Fault diagnosis and prognosis (FDP) tries to recognize and locate the faults from the
captured sensory data, and also predict their failures in advance, which can greatly help to …

The progressive growth in demand and requirements for bearing problem diagnostics in the
operating segment of trains has resulted from an increase in train speed and the …

The problem of fuel reloading optimization is very demanding, which requires to search for
the optimal suitable core configuration within a very huge solution space. To solve this …

Intelligent fault diagnosis based on multi-sensor fusion has gained considerable attention in
various modern industrial applications. However, it is still challenging to extract …

As an effective method, deep transfer learning is used to solve the problem of unsupervised
fault diagnosis of rolling bearings. In the process of obtaining domain invariant features, the …

The current deep learning-based intelligent diagnosis algorithms depend on large amounts
of well-labeled data, but they may not perform well in engineering practice where the fault …

Accurately locating the fault impacts and extracting sensitive fault features of vibration
signals are challenging problems in diesel engine fault diagnosis. To address the limited …

Due to adverse working conditions of rotating machinery in actual engineering, bearing fault
data are more difficult to acquire compared to normal data. That said, the real collected …

Deep transfer learning is an effective method for unsupervised fault diagnosis of rolling
bearings. In some works, the pseudo-label of target domain prediction is used to improve the …

Intelligent fault diagnosis algorithms require large amounts of data to train models, and the
fusion of heterogeneous information from multiple sensors increases the computational …