Reliability assessment is essential for ensuring the stable operation of industrial systems. This study presents a hybrid reliability assessment framework that integrates mechanism-based and data-driven approaches for online degradation evaluation. A representative degradation mode— impact fatigue—is investigated to demonstrate the method. The mechanistic component is formulated by using crack propagation theory and Miner’s cumulative damage rule; while the data-driven one employs a generalized power-law Wiener process with an additive nonlinear drift. Both sources of information are fused through an evolving unscented Kalman filter that performs optimal and unbiased online state estimation via prediction and observation updates. In the proposed framework, the Wiener process serves as the state equation, and the mechanistic model functions as the measurement equation. The reliability of impact fatigue is further evaluated by using the inverse Gaussian distribution, which is inherently associated with the Wiener process. Application to a thrust bearing in the main pump of a pressurized water reactor demonstrates the effectiveness and superiority of the proposed method over existing ones.

Training images with data transformations, e.g., crops, shifts, rotations and color distortions, have been suggested as contrastive examples to evaluate the robustness of deep neural networks against data noises [1]. In this work, we propose a practical framework ContRE (which is the meaning of “against” in French) that uses Contrastive examples for DNN Robustness Estimation. Specifically, ContRE follows the assumption in [2], [3] that robust DNN models with good generalization performance are capable of extracting a consistent set of features and making consistent predictions from the same image under varying data transformations. Incorporating with a set of randomized strategies for well-designed data transformations over the training set, ContREadopts classification errors and Fisher ratios on the generated contrastive examples to assess and analyze the robustness of DNN models, which correlates to the models’ generalization performance. To show the effectiveness and efficiency of ContRE, extensive experiments have been done using various DNN models, e.g., ResNet, VGGNet, DenseNet, EfficientNet, etc., on three open source benchmark datasets, i.e., CIFAR-10, CIFAR-100, and ImageNet, with thorough ablation studies and applicability analyses. Our experiment results confirm that ❨1❩ behaviors of deep models on contrastive examples are strongly correlated to what on the testing set, and ❨2❩ the robustness that ContRE calculates is a robust measure of generalization performance complementing to the testing set in various settings. Codes is to be publicly available.

Techniques to explain the predictions of deep neural networks (DNNs) have been largely required for gaining insights into the black boxes. We introduce InterpretDL, a toolkit of explanation algorithms based on PaddlePaddle, with uniformed programming interfaces and "plug-and-play" designs. A few lines of codes are needed to obtain the explanation results without modifying the structure of the model. InterpretDL currently contains 16 algorithms, explaining training phases, datasets, global and local behaviors of post-trained deep models. InterpretDL also provides a number of tutorial examples and showcases to demonstrate the capability of InterpretDL working on a wide range of deep learning models, e.g., Convolutional Neural Networks (CNNs), Multi-Layer Preceptors (MLPs), Transformers, etc., for various tasks in both Computer Vision (CV) and Natural Language Processing (NLP). Furthermore, InterpretDL modularizes the implementations, making efforts to support the compatibility across frameworks. The project is available at https://github.com/PaddlePaddle/InterpretDL.