Monitoring and Detection of Wind Turbine Vibration with KNN-Algorithm

Vives, Javier

doi:10.4236/jcc.2022.107001

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…Praveenkumar [5] Support Vector Machine Automobile gearbox Vibration signals 90% accurac Su and Huang [6] Random Forest Hard disk drive Vibration, temperature, and other variables 85% accurac Vives [7] K-Nearest Neighbors Wind turbine Vibration signals 95% accurac Hoang and Kang [8] Neural Network Rolling element Bearings Vibration signals 100.0% accurac Qin, Li and Liu [9] Gradient Boosting Diesel engine Vibration signals 99.9% accurac…”

Section: Authors ML Methods Equipment Description Of the Data Resultsmentioning

confidence: 99%

An Embedded Machine Learning Fault Detection System for Electric Fan Drive

Aung,

Kok,

Koh

et al. 2024

Electronics

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Industrial fans are critical components in industrial production, where unexpected damage of important fans can cause serious disruptions and economic costs. One trending market segment in this area is where companies are trying to add value to their products to detect faults and prevent breakdowns, hence saving repair costs before the main product is damaged. This research developed a methodology for early fault detection in a fan system utilizing machine learning techniques to monitor the operational states of the fan. The proposed system monitors the vibration of the fan using an accelerometer and utilizes a machine learning model to assess anomalies. Several of the most widely used algorithms for fault detection were evaluated and their results benchmarked for the vibration monitoring data. It was found that a simple Convolutional Neural Network (CNN) model demonstrated notable accuracy without the need for feature extraction, unlike conventional machine learning (ML)-based models. Additionally, the CNN model achieved optimal accuracy within 30 epochs, demonstrating its efficiency. Evaluating the CNN model performance on a validation dataset, the hyperparameters were updated until the optimal result was achieved. The trained model was then deployed on an embedded system to make real-time predictions. The deployed model demonstrated accuracy rates of 99.8%, 99.9% and 100.0% for Fan-Fault state, Fan-Off state, and Fan-On state, respectively, on the validation data set. Real-time testing further confirmed high accuracy scores ranging from 90% to 100% across all operational states. Challenges addressed in this research include algorithm selection, real-time deployment onto an embedded system, hyperparameter tuning, sensor integration, energy efficiency implementation and practical application considerations. The presented methodology showcases a promising approach for efficient and accurate fan fault detection with implications for broader applications in industrial and smart sensing applications.

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Section: Authors ML Methods Equipment Description Of the Data Resultsmentioning

confidence: 99%

An Embedded Machine Learning Fault Detection System for Electric Fan Drive

Aung,

Kok,

Koh

et al. 2024

Electronics

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“…It is very useful to diagnose problems with components like the small wind turbine prototype shown in Figure 2 as it can detect deterioration and wear on the parts and what its effects are [18]. The purpose of this system is to allow easy exchange of parts without waiting for deterioration to occur and, therefore, to test diagnostic techniques before deterioration occurs.…”

Section: Prototype and Sensor Distributionmentioning

confidence: 99%

SVM-Algorithm for Supervision, Monitoring and Detection Vibration in Wind Turbines

Vives¹,

Palací²,

Heart³

2022

JCC

Self Cite

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With the implementation of supervised machine learning techniques, wind turbine maintenance has been transformed. A wind turbine's electrical and mechanical components can be automatically identified, monitored, and detected to predict, detect, and anticipate their degeneration using this method of automatic and autonomous learning. Two different failure states are simulated due to bearing vibrations and compared with machine learning classifier and frequency analysis. A wind turbine can be monitored, monitored, and faulted efficiently by implementing SVM. With these technologies, downtime can be reduced, breakdowns can be anticipated, and aspects can be imported if they are offshore.

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“…The KNN algorithm is utilized to investigate the vibrations in the wind turbines to eradicate the failures and faults [8]. The rotor bearings in the wind turbines are checked for faults using the KNN algorithm to increase energy production and decrease the maintenance cost [9].…”

Section: Introductionmentioning

confidence: 99%

FPGA implementation of KNN Algorithm-Based Prediction for DPWM Methods

T. Anitha, M. Arulaalan

2023

tjjpt

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This paper presents the prediction of the DPWM methods using the FPGA device. For the sake of prediction, the parameters such as Clocking frequency, switching frequency, positive duty cycle value, and negative duty cycle value are considered by taking 23 samples of the real-time results obtained for the DPWM techniques. Based on the dataset, the parametric values are fed into the KKN algorithm to predict the suitable DPWM technique by identifying the K-nearest neighbor. The proposed KNN algorithm for the DPWM technique prediction is developed using the VHDL code and synthesized in the FPGA board for real-time validation. Also, the ASIC IC layout design for the proposed method is generated using the Cadence EDA tool. The performance analysis for the power, area, and delay are evaluated using the Xilinx and Cadence Tool.

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Monitoring and Detection of Wind Turbine Vibration with KNN-Algorithm

Cited by 5 publications

References 18 publications

An Embedded Machine Learning Fault Detection System for Electric Fan Drive

An Embedded Machine Learning Fault Detection System for Electric Fan Drive

SVM-Algorithm for Supervision, Monitoring and Detection Vibration in Wind Turbines

FPGA implementation of KNN Algorithm-Based Prediction for DPWM Methods

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