Analyses of the most influential factors for vibration monitoring of planetary power transmissions in pellet mills by adaptive neuro-fuzzy technique. (1st January 2017)

• Record Type: Journal Article
• Authors: Milovančević, Miloš; Nikolić, Vlastimir; Anđelković, Boban
• Is Part Of: Mechanical systems and signal processing. Volume 82(2017)
• Page Start: 356
• Page End: 375
• Publication Date: 2017-01-01
• Subjects: ANFIS -- Prediction -- Vibration -- Planetary power transmissions -- Pellet mills; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2016.05.028
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: Vibration-based structural health monitoring is widely recognized as an attractive strategy 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 operations. Potentials for further improvement of vibration monitoring lie in the improvement of current control strategies. One of the options is the introduction of model predictive 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 monitoring of pellet mills power transmission. The vibration data are collected by PIC (Programmable 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 preferable 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. Highlights: Vibration-based structural health monitoring for early damage detection in structures. Predictive models for vibration monitoring of planetary power transmissions. The models were developed based on ANFIS (adaptive neuro fuzzy inference system). Selecting the most relevant variables for predictive models of vibration monitoring. The obtained results could be used for simplification of predictive methods.