This study proposes a comprehensive framework of explainable remaining useful life (RUL) prediction. The proposed framework aims to not only enhance the accuracy and robustness of RUL estimation but also improve the interpretability of the estimated RUL. The proposed framework features three characteristics. First, raw measurements are transformed into informative features by accounting for the physics of degradation. This transformation results in five types of physics-informed features. The proposed method attenuates the adverse effect of distinct operational conditions while disclosing hidden information in the raw measurements. Second, a novel architecture of multi-scale deep convolutional neural network is addressed to extract temporal patterns from disparate time scales of transformed features, enabling accurate but robust prediction of RUL. Third, a novel interpretation method traces the root causes of the degradation. Specifically, layer-wise relevance propagation is deployed to create a relevance map, which provides fast but accurate identification of the failure modes regardless of the number of features. A systematic analysis on experiments with the commercial modular aero-propulsion system simulation dataset demonstrates the accuracy and interpretability of the proposed framework. The comparative study also confirms that the proposed framework would be effective in predicting and interpreting the RUL of systems in real-world applications, where disparate operational conditions and failure modes are complexly intertwined.

The study proposes a rotational multipyramid network (RoMP Net) with bounding-box transformation for object detection. The RoMP Net is a single-stage object detection neural network featuring three characteristics. First, the network uses a rotational bounding box to minimize the effect of background images when extracting features of objects. Bounding-box transformation was proposed to compensate for the limitation of the rotational bounding boxes, which have relatively low prediction accuracy for objects with a high aspect ratio. Second, the RoMP Net introduces a multi-scale and multi-level feature pyramid network to extract distinct and semantic features efficiently. This network architecture ensures high prediction accuracy and robustness regardless of the size and complexity of objects. Third, hyperparameters in the bounding boxes are automatically determined through an unsupervised clustering method. This optimization method is also critical in improving accuracy. The performance of the proposed network and preprocessing methods are validated through image-sets comprising critical components in power transmission facilities, which have a variety of sizes and aspect ratios. This case study demonstrates the effectiveness and robustness of the three key characteristics in the RoMP Net. Furthermore, the RoMP Net outperforms other state-of-the-art deep neural networks in prediction accuracy and robustness for object detection. Specifically, the mean average precision of the RoMP Net in the validation image-sets shows that it has the highest prediction accuracy, whereas its values in the test image-sets confirm the network's robustness. The fast yet accurate RoMP Net will expand the range of object detection through deep neural networks.