Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …

Vibration measurement and monitoring are essential in a wide variety of applications.
Vibration measurements are critical for diagnosing industrial machinery malfunctions …

Deep learning (DL)-based methods have advanced the field of Prognostics and Health
Management (PHM) in recent years, because of their powerful feature representation ability …

Planetary gearboxes have various merits in mechanical transmission, but their complex
structure and intricate operation modes bring large challenges in terms of fault diagnosis …

Abstract Electro-Hydrostatic Actuator (EHA) is a critical electro-hydraulic actuator system
widely used in aerospace equipment. To ensure its normal operation, the intelligent fault …

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 …

Unsupervised domain adaptation (UDA)-based methods have made great progress in
mechanical fault diagnosis under variable working conditions. In UDA, three types of …

The increased complexity and intelligence of automation systems require the development
of intelligent fault diagnosis (IFD) methodologies. By relying on the concept of a suspected …

Prediction of state-of-health and remaining useful life is crucial to the safety of lithium-ion
batteries. Existing state-of-health and remaining useful life prediction methods are not …

Bearings are commonly used to reduce friction between moving parts. Bearings may fail due
to lubrication failure, contamination, corrosion, and fatigue. To prevent bearing failures, it is …