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.