The existing fault diagnosis methods of rotating machinery constructed with both shallow
learning and deep learning models are mostly based on vibration analysis under steady …

Purpose This article aims to systematically review the recent research advances in data-
driven machinery fault diagnosis based on machine learning algorithms, and provide …

Effective intelligent fault diagnosis has long been a research focus on the condition
monitoring of rotary machinery systems. Traditionally, time-domain vibration-based fault …

Rotating machinery fault diagnosis is vital to enhance the reliability and safety of modern
equipment. Recently, deep learning (DL) models have achieved breakthrough …

The diagnosis of the key components of rotating machinery systems is essential for the
production efficiency and quality of manufacturing processes. The performance of the …

Fault diagnosis for rotating machinery requires both high diagnosis accuracy and time
efficiency. A rotating machinery fault diagnosis method based on intelligent feature self …

Fault diagnosis is important for the maintenance of rotating machinery. The detection of
faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this …

This study presents a new roller bearing fault diagnosis algorithm based on a sparsity and
neighborhood preserving deep extreme learning machine (SNP-DELM) and motor current …

Effective fault diagnosis of rotating machinery has always been an important issue in real
industries. In the recent years, data-driven fault diagnosis methods such as neural networks …

Vibration signals are closely linked with health conditions of rotating machines and widely
used in fault diagnosis. Unfortunately, traditional vibration signal-based fault diagnosis …