Rolling bearing fault detection is critical for improving production efficiency and lowering
accident rates in complicated mechanical systems, as well as huge monitoring data, posing …

The present study aims at monitoring and classifying the multi-variant wear behavior of
sliding bearings. For this purpose, acoustic emission (AE) technique was applied to a test rig …

On-line process monitoring increases product quality, improves process stability, and lowers
costs in manufacturing. This paper presents a study of using time-frequency representation …

What predicts cross-country differences in the recovery of socioeconomic activity from the
COVID-19 pandemic? To answer this question, we examined how quickly countries' …

Feature extraction from a signal is the most important step in signal-based fault diagnosis.
Deep learning or deep neural network (DNN) is an effective method to extract features from …

Fault diagnosis of rolling element bearings (REBs), one type of essential mechanical
element, has been actively researched; recent research has focused on the use of deep …

The Intelligent Fault Diagnosis of rotating machinery calls for a substantial amount of training
data, posing challenges in acquiring such data for damaged industrial machinery. This …

Lempel–Ziv complexity indicator (LZCI), as one of the complexity indicators, is effectively
used for identifying the bearing fault severity due to its own advantages. However, it has …

Rotating machinery failure analysis requires signal preprocessing to extract fault-related
information. However, to promote accurate condition monitoring of bearing following two …

Abstract Long Short-Time Memory (LSTM) deep neural networks are capable of learning
order dependence in sequence problems and capturing long-term, non-linear temporal …