Ninoslav Zuber

@uns.ac.rs

University of Novi Sad, Faculty of Technical Sciences
Faculty of Technical Sciences

https://researchid.co/ninoslavzuber

RESEARCH INTERESTS

Maintenance and reliability, Vibration diagnostics, Signal analysis, Signal processing

Scopus Publications

479

Scholar Citations

Scholar h-index

Scholar i10-index

Scopus Publications

Effect of changes in hydraulic parameters and tank capacity of the hydraulic press system on the heating of the hydraulic oil
Borivoj Zoran Novaković, Luka Djordjević, Mića Đurđev, Ljiljana Radovanović, Ninoslav Zuber, Eleonora Desnica, and Mihalj Bakator
Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
This study examines the influence of altering hydraulic parameters on the temperature of ISO HM VG 46 hydraulic mineral oil. Hydraulic mineral oils find extensive application in industrial power transmission systems, where precise temperature control is crucial for achieving optimal performance and prolonging system lifespan. Variations in hydraulic parameters, encompassing flow rate, pressure, and viscosity, can significantly impact the thermal characteristics of hydraulic oil.In this research, experimental studies were carried out to study the influence of changing hydraulic parameters on the temperature of hydraulic mineral oil. Experiments were performed on a hydraulic press, where different working conditions were simulated.The quality of hydraulic fluid is considered, according to all research, to be the most influential factor in ensuring the reliable operation of hydraulic systems.

The Influence of Fatigue Loading on the Durability of the Conveyor Belt
Nikola Ilanković, Dragan Živanić, and Ninoslav Zuber
MDPI AG
The conveyor belt is by its structure a textile composite. As a load-supporting element of the conveyor, the belt withstands variable loads during its operations. In order to investigate the influence of the level and variability of loading on the life of the belt, tests were carried out on specimens in laboratory conditions. A testing device was specially designed and made for these tests that enabled precise control and monitoring of the loading as well as number of loading cycles up to fracture. This research provides an overview of the influence of fatigue loading on the fatigue life of the belt. The methodology of the conducted research is explained with a description of important technical parameters of the testing device. A physical experiment and a corresponding numerical simulation using the FEM method were carried out with multiple loading levels of belt specimens. Based on the obtained results, appropriate conclusions were made; at loads less than 70% of the breaking strength, the lifetime of the belt is very long. Attention was drawn to additional influences that could not be covered by the experiment and possible directions for further research were indicated.

Analysis of the influence of hydraulic fluid quality on external gear pump performance
Borivoj Novaković, Ljiljana Radovanović, Ninoslav Zuber, Dragica Radosav, Luka Đorđević, and Mila Kavalić
Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
The basis of every hydraulic system is based on energy transformations, which are realized through hydraulic working fluid. Hydraulic oils are certainly subject to changes within their structure, meaning the basic characteristics and parameters of hydraulic oil, such as density, viscosity, humidity. The oils that are exploited are exposed to the process of degradation, which largely leads to significantly poorer quality of hydraulic fluid. The paper deals with the influence of changes in the characteristics of the hydraulic fluid on the hydraulic operating parameters of the gear pump installed on the hydraulic press. The parameters refer to pressure, flow, and temperature, as well as the quality of hydraulic oil, which affects the volumetric efficiency of the pump, and the results presented in the conclusion are based on the measured values of parameters before and after corrective measures. The control of parameters aims to increase the efficiency and reliability of the hydraulic system, a way of modern detection of deviations of parameters from standard, required values.

The analysis of influential parameters on calibration and feeding accuracy of belt feeders
Dragan Živanić, Nikola Ilanković, Ninoslav Zuber, Radomir Đokić, Nebojša Zdravković, and Atila Zelić
Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
Continual material feeding represents a process of great importance for process industries. Feeding with belt feeders represents one of the most common methods. Belt feeders are devices that require little space, they are not expensive and, most importantly, they do not interrupt material flow while feeding. Calibration of belt feeders, as well as other measuring devices, is a prerequisite for measuring and achieving a defined level of measurement accuracy. On the other hand, the defined level of measurement accuracy is often difficult to achieve in practice due to the multitude of factors that affect the operation of belt feeders. Existing mathematical models indicate a number of influential factors on measurement accuracy. The paper presents the measurement procedure performed on a belt feeder in laboratory conditions, with variable speeds and belt tensions and the known raised position of the measuring idler. Based on the obtained results, appropriate conclusions were made about the influences on calibration and measurement accuracy.

Gearbox faults feature selection and severity classificatiousing machine learning
Ninoslav Zuber and Rusmir Bajrić
Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
Condition monitoring systems (CMS) for gearboxes have received considerable attention in the last decade, primarily due to increased number of wind turbines installations and due to the fact that gearbox failure represents a significant part of wind turbines’ downtime. CMS helps to ensure the stability, extends the design life of drivetrain element especially gearboxes as a very important part and prevents complete failure, which could be very expensive. Thus, if applied properly, it allows significant savings. In addition to wind turbines, gearboxes are widely known to be used in other industries (energy, mining, petrochemical, automotive, etc.), as an element of high importance responsible for the smooth operation of many productions systems. Also, the availability of the entire system is almost always dependent on the usability of the gearbox. Gearbox fault can be identified while the defective component is still in operation and repair or replacement can be planned to reduce downtime, increase reliability, maximize availability with the ultimate goal of improving profitability. Gearbox condition monitoring often refers to gearbox diagnostics, which essentially process data with the evaluation of the functionality of a gearbox and detection or identification of faults using condition indicators. Different indicators could be used for gearbox condition monitoring process depending on the interested fault of the type we want to monitor – [3, 4, 13, 15, 25]. Establishing a reliable health detection system, especially for gear fitting faults is the key to ensuring smooth operation of industrial equipment [14]. In literature, fault detection and diagnostics of gears can be obtained by using the time domain analysis. In this case, statistical features, such as Root Mean Square (RMS), Standard Deviation (StD), Kurtosis (KUR), Skewness (SKE), etc., are extracted from vibration signals to perform condition monitoring [16]. On the other hand, the authors in [18] and [30] propose the use of the frequency analysis to identify the characteristic frequencies of the gear defects. Authors in [30] used time synchronous resample (TSR) to pre-process the raw signal to eliminate the interference of asynchronous shaft signal in process of in gearbox fault detection and then the adaptive variational mode decomposition is employed to process the fault synchronous shaft signals obtained by TSR to extract fault features. Many of these diagnostic methods are based on vibration spectrum analysis or demodulation techniques. The time-domain analysis is primarily performed to track changes during the gear meshing process, while time-domain analysis using the Cepstrum function is used for periodicity monitoring and modulation phenomena in the frequency domain from the sideband components. All of these methods and procedures have found application in the industry and provide relatively reliable diagnostics and often provide gear fault location ability for gearboxes with operation parameters at constant or fairly constant speed and load. However, this approach requires experienced users who will evaluate the status of the gearbox elements by looking at Keywords

Experimental determination of lateral forces caused by bridge crane skewing during travelling
Atila ZELIĆ, Ninoslav Zuber, and Rastislav ŠOSTAKOV
Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
The separate group of cyclically operating load transporting machines includes cranes, travelling along the invariable railway consisting of two parallel rails fastened onto the corresponding steel or concrete supporting beams, or onto the foundation on the ground. Some typical representatives of this group are bridge, gantry and semi-gantry cranes, ship-to-shore container gantry cranes, and slewing jib portal or semi-portal cranes. All the loads acting upon the crane are transmitted from the points of their action through the structure and wheels or guide rollers, to the runway rails. Crane wheels derailment is usually mechanically prevented under constraint by the guiding means, such as wheel flanges, or horizontal side rollers. Manufacturers of transducer technologies have already offered various electronic contactless guiding systems. However, their application is limited to the newer and valuable cranes. Nowadays in use are mainly bridge and gantry cranes without any additional electronic guiding devices. During the operation of slewing jib portal cranes, due to the slewing of their turntable, and derricking the jib, the position of the center of gravity projecting point upon the supporting plane is constantly altered. Asymmetric allocation of gravity forces at bridge and gantry cranes is caused by the loaded trolley traversing. Consequently, the general rule applies to all the previously mentioned cranes where during the load handling, vertical loads acting on the crane wheels and resistance forces change their values, thus causing the crane structure skewing in the horizontal plane. Their vertical wheels are rolling without disturbance in the „natural direction“, causing the deviation of the direction of resulting crane motion from the runway rail direction. However, the direction of motion alters when the guiding means comes into the contact with the rail head, and the crane keeps coming back into the runway direction. Such forced guiding along the runway realized by the successive interaction among the guiding means and the rail, causes complex planar motion of the crane, termed as skewing. The purpose of this research is to propose the concept of forming the experimental data basis concerning the influence of crane skewing on the fatigue of its structure elements and traveling drive components. Such data basis is indispensible for further advancement of probabilistic calculations of cranes. The paper gives the short survey of typical damages of crane wheels and rails caused by the undesirable consequences of excessive skewing. The main goal of the paper is to outline one of the possible ways of measuring the values of lateral forces due to skewing, without altering the function, the composition, or the form of the standardZELIĆ A, ZUBER N, ŠOSTAKOV R. Experimental determination of lateral forces caused by bridge crane skewing during travelling. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2018; 20 (1): 90–99, http://dx.doi.org/10.17531/ein.2018.1.12.

Reengineering the port equipment maintenance process

Application of artificial neural networks and principal component analysis on vibration signals for automated fault classification of roller element bearings
Ninoslav Zuber and Rusmir BAJRIĆ
Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
The ultimate goal of every maintenance strategy in a modern plant is to avoid high maintenance costs and productions risks due to the rotating machine’s fault. High costs are initiated through the production stops and losses while the production risks are related to the secondary failures of the neighboring machines. Monitoring the machine’s health through the implementation of condition based maintenance strategy is based on acquisition and trending the physical parameter that is found to be sensitive to machine degradation. Several methods of non-destructive testing are available nowadays, such as vibration measurement and analysis, infrared thermography, noise measurement, motor current signature analysis, wear particle analysis, ultrasound measurements etc. Mechanical vibration acquired at the bearing’s housing (absolute vibration) or directly on a rotating part (relative vibration) is one of the best parameter for early detection of a developing fault inside a machine. If appropriate vibration transducer is engaged and mounted properly and if proper signal processing methods has been used for the suspected fault, then we can say that the vibration signal contains unambiguous information on the existing state of the machine. Methods of vibration signature analysis enable the extraction of type and severity of a fault inside the rotating machine. However the existing guidelines are not universal due to the facts that there may be multiple faults inside the machine and that the content of the acquired vibration signals are dependent on the severity of the fault and on the variation of the rotating speed and load. As a result, derivation of incorrect conclusions and wrong estimation of machine criticality in the plant, is a very common situation. We can avoid this by engagement of highly skilled certified vibration analysts or by the implementation of artificial intelligence (AI) techniques for reliable extraction of an existing fault. In the absence of certified vibration analysts inside the maintenance team the implementation of AI methods through previously developed and validated fault identification algorithm has a promising potential. For the purpose of automatic machine health determination through automatic fault identification there are several applicable methods of AI such as supervised and unsupervised artificial neural networks (ANN), fuzzy logic, expert systems and hybrid intelligence systems. The most applied are ANN [14, 1] due to their ability to learn i.e. to adopt novelties. This adaptability of ANN results in a possibility for detection of an existence of a new condition (fault) based on the existing data [13, 6]. In addition, ANN are found to be efficient in modeling of highly complex nonlinear phenomena that are present in several types of rotating machinery faults. Several types of ANN are successfully ZUBER N, BAJRIĆ R. Application of artificial neural networks and principal component analysis on vibration signals for automated fault classification of roller element bearings. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2016; 18 (2): 299–306, http://dx.doi. org/10.17531/ein.2016.2.19.

Feature extraction using discrete wavelet transform for gear fault diagnosis of wind turbine gearbox
Rusmir Bajric, Ninoslav Zuber, Georgios Alexandros Skrimpas, and Nenad Mijatovic
Hindawi Limited
Vibration diagnosis is one of the most common techniques in condition evaluation of wind turbine equipped with gearbox. On the other side, gearbox is one of the key components of wind turbine drivetrain. Due to the stochastic operation of wind turbines, the gearbox shaft rotating speed changes with high percentage, which limits the application of traditional vibration signal processing techniques, such as fast Fourier transform. This paper investigates a new approach for wind turbine high speed shaft gear fault diagnosis using discrete wavelet transform and time synchronous averaging. First, the vibration signals are decomposed into a series of subbands signals with the use of a multiresolution analytical property of the discrete wavelet transform. Then, 22 condition indicators are extracted from the TSA signal, residual signal, and difference signal. Through the case study analysis, a new approach reveals the most relevant condition indicators based on vibrations that can be used for high speed shaft gear spalling fault diagnosis and their tracking abilities for fault degradation progression. It is also shown that the proposed approach enhances the gearbox fault diagnosis ability in wind turbines. The approach presented in this paper was programmed in Matlab environment using data acquired on a 2 MW wind turbine.

Gearbox faults identification using vibration signal analysis and artificial intelligence methods

Relations between pulverizing process parameters and beater wheel mill vibration for predictive maintenance program setup

Multiple fault identification using vibration signal analysis and artificial intelligence methods
Ninoslav Zuber, Dragan Cvetkovic, and Rusmir Bajrić
Trans Tech Publications, Ltd.
Paper addresses the implementation of feature based artificial neural networks and self-organized 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.

Rolling element bearings fault identification in rotating machines, existing methods of vibration signal processing techniques and practical considerations
Ninoslav Zuber and Dragan Cvetkovic
Trans Tech Publications, Ltd.
This paper addresses the suitability of vibration monitoring and analysis techniques to detect different types of defects in roller element bearings. Processing techniques are demonstrated on signals acquired from the test rig with defective bearings. As a result it is shown that there is no reliable universal method for bearing failure monitoring from its early occurence up to bearings failure. Two real life case studies with different types of bearing failures are presented with practical considerations on methods used for failure identification.

Recent advances in vibration signal processing techniques for gear fault detection - A review
Rusmir Bajrić, Ninoslav Zuber, and Safet Isić
Trans Tech Publications, Ltd.
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 nonstationary operation. 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.

Application of vibration signal analysis and artificial intelligence methods in fault identification of rolling element bearings

Experimental vibration investigation of an industrial beater wheel mill

Investigation of possible resonant problems during beater wheel mill operation

An innovative approach to the condition monitoring of excavators in open pits mines

Applied remote condition monitoring of the bucket wheel excavator

RECENT SCHOLAR PUBLICATIONS

Effect of changes in hydraulic parameters and tank capacity of the hydraulic press system on the heating of the hydraulic oil.
BZ Novaković, L Djordjević, M Đurđev, L Radovanović, N Zuber, ...
Maintenance & Reliability/Eksploatacja i Niezawodność 26 (4) 2024

The influence of fatigue loading on the durability of the conveyor belt
N Ilanković, D Živanić, N Zuber
Applied Sciences 13 (5), 3277 2023

Analysis of the influence of hydraulic fluid quality on external gear pump performance
B Novaković, L Radovanović, N Zuber, D Radosav, L Đorđević, M Kavalić
Eksploatacja i Niezawodność 24 (2) 2022

The analysis of influential parameters on calibration and feeding accuracy of belt feeders
D Živanić, N Ilanković, N Zuber, R Đokić, N Zdravković, A Zelić
Eksploatacja i Niezawodność 23 (3), 413-421 2021

Gearbox faults feature selection and severity classification using machine learning
N Zuber, R Bajrić
Eksploatacja i Niezawodność 22 (4), 748-756 2020

VIBRODIJAGNOSTIKA STANJA ELEKTROMOTORA SA OŠTEĆENIM ROTOROM
S Kraljević, N Zuber, D Reljić
Zbornik radova Fakulteta tehničkih nauka u Novom Sadu 35 (01), 25-28 2020

Experimental determination of lateral forces caused by bridge crane skewing during travelling
A Zelić, N Zuber, R Šostakov
Eksploatacja i Niezawodność 20 (1) 2018

Reengineering the port equipment maintenance process
B Stevanov, N Zuber, R Šostakov, Z Tešić, S Bojić, M Georgijević, A Zelić
International Journal of Industrial Engineering and Management 7 (3), 99 2016

Research Article Feature Extraction Using Discrete Wavelet Transform for Gear Fault Diagnosis of Wind Turbine Gearbox
R Bajric, N Zuber, GA Skrimpas, N Mijatovic
2016

Application of artificial neural networks and principal component analysis on vibration signals for automated fault classification of roller element bearings
N Zuber, R Bajrić
Eksploatacja i Niezawodność 18 (2) 2016

Feature extraction using discrete wavelet transform for gear fault diagnosis of wind turbine gearbox
R Bajric, N Zuber, GA Skrimpas, N Mijatovic
Shock and Vibration 2016 (1), 6748469 2016

Vibration feature extraction methods for gear faults diagnosis-a review
ZF Ninoslav, B Rusmir, D Cvetkovic
Facta Universitatis, Series: Working and Living Environmental Protection 12 2015

APPLICATION OF" RIGID" METHOD FOR DETERMINING THE SKEWING FORCES ON BRIDGE CRANES AND TROLLEYS ACCORDING TO EN 15 011
R Sostakov, A Zelic, I Knezevic, N Zuber, K Rafa
Machine design 6 (2) 2014

Skewing loadings in the scope of material fatigue phenomena of crane structure and travelling mechanism components
R Šostakov, A Zelić, N Zuber, H Ličen
Proceedings-The 5th International Conference" Transport and Logistics, 101-104 2014

Gearbox faults identification using vibration signal analysis and artificial intelligence methods
N Zuber, R Bajrić, R Šostakov
Eksploatacja i Niezawodność 16 (1), 61-65 2014

Relations between pulverizing process parameters and beater wheel mill vibration for predictive maintenance program setup
R Bajrić, N Zuber, R Šostakov
Eksploatacja i Niezawodność 16 (1) 2014

Rolling element bearings fault identification in rotating machines, existing methods of vibration signal processing techniques and practical considerations
N Zuber, D Cvetkovic
Applied Mechanics and Materials 430, 70-77 2013

Recent advances in vibration signal processing techniques for gear fault detection-A review
R Bajrić, N Zuber, S Isić
Applied Mechanics and Materials 430, 78-83 2013

Multiple fault identification using vibration signal analysis and artificial intelligence methods
N Zuber, D Cvetkovic, R Bajrić
Applied Mechanics and Materials 430, 63-69 2013

Remote condition monitoring of rotating machinery
N Zuber, R Šostakov
Tehnička dijagnostika 11 (1), 9-14 2012

MOST CITED SCHOLAR PUBLICATIONS

Gearbox faults identification using vibration signal analysis and artificial intelligence methods
N Zuber, R Bajrić, R Šostakov
Eksploatacja i Niezawodność 16 (1), 61-65 2014
Citations: 109

Feature extraction using discrete wavelet transform for gear fault diagnosis of wind turbine gearbox
R Bajric, N Zuber, GA Skrimpas, N Mijatovic
Shock and Vibration 2016 (1), 6748469 2016
Citations: 77

Review of vibration signal processing techniques towards gear pairs damage identification
R Bajric, D Sprecic, N Zuber
International Journal of Engineering & Technology 11 (4), 124-128 2011
Citations: 47

Experimental determination of lateral forces caused by bridge crane skewing during travelling
A Zelić, N Zuber, R Šostakov
Eksploatacja i Niezawodność 20 (1) 2018
Citations: 41

Gearbox faults feature selection and severity classification using machine learning
N Zuber, R Bajrić
Eksploatacja i Niezawodność 22 (4), 748-756 2020
Citations: 21

Remote online condition monitoring of the bucket wheel excavator SR1300: A case study
N Zuber, H Ličen, MA Klašnja
Facta universitatis-series: Working and Living Environmental Protection 5 (1 2008
Citations: 15

The influence of fatigue loading on the durability of the conveyor belt
N Ilanković, D Živanić, N Zuber
Applied Sciences 13 (5), 3277 2023
Citations: 10

Vibration feature extraction methods for gear faults diagnosis-a review
ZF Ninoslav, B Rusmir, D Cvetkovic
Facta Universitatis, Series: Working and Living Environmental Protection 12 2015
Citations: 10

Applied remote condition monitoring of the bucket wheel excavator
N Zuber, H Ličen, A Klašnja-Miličević
Istraživanja i projektovanja za 2009
Citations: 9

Recent advances in vibration signal processing techniques for gear fault detection-A review
R Bajrić, N Zuber, S Isić
Applied Mechanics and Materials 430, 78-83 2013
Citations: 8

Relations between pulverizing process parameters and beater wheel mill vibration for predictive maintenance program setup
R Bajrić, N Zuber, R Šostakov
Eksploatacja i Niezawodność 16 (1) 2014
Citations: 7

Mogućnosti primene metoda veštaĉke inteligencije u automatizaciji vibrodijagnostiĉkih metoda
N Zuber, H Liĉen
Ĉasopis „Tehniĉka dijagnostika 10, 9-14 2011
Citations: 7

An innovative approach to the condition monitoring of excavators in open pits mines
N Zuber, H Licen, R Bajric
Technics Technologies Education Management 2010
Citations: 6

Izveštaj o merenju i analizi vibracija na elektroagregatima tipa No-break, snage 100 kVA, u Radarskoj stanici, Koviona-Beograd
N Žegarac, H Ličen, N Zuber
Beograd 1999
Citations: 5

Multiple fault identification using vibration signal analysis and artificial intelligence methods
N Zuber, D Cvetkovic, R Bajrić
Applied Mechanics and Materials 430, 63-69 2013
Citations: 4

Proaktivno održavanje hidroturbinske opreme primenom 01DB-Metravib OneProd koncepta
H Ličen, N Zuber
Tehniĉka dijagnostika, Beograd 7, 3-10 2008
Citations: 4