Trustworthy Diagnostics With Out-of-Distribution Detection: A Novel Max-Consistency and Min-Similarity Guided Deep Ensembles for Uncertainty Estimation
X Zhang, C Wang, W Zhou, J Xu… - IEEE Internet of Things …, 2024 - ieeexplore.ieee.org
The unknow fault diagnosis technology in industrial systems implies significant engineering
application value and opportunities. The difficulty stems from the fact that the unknown fault …
application value and opportunities. The difficulty stems from the fact that the unknown fault …
Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles
Recent intelligent fault diagnosis technologies can effectively identify the machinery health
condition, while they are learnt based on a closed-world assumption, ie, the training and …
condition, while they are learnt based on a closed-world assumption, ie, the training and …
Uncertainty-Aware Deep Learning: A Promising Tool for Trustworthy Fault Diagnosis
Recently, intelligent fault diagnosis based on deep learning has been extensively
investigated, exhibiting state-of-the-art performance. However, the deep learning model is …
investigated, exhibiting state-of-the-art performance. However, the deep learning model is …
Trustworthy fault diagnosis with uncertainty estimation through evidential convolutional neural networks
H Zhou, W Chen, L Cheng, J Liu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Deep neural networks (DNNs) have been widely used for intelligent fault diagnosis under
the closed-world assumption that any testing data are within classes of the training data …
the closed-world assumption that any testing data are within classes of the training data …
An uncertainty-informed framework for trustworthy fault diagnosis in safety-critical applications
Deep learning-based models, while highly effective for prognostics and health management,
fail to reliably detect the data unknown in the training stage, referred to as out-of-distribution …
fail to reliably detect the data unknown in the training stage, referred to as out-of-distribution …
An ensemble method with DenseNet and evidential reasoning rule for machinery fault diagnosis under imbalanced condition
Fault diagnosis is of significant importance for intelligent manufacturing as it can increase
production efficiency and decrease the uncertain breakdown risk of machines. Previous …
production efficiency and decrease the uncertain breakdown risk of machines. Previous …
Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework
Fault diagnosis is efficient to improve the safety, reliability, and cost-effectiveness of
industrial machinery. Deep learning has been extensively investigated in fault diagnosis …
industrial machinery. Deep learning has been extensively investigated in fault diagnosis …
Reliable and intelligent fault diagnosis with evidential VGG neural networks
H Zhou, W Chen, L Cheng, D Williams… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
With the emergence of Internet-of-Things (IoT) and big data technologies, data-driven fault
diagnosis approaches, notably deep learning (DL)-based methods, have shown promising …
diagnosis approaches, notably deep learning (DL)-based methods, have shown promising …
HS-KDNet: A lightweight network based on hierarchical-split block and knowledge distillation for fault diagnosis with extremely imbalanced data
J Deng, W Jiang, Y Zhang, G Wang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Because of the cost, it is unrealistic to sample the failure state for a long time, which makes
the data collected from the scenario of engineering usually extremely imbalanced. However …
the data collected from the scenario of engineering usually extremely imbalanced. However …
Uncertainty-Aware Ensemble Combination Method for Quality Monitoring Fault Diagnosis in Safety-Related Products
With the advent of Industry 4.0 (I4. 0) leading to the proliferation of industrial process data,
deep learning (DL) techniques have become instrumental in developing intelligent fault …
deep learning (DL) techniques have become instrumental in developing intelligent fault …