The subversiveness of the work is a criterion of street art according to the theory of A. Baldini. This view, undoubtedly, represents a novelty in the understanding of this category of art; as such, ...it remains a contribution to aesthetics. Nevertheless, within the framework of the presented theory, subversiveness is combined with legal judgment and violation of the law. These circumstances situate the theory within the field of interest of legal studies; and from this perspective, it deserves criticism. This is because the theory does not contain precise determinants of the difference between legal and aesthetic judgments, nor does it equip the constructed criterion of street art with the ability to resolve conflicts between norms relevant to subversiveness; and it is the demonstration of these gaps, as well as proposals to fill them in the context of legal science that are the subject of this article.
In hazardous environments like mining sites, mobile inspection robots play a crucial role in condition monitoring (CM) tasks, particularly by collecting various kinds of data, such as images. ...However, the sheer volume of collected image samples and existing noise pose challenges in processing and visualizing thermal anomalies. Recognizing these challenges, our study addresses the limitations of industrial big data analytics for mobile robot-generated image data. We present a novel, fully integrated approach involving a dimension reduction procedure. This includes a semantic segmentation technique utilizing the pre-trained VGG16 CNN architecture for feature selection, followed by random forest (RF) and extreme gradient boosting (XGBoost) classifiers for the prediction of the pixel class labels. We also explore unsupervised learning using the PCA-K-means method for dimension reduction and classification of unlabeled thermal defects based on anomaly severity. Our comprehensive methodology aims to efficiently handle image-based CM tasks in hazardous environments. To validate its practicality, we applied our approach in a real-world scenario, and the results confirm its robust performance in processing and visualizing thermal data collected by mobile inspection robots. This affirms the effectiveness of our methodology in enhancing the overall performance of CM processes.
The belt conveyor (BC) is the main means of horizontal transportation of bulk materials at mining sites. The sudden fault in BC modules may cause unexpected stops in production lines. With the ...increasing number of applications of inspection mobile robots in condition monitoring (CM) of industrial infrastructure in hazardous environments, in this article we introduce an image processing pipeline for automatic segmentation of thermal defects in thermal images captured from BC idlers using a mobile robot. This study follows the fact that CM of idler temperature is an important task for preventing sudden breakdowns in BC system networks. We compared the performance of three different types of U-Net-based convolutional neural network architectures for the identification of thermal anomalies using a small number of hand-labeled thermal images. Experiments on the test data set showed that the attention residual U-Net with binary cross entropy as the loss function handled the semantic segmentation problem better than our previous research and other studied U-Net variations.
Mechanical industrial infrastructures in mining sites must be monitored regularly. Conveyor systems are mechanical systems that are commonly used for safe and efficient transportation of bulk goods ...in mines. Regular inspection of conveyor systems is a challenging task for mining enterprises, as conveyor systems' lengths can reach tens of kilometers, where several thousand idlers need to be monitored. Considering the harsh environmental conditions that can affect human health, manual inspection of conveyor systems can be extremely difficult. Hence, the authors proposed an automatic robotics-based inspection for condition monitoring of belt conveyor idlers using infrared images, instead of vibrations and acoustic signals that are commonly used for condition monitoring applications. The first step in the whole process is to segment the overheated idlers from the complex background. However, classical image segmentation techniques do not always deliver accurate results in the detection of target in infrared images with complex backgrounds. For improving the quality of captured infrared images, preprocessing stages are introduced. Afterward, an anomaly detection method based on an outlier detection technique is applied to the preprocessed image for the segmentation of hotspots. Due to the presence of different thermal sources in mining sites that can be captured and wrongly identified as overheated idlers, in this research, we address the overheated idler detection process as an image binary classification task. For this reason, a Convolutional Neural Network (CNN) was used for the binary classification of the segmented thermal images. The accuracy of the proposed condition monitoring technique was compared with our previous research. The metrics for the previous methodology reach a precision of 0.4590 and an F1 score of 0.6292. The metrics for the proposed method reach a precision of 0.9740 and an F1 score of 0.9782. The proposed classification method considerably improved our previous results in terms of the true identification of overheated idlers in the presence of complex backgrounds.
High-power turbomachines are equipped with flexible rotors and journal bearings and operate above their first and sometimes even second critical speed. The transient response of such a system is ...complex but can provide valuable information about the dynamic state and potential malfunctions. However, due to the high complexity of the signal and the nonlinearity of the system response, the analysis of transients is a highly complex process that requires expert knowledge in diagnostics, machine dynamics, and extensive experience. The article proposes the Multidimensional Data Driven Decomposition (MD3) method, which allows decomposing a complex transient into several simpler, easier to analyze functions. These functions have physical meaning. Thus, the method belongs to the Explainable Artificial Intelligence area. The MD3 method proposes three scenarios and chooses the best based on the MSE quality index. The approach was first verified on a test rig and then validated on data from a real object. The results confirm the correctness of the method assumptions and performance. Furthermore, the MD3 method successfully identified the failure of rotor unbalance, both on the test rig and the real object data (large generator rotor in the power plant). Finally, further directions for research and development of the method are proposed.
Conveying systems play an essential role in the continuous horizontal transportation of raw materials in mining sites. Regular inspections of conveyor system structures and their components, ...especially idlers, are essential for proper maintenance. Traditional inspection methods are labor-intensive and hazardous; therefore, robot-based thermography can be considered a quality assessment tool for the precise detection and localization of overheated idlers in opencast mining sites. This paper proposes an infrared image processing pipeline for the automatic detection and analysis of overheated idlers. The proposed image processing pipeline can be used for the identification of significant temperature anomalies such as hotspots and hot areas in infrared images. For the identification of such defects in idlers, firstly, the histogram of captured infrared images was analyzed and improved through the pre-processing stages. Afterward, the location of thermal anomalies in infrared images was extracted. Finally, for the validation of segmentation results, the shapes and locations of segmented hot spots were compared with RGB images that were synchronized by captured infrared images. A quantitative evaluation of the proposed method for the condition monitoring of belt conveyor idlers in an open-cast mining site shows the applicability of our approach.
Turbines and generators operating in the power generation industry are a major source of electrical energy worldwide. These are critical machines and their malfunctions should be detected in advance ...in order to avoid catastrophic failures and unplanned shutdowns. A maintenance strategy which enables to detect malfunctions at early stages of their existence plays a crucial role in facilities using such types of machinery. The best source of data applied for assessment of the technical condition are the transient data measured during start-ups and coast-downs. Most of the proposed methods using signal decomposition are applied to small machines with a rolling element bearing in steady-state operation with a shaft considered as a rigid body. The machines examined in the authors’ research operate above their first critical rotational speed interval and thus their shafts are considered to be flexible and are equipped with a hydrodynamic sliding bearing. Such an arrangement introduces significant complexity to the analysis of the machine behavior, and consequently, analyzing such data requires a highly skilled human expert. The main novelty proposed in the paper is the decomposition of transient vibration data into components responsible for particular failure modes. The method is automated and can be used for identification of turbogenerator malfunctions. Each parameter of a particular decomposed function has its physical representation and can help the maintenance staff to operate the machine properly. The parameters can also be used by the managing personnel to plan overhauls more precisely. The method has been validated on real-life data originating from a 200 MW class turbine. The real-life field data, along with the data generated by means of the commercial software utilized in GE’s engineering department for this particular class of machines, was used as the reference data set for an unbalanced response during the transients in question.
Power generation technologies are essential for modern economies. Modal Analysis (MA) is advanced but well-established method for monitoring of structural integrity of critical assets, including ...power ones. Apart from classical MA, the Operational Modal Analysis approach is widely used in the study of dynamic properties of technical objects. The principal reasons are its advantages over the classical approach, such as the lack of necessity to apply the excitation force to the object and isolate it from other excitation sources. However, for industrial facilities, the operational excitation rarely takes the form of white noise. Especially in the case of rotating machines, the presence of rotational speed harmonics in the response signals causes problems with the correct identification of the modal model. The article presents a hybrid approach where combination of results of two Operational Modal Analyses and Experimental Modal Analysis is performed to improve the models’ quality. The proposed approach was tested on data obtained from a 215 MW turbogenerator operating in one of Polish power plants. With the proposed approach it was possible to diagnose the machine’s excessive vibration level correctly.
The paper contains a survey of mobile scanning systems for measuring the railway clearance gauge. The research was completed as part of the project carried out for the PKP (PKP Polish Railway Lines ...S.A., Warsaw, Poland) in 2011-2013. The authors conducted experiments, including a search for the latest solutions relating to mobile measurement systems that meet the basic requirement. At the very least, these solutions needed to be accurate and have the ability for quick retrieval of data. In the paper, specifications and the characteristics of the component devices of the scanning systems are described. Based on experiments, the authors did some examination of the selected mobile systems to be applied for measuring the clearance gauge. The Riegl (VMX-250) and Z+F (Zoller + Fröhlich) Solution were tested. Additional test measurements were carried out within a 30-kilometer section of the Warsaw-Kraków route. These measurements were designed so as to provide various elements of the railway infrastructure, the track geometry and the installed geodetic control network. This ultimately made it possible to reduce the time for the preparation of geodetic reference measurements for the testing of the accuracy of the selected systems. Reference measurements included the use of the polar method to select profiles perpendicular to the axis of the track. In addition, the coordinates selected were well defined as measuring points of the objects of the infrastructure of the clearance gauge. All of the tested systems meet the accuracy requirements initially established (within the range of 2 cm as required by the PKP). The tested systems have shown their advantages and disadvantages.
Contemporary risk management is based on statistical analysis. Such an approach has a few crucial disadvantages. First of all, it has limited applicability to new technological solutions. In this ...paper, a new idea for risk evaluation and management is put forward. The proposed approach is based on the autonomous systems theory. The theoretical foundation of the proposed idea is described and its prospective applications are discussed. The proposed measures of risk are based on the idea of the controllability of the system—the greater the level of controllability, the lower the risk. Various aspects of controllability are analyzed—economic, technological, and industrial. For each aspect of controllability, the problem of defining adequate measures for the level of risk is discussed. The proposed approach allows the risk assessor to analyze the system deeply. As a consequence, the analyst can assess the risk based not only on a posteriori statistics but also on an analysis of the crucial properties of the system. This allows the investigator to predict a priori possibilities of critical events. The proposed methodology is applied to the power industry.
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