Data-driven methods have pushed mechanical fault diagnostics to an unprecedented height
recently. However, their satisfactory performance heavily relies on the availability of …

In actual industrial scenarios, fault data is rare and fault labels are difficult to obtain, which
brings many obstacles for fault diagnosis. For this situation, this research proposes a novel …

Applications in industrial production have indicated that the challenges of sparse fault
samples and singular monitoring data will diminish the performance of deep learning-based …

The rarity of equipment failures results in a high level of imbalance between failure data and
normal operation data, which makes the effective classification and prediction of such data …

The development of Internet of Things technology provides abundant data resources for
prognostics health management of industrial machinery, and data-driven methods have …

This article proposes an active learning scheme to detect multiple faults in permanent
magnet synchronous motors in dynamic operations without using historical labelled faulty …

Data-driven prognostic and health management technologies are instrumental in accurately
monitoring the health of mechanical systems. However, the availability of few-shot source …

Data-driven intelligent diagnosis models need massive monitoring data to train themselves
for satisfactory recognition performance. Nevertheless, in many industrial practices …

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

Deep-learning-based methods used for fault diagnosis show remarkable performance, and
these methods primarily learn features based on the time or frequency domain. Generally …