Vibration Analysis (VA) is the most commonly used technique in predictive maintenance. It
allows the diagnosis of faults, especially those in the early stages. The use of VA is important …

Traditional deep learning fails to identify new faults when the number of faulty samples is
limited. Existing meta-learning studies on cross-domain small-sample fault diagnosis do not …

As the digitalization of process industry deepens, process fault detection and diagnosis
(FDD) is an essential tool to ensure safe production in chemical industries. However, FDD …

Detecting faults in high-speed and high-power diesel engines under complex variable
operating conditions is highly challenging. Online vibration monitoring systems have been …

A non-parametric complementary ensemble empirical mode decomposition (NPCEEMD) is
proposed for identifying bearing defects using weak features. NPCEEMD is non-parametric …

The failure of the ball screw in the machine tool presents various types and complex
coupling characteristics, which pose challenges in extracting fault features from vibration …

Predictive Maintenance is crucial for production systems as it helps maintaining the
reliability and availability of components/equipment as well as preventing unexpected …

It is always an important and challenging issue to achieve an effective fault diagnosis in
rotating machinery in industries. In recent years, deep learning proved to be a high-accuracy …

With the advent of intelligent manufacturing era, smart mobile robots have taken the major
roles on transporting materials through intelligent dynamic production environment. It is …

The acoustic emission (AE)-based early fault diagnosis of high-speed train bearings under
non-Gaussian noise is attractive but challenging. Maximum generalized Gaussian …