Ren Lei, Liu Yuxin, Huang Di, Huang Keke, Yang Chunhua
IEEE Trans Neural Netw Learn Syst. 2023 Sep;34(9):6456-6467. doi: 10.1109/TNNLS.2021.3136768. Epub 2023 Sep 1.
Health indicator prediction, such as remaining useful life prediction and product quality prediction, is an important aspect of industrial intelligence. It is essential to process the massive multichannel industrial time series collected from the Industrial Internet of Things for the industrial health indicator prediction. At present, there are still three issues that need to be considered for industrial health indicator prediction. First, it is difficult to directly connect the distant positions in the industrial time series to extract the temporal relations, which decreases the efficiency of extracting the potential long-distance temporal relations and training networks. Second, it should be fully considered that data from different channels have different contributions. Equally dealing with the contributions of each channel will weaken the representational ability of prediction networks. Third, the loss function deals with early predictions and delay predictions equally, which will lead to high risks caused by delay predictions. In this article, for these issues, a novel multichannel temporal attention-based network (MCTAN) is proposed for industrial health indicator prediction, which can weigh contributions of different channels through the channel attention while avoiding the loss of the temporal information and directly connect each time series position to the local fields of the sequence through the multi-head local attention mechanism to efficiently extract potential long-distance temporal relations. Then, a weighted mean square error loss function differently dealing with early predictions and delay predictions by setting dynamic weights is presented to reduce delay predictions. Next, to deal with the above-mentioned issues systematically, a framework combining data preprocessing and MCTAN collaboratively is introduced to predict industrial health indicators through multichannel time series. Finally, the experiments are carried out on the commercial modular aero-propulsion system simulation dataset to measure the performances, including the accuracy of industrial health indicator predictions and the inference speed.
健康指标预测，如剩余使用寿命预测和产品质量预测，是工业智能的一个重要方面。对于工业健康指标预测而言，处理从工业物联网收集的海量多通道工业时间序列至关重要。目前，工业健康指标预测仍有三个问题需要考虑。首先，难以直接连接工业时间序列中的远距离位置以提取时间关系，这降低了提取潜在长距离时间关系和训练网络的效率。其次，应充分考虑不同通道的数据有不同贡献。同等对待每个通道的贡献会削弱预测网络的表征能力。第三，损失函数对早期预测和延迟预测一视同仁，这将导致延迟预测带来的高风险。在本文中，针对这些问题，提出了一种新颖的基于多通道时间注意力的网络（MCTAN）用于工业健康指标预测，该网络可以通过通道注意力对不同通道的贡献进行加权，同时避免时间信息的损失，并通过多头局部注意力机制将每个时间序列位置直接连接到序列的局部字段，以高效提取潜在的长距离时间关系。然后，提出了一种加权均方误差损失函数，通过设置动态权重来区别对待早期预测和延迟预测，以减少延迟预测。接下来，为了系统地处理上述问题，引入了一个将数据预处理和MCTAN协作结合的框架，通过多通道时间序列预测工业健康指标。最后，在商用模块化航空推进系统仿真数据集上进行实验，以衡量性能，包括工业健康指标预测的准确性和推理速度。
