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 …

Early classification predicts the class of the incoming sequences before it is completely
observed. How to quickly classify streaming time series without losing interpretability …

A systematic review on interpretability research of intelligent fault diagnosis models Page 1
Measurement Science and Technology ACCEPTED MANUSCRIPT A systematic review on …

Few-shot new faults are constantly emerging due to the dynamic environments and
operations in the industrial process. It is a challenge for existing fault diagnosis methods to …

Unknown abnormal working condition discovery is the key of refinement industrial
production. Clustering industrial time series is an effective way to discover unknown working …

Autonomous underwater vehicles (AUVs) acquire large-scale multivariate time series (MTS)
data during navigation, which can be utilised to realise fault diagnosis, condition monitoring …

Shape shift refers to the phenomenon where by amplitude and scaling of samples of the
same fault vary with the environment. How to incrementally diagnose industrial faults under …

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 …

Feature extraction is an essential part of bearing fault diagnosis. Robust principal
component analysis (RPCA) provides a general technique for extracting fault features …

To address the challenges of accurately diagnosing few-shot fault samples obtained from
rolling bearings under variable operating conditions, as well as the issues of black box …