Jacek Dybała
Warsaw University of Technology
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Featured researches published by Jacek Dybała.
IEEE Transactions on Reliability | 2016
Yaguo Lei; Naipeng Li; Szymon Gontarz; Jing Lin; Stanisław Radkowski; Jacek Dybała
Remaining useful life (RUL) prediction allows for predictive maintenance of machinery, thus reducing costly unscheduled maintenance. Therefore, RUL prediction of machinery appears to be a hot issue attracting more and more attention as well as being of great challenge. This paper proposes a model-based method for predicting RUL of machinery. The method includes two modules, i.e., indicator construction and RUL prediction. In the first module, a new health indicator named weighted minimum quantization error is constructed, which fuses mutual information from multiple features and properly correlates to the degradation processes of machinery. In the second module, model parameters are initialized using the maximum-likelihood estimation algorithm and RUL is predicted using a particle filtering-based algorithm. The proposed method is demonstrated using vibration signals from accelerated degradation tests of rolling element bearings. The prediction result identifies the effectiveness of the proposed method in predicting RUL of machinery.
Archive | 2012
Jacek Dybała; Radoslaw Zimroz
Bearings damage detection is one of the most important topic in condition monitoring. Main problem in industrial application of bearing vibration diagnostics is the masking of informative signal by interfering signals. It requires the usage of techniques based on advanced signal enhancement in order to extract useful diagnostic components from the measured vibration signals. The paper shows application of Empirical Mode Decomposition (EMD) in extraction of weak impulsive signal from raw vibration signals generated by complex mechanical systems employed in the industry (driving units of belt conveyors). Impulsive character of the vibration signals is very often associated with a mechanical fault. The purpose of this processing is decomposition of the signal in order to detect impacts related to the damages in rolling element bearings (REB).
Archive | 2014
Jacek Dybała; Adam Gałęzia
Nowadays, many industrial types of machinery rely on different types of gears to transmit rotational torque. Gearbox faults are one of the major reasons for breakdown of industrial machinery. Therefore, gearbox diagnosing is one of the most important topics in machine condition monitoring. A number of signal processing techniques are described for the vibrodiagnostics of gearboxes, but there are also different limitations for vibration based gear diagnostic methods. For some specific requirements (e.g. time-triggered signal acquisition), not all of described techniques can be always applied in industrial reality. This paper introduces a novel, easy to use method of gearbox health vibromonitoring based on Empirical Mode Decomposition (EMD) and a time-domain analysis of vibration signal parts. Six sets of data collected from gearboxes are used to validate the proposed method. The experimental results demonstrate that the gear tooth defect can be detected and evaluated at an early stage of development when both Empirical Mode Decomposition and statistical analysis technique are used.
Key Engineering Materials | 2013
Jacek Dybała; Radoslaw Zimroz
In rotating machinery, the detection of local damage is one of the most important issues. This kind of change of technical condition produce local disturbance according to temporal (local) change of stiffness of kinematic pair (tooth-tooth contact, rolling element-outer/inner race etc). In many practical, i.e. industrial cases, vibration signature of such change is weak in sense of produced energy, so consequently, completely masked by other vibration sources in machine. The general concept of signal processing for local damage detection is to use so called signal enhancement, i.e. a kind of tool that may improve signal to noise ratio. One may find many approaches used in the literature. Most of them use signal filtering (classical, adaptive and optimal filters), decomposition (wavelets) or extraction (blind source separation). Empirical Mode Decomposition (EMD) is one of such techniques that can be used with signal decomposition problem. In this paper, EMD will be used for vibration signal decomposition in order to extract information about local perturbation of arm (carrier) in planetary gearbox used in heavy mining machine, i.e. bucket wheel excavator. As a result of application of EMD, one may obtain several time series with different properties of sub-signal. Due to predefined task, namely local disturbance detection, several criteria have been investigated in order to select the most informative empirical mode. First criterion was kurtosis calculated for every mode with very simple decision rule (max kurtosis is the best). It was found that such approach is not optimal due to some random impulses that are not related to damage. To improve results, it is proposed to combine envelope spectrum and kurtosis. If envelope spectrum contains family of components related to arm (carrier) shaft frequency and signal is spiky (kurtosis is high) result of EMD for given mode is optimal in sense of carried information. However, in this approach decision was made based on visual inspection of the envelope spectra of each mode, which is non-effective way. Finally two parameters have been proposed: 1) Pearson correlation coefficient of an empirical mode and the empirically determined local mean of original signal; 2) a relative power of an empirical mode.
international conference on reliability, maintainability and safety | 2009
Jacek Dybała
The paper will present the original NBV (Nearest Boundary Vector) classifier whose structure has been inspired by the structure of CP (Counter Propagation) neural network, which uses the methods applied in the minimum-distance classification while in its operation drawn on the idea of functioning of SVM (Support Vector Machines) classifiers. The classification algorithm which is used by it relies on the original concept of a set of Boundary Vectors. It is characterized by the possibility of creation of various shapes of decision-making regions and it enables effective multi-class recognition. Recognition efficiency of NBV classifier will be confronted with efficiency of SVM classifiers.
Archive | 2018
Jacek Dybała; Jakub Komoda
In recent years proactive diagnostic strategies have gained more significance. Due to the need of reduction of production costs, machine downtime must be held at the lowest possible limits. This forces maintenance services to predict possible failures and plan repairs in advance. Rolling bearing faults are among the major reasons for breakdown of industrial machinery and bearing diagnosing is one of the most important topics in machine condition monitoring . Vibration signals offer great opportunity to provide reliable information about machine condition. However, in complex industrial environments the vibration signal of the rolling bearing may be covered or concealed by other vibration sources, such as gears. In case of masking the informative bearing signal by machine noise, extraction of useful diagnostic information from vibration signals becomes very difficult. The following paper presents two rolling bearing diagnosing approaches enabling early detection of rolling bearing faults at the low-energy stage of their development. By using empirical signal decomposition methods a raw vibration signal is divided into two parts: an informative bearing signal and a signal emitted from other machinery elements. For further bearing fault-related feature extraction from the informative bearing signal, the spectral analysis of the empirically determined local amplitude is applied. To test the operational effectiveness of the developed signal decomposition methods, raw vibration signals generated by complex mechanical systems employed in the industry are used. The test results show that the developed methods allow early identification of bearing fault in case of masking the informative bearing signal by signals derived from other sources.
International Congress on Technical Diagnostic | 2016
Jacek Dybała; Krzysztof Nadulicz
Welding is one of the basic methods of combining construction materials. Since the quality of welded joints translates into the possibility of load transfer through structures, welded joints are naturally objects of diagnostic tests. The main objective of the welded joints research is the search of their weak link, that is to say finding and assessment of areas where there are metal discontinuities. In these places, aggregate impact of stresses arising during welding and stresses arising under operational load of object occur. The existence of the natural Earth’s magnetic field as well as the fact, that the materials of critical elements of construction are largely ferromagnetic materials allows for obtaining diagnostic information concerning the structure with the use of magnetometers. The article presents a method of passive magnetic survey of welded joints based on the analysis of the distribution of diagnosed object’s own magnetic field. The essence of the method is the measurement and interpretation of the local magnetic field disturbances caused by the occurrence of the local stress in the material, local plastic deformation of the material or the presence of material discontinuities, both mechanical (crack, stratification) and structural (inclusions of other materials). The article shows that the presented diagnostic approach allows for performing a quick quality check of welded joints without the need for special preparation of welded joints for diagnostic testing, which is of great utility importance and which translates directly to the low cost of such research.
Dynamical Systems Theory and Applications | 2015
Michał Wikary; Stanisław Radkowski; Jacek Dybała; K. Lubikowski
Finite Element Method (FEM) is an effective and productive tool which is able to deal with sophisticated engineering requirements and successfully calculates expected output values often based on advanced boundary conditions and solution settings. The paper refers to modelling of thermoelectric generators in FEM environment (ANSYS software) which are based on popular Peltier modules that are frequently used in energy cogeneration branch of industry. The modelling process consists of geometry design, sensitivity study which focus on solver settings, discretization level and their impact into results (optimization of solution process total time). Last step engages parameter of Seebeck coefficient. Its modification allows adjusting the FE analysis to experimental data. The verified thermoelectric module will be able to reflect real capabilities of the commercially available thermoelectric devices. The main purpose of the process development is creation of Peltier modules (accessible in industry) FE models database. The devices from the database could easily be used in sophisticated FE analysis which consists of various physics systems (coupled) where simplified approach or indirect method will be limited or impossible to use.
Applied Acoustics | 2014
Jacek Dybała; Radoslaw Zimroz
Mechanical Systems and Signal Processing | 2013
Jacek Dybała; Stanisław Radkowski