PHM technology is vital in industrial production and maintenance, identifying and predicting
potential equipment failures and damages. This enables proactive maintenance measures …

With the rapid development of pattern recognition represented by deep learning, the
massive excellent bearing fault diagnosis methods have emerged. However, the majority of …

Deep learning-based fault diagnosis models constructed from imbalanced datasets would
meet severe performance degradation when the number of samples for fault classes is much …

A surge of transfer fault diagnosis techniques has been proposed to guarantee the safe
operation of traction motor systems. However, existing efforts highly depend on the …

Due to the extremely limited prior knowledge, machinery fault diagnosis under varying
working conditions with limited annotation data is a very challenging task in practical …

Deep learning has gained significant success in fault diagnosis. However, the number of
gearbox health samples is inevitably much larger than that of fault samples in real-world …

In actual industrial scenarios, fault data is rare and fault labels are difficult to obtain, which
brings many obstacles for fault diagnosis. For this situation, this research proposes a novel …

Deep-learning-based intelligent diagnosis is a popular method to ensure the safe operation
of rolling bearings. However, practical diagnostic tasks are often subject to a lack of labeled …

Benefiting from the excellent capability of data processing, deep learning-based methods
have been well applied in fault diagnosis. However, these methods may perform poorly due …

Knowledge and data dual-driven (KDDD) represents a novel paradigm that leverages the
strengths of data-driven methods in feature representation and knowledge transfer, while …