An insight on VMD for diagnosing wind turbine blade faults using C4.5 as feature selection and discriminating through multilayer perceptron

Joshuva, A.; Kumar, Ravi; Sivakumar, S.; Deenadayalan, G.; Vishnuvardhan, R.

doi:10.1016/j.aej.2020.06.041

Cited by 52 publications

(16 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Prediksi kelangsungan hidup kanker paru-paru [2] dengan metode SVM, Naïve Bayes dan C4.5, hasil akurasi menunjukkan algoritma C4.5 memiliki kinerja lebih baik dibandingkan yang lain dalam memprediksi kanker paru-paru. Algoritma C4.5 juga digunakan [3] untuk diagnosis turbin angin, deteksi dan diagnosis kesalahan sistem photovoltaic (PV) yang terhubungan dengan jaringan [4] yang mempunyai akurasi pengujian sebesar 99,8%.…”

Section: Pendahuluanunclassified

Prediksi Penyakit Demam Berdarah Di Puskesmas Ngemplak Simongan Menggunakan Algoritma C4.5

2020

View full text Add to dashboard Cite

Dengue Hemorrhagic Fever (DHF) is a disease whose main cause is the flaviviridae virus. This virus can be transmitted through mosquito bites. The spread of this disease is faster in urban areas than in rural areas due to the high population density. Aedes aegypti mosquito is very easy to spread dengue virus from one person to another because it has a domestic nature. The Ministry of Health has collaborated with local health centers in the DHF prevention program. Ngemplak Simongan Health Center is one of the public health centers located in the District of West Semarang that serves a variety of treatments for this type of disease, one of which is a patient with Dengue Hemorrhagic Fever (DHF). C4.5 algorithm is used to predict dengue fever which aims to produce a decision tree. The choice of using Algortima is because it is widely used to describe a pattern / knowledge / information in the form of a decision tree explicitly. Application created using PHP programming language that produces prediction of dengue fever. The test results obtained an accuracy value of 94.44% so that the application program built can be used correctly.

show abstract

Section: Pendahuluanunclassified

Prediksi Penyakit Demam Berdarah Di Puskesmas Ngemplak Simongan Menggunakan Algoritma C4.5

2020

View full text Add to dashboard Cite

show abstract

“…Li et al [2] proposed an adaptive denoising method based on VMD, which is applied to water supply pipeline leakage location. Compared with EMD, VMD can effectively eliminate modal aliasing and is robust to noise and sampling [3,4].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearing Based on Improved VMD and KNN

Shen

Wang

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Variational modal decomposition (VMD) has the end effect, which makes it difficult to efficiently obtain fault eigenvalues from rolling bearing fault signals. Inspired by the mirror extension, an improved VMD is proposed. This method combines VMD and mirror extension. The mirror extension is a basic algorithm to inhibit the end effect. A comparison is made with empirical mode decomposition (EMD) for fault diagnosis. Experiments show that the improved VMD outperforms EMD in extracting the fault eigenvalues. The performance of the new algorithm is proven to be effective in real-life mechanical fault diagnosis. Furthermore, in this article, combining with singular value decomposition (SVD), fault eigenvalues are extracted. In this way, fault classification is realized by K-nearest neighbor (KNN). Compared with EMD, the proposed approach has advantages in the recognition rate, which can accurately identify fault types.

show abstract

“…For the question raised above, the scholars have presented and constructed some models such as Empirical Mode Decomposition (EMD), Wavelet Transform (WT), Local Mean Value Decomposition (LMVD), and Variational Mode Decomposition (VMD) in references [2][3][4]. e experiment results show that these methods may acquire the most of the valuable information in the specified scenarios.…”

Section: Introductionmentioning

confidence: 99%

A Classification Algorithm of Fault Modes-Integrated LSSVM and PSO with Parameters’ Optimization of VMD

Huang

Qin

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

To overcome the shortcomings that the early fault characteristics of rolling bearing are not easy to be extracted and the identification accuracy is not high enough, a novel collaborative diagnosis method is presented combined with VMD and LSSVM for incipient faults of rolling bearing. First, the basic concept of VMD was introduced in detail, and then, the adaptive selection principle of parameter K in VMD was constructed by instantaneous frequency mean. Furthermore, we used Lagrangian polynomial and Euclidean norm to verify the value of K accurately. Secondly, we proposed a classification algorithm based on PSO-optimized LSSVM. Meanwhile, the flowchart of the classification algorithm of fault modes may be also designed. Third, the experiment shows that the presented algorithm in this paper is effective by using the existing failure data provided by the laboratory of Guangdong Petrochemical Research Institute. Finally, some conclusions and application prospects were discussed.

show abstract

An insight on VMD for diagnosing wind turbine blade faults using C4.5 as feature selection and discriminating through multilayer perceptron

Cited by 52 publications

References 32 publications

Prediksi Penyakit Demam Berdarah Di Puskesmas Ngemplak Simongan Menggunakan Algoritma C4.5

Prediksi Penyakit Demam Berdarah Di Puskesmas Ngemplak Simongan Menggunakan Algoritma C4.5

Fault Diagnosis of Rolling Bearing Based on Improved VMD and KNN

A Classification Algorithm of Fault Modes-Integrated LSSVM and PSO with Parameters’ Optimization of VMD

Contact Info

Product

Resources

About