Rolling element bearing is a crucial component in rotating machines. The existence of faults in the bearing causes sudden failures, resulting in catastrophic failure of machines. Incipient fault detection, health state assessment, and Remaining Useful Life (RUL) prediction are the essential tasks in condition-based maintenance to avoid machine failures. This work proposes a new method, the Pruned Exact Linear Time (PELT) method, for identifying the incipient faults and subsequent damage states in the bearing. When a fault initiates in the bearing, there is an increment in the vibration response. This increment can be quantified by the degradation indicator computed from the vibration signal. The Variational Mode Decomposition (VMD) technique is used to de-noise the vibration signals. Various statistical features are derived from the de-noised signal, and the best feature subset is chosen by the Recursive Feature Elimination method. Then, the significant bearing life degradation indicator is computed using the Reconstruction Independent Component Analysis (RICA) method by fusing selected features. A novel index is formulated for computing the percentage of failure, which can identify whether the bearing is in a mild failure state or medium failure state. Finally, Transfer Long Short-Term Memory technique is used to predict the RUL of the bearing under multiple operating conditions. The efficiency of the proposed framework is demonstrated using experimental bearing datasets