Quantifying the Impact of Data Cleaning on Deep Learning‐Based Bearing Fault Diagnosis: A Multidataset, Multiarchitecture Study

ABSTRACT The reliability of deep learning models for machinery fault diagnosis is frequently compromised by poor data quality in industrial environments, yet the literature remains dominated by architectural innovations validated on pristine laboratory datasets. This study addresses this gap through... View More