This PhD project will focus on developing, evaluating, and demonstrating an intelligent solution of diagnosis and prognosis for rotating machinery to enhance safety, reliability, maintainability and readiness. A comprehensive test-bed for in-depth studies will be used for experiments for demonstration and evaluation. Rotating machinery has a fundamental role in many industries. Therefore, there is a need for a Condition-based maintenance (CBM) which holds the promise of predicting machinery maintenance requirements based on process performance measurements. Diagnostics and Prognostics are essential parts of CBM. Therefore, diagnostics and prognostics of rotating machinery can help to reduce machine downtime and cost. Many techniques such as vibration analysis, current signature analysis, acoustic emission analysis, wear and oil analysis,,etc. have been used, through condition monitoring of the rotating machinery, to diagnose and prognosis different faults such as, bearing, crack shaft, gearbox, belt drive, reciprocating mechanism, mechanical rub, induction motor, pump, compressor, and fan.