In engineering, due to strong noise interference, it is a challenging issue for decision-making
of the neural network to accurately detect healthy conditions of aero-engine bearings. As …

Spectral amplitude modulation (SAM) method, as an automated and empirical nonlinear
filtering approach, has shown great promise for rotating machinery fault diagnosis. However …

Adversarial transfer learning is an effective method for diagnosing rotating machinery faults.
However, it does not extract features completely and underutilizes unlabeled target data …

Abstract Domain adaptation (DA) is a well-established method to tackle the transfer
diagnosis of rotating machinery. However, current DA methods encounter challenges in …

The complexity of the internal structure of rolling bearings and the harshness of their
operating environment result in strong non-stationarity and nonlinearity of the vibration …

Low-rank matrix learning techniques, especially support matrix machine (SMM) approach,
have significantly altered mechanical fault diagnosis by efficiently uncovering correlations …

The performance of gas turbine inevitably grades slowly in service. In order to obtain high-
precision state assessment, an intelligent online real-time sensor fault diagnosis algorithm …

Variable speed is one of the common working conditions of mechanical equipment, which
poses an important challenge to equipment fault diagnosis. The current solutions have the …

AI-enabled industrial equipment monitoring, diagnosis and health management -
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Traction motor bearings are crucial for guaranteeing the safe operation of metro vehicles.
However, in the metro traction motor bearing fault diagnosis, there are usually problems of …