最佳分类策略,从而减少了对人工干预和人类专业知识的需求。实验结果表明,即使在嘈杂的条件下,也能达到 98.5% 以上的准确率,具有出色的泛化和稳定性。它优于现有的方法,并突出了其在具有挑战性的操作环境中的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gearbox fault diagnosis based on temporal shrinkage interpretable deep reinforcement learning under strong noise
Gearbox fault diagnosis is crucial for the safe operation of mechanical systems. While Deep Learning (DL) has demonstrated promising results in this area, most existing methods rely on static supervised learning, lacking the dynamic, interactive learning capabilities similar to human decision-making. To tackle this issue, this study presents a novel approach that combines the strengths of Deep Reinforcement Learning (DRL) with the interpretability of a temporal shrinkage interpretable network. DRL integrates the perception abilities of DL with the decision-making capabilities of Reinforcement Learning (RL), offering a more comprehensive solution for complex challenges. In this method, gearbox fault diagnosis is formulated as a sequential decision problem within a Classification Markov Decision Process (CMDP). A multi-scale temporal shrinkage module is utilized to construct an interpretable network, which enhances model interpretability and reduces the negative impact of noisy data in harsh working conditions. The diagnosis agent autonomously learns the optimal classification policy, reducing the need for manual intervention and human expertise. Experimental results show excellent generalization and stability, achieving over 98.5% accuracy even in noisy conditions. It outperforms existing methods and highlights its robustness in challenging operational environments.
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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