基于虚拟现实交互的智能设备健康监测与智能预警模型及应用

Yaohua Deng, Zilin Zhang, Xiali Liu, Yujian Lu, Guanhao Chen, Q. Lu
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引用次数: 0

摘要

复杂装备可靠性建模存在可靠性数据来源多、数据分布极不平衡、信息不确定、模型解释能力弱、误判率高等问题。本文将网络与物理系统、深度学习与可解释性人工智能相结合,构建海洋设备健康监测的虚实集成体系结构,将人类的“知识”转化为实际模型,嵌入深度学习网络,提出了一种基于虚拟现实交互的大型海洋设备起重系统迁移健康诊断方法。此外,将船舶装备健康预警问题转化为智能预警系统与船舶装备持续交互的强化学习问题,建立船舶装备智能预警决策“健康状态预警策略”端到端映射的深度强化学习模型。最后,以船舶设备起重系统为例,介绍了上述模型的应用方法,并对其有效性进行了验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Model and Application of Intelligent Equipment Health Monitoring and Intelligent Warning Based on the Interaction of Virtual Reality
The modeling of reliability of the complex equipment has many problems, such as multi-source reliability data, extremely unbalanced data distribution, uncertain information, weak model interpretation ability, and high misjudgment rate. This paper integrates cyber and physical system, deep learning and interpretable artificial intelligence to build virtual and real integration architecture for the health monitoring of marine equipment, to convert human “knowledge” into actual model and embed into deep learning network, and then proposes a migration health diagnosis method of large marine equipment lifting system based on the interaction of virtual reality. In addition, to transform the health warning problem of marine equipment into reinforcement learning problem of the continuous interaction between intelligent warning system and marine equipment, to establish the deep reinforcement learning model of the end-to-end mapping of “health state-warning strategy” for the intelligent warning decision of marine equipment. Finally, taking the marine equipment lifting system as an example, the application method of the model proposed above is introduced and its validity is verified.
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