Rolling element bearings are used in rotating machines in aviation, chemical, and nuclear industries. A failure to detect faults in the rolling bearing causes unexpected breakdown of rotating machines. Detecting bearing defects early on is still a challenge since micro-faults have less energy. Early-stage defects from fatigue, misalignment, overload and poor lubrication create low-energy signals that can be masked. This article summarises some key measurement methods, i.e. vibration and acoustic signal measurement, tribological parameter analysis, wear debris analysis, and thermal measurement, in rolling element bearings. All the methods will be evaluated with respect to a sensing mechanism, signal processing algorithm, micro-defect sensitivity, and operational constraints of the anti-friction bearing. To deal with the drawbacks of single sensors, we demonstrate the use of recent advancements in multi-sensor data fusion and machine learning models through examples where kinetic, acoustic and chemical metrics are fused for improved classification outcomes. In summary, multi-sensor data fusion greatly improves the accuracy of early fault detection, and it can greatly enhance predictive maintenance systems.