This paper proposes a novel fault diagnosis method by fusing the information from multi-
sensor signals to improve the reliability of the conventional vibration-based wind turbine …

Diagnostics and prognostics of electrical energy conversion systems are moving forward
with the rapid development of IT and artificial intelligence possibilities. This also broadens …

This study proposes a model for detecting anomalies in induction motors using audio
signals. Compared to existing anomaly detection methods based on deep generative …

In complex environments, signals from rotor bearing systems can easily be drowned out by
strong noise, leading to low fault diagnosis accuracy. In order to reduce noise and improve …

Metallic wire rope is the core component unit in construction, and vision-based
nondestructive inspection methods for condition monitoring of metallic wire ropes have …

Bearings are critical components found in most rotating machinery; their health condition is
of immense importance to many industries. The varied conditions and environments in …

Fault diagnosis of rolling bearings is significant for the safe operation of engineering
equipment. Many intelligent diagnostic methods have been successfully developed …

Current signatures from an induction motor are normally used to detect anomalies in the
condition of the motor based on signal processing techniques. However, false alarms might …

This paper aims to classify local demagnetisation and inter-turn short-circuit (ITSC) on
position sensorless permanent magnet synchronous motors (PMSM) in transient states …

Owing to the merits of automatic feature extraction and depth structure, intelligent fault
diagnosis based on deep neural networks has become a great concern. However, the non …