1,716 research outputs found
Inspection Robotic UGV Platform and the Procedure for an Acoustic Signal-Based Fault Detection in Belt Conveyor Idler
No full textBelt conveyors are commonly used for the transportation of bulk materials. The most characteristic design feature is the fact that thousands of idlers are supporting the moving belt. One of the critical elements of the idler is the rolling element bearing, which requires monitoring and diagnostics to prevent potential failure. Due to the number of idlers to be monitored, the size of the conveyor, and the risk of accident when dealing with rotating elements and moving belts, monitoring of all idlers (i.e., using vibration sensors) is impractical regarding scale and connectivity. Hence, an inspection robot is proposed to capture acoustic signals instead of vibrations commonly used in condition monitoring. Then, signal processing techniques are used for signal pre-processing and analysis to check the condition of the idler. It has been found that even if the damage signature is identifiable in the captured signal, it is hard to automatically detect the fault in some cases due to sound disturbances caused by contact of the belt joint and idler coating. Classical techniques based on impulsiveness may fail in such a case, moreover, they indicate damage even if idlers are in good condition. The application of the inspection robot can “replace” the classical measurement done by maintenance staff, which can improve the safety during the inspection. In this paper, the authors show that damage detection in bearings installed in belt conveyor idlers using acoustic signals is possible, even in the presence of a significant amount of background noise. Influence of the sound disturbance due to the belt joint can be minimized by appropriate signal processing methods
Application of Machine Learning Tools for Long-Term Diagnostic Feature Data Segmentation
No full textIn this paper, a novel method for long-term data segmentation in the context of machine health prognosis is presented. The purpose of the method is to find borders between three data segments. It is assumed that each segment contains the data that represent different statistical properties, that is, a different model. It is proposed to use a moving window approach, statistical parametrization of the data in the window, and simple clustering techniques. Moreover, it is found that features are highly correlated, so principal component analysis is exploited. We find that the probability density function of the first principal component may be sufficient to find borders between classes. We consider two cases of data distributions, Gaussian and α-stable, belonging to the class of non-Gaussian heavy-tailed distributions. It is shown that for random components with Gaussian distribution, the proposed methodology is very effective, while for the non-Gaussian case, both features and the concept of moving window should be re-considered. Finally, the procedure is tested for real data sets. The results provided here may be helpful in understanding some specific cases of machine health prognosis in the presence of non-Gaussian noise. The proposed approach is model free, and thus it is universal. The methodology can be applied for any long-term data where segmentation is crucial for the data processing
Data-driven segmentation of long term condition monitoring data in the presence of heavy-tailed distributed noise with finite-variance
No full textMachinery condition prognosis systems use long-term historical data to predict the remaining useful life (RUL). One of the critical steps to reach this purpose is to segment long-term data into two or several degradation stages (healthy, unhealthy, critical stage). Finding a changing point between stages may be a crucial preliminary task for further prediction of degradation process. However, finding the accurate partition into two or more stages is a challenging task in actual application when noise inherent in the observed process exhibits non-Gaussian characteristics. In this paper, a framework for data-driven segmentation is presented for prognosis of machinery long-term data in presence of heavy-tailed distributed noise with finite variance. It is assumed that three different stages are inherent in degradation process and each segment of data follows a specific trend (constant, linear, exponential or polynomial). At first, data is divided into three parts. Trend functions are fitted to the data by using robust regression method, and cumulative error is calculated. This process is done iteratively for all possible partitions into three intervals to find the segmentation which minimizes the error. The framework has been tested via empirical analysis of estimators of the changing points obtained in Monte Carlo simulations. Also, discussed approaches are applied to the real data. In such measurement, data that are commonly available (in condition monitoring systems) is aggregated from the raw signal and sampled at long intervals. Finally, effectiveness of the segmentation results is assessed by comparing them with envelope frequency analysis of raw signal to confirm the fact that detected changing points coincide with start time of the fault in the machine or not
Using long-term condition monitoring data with non-Gaussian noise for online diagnostics
No full textThe number of timely diagnoses based on condition monitoring data is increasing with the growing usage of monitoring systems. In most of the methods used in these systems, a pre-established fault detection threshold is needed, while there are no specific limit values or thresholds in many cases, especially when the machine is unique. Also, in most actual applications, due to the kind of process and harsh environment, the noise inherent in the observed process exhibits non-Gaussian characteristics, making it a challenging task for diagnostics based on condition monitoring (CM) data. Therefore, this paper introduced a robust methodology based on the switching maximum correntropy Kalman filter (SMCKF) to address the mentioned problems (threshold and online diagnostics in the presence of non-Gaussian noise by using CM data). This approach uses multiple dynamic system models to explain different degradation stages, utilizing robust Bayesian estimation. As this approach is based on dynamic behavior, a threshold for diagnostics is no longer needed. Ultimately, the proposed approach is applied to the online diagnosis of simulated and actual data sets. The results of both simulated and real data sets prove the method’s efficacy
MIN3D Dataset: MultI-seNsor 3D Mapping with an Unmanned Ground Vehicle
No full textThe research potential in the field of mobile mapping technologies is often hindered by several constraints. These include the need for costly hardware to collect data, limited access to target sites with specific environmental conditions or the collection of ground truth data for a quantitative evaluation of the developed solutions. To address these challenges, the research community has often prepared open datasets suitable for developments and testing. However, the availability of datasets that encompass truly demanding mixed indoor–outdoor and subterranean conditions, acquired with diverse but synchronized sensors, is currently limited. To alleviate this issue, we propose the MIN3D dataset (MultI-seNsor 3D mapping with an unmanned ground vehicle for mining applications) which includes data gathered using a wheeled mobile robot in two distinct locations: (i) textureless dark corridors and outside parts of a university campus and (ii) tunnels of an underground WW2 site in Walim (Poland). MIN3D comprises around 150 GB of raw data, including images captured by multiple co-calibrated monocular, stereo and thermal cameras, two LiDAR sensors and three inertial measurement units. Reliable ground truth (GT) point clouds were collected using a survey-grade terrestrial laser scanner. By openly sharing this dataset, we aim to support the efforts of the scientific community in developing robust methods for navigation and mapping in challenging underground conditions. In the paper, we describe the collected data and provide an initial accuracy assessment of some visual- and LiDAR-based simultaneous localization and mapping (SLAM) algorithms for selected sequences. Encountered problems, open research questions and areas that could benefit from utilizing our dataset are discussed. Data are available at https://3dom.fbk.eu/benchmarks
Jacek Dehnel – obraz tłumacza
No full textThis article attempts to present Jacek Dehnel as a translator. For this purpose, the author refers to selected works discussing Jacek Dehnel as a writer, poet, translator, and artist. In addition, the author uses Jacek Dehnel's statements on translation made in interviews and his critical comments on poetry and prose translations. The author assumes that a discussion on Jacek Dehnel's (or any other translator's) professional activity solely from the perspective of the texts of their translations would be insufficient. In the article, the author suggests that Jacek Dehnel pays particular attention to non-standard varieties of language in his prose, which in turn may influence his translation decisions. The article notes that when it appears impossible to convey the character of the original language in its entirety, Dehnel has been a proponent of departing from the form of the source text to save its exact meaning - also in poetry translation. The article concludes that Jacek Dehnel is familiar with many views on literary translation, he is interested in its theory, willingly participates in discussions in this field, and reviews other translations. Dehnel treats literature as the best source of knowledge about writing and translation. Despite his unquestionable literary, poetic, and translation achievements, Dehnel does not deny that he is still learning how to write and translate from others. The article is also an invitation to further discussion and critical analysis of Jacek Dehnel's translations.Artykuł jest próbą przedstawienia sylwetki Jacka Dehnela jako tłumacza. Autor opiera się na wybranych pracach, omawiających dotychczasowy dorobek Jacka Dehnela jako pisarza, poety, tłumacza oraz artysty. W artykule wykorzystano wypowiedzi Jacka Dehnela na temat tłumaczenia udzielone w wywiadach oraz jego komentarze krytyczne, dotyczące przekładów poezji i prozy. Autor wychodzi z założenia, że omówienie translatorskiej działalności Jacka Dehnela (oraz każdego innego tłumacza) jedynie przez pryzmat tekstów jego przekładów byłoby niewystarczające. Artykuł jest zatem zaproszeniem do dalszej dyskusji i krytycznej analizy tłumaczeń pióra Jacka Dehnela.  
45' Reception, Jacek Bocheński Divine Julius: Episode 10
No full text"Divine Julius" by Jacek Bocheński, the author of the "Roman Trilogy".
Video prepared by Olga Strycharczyk and Marta Pszczolińska.
"45 Seconds Reception" series was created as part of the European Research Council (ERC) funded project: Our Mythical Childhood... The Reception of Children's and Young Adults' Culture in Response to Regional and Global Challenges (Grant Agreement 681202; ERC Consolidator Grant led by Prof Katarzyna Marciniak).Information about "Our Mythical Childhood..." is available at http://omc.obta.al.uw.edu.pl
The YouTube channel of the project is available at https://www.youtube.com/channel/UC6zvu9EXsI0gK5rSvgnQse
Jacek Salij's Contemporary Apology of Church
No full textTrudno mówić o Kościele i bronić go we współczesnym świecie. Nie boi się jednak tego zadania polski dominikanin Jacek Salij, który dokonuje swoistej apologii Kościoła. W niniejszym artykule w kilku punktach autor ukazał oryginalność i aktualność eklezjologicznej myśli Jacka Salija. Wielką zaletą teologii o. Salija jest komunikatywność i obrazowość. Apologia Kościoła tego dominikanina bez wątpienia pozwala na nowo spojrzeć na Kościół dzisiejszemu człowiekowi.Nowadays speaking about the Church and defending it is a very big challenge. However a Polish Dominican Jacek Salij is not afraid of this task and he performs a kind of apology for the Church. In this article in several points the author shows originality and timeliness of the ecclesiological thought of Jacek Salij. The great advantage of his theology is communication and imagery. Certainly the apology of Church of the Black Friar gives modern man a new look at the Church
Nonnegative Matrix Factorization of time frequency representation of vibration signal for local damage detection – comparison of algorithms
No full textLocal damage detection in rotating machine elements is very important problem widely researched in the literature. One of the most common approaches is the vibration signal analysis. Since time domain processing is often insufficient, other representations are frequently favored. One of the most common one is time-frequency representation hence authors propose to separate internal processes occurring in the vibration signal by spectrogram matrix factorization. In order to achieve this, it is proposed to use the approach of Nonnegative Matrix Factorization (NMF). In this paper three NMF algorithms are tested using real and simulated data describing single-channel vibration signal acquired on damaged rolling bearing operating in drive pulley in belt conveyor driving station. Results are compared with filtration using Spectral Kurtosis, which is currently recognized as classical method for impulsive information extraction, to verify the validity of presented methodology
Understanding Reform: The Case of Poland
No full textThis report reviews the reform process in Poland in the period 1989-2001, from the formation of the first non-Communist government to the defeat of right-wing forces in the 2001 parliamentary elections and the formation of a governing left-wing coalition of social democrats and the peasants’ party (both of them with roots in the Communist era). It reconstructs the sequence of reforms, assesses their relative successes, and focuses on the problem of the stagnation of the reform process at the end of the 1990s.Poland, reform, transition
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