  In recent years, intelligent condition-based monitor-ing of rotary machinery
systems has become a major researchfocus of machine fault diagnosis. In
condition-based monitoring,it is challenging to form a large-scale
well-annotated datasetdue to the expense of data acquisition and costly
annotation.The generated data have a large number of redundant featureswhich
degraded the performance of the machine learning models.To overcome this, we
have utilized the advantages of minimumredundancy maximum relevance (mRMR) and
transfer learningwith a deep learning model. In this work,mRMRis combinedwith
deep learning and deep transfer learning framework toimprove the fault
diagnostics performance in terms of accuracyand computational complexity.
ThemRMRreduces the redundantinformation from data and increases the deep
learning perfor-mance, whereas transfer learning, reduces a large amount of
datadependency for training the model. In the proposed work, twoframeworks,
i.e.,mRMRwith deep learning andmRMRwith deeptransfer learning, have explored
and validated on CWRU andIMS rolling element bearings datasets. The analysis
shows thatthe proposed frameworks can obtain better diagnostic accuracycompared
to existing methods and can handle the data with alarge number of features more
quickly.
