Aiming at fault visualization and automatic feature extraction, this article presents a new and
intelligent bearing fault diagnostic method by combining symmetrized dot pattern (SDP) …

Effective fault diagnosis of rotating machinery plays a pretty important role in the enhanced
reliability and improved safety of industrial informatics applications. Although traditional …

In recent years, deep learning (DL)-based fault diagnosis methods have demonstrated
significant success in various domains due to their high accuracy. Similar to other data …

The accurate monitoring of blade vibration under operating conditions is essential in turbo-
machinery testing. Blade tip timing (BTT) is a promising non-contact technique for the …

Sparse priors for signals play a key role in sparse signal modeling, and sparsity-assisted
signal processing techniques have been studied widely for machinery fault diagnosis. In this …

Improving diagnostic efficiency and shortening diagnostic time is important for improving the
reliability and safety of rotating machinery, and has received more and more attention. When …

The localization of cyclostationary sound sources is important for rotating machinery noise
source identification and fault diagnosis. Cyclostationary sound sources are a special class …

Aiming at the problems of traditional fault diagnosis methods that do not represent the time
correlation between signals, low recognition accuracy under complex working conditions …

Incipient faults for analogue circuits in modern electronic systems are difficult to diagnose
due to poor fault features. To address this issue, a method based on the attention weighted …

The absorption peak contains a great amount of important chemical information that is
critical for the qualitative/quantitative analysis of organic compounds in high-dimensional …