转辙机作为转换道岔的关键设备, 其工作状态的及时诊断对铁路行车安全和运输效率至关重要.
针对铁路现场转辙机型号多, 故障样本少, 故障类别不均衡等实际问题, 提出一种基于PLATIPUS …

In the era of big data, mechanical intelligent fault diagnosis is more intelligent due to the
large-scale collection of data. However, fault data is often difficult to obtain, resulting in small …

Current-based fault diagnosis has become a promising solution for electromechanical
systems due to the low cost and easy access. However, most of the existing studies require …

The visual anomaly diagnosis can automatically analyze the defective products, which has
been widely applied in industrial quality inspection. The anomaly classification can classify …

多任务学习网络结构和参数冗余, 网络规模过大, 导致网络实时性差的问题; 无法获取元件的部分
或者全部故障类型样本, 导致零样本问题. 针对上述问题, 提出一种基于元学习优化的轻量化多 …

Existing deep learning (DL) algorithms are based on a large amount of training data and
they face challenges in effectively extracting fault features when dealing with few-shot fault …

The performance of diagnosis model highly depends on the amount and quality of data used
in training. However, it is difficult to collect sufficient fault data in practical applications …

Safety-critical systems frequently encounter few-shot fault diagnosis (FSFD) scenarios,
where conventional deep learning models may not be as effective. For effective diagnosis of …

Motor is an important component in industrial production, so it is very important to detect and
diagnose the motor fault in time. The performance of traditional deep learning methods is …

Few-shot fault diagnosis is a challenging issue in manufacturing area, which rely on
knowledge learned from historical data and limited data in new work condition …