Deep transfer learning algorithm is regarded as a promising method to address the issue of
rolling bearing fault diagnosis with limited labeled data. Stacked autoencoder (SAE) has …

Bearing fault diagnosis is a significant part of rotating machine health monitoring. In the era
of big data, various data-driven methods, which are mainly based on deep learning (DL) …

Nowadays, the industrial environment is characterised by growing competitiveness, short
response times, cost reduction and reliability of production to meet customer needs. Thus …

Efficient fault diagnosis of electrical and mechanical anomalies in induction motors (IMs) is
challenging but necessary to ensure safety and economical operation in industries …

Effective fault diagnosis is a crucial way to reduce the occurrence of severe damages of
many industrial products. With the increasing amount of condition monitoring data, deep …

Vibration signals always contain noise and irregularities, which makes spectrum analysis
difficult to extract high-level features. Recently, graph theory has been applied to spectrum …

It is important to minimize the unscheduled downtime of machines caused by outages of
machine components in highly automated production lines. Considering machine tools such …

As a new classification method with the matrix as the input, support matrix machine (SMM)
makes full use of the structured information between rows and columns of the input matrix to …

Due to their special characteristics, angular contact ball bearings (ACBBs) are broadly
applied in various mechancial systems. The working performance of rotating machinery can …

Over the years, anomaly detection of fluid pipes has been a focus of water authorities as well
as oil and gas operators to advance the operation and management of these mechanical …