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Industrial fault diagnosis (FD) is crucial for ensuring a manufacturing process’s safety, reliability, and efficiency. However, significant challenges arise from small fault sample sizes and the inherent variability of operating conditions. To tackle the above challenges, this article presents a novel small-sample-oriented FD framework, CF-DDIM-MCCL, leveraging the classifier-free denoising diffusion implicit model combined with multiclass contrastive learning. Additionally, CF-DDIM-MCCL incorporates a dynamic training termination strategy to enhance training efficiency by optimizing the learning process. It also employs a K-nearest neighbor (KNN) classifier instead of the traditional Softmax classifier, resulting in notable improvements in fault identification stability and accuracy. Extensive confirmatory and comparative experiments were conducted on two benchmark datasets, the Case Western Reserve University (CWRU) and Spectra Quest (SQ) bearing datasets, and in a real industrial application scenario, axial flow fan FD from a steel-making plant. Experimental results demonstrate the fault classification accuracies of 99.361%, 98.140%, and 98.542% across the three test cases. The outstanding performance of the proposed approach across diverse benchmark datasets and practical applications underscores its potential to revolutionize FD practices in natural industrial environments.

Few-shot new faults are constantly emerging due to the dynamic environments and operations in the industrial process. It is a challenge for existing fault diagnosis methods to diagnose few-shot new faults without forgetting old faults by fine tuning the base model. This article defines this challenge as few-shot fault incremental learning problem, and proposes a multiview shapelet prototypical network to solve this problem. First, a multiview metalearning framework that simulates true incremental tasks and combines multiview information is proposed in this article to build a generalizable feature space for unseen classes. Second, a multiview shapelet prototypical classifier is proposed to enhance the generalization ability of shapelets in adapting new faults with few samples. Third, multiview metacalibration modules based on transformers are proposed to fuse multiview information and calibrate prototypes and embedded features into a distinguishable space. Finally, experiments are conducted on the benchmark Tennessee Eastman process and the real-world aluminum electrolysis process. Experimental results illustrate that the proposed method is better than the existing methods in terms of interpretability, alleviating catastrophic forgetting, and reducing time complexity.

Early classification predicts the class of the incoming sequences before it is completely observed. How to quickly classify streaming time series without losing interpretability through early classification method is a challenging problem. A novel memory shapelet learning framework for early classification is proposed in this article. First, a memory distance matrix is introduced to store the historical characteristics of streaming time series, which can alleviate repetitive calculations caused by the growing length of time series. Second, early interpretable shapelets are extracted in the proposed method by optimizing both accuracy objective and earliness objective simultaneously. The proposed method employs end-to-end learning, which allows the model to directly learn early shapelets without the necessity of searching for numerous candidate shapelets. Third, an objective function of memory shapelet learning is proposed by overall considering accuracy and earliness, which can be optimized by gradient descent algorithm. Finally, experiments are conducted on benchmark dataset UCR, Tennessee Eastman process, and real-world aluminum electrolysis process in China. Comparable results with other state-of-the-art methods demonstrate the superior performance of the proposed method in interpretability, accuracy, earliness, and time complexity.

Accurate segmentation of coronavirus disease 2019 (COVID-19)-infected lung areas from chest computed tomography (CT) scans is critical for COVID-19 diagnosis and prognosis evaluations. However, current mainstream 2-D slice-wise segmentation models fail to fully utilize 3-D volumetric context across slices, leading to inaccurate segmentation results. Although 3-D segmentation models can exploit volumetric information, they are prone to overfitting due to the limited open datasets of COVID-19 and the lack of clinical expert labels. To address the above issues, we propose a novel COVID-19 lesion segmentation model, called 2.5-D DPE-Net, based on a 2.5-D dual-path encoder network model with a multiscale dynamic weight deep supervision mechanism (MsDWDSM). MsDWDSM guides layer-by-layer refinement of multiscale feature maps, enabling the model to learn hierarchical representations of features at different scales to improve the explicitness of the hidden layer learning process. The 2.5-D DPE-Net can fuse spatial and interslice features to effectively enhance and convey spatial location information about the lesion and improve segmentation accuracy. By combining two public datasets, we conducted extensive confirmatory and comparative experiments on a multivendor, multicenter COVID-19 CT image segmentation dataset. Our experimental results show that the 2.5-D DPE-Net performs significantly better than the state-of-the-art methods, with sensitivity, specificity, precision, and Dice of 88.57%, 99.84%, 68.55%, and 75.89%, respectively.