Integration of multidimensional fault diagnostic indicators on the example of rolling element bearings

Diagnostics of rotating machinery relies on examining of many dozens of fault indicators that enable recognition of malfunction symptoms at the earliest stage possible. Unfortunately, in many industrial applications and especially in large machinery parks, the number of diagnostic features to monitor goes beyond the perception capabilities of responsible maintenance personnel. Therefore, there is need for a data reduction techniques that simplify and provide the most important information within the condition monitoring system, starting from a single kinematic element. In this paper it is proposed to employ a simple Euclidean distance that relates the object’s condition to the difference between the vibration-based indicators and the initial state. As an example, the authors examine the integration of diagnostic features used to identify localized and extended fault of rolling element bearings for simulated data and real industrial event that occurred at wind turbine’s generator bearing

Konwencja stambulska a płeć i liczba małżonków

W artykule wykazuję, że koncepcja małżeństwa zawarta w Konwencji Rady Europy o zapobiega­niu i zwalczaniu przemocy wobec kobiet i przemocy domowej, sporządzonej w Stambule dnia 11 maja 2011 r., nie obejmuje rozstrzygnięć co do liczby osób w małżeństwie i odnośnie do płci małżonków. Podejmując się tego tematu, w pierwszej kolejności badam tekst Konwencji z perspektywy podstawowych reguł wykładni pra­wa, dekodując w ten sposób atrybuty małżeństwa expressis verbis ulokowane w tekście Konwencji. W dalszej części, angażując okoliczności zewnętrzne wobec tego tekstu, uzasadniam tezy: poligamiczność jest wykluczo­na z zakodowanego w Konwencji pojęcia małżeństwa (1); w Konwencji jest złożona koncepcja małżeństwa, z której wyłączono realizowanie się między osobami tej samej płci (2) oraz pojęcie małżeństwa z Konwencji denotuje związki poligamiczne lub homoseksualne (3). Następnie zaś każdą tezę – (1), (2) i (3) oraz ich uzasad­nienia oceniam według wymogów prawidłowej wykładni prawa

Analysis of Compliance with WCAG Guidelines Regarding Contrast Im-plementation in an E-Learning Quiz

In the process of distance learning, modern information and educational solutions are increasingly employed. The introduction of tools facilitating compliance with WCAG guidelines to course creators, enhances content accessibility for individuals with disabilities, eliminating barriers to education access. This aligns with the concept of universal design, aiming to create courses and instructional materials accessible to a broad audience, regardless of individual needs, abilities or conditions. While this is a standard in web design, it is often overlooked in distance learning and course de-sign. This paper focuses on analyzing the correctness of implementing a component allowing the assessment of WCAG compliance in designed quizzes using an exercise creator. The implementation was based on the Quizer e-learning plat-form. Quizzes in the field of cybersecurity were designed to meet contrast guidelines according to the WCAG 2.1 stand-ard, followed by user testing. The research results confirmed the correctness of the applied solutions and emphasized the necessity of designing IT tools considering potential disabilities of future users of e-learning quizzes

The use of a fuzzy logic approach for integration of vibration-based diagnostic features of rolling element bearings

Modern condition monitoring systems (CMS) collect and process enormous amount of data in order to provide the earliest and most dependable information of fault development within any of the machine components and their operation combined. According to numerous studies one of the most fault susceptible mechanical elements in rotating machinery are rolling element bearings. Although reliable techniques for their diagnostics are already proposed, the new investigation is needed. According to authors experience in many industrial applications the operators are obligated to simultaneously track hundreds of diagnostic estimates, such as signals energy, its peakedness or narrowband characteristics for localized faults. As mentioned, for a vibration-based CMS of single wind turbine there are nearly 150 of them. Therefore, the authors employ a fuzzy logic approach for integration of bearing diagnostic features. A new estimate that carry most relevant information about bearing condition is discussed. The reasoning is presented on simulated data that mimics real rotating machine

Informative frequency band identification for automatic extraction of impulsive components in vibration data from rotating machinery

In this paper authors address the issue of local damage detection in rolling element bearings in the presence of non-Gaussian noise. Typically damage detection problems concern the techniques of filtration, decomposition, separation, extraction etc. In such real-life cases, main difficulty lies in non-Gausianity of the noise present in the operational environment, hence popular denoising techniques cannot be used. In presented article, a real-life industrial scenario will be discussed and a new approach to cyclic component extraction will be presented. Classical detection methods are often not sufficient for the task because of high energy of impulsive noise in comparison to spectral structure of the damage. Proposed method utilizes Cyclic Spectral Coherence map as two-dimensional data representation, and Nonnegative Matrix Factorization as analytical tool to extract individual components

Automatic and Full-band Demodulation for Fault Detection. Validation on a Wind Turbine Test Bench

International audienceIn order to improve vibration analysis of a signal measured on multi-component systems and especially on systems operating under non-stationary conditions, advanced signal processing methods are required. To contribute to this need a new diagnostic approach is presented in this work. This paper focuses on some parts of an autonomous full-band spectrum analyzer, referred to as AStrion. AStrion consists different function blocks, referred to as "modules". In the first module, among other processing tasks, the signal is pre-processed with a new order tracking algorithm described in this paper. The subsequent modules, referred to as AStrion-I and AStrion-H, automatically identify all the harmonic series and modulation side-bands in all the frequency band of the signal [1]. The first module described in this paper is called AStrion-K, where K stands for Kinematic. This module associates the detected spectral structure, harmonic series and modulation side-bands, with the characteristic fault frequencies of the monitored system. This approach has the advantage of analyzing all the frequency band of the signal and of being able to monitor a system even if the kinematic of the system is unknown. The second part of this paper describes the demodulation task, done by AStrion-M module. The demodulation is conducted over each carrier frequency along with its side-bands detected by AStrion-H. A multi-rate filtering process is applied over each band to isolate the component. Such filtering method downsamples the signal in such a way that the digital filter can achieve a satisfactory precision and stability over the targeted demodulation band, even if it is extremely narrow. In order to extract the corresponding deterministic component and to increase the signal to noise ratio, a time synchronous averaging is carried out over the filtered signal sampled according to the corresponding triggering frequency. Therefore, after computing the analytical signal of the averaged signal, a demodulation process estimates the amplitude and frequency functions, which are employed to derive fault indicators both in time and in frequency domains. Finally, results are presented on real data gathered on a test bench designed in KAStrion project for simulating a wind turbine operation. This test bench was prepared to simulate all components of a wind turbine drive train and is composed of mechanisms for accelerated deterioration of bearings and gearbox, which allows the investigation of the wear of selected elements separately. This paper is focused on the fault of the main bearing. A comparison with commonly used fault indicators is carried out

Assessment of background noise properties in time and time-frequency domains in the context of vibration-based local damage detection in real environment

Any measurement in condition monitoring applications is associated with disturbing noise. Till now, most of the diagnostic procedures have assumed the Gaussian distribution for the noise. This paper shares a novel perspective to the problem of local damage detection. The acquired vector of observations is considered as an additive mixture of signal of interest (SOI) and noise with strongly non-Gaussian, heavy-tailed properties, that masks the SOI. The distribution properties of the background noise influence the selection of tools used for the signal analysis, particularly for local damage detection. Thus, it is extremely important to recognize and identify possible non-Gaussian behavior of the noise. The problem considered here is more general than the classical goodness-of-fit testing. The paper highlights the important role of variance, as most of the methods for signal analysis are based on the assumption of the finite-variance distribution of the underlying signal. The finite variance assumption is crucial but implicit to most indicators used in condition monitoring, (such as the root-mean-square value, the power spectral density, the kurtosis, the spectral correlation, etc.), in view that infinite variance implies moments higher than 2 are also infinite. The problem is demonstrated based on three popular types of non-Gaussian distributions observed for real vibration signals. We demonstrate how the properties of noise distribution in the time domain may change by its transformations to the time-frequency domain (spectrogram). Additionally, we propose a procedure to check the presence of the infinite-variance of the background noise. Our investigations are illustrated using simulation studies and real vibration signals from various machines

Nonlinear cointegration approach for condition monitoring of wind turbines

Monitoring of trends and removal of undesired trends from operational/process parameters in wind turbines is important for their condition monitoring. This paper presents the homoscedastic nonlinear cointegration for the solution to this problem. The cointegration approach used leads to stable variances in cointegration residuals. The adapted Breusch-Pagan test procedure is developed to test for the presence of heteroscedasticity in cointegration residuals obtained from the nonlinear cointegration analysis. Examples using three different time series data sets-that is, one with a nonlinear quadratic deterministic trend, another with a nonlinear exponential deterministic trend, and experimental data from a wind turbine drivetrain-are used to illustrate the method and demonstrate possible practical applications. The results show that the proposed approach can be used for effective removal of nonlinear trends form various types of data, allowing for possible condition monitoring applications

Automatic Characteristic Frequency Association and All-Sideband Demodulation for Detection of a Bearing Fault of a Test Rig

International audienceThis paper proposes advanced signal-processing techniques to improve condition monitoring of operating machines. The proposed methods use the results of a blind spectrum interpretation that includes harmonic and sideband series detection. The rst contribution of this study is an algorithm for automatic association of harmonic and sideband series to characteristic fault frequencies according to a kinematic conguration. The approach proposed has the advantage of taking into account a possible slip of the rolling-element bearings. In the second part, we propose a full-band demodulation process from all sidebands that are relevant to the spectral estimation. To do so, a multi-rate ltering process in an iterative schema provides satisfying precision and stability over the targeted demodulation band, even for unsymmetrical and extremely narrow bands. After synchronous averaging, the ltered signal is demodulated for calculation of the amplitude and frequency modulation functions, and then any features that indicate faults. Finally, the proposed algorithms are validated on vibration signals measured on a test rig that was designed as part of the Eu-ropean Innovation Project KAStrion'. This rig simulates a wind turbine drive * Corresponding author Email address: [email protected] (Marcin Firla) Preprint submitted to Mechanical Systems and Signal Processing March 11, 2016 train at a smaller scale. The data show the robustness of the method for localizing and extracting a fault on the main bearing. The evolution of the proposed features is a good indicator of the fault severity

PROGRAMMABLE LOGIC DEVICES AS A TOOL IN STRUCTURE'S DAMAGE DETECTION APPLICATIONS

Programmable logic devices is one of the most dynamically developing fields of technology today. It is widely used in many kinds of signal acquisition as well as data processing systems mainly because of it's large flexibility and contin­uously growing abilities. It gives a designer a powerful tool, which allows for the creation of almost any kind of logic topology and any kind of data processing system, which additionally may be freely reprogrammable without any phys­ical changes in the platform device. This paper briefly describes programmable devices technology contribution in the development of various SHM (System Health Monitoring) systems. The SHM systems mentioned in this paper have already been designed, built and successfully tested on real structures