Performance Analysis of Mesh Based Enterprise Network Using RIP, EIGRP and OSPF Routing Protocols

Kabir, Md. Ahasan; Islam, Md. Saiful; Mortuza, Mohammad Golam; Mohiuddin, Mohammad

doi:10.3390/ecsa-8-11285

“…There are two parts to bearing fault identification problem. The initial part focuses on the extraction of fault information-related features from vibration signals, and the latter one on fault identification, which makes use of the extracted features for problem detection by applying a variety of artificial intelligence (AI) approaches, including an artificial neural network (ANN), a decision tree (DT), the k-nearest neighbors (k-NN) algorithm, a support vector machine (SVM), neuro-fuzzy [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ], etc.…”

Section: Introductionmentioning

confidence: 99%

“…There are two parts to bearing fault identification problem. The initial part focuses on the extraction of fault information-related features from vibration signals, and the latter one on fault identification, which makes use of the extracted features for problem detection by applying a variety of artificial intelligence (AI) approaches, including an artificial neural network (ANN), a decision tree (DT), the k-nearest neighbors (k-NN) algorithm, a support vector machine (SVM), neuro-fuzzy [4][5][6][7][8][9][10][11], etc. Samanta et al [12] conducted a study comparing the effectiveness of three different ANN types for detecting bearing faults: multi-layer perception (MLP), the radial basis function (RBF) network, and the probabilistic neural network (PNN).…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of Rolling Element Bearings Based on Modified AlexNet

Mohiuddin,

Islam,

Islam

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Because of this, it is expected that AI-driven DT technology will be able to successfully assist decision making in multi-objective issues by adapting traditional model-based methodologies to shifting boundary circumstances and providing a demand-oriented, real-time assessment foundation. Many studies have previously provided descriptions and definitions of DTs from the standpoint of broad ideas and technological frameworks [11]. Not only that, but product design, simulation, and modeling would not be able to take advantage of their own unique enabler, artificial intelligence diagnostics and prognostics for To examine various situations and forecast decision outcomes, computer simulations are performed.…”

Section: Introductionmentioning

confidence: 99%