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Sarajevo, Bosnia and Herzegovina

Bajric R.,Public enterprise Elektroprivreda BiH | Zuber N.,University of Novi Sad | Isic S.,Dzemal Bijedic University of Mostar
Applied Mechanics and Materials | Year: 2013

This paper provides a review of the literature, progress and changes over the years on fault detection of gears using vibration signal processing techniques. Analysis of vibration signals generated by gear in mesh has shown its usefulness in industrial gearbox condition monitoring. Vibration measurement provides a very efficient way of monitoring the dynamic conditions of a machine such as gearbox. Various vibration analysis methods have been proposed and applied to gear fault detection. Most of the traditional signal analysis techniques are based on the stationary assumption. Such techniques can only provide analyses in terms of the statistical average in the time or frequency domain, but can not reveal the local features in both time and frequency domains simultaneously. Frequency/quefrency analysis, time/statistical analysis, time-frequency analysis and cyclostationarity analysis are reviewed in regard for stationary and non-stationary operations. The use of vibration signal processing detection techniques is classified and discussed. The capability of each technique, fundamental principles, advantages and disadvantages and practical application for gear faults detection have been examined by literature review. © (2013) Trans Tech Publications, Switzerland. Source


Zuber N.,University of Novi Sad | Cvetkovic D.,University of Nis | Bajric R.,Public enterprise Elektroprivreda BiH
Applied Mechanics and Materials | Year: 2013

Paper addresses the implementation of feature based artificial neural networks and selforganized feature maps with the vibration analysis for the purpose of automated faults identification in rotating machinery. Unlike most of the research in this field, where a single type of fault has been treated, the research conducted in this paper deals with rotating machines with multiple faults. Combination of different roller elements bearing faults and different gearbox faults is analyzed. Experimental work has been conducted on a specially designed test rig. Frequency and time domain vibration features are used as inputs to fault classifiers. A complete set of proposed vibration features are used as inputs for self-organized feature maps and based on the results they are used as inputs for supervised artificial neural networks. The achieved results show that proposed set of vibration features enables reliable identification of developing bearing and gear faults in geared power transmission systems. © (2013) Trans Tech Publications, Switzerland. Source


Beater wheel mills are designed to prepare a coal powder air fuel mixture for combustion in furnace chambers of coal-freed power plants by coal drying, pulverizing, classifying and transport. Their multipurpose function usually results in operation instability accompanied by unacceptable vibration. This usually is a signifcant problem due to unplanned shutdowns. Beater wheel mill maintenance program requires special attention due to operation under non-stationary conditions. The purpose of this paper was to identify pulverizing process parameter that affect the beater wheel mill vibration level and severity at the same time by using statistical principles under a wide range of operating conditions. This paper intends to establish the foundations to investigate correlation of pulverizing process parameter with beater wheel mill vibration in order to setup a better predictive maintenance program. To achieve this goal, the beater wheel mill vibration under different combinations of selected pulverizing process parameters are analyzed using statistical tools. Experiments were carried out under different conditions for two identical but separated beater wheel mills. The infuence of pulverizing process parameter, such as electrical current of the driving motor, mill capacity, boiler production, coal types on mill vibration are investigated to identify the potential malfunction of beater wheel mills and their associated components for predictive maintenance purposes. The results have demonstrated that the selected pulverizing process parameters do not have signifcant infuence on beater wheel mill vibration severity. Unlike most coal mills where pulverizing process parameters must take into account, here with beater wheel impact mills it is not the case and condition monitoring of these mills could be conducted offline or online using standard vibration condition monitoring methods. Source


Zuber N.,University of Novi Sad | Bajric R.,Public enterprise Elektroprivreda BiH | Sostakov R.,University of Novi Sad
Eksploatacja i Niezawodnosc | Year: 2014

The paper addresses the implementation of feature based artificial neural networks and vibration analysis for the purpose of automated gearbox faults identification. Experimental work has been conducted on a specially designed test rig and the obtained results are validated on a belt conveyor gearbox from a mine strip bucket wheel excavator SRs 1300. Frequency and time domain vibration features are used as inputs to fault classifiers. A complete set of proposed vibration features are used as inputs for self-organized feature maps and based on the results a reduced set of vibration features are used as inputs for supervised artificial neural networks. Two typical gear failures were tested: worn gears and missing teeth. The achieved results show that proposed set of vibration features enables reliable identification of developing faults in power transmission systems with toothed gears. Source


Salihbegovic I.,Public enterprise Elektroprivreda BiH | Steinhart H.,Aalen University of Applied Sciences
2011 23rd International Symposium on Information, Communication and Automation Technologies, ICAT 2011 | Year: 2011

Magnetic circuit design of wound rotor low-voltage three-phase synchronous generator for autonomous operation is presented in this paper. The Maxwell software based on two-dimensional finite element method (2D FEM) is used to study the performance of the synchronous generator in specified static operating regime. Two machines with different configurations are analyzed. These machines are characterized by the same stator and rotor external dimensions and used materials. Moreover, stators of all machines are exactly the same while the rotor magnetic circuit structures and excitation windings are different. Calculated characteristics and their comparisons are presented and discussed. Some characteristics of prototype, which is currently under construction, are given. © 2011 IEEE. Source

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