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

With movement toward complication and automation, modern machinery equipment
encounters the problems of diversity and complex origination of faults, incipient weak faults …

Despite the recent advances on intelligent data-driven machinery fault diagnostics, large
amounts of high-quality supervised data are mostly required for model training. However, it …

In real engineering scenarios, it is difficult to collect adequate cases with faulty conditions to
train an intelligent diagnosis system. To alleviate the problem of limited fault data, this paper …

Massive volumes of data are needed for deep learning (DL) models to provide accurate
diagnosis results. Numerous studies of fault diagnosis systems have demonstrated the …

Intelligent fault diagnosis (IFD) refers to applications of machine learning theories to
machine fault diagnosis. This is a promising way to release the contribution from human …

In the age of industry 4.0, deep learning has attracted increasing interest for various
research applications. In recent years, deep learning models have been extensively …

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 …

Mechanical equipment in modern industries becomes more automatic, precise and efficient.
To fully inspect its health conditions, condition monitoring systems are used to collect real …

Intelligent fault diagnosis is a promising tool to deal with mechanical big data due to its
ability in rapidly and efficiently processing collected signals and providing accurate …