In the pursuit of smart manufacturing, predictive maintenance (PdM) holds significant
importance as it allows manufacturing firms to effectively mitigate avoidable downtime and …

Health monitoring in rotatory machinery is a process of develo** a mechanism to
determine its state of deterioration. It involves analysing the presence of damage, locating …

Recent studies on machinery anomaly detection only based on normal data training models
have yielded good results in improving operation reliability. However, most of the studies …

Hydraulic piston pump is known as one of the most critical parts in a typical hydraulic
transmission system. It is imperative to probe into an accurate fault diagnosis method to …

Bearings are indispensable components of machinery, playing a critical role in effective
health monitoring. This monitoring is vital in detecting equipment incipient failure and …

Most transfer learning (TL) models generally need the fault data from similar scenarios to
achieve cross-domain bearing fault diagnosis. However, due to the bearings are mostly in …

This study aims to establish a novel entropy-based sparsity measure for two main purposes:
first is for the prognosis of bearing defects, secondly it is employed to construct sparsogram …

For highly reliable gas turbines that rarely suffer faults, the overwhelming majority of
historical data are collected under healthy state, while only a very small number of them are …

Accurate and science-based prediction of bearing performance degradation has been a
principal concern and a critical challenge issue in the sector of Prognostics and Health …

Remaining useful life (RUL) prediction is a vital task in rolling bearing prognostics and
health management (PHM) process. Kalman filtering (KF) is one of the hot spots in the …