Preventive Maintenance for Fault Detection in Transfer Nodes using Machine Learning

Dsouza, Joanita; S, Senthil Velan

doi:10.1109/iccike47802.2019.9004230

Cited by 13 publications

(4 citation statements)

References 8 publications

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“…Model training [14] involves selecting an appropriate machine learning algorithm and training it on the pre-processed data. Decision trees, in essence, can solve both classification and regression issues.…”

Section: Related Workmentioning

confidence: 99%

“…These branches represent different possibilities based on the dataset and offer a well-defined goal, while the root node signifies the optimal choice. Several algorithms have been used in [14], such as linear regression, decision trees, and random forests for model training. Performance evaluation involves assessing the accuracy of the trained models.…”

Section: Related Workmentioning

confidence: 99%

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Enhancing LAN Failure Predictions with Decision Trees and SVMs: Methodology and Implementation

Rzayeva,

Myrzatay,

Abitova

et al. 2023

Electronics

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Predicting Local Area Network (LAN) equipment failure is of utmost importance to ensure the uninterrupted operation of modern communication networks. This study explores the use of machine learning algorithms to enhance the accuracy of equipment failure prediction in LAN environments. Using these algorithms to enhance LAN failure predictions involves collecting and analyzing network data, such as packet loss rates and latency, to identify patterns and anomalies. These algorithms can then predict potential LAN failures by recognizing early warning signs and deviations from normal network behavior. By leveraging machine learning, network administrators can proactively address issues, reduce downtime, and improve overall network reliability. In our study, two powerful machine learning algorithms—decision tree and support vector machine (SVM)—are used. To evaluate the effectiveness of the proposed models, a comprehensive dataset comprising various LAN equipment parameters and corresponding failure instances is utilized. The dataset is pre-processed to handle missing values and normalize features, ensuring the algorithms’ optimal performance. Performance metrics, such as accuracy, precision, recall, and F1-score, are employed to assess the predictive capabilities of the models. The excremental results of our study lead to more reliable and stable network operations by allowing early detection of potential issues and preventive maintenance. This leads to reduced downtime, improved network performance, and enhanced overall user satisfaction. They demonstrate the efficacy of both decision tree and SVM algorithms in accurately predicting LAN equipment failure.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Enhancing LAN Failure Predictions with Decision Trees and SVMs: Methodology and Implementation

Rzayeva,

Myrzatay,

Abitova

et al. 2023

Electronics

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“…Various algorithms and models can be used on the cleaned dataset in order to gain insights to make better decisions. Preventive Maintenance can also be used to predict the negative impacts which may be caused due to certain measure or decisions taken [11]. However, exploratory data analysis is an essential step before performing algorithms and models.…”

Section: B Application To Covid-19 Datamentioning

confidence: 99%

Using Exploratory Data Analysis for Generating Inferences on the Correlation of COVID-19 cases

Dsouza

2020

2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

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Exploratory Data Analysis (EDA) is a field of data analysis used to visually represent the knowledge embedded deep in the given data set. The technique is widely used to generate inferences from a given data set. Data set of current pandemic, the COVID-19 is widely made available by the standard dataset repository. EDA can be applied to these standard dataset to generate inferences. In this paper, data visualization technique is applied to the dataset and is used to formulate patterns for better insights on the effects of the pandemic with respect to the variables/ labels given in the dataset. A Web application tool called Jupyter Notebook is used to generate graphs using python language as it consists of libraries which are used for the process of EDA and the visualization is depicted for the attributes showing higher correlation. Based on the graphs obtained, we can draw conclusions from the current situation based on the data available, understand why a certain variable is increasing/decreasing with respect to another and what can be done to improve the drawbacks found.

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“…Применение методов машинного обучения и искусственных нейронных сетей на этапе анализа данных для обучения математической модели прогнозирования технического состояния оборудования на основе исторических данных о работе позволяет перейти к прогностической модели технического обслуживания [13][14][15]. Исследование проводилось на данных, собранных с приводов промышленных роботов (как манипуляторов, так и отдельных двигателей), которые выполняли однотипные рабочие операции в разных режимах работы (без нагрузки, с нагрузкой, с перегрузкой).…”

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Approach to an automatic prediction of the state of industrial manipulators using machine learning methods

Goncharov¹,

Savelev²,

Pisankin³

et al. 2021

Proceedings of TUSUR University

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Due to intensive development of information technologies and the onset of 4th industrial revolution the number of robotic industries is steadily growing. The volume of production and the use of robots is also increasing. At the same time, the support and the management of digital production is being rapidly developing. The robotic systems are incapable of completely excluding a person from the technological chain, since they need timely maintenance and personnel working out the emergency situations. One of the solutions to reduce the risk of unexpected breakdowns is a predictive approach to the maintenance. The implementation of this approach is carried out using data analysis tools. This study presents the results of applying machine learning methods to analyze data from industrial robots in order to predict potential failures

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Preventive Maintenance for Fault Detection in Transfer Nodes using Machine Learning

Cited by 13 publications

References 8 publications

Enhancing LAN Failure Predictions with Decision Trees and SVMs: Methodology and Implementation

Enhancing LAN Failure Predictions with Decision Trees and SVMs: Methodology and Implementation

Using Exploratory Data Analysis for Generating Inferences on the Correlation of COVID-19 cases

Approach to an automatic prediction of the state of industrial manipulators using machine learning methods

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