This paper proposes a supervised dimension reduction methodology for tensor data which has two advantages over most image-based prognostic models. First, the model does not require tensor data to be complete which expands its application to incomplete data. Second, it utilizes time-to-failure (TTF) to supervise the extraction of low-dimensional features which makes the extracted features more effective for the subsequent prognostic. Besides, an optimization algorithm is proposed for parameter estimation and closed-form solutions are derived under certain distributions.
翻译:本文建议对压力数据采用监督的减少维度方法,该方法比大多数基于图像的预测模型具有两个优势。首先,该模型并不要求压力数据必须完整,从而将其应用扩大到不完整的数据。其次,它利用时间到故障(TTF)来监督低维特征的提取,从而使提取的特征对随后的预测更加有效。此外,还提议为参数估计采用优化算法,在某些分布中得出封闭式解决方案。