Vibration source information (source number, source waveforms, and source contributions) of gears, bearings, motors, and shafts is very important for machinery condition monitoring, fault diagnosis, and especially vibration monitoring and control. However, it has been a challenging to effectively extract the source information from the measured mixed vibration signals without a priori knowledge of the mixing mode and sources. In this paper, we propose source number estimation, source separation, and source contribution evaluation methods based on an enhanced independent component analysis (EICA). The effects of nonlinear mixing mode and different source number on source separation are studied with typical vibration signals, and the effectiveness of the proposed methods is validated by numerical case studies and experimental studies on a thin shell test bed. The conclusions show that the proposed methods have a high accuracy for thin shell structures. This research benefits for application of independent component analysis (ICA) to solve the vibration monitoring and control problems for thin shell structures and provides important references for machinery condition monitoring and fault diagnosis.