With the increasing dependence on communication networks, the need for reliable and efficient network maintenance has become crucial. However, current maintenance techniques are limited to routine checks and post-maintenance, and lack a comprehensive monitoring system to assess the state of the network. This can lead to delays in identifying and fixing faults and often requires specialized expertise. The study proposes a deep learning-based model for network fault analysis and investigates its effectiveness through experiments and simulations. The proposed technique is aimed to prevent errors produced by the created model and enhance the resilience, universality, and accuracy of the network fault diagnostic model. The results of the study suggest that the proposed technique is capable of identifying and locating faults in a timely and efficient manner, thus reducing network interruption, and increasing network reliability.
Top1. Introduction
The use of artificial intelligence (AI) in the industrial sector has the potential to revolutionize the way in which production and communication lines are managed. One key area in which AI can be particularly useful is in the detection of faults. Immediate fault detection can help to minimize downtime, reduce costs, and improve overall efficiency (Wu et al. 2020). In this research article, we will explore the use of AI for immediate fault detection in production and communication lines in the industrial sector. We will examine the current state of the art in AI-based fault detection, as well as the challenges and opportunities associated with this technology (Sachdeva et al. 2022). The article will also present case studies and real-world examples to demonstrate the effectiveness of AI-based fault detection in the industrial sector. Overall, this research aims to provide a comprehensive overview of the potential of AI for immediate fault detection in production and communication lines, and to encourage further research and development in this area (Natrayan L & Kumar MS. 2020).
One of the main approaches used in AI-based fault detection is the use of machine learning (ML) algorithms. These algorithms can be trained to detect patterns and anomalies in data, which can then be used to identify potential faults. Several studies have shown the effectiveness of using ML algorithms for fault detection in industrial systems (Veeman et al., 2021). For example, in a study by a ML-based fault detection method was used to detect faults in a wind turbine system (Natrayan L et al., 2021). The results showed that the proposed method was able to detect faults with high accuracy and low false alarm rate. Another study by (Swain and Khilar 2017)used a ML-based approach to detect faults in a gas turbine. The results showed that the proposed method was able to detect faults with high accuracy and low false alarm rate, and it outperformed traditional methods (Pragadish et al., 2022).
Another approach that has been used in AI-based fault detection is the use of artificial neural networks (ANNs). ANNs are a type of ML algorithm that can be used to model complex nonlinear systems (Balaji et al., 2022), making them well-suited for fault detection in industrial systems. Several studies have shown the effectiveness of using ANNs for fault detection (Natrayan L et al., 2018). For example, in a study by (Priya et al. 2022)an ANN-based method was used to detect faults in a centrifugal pump. The results showed that the proposed method was able to detect faults with high accuracy and low false alarm rate. Another study by (Chen et al. 2023) used an ANN-based approach to detect faults in a steam turbine. The results showed that the proposed method was able to detect faults with high accuracy and low false alarm rate, and it outperformed traditional methods (Ramaswamy et al., 2022).
Another promising approach that has been used in AI-based fault detection is the use of deep learning (DL) algorithms. DL algorithms, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have been shown to be effective in a wide range of applications, including image and speech recognition, and natural language processing Yogeshwaran et al., (2015). Several studies have also shown the effectiveness of using DL algorithms for fault detection in industrial systems (Polenghi et al. 2023). For example, in a study a CNN-based method was used to detect faults in a bearing. The results showed that the proposed method was able to detect faults with high accuracy and low false alarm rate. Another study by Li et al. (2020) used a RNN-based approach to detect faults in a gearbox. The results showed that the proposed method was able to detect faults with high accuracy and low false alarm rate, and it outperformed traditional methods (Mishra and Ray 2018; Song et al. 2020).