Impact of Crack Length into Pipe Conveying Fluid Utilizing Fast Fourier transform Computer Algorithm

AL-Sagheer, Radhwan Hussein Abdulzhraa; Mezher, Alaa Abdul Hussein; Habeeban, Karrar Abdullah Mohammed

doi:10.11591/ijece.v9i4.pp2541-2547

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Random Forest also achieves high detection accuracy (95.11%) and a low false positive rate (0.103). The Ensemble with JRip Classifiers model has been compared with five different methods which are clustering, Neural Network, Recurrent Neural Network [37,38], K-medoids, K-means [12], Long Short-Term Memory (LSTM) [11], anddecision trees [10]. The comparative of results in Table 2 show that our proposal Ensemble with JRip Classifiers model achieves better detection accuracythan the existing systems for botnet detection.…”

Section: Resultsmentioning

confidence: 99%

Botnet detection using ensemble classifiers of network flow

Algelal¹,

Aldhaher²,

Abdul-Wadood³

et al. 2020

IJECE

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Recently, Botnets have become a common tool for implementing and transferring various malicious codes over the Internet. These codes can be used to execute many malicious activities including DDOS attack, send spam, click fraud, and steal data. Therefore, it is necessary to use Modern technologies to reduce this phenomenon and avoid them in advance in order to differentiate the Botnets traffic from normal network traffic. In this work, ensemble classifier algorithms to identify such damaging botnet traffic. We experimented with different ensemble algorithms to compare and analyze their ability to classify the botnet traffic from the normal traffic by selecting distinguishing features of the network traffic. Botnet Detection offers a reliable and cheap style for ensuring transferring integrity and warning the risks before its occurrence.

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Section: Resultsmentioning

confidence: 99%

Botnet detection using ensemble classifiers of network flow

Algelal¹,

Aldhaher²,

Abdul-Wadood³

et al. 2020

IJECE

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show abstract

“…As discussed before, among them, ultrasound signals have the ability to distinguish industrial faults more efficiently [7]. However, in order to analyze ultrasonic signals, conventional methods like FFT [8], filtering, and windowing [9,10], due to the overlapping of noise and signal, are not effective. Alternatively, intelligent methods like machine learning (ML) can detect subtle changes in the received signals from the ultrasonic sensor [1,11].…”

Section: Introductionmentioning

confidence: 99%

Industrial Fault Detection Employing Meta Ensemble Model Based on Contact Sensor Ultrasonic Signal

Moshrefi,

Tawfik,

Elsayed

et al. 2024

Sensors

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Ultrasonic diagnostics is the earliest way to predict industrial faults. Usually, a contact microphone is employed for detection, but the recording will be contaminated with noise. In this paper, a dataset that contains 10 main faults of pipelines and motors is analyzed from which 30 different features in the time and frequency domains are extracted. Afterward, for dimensionality reduction, principal component analysis (PCA), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) are performed. In the subsequent phase, recursive feature elimination (RFE) is employed as a strategic method to analyze and select the most relevant features for the classifiers. Next, predictive models consisting of k-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM) are employed. Then, in order to solve the classification problem, a stacking classifier based on a meta-classifier which combines multiple classification models is introduced. Furthermore, the k-fold cross-validation technique is employed to assess the effectiveness of the model in handling new data for the evaluation of experimental results in ultrasonic fault detection. With the proposed method, the accuracy is around 5% higher over five cross folds with the least amount of variation. The timing evaluation of the meta model on the 64 MHz Cortex M4 microcontroller unit (MCU) revealed an execution time of 11 ms, indicating it could be a promising solution for real-time monitoring.

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A Review of Inspection Methods for Continuously Monitoring PVC Drinking Water Mains

Tran¹,

Le²,

Yntema³

et al. 2022

IEEE Internet Things J.

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The drinking water mains, mostly buried underground and stay there for decades, require proper maintenance to prevent failures. Among different kinds of material, polyvinyl chloride (PVC) has been widely used due to positive features such as high durability, corrosion resistance, low price, and easy installation. To the best of our knowledge, this is the first paper that reviews the inspection methods toward continuously monitoring the structural health of PVC drinking water mains. To understand which properties need inspecting, we first investigated the attributes that influence PVC pipe and joint failures. Then, we reviewed the methods that have already been applied or can inspect these influencing attributes. We categorized the prospects into five groups: sound wave, fiber optic sensing, hydraulic monitoring, multiple discrete sensors, and other inline methods. Finally, we discussed the possibility and challenges in implementing these methods into a continuous monitoring system of PVC water mains for early failure warning. The result, which includes active sound wave, fiber optic sensing, hydraulic vibration, multiple discrete sensors methods, can help future researchers select the appropriate methods to develop the continuous monitoring system for the PVC water mains.Index Terms-drinking water mains, PVC pipe, pipeline inspection, continuous monitoring, early failure warning, leak, pipe crack. I. INTRODUCTIONG RADUALLY, polyvinyl chloride (PVC) has become the dominant material in the Dutch drinking water mains due to its many advantages such as high durability, resistance to many chemicals, low price, and great flexibility [1]. Although more reliable than other pipes [2], PVC mains also become old

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Impact of Crack Length into Pipe Conveying Fluid Utilizing Fast Fourier transform Computer Algorithm

Cited by 3 publications

References 5 publications

Botnet detection using ensemble classifiers of network flow

Botnet detection using ensemble classifiers of network flow

Industrial Fault Detection Employing Meta Ensemble Model Based on Contact Sensor Ultrasonic Signal

A Review of Inspection Methods for Continuously Monitoring PVC Drinking Water Mains

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