• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Sensors and Systems for Space Applications Conference >Smart Robot-Enabled Remaining Useful Life Prediction and Maintenance Optimization for Complex Structures using Artificial Intelligence and Machine Learning
【24h】

Smart Robot-Enabled Remaining Useful Life Prediction and Maintenance Optimization for Complex Structures using Artificial Intelligence and Machine Learning

机译：通过人工智能和机器学习实现智能机器人的剩余使用寿命预测和维护优化，适用于复杂结构

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

To replace current legacy inspection/maintenance methods with autonomous real-time health status tracking , the paper proposes a smart robotic system with integrated remaining useful life (RUL) prediction tailored for complex components, structures and systems (CSSs). Capabilities like artificial intelligence (AI)/machine learning (ML) utilizing sensing data along with other monitoring data assist in maintenance optimization. The designed system is based on the state-of-the-art reinforcement learning (RL) and deep learning (DL) framework, which consists of an input, modeling, and decision layer. To achieve better prediction accuracy with higher autonomy, a novel active robot-enabled inspection/maintenance system is deployed in the input layer to collect whole-field infrastructure sensing data and inspect critical CSSs. The deep RL approach is integrated with failure diagnostic and prognostic algorithms to train a risk-informed AI-based agent for controlling the robots. With the data collected from the input layer, the modeling layer first conducts data fusion and predicts RUL of components using an efficient Bayesian convolutional neural network (BCNN) algorithm. In the decision layer, a resilience-driven probabilistic decision-making framework will be developed to control the robot for automatically detecting local damage, e.g. defects, degradation, and recommend mitigation/recovery actions for the health management of infrastructure under uncertainty. The combined layers comprise a AI-risk-driven sensing system (AIRSS) which was tested on an Aero-Propulsion System turbofan engine.
机译：为了用自动实时健康状态跟踪更换电流遗留检查/维护方法，本文提出了一种智能机器人系统，具有用于复杂组件，结构和系统（CSSS）的集成剩余的使用寿命（RUL）预测。利用感测数据以及其他监控数据辅助在维护优化中使用感应数据等人工智能（AI）/机器学习（ML）等功能。设计的系统基于最先进的加强学习（RL）和深度学习（DL）框架，其包括输入，建模和决策层。为了实现更好的自主权预测准确性，在输入层中部署了一种新的主​​动机器人的检查/维护系统，以收集全场基础设施感应数据并检查关键CSSS。深度RL方法与故障诊断和预后算法集成，培训一种用于控制机器人的风险信息的基于AI的代理。利用从输入层收集的数据，建模层首先使用高效的贝叶斯卷积神经网络（BCNN）算法进行数据融合并预测组件的RUL。在决策层中，将开发弹性驱动的概率决策框架以控制机器人，以便自动检测局部损坏，例如，在不确定性下，对基础设施健康管理的缺陷，退化和建议缓解/恢复行动。组合层包括在航空推进系统涡轮机发动机上测试的Ai-Rustrive驱动的感测系统（空气）。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号