A Preliminary Studies of the Impact of a Conveyor Belt on the Noise Emission

Bortnowski, Piotr; Król, R.; Nowak-Szpak, Anna; Ozdoba, Maksymilian

doi:10.3390/su14052785

Cited by 9 publications

(4 citation statements)

References 41 publications

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“…The main sources of noise in electrical motors are divided into three main categories: aerodynamic, mechanical and electromagnetic [60]. Each of these sources was analyzed in various studies [61,62]. The level of noise produced by an electromagnetic source is greatly dependent on the geometrical parameters of the machine, such as dimensions of slots and poles, airgap length [63], ratio between housing outer diameter and height of the yoke [64].…”

Section: B Numerical Structural Analysis Of the Studied Iwm-imgmentioning

confidence: 99%

Structural Analysis of an In-Wheel Motor with Integrated Magnetic Gear Designed for Automotive Applications

2022

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This paper is focused on the structural analysis of a new propulsion system defined by an in-wheel motor with an integrated magnetic gear (IWM-IMG). The IWM-IMG has two levels of torque and speed. First, the state of the art of magnetic gears (MGs) is presented. Next, the proposed structure and the main designing steps are depicted. The advantages of the proposed structure are emphasized, in terms of speed increase operation, knowing that this is achieved without it being necessary to increase the fed frequency via the inverter. Moreover, the electromagnetic characteristics are briefly introduced to prove the suitability of the proposed solution for the considered application. Next, the structural analysis is detailed, where through an analytical, numerical and experimental approach, the study is validated.

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Section: B Numerical Structural Analysis Of the Studied Iwm-imgmentioning

confidence: 99%

Structural Analysis of an In-Wheel Motor with Integrated Magnetic Gear Designed for Automotive Applications

2022

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“…Compliance with internal regulations at mining sites necessitates daily inspections of different BC components. The extensive length of BCs, spanning thousands of meters, coupled with the need to individually inspect dozens of components under harsh environmental conditions, underscores the complexity of this task [ 10 , 11 , 12 , 13 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Advanced Image Analytics for Mobile Robot-Based Condition Monitoring in Hazardous Environments: A Comprehensive Thermal Defect Processing Framework

Siami,

Barszcz,

Zimroz

2024

Sensors

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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.

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“…The idlers are responsible for supporting the loaded bulk materials' weights that are carried by the belt [7,8]. Faulty idlers can cause serious damage in conveyor systems [9,10].…”

Section: Introductionmentioning

confidence: 99%

Automated Identification of Overheated Belt Conveyor Idlers in Thermal Images with Complex Backgrounds Using Binary Classification with CNN

Siami¹,

Barszcz

Wodecki

et al. 2022

Sensors

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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.

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A Preliminary Studies of the Impact of a Conveyor Belt on the Noise Emission

Cited by 9 publications

References 41 publications

Structural Analysis of an In-Wheel Motor with Integrated Magnetic Gear Designed for Automotive Applications

Structural Analysis of an In-Wheel Motor with Integrated Magnetic Gear Designed for Automotive Applications

Advanced Image Analytics for Mobile Robot-Based Condition Monitoring in Hazardous Environments: A Comprehensive Thermal Defect Processing Framework

Automated Identification of Overheated Belt Conveyor Idlers in Thermal Images with Complex Backgrounds Using Binary Classification with CNN

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