The advent of Industry 4.0 has resulted in the widespread usage of novel paradigms and
digital technologies within industrial production and manufacturing systems. The objective of …

Despite a few recent meta-learning studies have facilitated few-shot cross-domain fault
diagnosis of bearing, they are limited to homogenous signal analysis and have challenges …

Sensor techniques and emerging CNN models have greatly facilitated the development of
collaborative fault diagnosis. Existing CNN models apply different fusion schemes to …

Due to often working in the environment of variable speeds and loads, it is an enormous
challenge to achieve high-accuracy fault diagnosis (FD) of rolling bearings (RB) via existing …

As a key component widely used in electric multiple units (EMU), fault diagnosis of EMU
bearing is an important link. Typically, labeled data from different conditions provides the …

A rolling bearing fault diagnosis method based on whale gray wolf optimization algorithm-
variational mode decomposition-support vector machine (WGWOA-VMD-SVM) was …

Induction machines (IMs) are utilized in different industrial sectors such as manufacturing,
transportation, transmission, and energy due to their ruggedness, low cost, and high …

Intelligent fault diagnosis based on deep learning (DL) has been widely used in various
engineering practices. However, when confronting massive unlabeled industrial data …

Axial piston pump plays a pivotal role in a hydraulic transmission system since it can supply
the core power source. The complexity of structure and the invisibility of failure feature bring …

Rolling bearing is a key component of machinery, its fatigue failure will affect the reliability of
machinery. The bearing vibration signal has strong nonlinearity, resulting in weak fault …