Gui Zhenwen, He Shuaishuai, Lin Yao, Nan Xin, Yin Xiaoyan, Wu Chase Q
The 7th Rescarch Institute of Electronics Technology Group Corporation, Guangzhou 510310, China.
School of Information Science and Technology, Northwest University, Xi'an 710127, China.
Sensors (Basel). 2023 Aug 9;23(16):7040. doi: 10.3390/s23167040.
Existing fault prediction algorithms based on deep learning have achieved good prediction performance. These algorithms treat all features fairly and assume that the progression of the equipment faults is stationary throughout the entire lifecycle. In fact, each feature has a different contribution to the accuracy of fault prediction, and the progress of equipment faults is non-stationary. More specifically, capturing the time point at which a fault first appears is more important for improving the accuracy of fault prediction. Moreover, the progress of the different faults of equipment varies significantly. Therefore, taking feature differences and time information into consideration, we propose a usal-actors-ware Attention work, , for equipment fault prediction in the Internet of Things. Experimental results and performance analysis confirm the superiority of the proposed algorithm over traditional machine learning methods with prediction accuracy improved by up to 15.3%.
现有的基于深度学习的故障预测算法已经取得了良好的预测性能。这些算法平等对待所有特征，并假设设备故障在整个生命周期内的发展是平稳的。事实上，每个特征对故障预测的准确性贡献不同，而且设备故障的发展是不平稳的。更具体地说，捕捉故障首次出现的时间点对于提高故障预测的准确性更为重要。此外，设备不同故障的发展差异很大。因此，考虑到特征差异和时间信息，我们提出了一种用于物联网中设备故障预测的通用行为感知注意力模型。实验结果和性能分析证实了所提算法优于传统机器学习方法，预测准确率提高了15.3%。
