Bearing vibration analysis plays a crucial role in the condition monitoring and predictive maintenance of rotating machinery. However, when rotating industrial equipment operates at low speeds, the vibration signal is affected with problems, which makes it more challenging to extrapolate information regarding the health status of the components. This thesis, written in collaboration with Remote Diagnostic Center at SKF Industrie S.p.a., aims to explore advanced vibration monitoring techniques for rolling bearings at low speeds in order to improve fault diagnosis. Vibration diagnostics makes use of the signal from accelerometers placed on the housing close to the bearings. Successively, the acquired signal is processed to obtain information about the health of the bearings.