国外文献 > 1.轴承 > 5. 监测与诊断 > 寿命与维护
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| 标 题： |
A Predictive Maintenance Solution for Bearing Production Line Based on Edge-Cloud Cooperation
基于边缘云协同的轴承生产线预测性维护解决方案 |
| 作 者： |
Cheng Cheng
Bei-ke Zhang
Dong Gao |
| 关键词： |
Predictive maintenance [预测性维护]
Internet of things [ 物联网]
Edge-Cloud [ 边缘云]
Machine learning [ 机器学习] |
| 出版年： |
2019 |
| 会议名： |
2019 Chinese Automation Congress (CAC 2019)
2019年中国自动化大会（CAC 2019） |
| 届 次： |
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| 摘 要： |
Effective and accurate maintenance of equipment is a difficult problem for the manufacturing industry. With the development of technologies such as the Internet of Things, artificial intelligence, and edge computing, predictive maintenance (PdM) has brought new solutions to this problem. Taking the bearing production line as the application object, this paper proposes a predictive maintenance solution of "edge cloud coordination". By deploying sensors in the production line equipment to realize equipment data collection, data cleaning, data standardization and real-time control are completed at the edge end. The cloud analyzes the data through machine learning algorithms to predict equipment failure and remaining service life, thereby reducing equipment maintenance costs and improving equipment utilization. Experiments show that this solution provides a powerful means to ensure efficient and stable operation of the bearing production line, which can effectively predict equipment failure and equipment life, and reduce operation and maintenance costs, with feasibility and good market application prospects.
设备的有效与精准维护是制造业面临的一大难题。随着物联网、人工智能和边缘计算等技术的发展，预测性维护（PdM）为解决这一问题带来了新的方案。本文以轴承生产线为应用对象，提出了一种“边缘云协同”的预测性维护解决方案。通过在生产线设备中部署传感器，实现设备数据的采集、清洗、标准化以及边缘端的实时控制。云端利用机器学习算法对数据进行分析，从而预测设备故障及剩余使用寿命，有效降低设备维护成本并提高设备利用率。实验表明，该方案能够有力保障轴承生产线高效稳定运行，可有效预测设备故障和寿命，降低运行与维护成本，具有良好的可行性与市场应用前景。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
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