In recent years, supervised deep learning methods have proven to be feasible and
advanced for fault diagnosis. They do, however, rely on large amounts of accurately labeled …

With the recent development of deep learning, machine-learning-based methods have
gained promising achievements for fault diagnosis. However, most of these methods are …

Abnormal signals are inevitable in big data acquired from harsh industrial environments.
Abnormal data detection is a crucial component of condition monitoring for rotating parts and …

Modern machine degradation trend evaluation relies on the unsupervised model-based
estimation of a health index (HI) from asset measurement data. This minimizes the need for …

Federated learning does well in jointly training multiple deep learning models for fault
diagnosis. The accuracy of local model may affect the effectiveness of federated learning. In …

Squeeze film damper (SFD) is one of the key components in the sophisticated rotating
machines in the high-end aerospace industry. Rolling bearings in the aeroengine spindle …

A novel fusion method is proposed for fault diagnosis and prognosis in nonlinear stochastic
distribution control systems. Technically, a mapping relationship between fault increments …