Intelligent Condition-Based Monitoring Techniques for Bearing Fault Diagnosis

In recent years, intelligent condition-based monitoring of rotary machinery systems has become a major research focus machine fault diagnosis. monitoring, it is challenging to form large-scale well-annotated dataset due the expense data acquisition and costly annotation. The generated have large number redundant features which degraded performance learning models. To overcome this, we utilized advantages minimum redundancy maximum relevance (mRMR) transfer with deep model. this work, mRMR combined framework improve diagnostics in terms accuracy computational complexity. reduces information from increases performance, whereas learning, amount dependency for training proposed two frameworks, i.e., explored validated on CWRU IMS rolling element bearings datasets. analysis shows that frameworks can obtain better diagnostic compared existing methods handle more quickly.