Prognostic health management minimizes system downtime and improves overall equipment effectiveness. Accurate prediction of remaining useful life (RUL) is key to prognostics. Prominent machine learning algorithms implement handcrafted feature extraction to improve RUL prediction. Deep learning automates feature extraction from raw data but requires large datasets and computationally expensive fine-tuning. Data-specific handcrafting and fine-tuning limit the generalization capability of existing models. Proposed framework addresses these challenges using Temporal Multivariate 3D Convolutional Network (TM3C) and Kernel-based Transformation (KT) of features. KT generates 3D features that incorporate trendable degradation patterns from multivariate temporal relationship among sensor data. TM3C implements 3D convolutional layers with temporal filters for RUL prediction. KT is generalizable and improves feature relevance. Full-width filters in TM3C reduce number of tunable parameters and convolution operations. Proposed TM3C-KT capitalizes on the strength of deep learning while lowering the cost for feature discovery, parameter learning, and model fine-tuning. TM3C-KT is evaluated on three prognostics applications, (1) RUL prediction for turbofan engines, (2) Failure state estimation for hydraulic pumps, and (3) Component wear prediction for milling machines. Performance of the framework is comparable and better than benchmark methods in literature. Characteristics of the framework are reviewed on generalizability, prognosability and versatility metrics. Results and corresponding analysis demonstrate suitability of TM3C-KT for industrial applications of machine health prognostics.

预后健康管理可最大程度地减少系统停机时间，并提高整体设备效率。剩余使用寿命（RUL）的准确预测是预测的关键。杰出的机器学习算法实现了手工特征提取，以改善RUL预测。深度学习可自动从原始数据中提取特征，但需要大型数据集和计算量巨大的微调。特定于数据的手工制作和微调限制了现有模型的泛化能力。拟议的框架使用功能的时间多元3D卷积网络（TM3C）和基于内核的转换（KT）解决了这些挑战。KT会根据传感器数据之间的多元时间关系生成3D特征，这些特征将趋势性退化模式纳入其中。TM3C通过用于RUL预测的时间滤波器实现3D卷积层。KT具有通用性，可以提高功能的相关性。TM3C中的全宽滤波器减少了可调参数和卷积运算的数量。拟议的TM3C-KT利用深度学习的优势，同时降低了特征发现，参数学习和模型微调的成本。TM3C-KT在以下三个预测应用程序上进行了评估：（1）涡轮风扇发动机的RUL预测，（2）液压泵的故障状态估计，以及（3）铣床的部件磨损预测。该框架的性能是可比的，并且优于文献中的基准方法。对该框架的特征进行了概括性，可预见性和多功能性指标的审查。