Ahn Jisu, Lee Younjeong, Kim Namji, Park Chanho, Jeong Jongpil
Department of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si 16419, Gyeonggi-do, Republic of Korea.
AI Research Center, Gfyhealth, 20 Pangyo-ro, Bundang-gu, Seongnam-si 13488, Gyeonggi-do, Republic of Korea.
Sensors (Basel). 2023 Aug 22;23(17):7331. doi: 10.3390/s23177331.
In the manufacturing process, equipment failure is directly related to productivity, so predictive maintenance plays a very important role. Industrial parks are distributed, and data heterogeneity exists among heterogeneous equipment, which makes predictive maintenance of equipment challenging. In this paper, we propose two main techniques to enable effective predictive maintenance in this environment. We propose a 1DCNN-Bilstm model for time series anomaly detection and predictive maintenance of manufacturing processes. The model combines a 1D convolutional neural network (1DCNN) and a bidirectional LSTM (Bilstm), which is effective in extracting features from time series data and detecting anomalies. In this paper, we combine a federated learning framework with these models to consider the distributional shifts of time series data and perform anomaly detection and predictive maintenance based on them. In this paper, we utilize the pump dataset to evaluate the performance of the combination of several federated learning frameworks and time series anomaly detection models. Experimental results show that the proposed framework achieves a test accuracy of 97.2%, which shows its potential to be utilized for real-world predictive maintenance in the future.
在制造过程中，设备故障与生产率直接相关，因此预测性维护起着非常重要的作用。工业园区分布广泛，异构设备之间存在数据异质性，这使得设备的预测性维护具有挑战性。在本文中，我们提出了两种主要技术，以在这种环境中实现有效的预测性维护。我们提出了一种用于制造过程时间序列异常检测和预测性维护的1DCNN-Bilstm模型。该模型结合了一维卷积神经网络（1DCNN）和双向长短期记忆网络（Bilstm），在从时间序列数据中提取特征和检测异常方面非常有效。在本文中，我们将联邦学习框架与这些模型相结合，以考虑时间序列数据的分布变化，并在此基础上进行异常检测和预测性维护。在本文中，我们利用泵数据集来评估几种联邦学习框架与时间序列异常检测模型组合的性能。实验结果表明，所提出的框架实现了97.2%的测试准确率，这表明其在未来用于实际预测性维护的潜力。
