## 4 Discussion and Conclusions  
The paper presented a novel method for implementing a cost-effective video-tachometer through a GA-adaptive Template Matching. The target was an offline implementation, as the proposed algorithm is not optimized enough and results slow if running on nowadays PCs. To give an idea, the software execution takes, per each frame, about 2s for the first GA, 2s for the second GA, and 10s for the third GA when using MATLAB R2018b on a machine with 8 GB of ram and an INTEL i7-7700 CPU at 3,60 GHz. Clearly, for obtaining just the Hilbert demodulation estimate of the IAS, only the first optimization step is needed, so that the computational burden can be strongly reduced, but it is not enough for real-time implementation.  
In any case, the method proved to be effective in estimating the IAS of the fan despite the limits of the SURVISHNO 2019 video, acquired using a mobile phone. In fact, the wise selection of the search space effectively dealt with spatial aliasing (i.e., the rolling shutter effect), temporal aliasing (i.e., because of the 10 equal blades of the fan combined with a 30-fps sampling rate), and the additional autofocus distortions. Furthermore, the GA-adaptive implementation of TM reconstructing the binary edge template from a geometrical parametric model demonstrated its robustness to illumination changes and noise in general, as well as to rigid and non-rigid transformations. The issue of background changes and clutter was also tackled both in a pre-processing stage, by cropping and binarizing the search frame, and in the three-step GA optimization, exploiting the information from the previous stages for further focusing the TM on a smaller region of interest.  
The here described GA-adaptive TM has the great advantage of allowing the localization of the template with a super-resolution that goes beyond the pixel-grid discretization. This allowed obtaining a robust and reliable estimate of the IAS, avoiding the need of expensive high-resolution encoders or tachometers, which are otherwise necessary for non-stationary machine diagnostics. When the speed is variable, in fact, the machine signature can only be highlighted by resampling the signal synchronously with the angular position of a reference shaft (i.e., performing the so-called Computed Order Tracking to get to the order domain). This way, the events which are phase-locked to the reference shaft (e.g., the intake of a 4 strokes diesel engine to the crankshaft, or the meshing of a broken tooth of a gearwheel to the supporting shaft, etc.) are put in evidence. Furthermore, the IAS is considered a precious diagnostic information per se, so that the analysis of IAS anomalies is spreading in the field of condition monitoring.  
To conclude, given the here underlined strengths, the proposed signal processing gives an effective and reliable tool able to foster the IAS-based condition monitoring, setting the state of the art for video-tachometric acquisitions.  
The video was downloaded online during the international conference SURVISHNO 2019 at [[https://survishno.sciencesconf.org/resource/page/id/18](https://survishno.sciencesconf.org/resource/page/id/18)]([https://survishno.sciencesconf.org/resource/page/id/18](https://survishno.sciencesconf.org/resource/page/id/18)).  
Conceptualization, A.P.D.; data curation, A.P.D.; formal analysis, A.P.D.; funding acquisition, L.G.; investigation, A.P.D.; methodology, A.P.D.; project administration, L.G.; software, A.P.D.; supervision, L.G.; validation, L.G.; visualization, A.P.D.; writing--original draft, A.P.D.; writing--review and editing, A.P.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
We thank the SURVISNO 2019 organization committee and in particular [PERSON], [PERSON] and [PERSON] for acquiring the video and organizing the challenge.  
The authors declare no conflict of interest.