Sparse random mode decomposition (SRMD) is a decomposition approach established by
combining sparse random feature model with clustering algorithm. It is not subject to the …

Traditional signal processing methods cannot effectively segment the optimal frequency
band, resulting in the fault characteristics are not obvious. Therefore, this paper proposes a …

To address the limitations of existing stall/surge warning methods—including variations in
research subjects, sensor configurations, computational complexity, and the universality of …

The supercritical carbon dioxide (sCO 2) power cycle system is a potential thermoelectric
conversion system with the advantages of high efficiency and compactness, which can be …

Extracting incipient fault features is a critical aspect of monitoring the rotating machinery
operation condition. However, existing methods based on symplectic geometry mode …

Rub-impact is a common nonlinear fault of the rotor system, occurring in rotating machines
with radial clearance between the rotor and the stator, which may lead to serious …

This work presents a fully automatic method for extracting running-in information from data of
hermetic reciprocating compressors by analyzing clusters of subsequenced time series data …

In recent years, deep learning-based intelligent diagnosis approaches have showcased
broad application prospects in the field of rolling bearing fault identification in Industrial …

The periodical impulses caused by localized defects of components are the vital
characteristic information for fault detection and diagnosis of rotating machines. In recent …

An effective structural health monitoring method of traction motor bearings is a powerful
guarantee for the safety operation of high-speed trains. However, it is exceptionally difficult …