In the vibration-based diagnosis of rolling element bearings, the complexity of the signals requires an expert to use advanced signal processing tools and to interpret the results based on his/her experience. Recently, a few autonomous methods have been proposed to alleviate the demand on the user's expertise, yet they have been mainly focused on fault detection. They ideally track certain properties in the signal, whose occurrence is correlated with the symptom of a fault. This paper follows a similar direction but with wider objectives: it aims to develop an indicator that is sensitive to both non-stationarity, non-Gaussianity and to the modification of the acoustic signature of the vibratory signal. The indicator is based on the recently developed Fast Spectral Coherence, a key tool of the theory of second-order cyclostationary processes. It condenses the whole information initially displayed in three dimensions into a scalar. it initially addresses the case where the faults frequencies are unknown. In addition, the proposed indicator is able to return information for different levels of damages in both stationary and non-stationary operating conditions. A new pre-processing step is provided to ensure an efficient and constant statistical threshold. The proposed indicator is intended to be used in an autonomous process without the need for visual analysis and human interpretation. The proposed indicator is compared with a recent indicator based on the Envelop Spectrum, in terms of classification and detection performance. Several applications using real and benchmarked data eventually illustrate the capability for self-running diagnosis.