The monitoring of rotating machinery has now become a fundamental activity in the industry, given the high criticality in production processes. Extracting useful information from relevant signals is a key factor for effective monitoring: studies in the areas of Informative Frequency Band selection (IFB) and Feature Extraction/Selection have demonstrated to be effective approaches. However, in general, typical methods in such areas focuses on identifying bands where impulsive excitations are present or on analyzing the relevance of the features after its signal extraction: both approaches lack in terms of procedure automation and efficiency. Typically, the approaches presented in the literature fail to identify frequencies relevant for the vibration analysis of a rotating machinery; moreover, with such approaches features can be extracted from irrelevant bands, leading to additional complexity in the analysis. To overcome such problems, the present study proposes a new approach called Band Relevance Factor (BRF). BRF aims to perform an automatic selection of all relevant frequency bands for a vibration analysis of a rotating machine based on spectral entropy. The results are presented through a relevance ranking and can be visually analyzed through a heatmap. The effectiveness of the approach is validated in a synthetically created dataset and two real dataset, showing that the BRF is able to identify the bands that present relevant information for the analysis of rotating machinery.