Deep learning has made notable advances in intelligent fault diagnosis. However, industrial
application of deep learning models faces challenges due to noise interference and scarce …

Intelligent fault diagnostic techniques are crucial for ensuring the long-term reliability of
manufacturing. The process variables collected by sensors in real industrial systems …

Sensors, vital elements in data acquisition systems, play a crucial role in various industries.
However, their exposure to harsh operating conditions makes them vulnerable to faults that …

Acquiring sufficient fault data labels for new tasks in rotating machinery fault diagnosis is
tricky. Accurately identifying faults in unlabeled scenarios is a critical and urgent practical …

Pipeline feature recognition is crucial for the reliability and safety of long-distance natural
gas pipelines. Utilizing manual or machine learning methods to recognize pipeline features …

Condition-based maintenance of aero-engines requires real-time gas-path fault diagnosis,
which is crucial for reducing costs and enhancing aircraft attendance. It is imperative to …

Intelligent fault diagnosis (IFD) based on deep learning (DL) has demonstrated its powerful
performance to promote the reliability and safe operation of rotating machinery. In industrial …

To enhance the generalization capability of rotating machinery fault diagnosis, a novel
generalized fault diagnosis framework is proposed. Phase entropy is introduced as a new …

There are hundreds of various sensors used for online Prognosis and Health Management
(PHM) of LREs. Inspired by the fact that a limited number of key sensors are selected for …

This study introduces a fault diagnosis algorithm based on particle filtering for open-cycle
liquid-propellant rocket engines (LPREs). The algorithm serves as a model-based method …