In existing fault diagnosis methods, fault categories are generally coarse-grained, which
may result in failure to precisely identify fault details. Therefore, fine-grained fault diagnosis …

With the improvement of intelligence and integration, automatic supervision of large-scale
systems is a current challenge in guaranteeing the high-reliability of edge devices. Hence …

The motor fault detection based on data-driven approach has achieved some results.
However, there are still some problems, such as complex signal processing, long training …

Deep learning methods are promising tools for machinery fault diagnosis in modern
industry. However, the diagnostic performance of these methods will deteriorate significantly …

Plant-wide process monitoring and fault diagnosis are key technologies to ensure the high-
efficiency and safety of the production. This article pays close attention to the new …

Although doubly fed induction generators (DFIG) are widely used, difficulties in early fault
detection and severity assessment for inter-turn short-circuit (ITSC) faults are highly …

In real industrial scenarios, the limited available labeled fault data and existed significant
data distribution differences between the source domain and the target domain emplace …

Due to the frequent switch of the working condition, fault diagnosis model for rotating
machinery established on the training set (the source domain) cannot be effectively applied …

The transfer learning method performs better than conventional deep learning when dealing
with the few-shot diagnosis situation where obtaining the true bearing defect signal is …

摘要分组密码算法分析中需评估其抵抗差分和线性攻击的能力, 而这一能力往往是通过算法的
最小差分/线性活跃S 盒个数体现的. 论文给出基于混合整数线性规划的方法自动分析活跃S …