Abstract Unsupervised Domain Adaptation (UDA) has gained widespread application in
bearing fault diagnosis across various operational conditions, attributed to its commendable …

With the rapid development of industrial applications using permanent magnet synchronous
motors (PMSMs) and the Internet of Things, the demand for using robust fault diagnosis …

The speed variation poses great hardships to the intelligent fault diagnosis of mechanical
equipment. Existing solutions rarely consider the interpretable representation of fault …

In smart home services, non-intrusive load monitoring (NILM) can reveal individual
appliances' power consumption from the aggregate power and requires only one …

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 …

Fault patterns are often unavailable for machine fault diagnosis without prior knowledge.
This makes it challenging to diagnose the existence of machine faults and their types. To …

Deep Learning-based fault diagnosis has aroused widespread attention in machine fault
diagnosis. Helicopter is an important transport for its special purpose. How to ensure its …

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 …