Local Maximum Acceleration Based Rotating Machinery Fault Classification Using KNN

Paudyal, Santosh; Atique, Saifuddin Ahmed; Yang, Cai Xia

doi:10.1109/eit.2019.8833788

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…22 It has been widely used for damage detection. Paudyal 23 used KNN to identify unbalanced and misalignment faults for shafts based on vibration signals. The results show the accuracy can achieve 96%.…”

Section: K-nearest-neighbours Algorithmmentioning

confidence: 99%

Machine learning methods for multi-rotor UAV structural damage detection based on MEMS sensor

Ma,

Mustapha,

Ishak

et al. 2023

International Journal of Aeroacoustics

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Multi-rotor Unmanned Aerial Vehicles (UAVs) have become increasingly important in industries and early detection of structural damage is crucial to prevent unexpected breakdowns, ensure production efficiency, and maintain operational safety. This paper proposes machine learning techniques for detecting damage caused by loosened screws which is not easy founded based on vibration signals. An independent data acquisition device with a Micro Electro Mechanical Systems (MEMS) sensor is designed and fixed onto the multi-rotor UAVs to acquire the vibration data. Four machine learning algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree, and Random Forest, are employed for damage detection. The results demonstrate successful utilization of the vibration data from the MEMS sensor for damage detection, with the random forest model outperforming other models with an accuracy of 90.07.

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Section: K-nearest-neighbours Algorithmmentioning

confidence: 99%

Machine learning methods for multi-rotor UAV structural damage detection based on MEMS sensor

Ma,

Mustapha,

Ishak

et al. 2023

International Journal of Aeroacoustics

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“…Logistic regression predicts the probability of a target variable that predicts a binary outcome [29]. We include K-nearest neighbor (KNN) which is used to classify data points into separate classes [30]. We, finally, use SVM to effectively handle binary classification problems.…”

Section: Model Development Using Ensemble Learningmentioning

confidence: 99%

Software Fault Prediction Using Combinations of Code Smells, Code Metrics, and Code Smell Metrics With Ensemble and Deep Learning

Khan,

Ashraf

2023

Preprint

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Code smells are the structural characteristics of the software under development that indicate poor code choices and can cause errors or failures in the software and they can degrade the software maintenance and evolution processes. Software fault prediction (SFP) helps predict the probability of the existence of software faults utilizing code characteristics and metrics. Code smells-based datasets are based on a variety of code measures such as information about the presence of certain code smells or a combination of code smells with code metrics and code smell metrics with code metrics. We investigate the effectiveness and usefulness of these different combinations for performance evaluation and improvement of SFP models. We label the unlabeled datasets using clustering and pseudo-labeling techniques. We implement models considering ensemble methods and deep learning algorithms and compare performance. We use k-fold cross-validation and our results outperform existing benchmark studies. We conclude that code smells-based software defect prediction has optimal accuracy and precision.

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“…Classification can be logic-based statistical learning (SVM) and instance-based (KNN) algorithms [7]. Machine learning gets more popularity in the recent years because of their swiftness to unexperienced scenarios and ability to solve complicated jobs, which are difficult to solve by using mathematical model [8]. The statistical features extraction are very important for classification or recognition process and the supervised learning K-Nearest Neighbors (KNN) has been used for classification and compare the results with Support Vector machine (SVM) [9].…”

Section: Literature Reviewmentioning

confidence: 99%

In-Situ Event Localization for Pipeline Monitoring System Based Wireless Sensor Network Using K-Nearest Neighbors and Support Vector Machine

Hadi

Shareef

2020

J. Al-Qadisiyah Comp. Sci. Math.

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Pipeline Monitoring Systems (PMS) benefits the most of recent developments in wireless remote monitoring since each pipeline would span for long distances which make conventional methods unsuitable. Precise monitoring and detection of damaging events requires moving large amounts of data between sensor nodes and base station for processing which require high bandwidth communication protocol. To overcome this problem, In-Situ processing can be practiced by processing the collected data locally at each node instead of the base station. However, this introduce a challenge to the limited resources available on the nodes. In this paper, a Wireless Sensor Network (WSN) was implemented for In-Situ Pipeline Monitoring System with proposed algorithms for event location estimation. The proposed algorithms include feature extraction (using ANOVA), dimensionality reduction using statistical procedure that is (Principle Component Analysis PCA) and data classification using supervised learning K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). The proposed system was tested on pipelines in Al-Mussaib Gas Turbine Power Plant. During test, knocking events are applied at several distances relative to the nodes locations. Data collected at each node is filtered and processed locally in real time in each two adjacent nodes. The results of the estimation is then sent to the supervisor at base-station for display. The results show the proposed system ability to estimate the location of knocking event.

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Local Maximum Acceleration Based Rotating Machinery Fault Classification Using KNN

Cited by 10 publications

References 11 publications

Machine learning methods for multi-rotor UAV structural damage detection based on MEMS sensor

Machine learning methods for multi-rotor UAV structural damage detection based on MEMS sensor

Software Fault Prediction Using Combinations of Code Smells, Code Metrics, and Code Smell Metrics With Ensemble and Deep Learning

In-Situ Event Localization for Pipeline Monitoring System Based Wireless Sensor Network Using K-Nearest Neighbors and Support Vector Machine

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