Selection, Dimensioning and Maintenance of Roller Bearings

Desnica, Eleonora; Ašonja, Aleksandar; Radovanović, Ljiljana; Palinkas, Ivan; Kiss, I

doi:10.1007/978-3-031-21429-5_12

Cited by 13 publications

(16 citation statements)

References 6 publications

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“…This means there are precise data on the element's behavior for the previous period. For the next period, there is only an expectation (probability) that does not necessarily come true [24].…”

Section: Reliability Of the Cable Carmentioning

confidence: 99%

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Analysis of the Reliability of Automated Electric Motor Drives of Cable Cars at Ski Fields

Bogićević¹,

Petković²,

Kojić³

2023

adeletters

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The basic questions that arise when maintaining an automated electric drive relate to the safety and reliability of the elements in operation. The maintenance strategy of these elements is planned to start from the basic facts related to the plant’s importance, type and purpose. The main purpose of maintenance is the timely detection of a fault and its elimination, i.e. ensuring continuous operation of the plant. In this work, the operation of the cable car was analyzed by monitoring the reliability of the electric drive. An analysis of the significance of reliability coefficients is a potentially new method for maintenance planning. The aim of the research was to define a new strategy for the maintenance of the facility and ensure the maximum safety of skiers through the analysis of previous events. Research is based on coefficients that indicate the reliability, frequency of failures, availability and unavailability of the elements that make up a drive. The essence of the conducted research is based on a new strategy for planning and maintaining such a facility, aiming to ensure skiers’ safety and protection.

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Section: Reliability Of the Cable Carmentioning

confidence: 99%

“…The probability of failure is higher in more complex systems (systems with more elements) [15]. It is approximately equal to the sum of the failure probabilities of its parts.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Reliability of Automated Electric Motor Drives of Cable Cars at Ski Fields

Bogićević¹,

Petković²,

Kojić³

2023

adeletters

View full text Add to dashboard Cite

show abstract

“…Bearings are critical rotating machinery components and one of these machines' weakest links [1,2]. Their performance directly affects the system's stable operation and production efficiency [3,4].…”

Section: Introductionmentioning

confidence: 99%

Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions

Wang,

Ahmed,

Chen

et al. 2024

Applied Sciences

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Bearings are one of the critical components of rotating machinery, and their failure can cause catastrophic consequences. In this regard, previous studies have proposed a variety of intelligent diagnosis methods. Most existing bearing fault diagnosis methods implicitly assume that the training and test sets are from the same distribution. However, in real scenarios, bearings have been working in complex and changeable working environments for a long time. The data during their working processes and the data used for model training cannot meet this condition. This paper proposes an improved adversarial transfer network for fault diagnosis under variable working conditions. Specifically, this paper combines an adversarial transfer network with a short-time Fourier transform to obtain satisfactory results with the lighter network. Then, this paper employs a channel attention module to enhance feature fusion. Moreover, this paper designs a novel domain discrepancy hybrid metric loss to improve model transfer learning performance. Finally, this paper verifies the method’s effectiveness on three datasets, including dual-rotor, a Case Western Reserve University dataset and the Ottawa dataset. The proposed method achieves average accuracy, surpassing other methods, and shows better domain alignment capabilities.

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“…The most crucial part of rotating machines is the bearing, whose main purposes are to sustain the mechanical rotating body and decrease the friction coefficient while it is in motion [ 1 ]. Rolling bearings are employed to transmit loads from moving to stationary components or vice versa as well as to create conditions for the relative movement of rotating parts [ 2 , 3 ]. Efficient bearing fault diagnosis is required to ensure the smooth operation of mechanical system.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of Rolling Element Bearings Based on Modified AlexNet

Mohiuddin,

Islam,

Islam

et al. 2023

Sensors

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The reliable and safe operation of industrial systems needs to detect and diagnose bearing faults as early as possible. Intelligent fault diagnostic systems that use deep learning convolutional neural network (CNN) techniques have achieved a great deal of success in recent years. In a traditional CNN, the fully connected layer is located in the final three layers, and such a layer consists of multiple layers that are all connected. However, the fully connected layer of the CNN has the disadvantage of too many training parameters, which makes the model training and testing time longer and incurs overfitting. Additionally, because the working load is constantly changing and noise from the place of operation is unavoidable, the efficiency of intelligent fault diagnosis techniques suffers great reductions. In this research, we propose a novel technique that can effectively solve the problem of traditional CNN and accurately identify the bearing fault. Firstly, the best pre-trained CNN model is identified by considering the classification’s success rate for bearing fault diagnosis. Secondly, the selected CNN model is modified to effectively reduce the parameter quantities, overfitting, and calculating time of this model. Finally, the best classifier is identified to make a hybrid model concept to achieve the best performance. It is found that the proposed technique performs well under different load conditions, even in noisy environments, with variable signal-to-noise ratio (SNR) values. Our experimental results confirm that this proposed method is highly reliable and efficient in detecting and classifying bearing faults.

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Selection, Dimensioning and Maintenance of Roller Bearings

Cited by 13 publications

References 6 publications

Analysis of the Reliability of Automated Electric Motor Drives of Cable Cars at Ski Fields

Analysis of the Reliability of Automated Electric Motor Drives of Cable Cars at Ski Fields

Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions

Intelligent Fault Diagnosis of Rolling Element Bearings Based on Modified AlexNet

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