Deep learning for machine health prognostics using Kernel-based feature transformation

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.