# IoT System for Detecting the Condition of Rotating Machines Based on Acoustic Signals
## 5 Conclusions  
In this paper, an Industrial IoT solution for state detection of rotary machines based on acoustic signals is presented. The proposed system consists of an inexpensive portable device that is used for the recording of acoustic signals, as well as on-site state detection of the observed machine. The device itself can be used on a variety of different machines with fixed rotation frequencies. Furthermore, due to internal power supply and wireless communication, it can communicate with a remote server and various stakeholders. The AI algorithm for state detection, implemented on this device, consists of a combination of DWT (for feature extraction), NN (for state classification), and GA (for hyperparameter tuning).  
The main application of the proposed system is early state estimation and machine fault prevention in industrial surroundings. With that in mind, the device and the algorithm are tested on real acoustic signals recorded in thermal power plants for the purpose of state detection of 3 different rotary coal mills. The algorithm was first tested with default hyperparameter values, and the results seemed promising. There was only one instance in which the value of the F1 score was lower than 0.9, and that was because the microphone was placed near the source of ambient noise, and thus the signal-to-noise ratio was unnecessarily reduced. The second test was conducted using hyperparameters obtained with GA optimization, and in this case, the results are significantly improved even in the case where the noise is dominant in the recorded signal. The obtained results are comparable to or better than the result achieved in other relevant works. Since the research on the condition of fan mill impact plates is scarce in the literature, the aforementioned comparison group consists of techniques for state detection of similar rotary machines.  
The algorithm was tested on different mills with different rotating frequencies and spectral components and showed very good performance. Such results indicate the applicability of the proposed solution to other types of rotary machines with fixed rotating frequencies. Bearing in mind the wide range of rotary machines which are used in the industry (pumps, motors, fans...) this device could contribute to a number of different systems that need predictive maintenance.  
The Industrial IoT device for acoustic-based machine state detection successfully passed the tests on rotary machines with fixed rotating frequency in real industrial surroundings in which stationary noise is present. The inexpensive and mobile nature of this device coupled with the ability to communicate with remote stakeholders makes it ideal for Industry 4.0 paradigm. Future research will include further tests on different machines with fixed rotating frequencies, as well as exploring the possibility of the use of the algorithm on rotary machines with variable rotating frequencies. Special attention will be paid to the adjustment of the structure and parameters of the algorithm for each specific machine. Finally, the possible direction of improvement of the proposed solution will be reflected in developing filtering methods for elimination of the potential non-stationary ambient noise.  
Conceptualization, M.R. and S.V.; methodology, M.R. and S.V.; software, M.R. and S.V.; validation, M.R., S.V., A.K. and Z.Z.; investigation, M.R. and S.V.; resources, A.K. and Z.Z.; data curation, A.K. and Z.Z.; writing--original draft preparation, M.R., S.V., A.K. and Z.Z.; writing--review and editing, M.R., S.V., A.K. and Z.Z.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by EUREKA, grant number E113084 and Ministry of Economic Development, Montenegro, grant number 01-1865/6. The APC was funded by Ministry of Economic Development, Montenegro, grant number 01-1865/6.  
Not applicable.  
The authors declare no conflict of interest.