联合学习用于预测性维护和异常检测,使用时间序列数据在制造过程中的分布变化
Jisu Ahn1,2, Younjeong Lee1,2, Namji Kim1
1Department of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si 16419, Gyeonggi-do, Republic of Korea.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
使用1DCNN-Bilstm模型结合联合学习的预测性维护有效地检测制造设备中的异常. 这种方法实现了97.2%的测试准确度,提高了工业生产率和设备可靠性.
科学领域:
- 工业工程 工业工程 工业工程
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 设备故障严重影响制造业的生产率,需要强大的预测性维护策略.
- 分布式工业环境由于各种设备的数据异质性而带来了挑战,使预测性维护工作复杂化.
研究的目的:
- 为具有异质设备的分布式制造环境开发和评估有效的预测性维护框架.
- 解决时间序列数据中的数据分布变化,以改善异常检测和维护预测.
主要方法:
- 提出了一个新的1DCNN-Bilstm模型用于时间序列异常检测,结合1D卷积神经网络和双向LSTM用于特征提取.
- 与1DCNN-Bilstm模型集成了一个联合学习框架,以处理工业环境中的数据异质性和分布变化.
- 使用数据集评估综合框架,以评估异常检测和预测性维护方面的性能.
主要成果:
- 拟议的联合学习框架与1DCNN-Bilstm模型集成,实现了高测试准确率97.2%.
- 证明了模型在从时间序列数据中提取特征和准确检测异常方面的有效性.
- 验证了该方法在现实世界预测性维护应用中的潜力.
结论:
- 联合联合学习和1DCNN-Bilstm方法为复杂工业环境中的预测性维护提供了一个有希望的解决方案.
- 该框架有效地处理数据异质性和分布变化,从而在异常检测中获得高精度.
- 这项研究强调了通过先进的人工智能技术显著提高制造生产率和设备可靠性的潜力.
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