In order to improve the diagnosis accuracy and solve the weak fault signal of rolling element
of rolling bearings due to long transmission path, a novel fault diagnosis method based on …

This paper presents a comprehensive survey on interpretable intelligent fault diagnosis for
rotating machinery, addressing the challenge of the “black box” nature of machine learning …

This paper proposes a novel multiple amplitude modulation and frequency modulation (AM–
FM) demodulation method based on local modulation signal bispectrum (LMSB), which can …

Shapelets are discriminative subsequences of sequential data that best predict the target
variable and are directly interpretable, which have attracted considerable interest within the …

Due to high transmission efficiency, gearboxes have become indispensable components of
industrial mechanical equipment. It is paramount for gearbox fault diagnosis to extract …

This study examines the issue of interpretability in fault diagnosis for rolling bearings using a
symbolic learning technique. We propose the adoption of weighted signal temporal logic …

This paper addresses the critical challenge of interpretability in machine learning methods
for machine fault diagnosis by introducing a novel ad hoc interpretable neural network …

Machine learning-based methods have achieved successful applications in machinery fault
diagnosis. However, the main limitation that exists for these methods is that they operate as …

The marine diesel engine is an important power machine for ships. Traditional machine
learning methods for diesel engine fault diagnosis usually require a large amount of labeled …

Timed failure propagation graphs (TFPGs) perform fault diagnosis in a transparent way.
However, accurate TFPGs depend on experts' knowledge or accurate model of the system …