Rotating machines have been widely used in industrial engineering. The fault diagnosis of
rotating machines plays a vital important role to reduce the catastrophic failures and heavy …

The failure of a rolling bearing may cause the shutdown of mechanical equipment and even
induce catastrophic accidents, resulting in tremendous economic losses and a severely …

This paper intends to provide an overview on recent development of knowledge transfer for
rotary machine fault diagnosis (RMFD) by using different transfer learning techniques. After …

This paper presents a novel hybrid model for fault detection and classification of motor
bearing. In the proposed model, permutation entropy (PE) of the vibration signal is …

Intelligent fault diagnosis of rotating machinery is essentially a pattern recognition problem.
Meanwhile, effective feature extraction from the raw vibration signal is an important …

Due to difficulties in identifying localized and incipient bearing faults, most proposed fault
diagnosis methods focus on detecting these faults. However, it is not clear to what extent of …

Feature extraction is one of the most important, pivotal, and difficult problems in mechanical
fault diagnosis, which directly relates to the accuracy of fault diagnosis and the reliability of …

Bearings are fundamental mechanical components in rotary machines (engines, gearboxes,
generators, radars, turbines, etc.) that have been identified as one of the primary causes of …

At present, the fourth industrial revolution is pushing factories toward an intelligent,
interconnected grid of machinery, communication systems, and computational resources …

Based on the combination of improved Local Mean Decomposition (LMD), Multi-scale
Permutation Entropy (MPE) and Hidden Markov Model (HMM), the fault types of bearings …