This paper investigates the fault detection and diagnosis for a class of rolling-element bearings using signal-based methods based on the motor's vibration and phase current measurements, respectively. The envelope detection method is employed to preprocess the measured vibration data before the FFT algorithm is used for vibration analysis. The average of a set of Short-Time FFT (STFFT) is used for the current spectrum analysis. A set of fault scenarios, including single and multiple point-defects as well as generalized roughness conditions, are designed and tested under different operational conditions, including different motor speeds, different load conditions and samples from different operating time intervals. The experimental results show the powerful capability of vibration analysis in the bearing point-defect fault diagnosis under stationary operation. The current analysis showed a subtle capability in diagnosis of pointdefect faults depending on the type of fault, severity of the fault and operational condition. The generalized roughness fault can not be detected by the proposed frequency methods. The temporal features of the considered faults and their impact on the diagnosis analysis are also investigated.