In a practical production environment, machinery operators would consider using contactless sensing technology to monitor machinery degradation condition due to the concern of interference on production. A stream of time-series infrared images as a contactless sensing technology could capture the spatial and temporal information of a machinery degradation process. However, due to four dimensions (4D) of image stream tensor data, most existing remaining useful life (RUL) prediction methods are not capable of processing this kind of data. To fill this gap, image stream based regressors consisting of a neural network and multiple weighted time window policy are proposed. In the regressors, neural networks are utilized and trained to model the correlation between degradation image stream and its associated RUL. The networks are respectively designed based on some primary neural network blocks, including a fully-connected layer, long short-term memory network (LSTM) and convolutional neural network (CNN). To achieve the processing capability of 4D data, various input preprocessing means of raw image stream data are investigated. Meanwhile, to increase the prediction accuracy of the trained networks, a multiple weighted time window (MWTW) policy is developed. The policy aims to use whole monitoring data rather than a recent time window in existing neural network based RUL prediction methods. The proposed image stream based regressors are validated by using two datasets of degradation infrared images. Results showed that the regressors can predict RUL based on image stream well and MWTW policy has a significant effect on the increase of the prediction accuracy.

在实际的生产环境中，由于担心对生产的干扰，机械操作人员会考虑使用非接触式传感技术来监视机械的退化情况。时间序列红外图像流作为非接触式传感技术可以捕获机械退化过程的时空信息。但是，由于图像流张量数据的四个维度（4D），大多数现有的剩余使用寿命（RUL）预测方法无法处理此类数据。为了填补这一空白，提出了一种基于图像流的回归器，该回归器由神经网络和多个加权时间窗口策略组成。在回归器中，利用神经网络并对其进行训练以对退化图像流及其关联的RUL之间的相关性进行建模。这些网络分别基于一些主要的神经网络模块进行设计，包括完全连接的层，长短期记忆网络（LSTM）和卷积神经网络（CNN）。为了实现4D数据的处理能力，研究了原始图像流数据的各种输入预处理手段。同时，为了提高训练网络的预测精度，制定了多重加权时间窗（MWTW）策略。该策略旨在在现有的基于神经网络的RUL预测方法中使用整个监视数据，而不是最近的时间窗口。所提出的基于图像流的回归器通过使用两个降解红外图像数据集进行了验证。