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Abstract：
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 Causal-Factors-Aware Attention Network, CaFANet, 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%.
摘要：
现有的基于深度学习的故障预测算法取得了良好的预测性能。这些算法公平地对待所有特征,并且假设设备故障的进展在整个生命周期中是固定的。事实上,每个特征对故障预测的准确性有不同的贡献，设备故障的进展是非平稳的。更具体地说,捕获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，设备不同故障的进度差异很大。因此,考虑到特征差异和时间信息，我们提出了一个因果因素感知的注意力网络，CaFANet,物联网中的设备故障预测。实验结果和性能分析证实了该算法相对于传统机器学习方法的优越性，预测精度提高了15.3%。
