Compression-ignition (CI) engines, aka diesel engines, are responsible for an essential percentage of the world-polluting emissions. Moreover, bearings installed in industrial machinery constitute the most common failure affecting global energy consumption. Since industries’ energy demand has a growing tendency, efficient maintenance is a must. Maintenance requires a fast and accurate diagnosis, commonly based on an intrusive or expensive sensor to capture monitoring signals, i.e. pressure, emissions, temperature, fuel consumption and rotational speed. Here, a vibration signal-based approach is introduced to combustion engines and bearings diagnosis. Namely, a multi-scale permutation entropy (MPE)-based feature extraction is conducted within a variability-based relevance analysis (VRA) stage to feed a straightforward classifier, the K-nearest neighbours (KNN). Accuracy was validated using a signals’ database from a single-cylinder engine under multiple work conditions. Also, the methodology is compared through classification accuracy of a widely known bearing vibration signal database obtaining an outstanding performance.