Analyses of the most influential factors for vibration monitoring of planetary power transmissions in pellet mills by adaptive neuro-fuzzy technique Miloš Milovančević n , Vlastimir Nikolić, Boban Anđelković University of Nis, Faculty of Mechanical Engineering, Aleksandra Medvedeva 14, 18000 Nis, Serbia article info Article history: Received 15 March 2016 Received in revised form 16 April 2016 Accepted 22 May 2016 Keywords: ANFIS Prediction Vibration Planetary power transmissions Pellet mills abstract Vibration-based structural health monitoring is widely recognized as an attractive strat- egy for early damage detection in civil structures. Vibration monitoring and prediction is important for any system since it can save many unpredictable behaviors of the system. If the vibration monitoring is properly managed, that can ensure economic and safe op- erations. Potentials for further improvement of vibration monitoring lie in the improve- ment of current control strategies. One of the options is the introduction of model pre- dictive control. Multistep ahead predictive models of vibration are a starting point for creating a successful model predictive strategy. For the purpose of this article, predictive models of are created for vibration monitoring of planetary power transmissions in pellet mills. The models were developed using the novel method based on ANFIS (adaptive neuro fuzzy inference system). The aim of this study is to investigate the potential of ANFIS for selecting the most relevant variables for predictive models of vibration mon- itoring of pellet mills power transmission. The vibration data are collected by PIC (Pro- grammable Interface Controller) microcontrollers. The goal of the predictive vibration monitoring of planetary power transmissions in pellet mills is to indicate deterioration in the vibration of the power transmissions before the actual failure occurs. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the prediction of vibration monitoring. It was also used to select the minimal input subset of variables from the initial set of input variables – current and lagged variables (up to 11 steps) of vibration. The obtained results could be used for simplification of predictive methods so as to avoid multiple input variables. It was pre- ferable to used models with less inputs because of overfitting between training and testing data. While the obtained results are promising, further work is required in order to get results that could be directly applied in practice. & 2016 Elsevier Ltd. All rights reserved. 1. Introduction Structural health monitoring is a discipline that aims to identify the health of a mechanical system through its lifecycle [1]. In the structural health monitoring field, vibration-based monitoring of structures is becoming more popular in recent years. This is owing to the fact that this type of systems will enable to keep track of the genuine health status of real structures under the disturbance of environmental and operational factors. Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/ymssp Mechanical Systems and Signal Processing http://dx.doi.org/10.1016/j.ymssp.2016.05.028 0888-3270/& 2016 Elsevier Ltd. All rights reserved. n Corresponding author. E-mail address: milovancevicmilos2016@gmail.com (M. Milovančević). Mechanical Systems and Signal Processing ∎ (∎∎∎∎) ∎∎∎–∎∎∎ Please cite this article as: M. Milovančević, et al., Analyses of the most influential factors for vibration monitoring of planetary power transmissions in pellet mills by adaptive neuro-fuzzy technique, Mech. Syst. Signal Process. (2016), http: //dx.doi.org/10.1016/j.ymssp.2016.05.028i