A Novel Fault Detection Scheme Based on Mutual k-Nearest Neighbor Method: Application on the Industrial Processes with Outliers

Wang, Jian; Zhou, Zhe; Li, Zuxin; Du, Shuxin

doi:10.3390/pr10030497

Cited by 14 publications

(10 citation statements)

References 30 publications

(36 reference statements)

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“…The data are symmetric when the mean, median, and mode are at the same point. The data show positive skewness when the distribution of the tail to the right side is longer or fatter, which means that the mean and median are greater than the mode [47].…”

Section: Detecting Outliersmentioning

confidence: 96%

Automatic and Early Detection of Parkinson’s Disease by Analyzing Acoustic Signals Using Classification Algorithms Based on Recursive Feature Elimination Method

et al. 2023

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Parkinson’s disease (PD) is a neurodegenerative condition generated by the dysfunction of brain cells and their 60–80% inability to produce dopamine, an organic chemical responsible for controlling a person’s movement. This condition causes PD symptoms to appear. Diagnosis involves many physical and psychological tests and specialist examinations of the patient’s nervous system, which causes several issues. The methodology method of early diagnosis of PD is based on analysing voice disorders. This method extracts a set of features from a recording of the person’s voice. Then machine-learning (ML) methods are used to analyse and diagnose the recorded voice to distinguish Parkinson’s cases from healthy ones. This paper proposes novel techniques to optimize the techniques for early diagnosis of PD by evaluating selected features and hyperparameter tuning of ML algorithms for diagnosing PD based on voice disorders. The dataset was balanced by the synthetic minority oversampling technique (SMOTE) and features were arranged according to their contribution to the target characteristic by the recursive feature elimination (RFE) algorithm. We applied two algorithms, t-distributed stochastic neighbour embedding (t-SNE) and principal component analysis (PCA), to reduce the dimensions of the dataset. Both t-SNE and PCA finally fed the resulting features into the classifiers support-vector machine (SVM), K-nearest neighbours (KNN), decision tree (DT), random forest (RF), and multilayer perception (MLP). Experimental results proved that the proposed techniques were superior to existing studies in which RF with the t-SNE algorithm yielded an accuracy of 97%, precision of 96.50%, recall of 94%, and F1-score of 95%. In addition, MLP with the PCA algorithm yielded an accuracy of 98%, precision of 97.66%, recall of 96%, and F1-score of 96.66%.

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Section: Detecting Outliersmentioning

confidence: 96%

Automatic and Early Detection of Parkinson’s Disease by Analyzing Acoustic Signals Using Classification Algorithms Based on Recursive Feature Elimination Method

et al. 2023

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“…Prinsip algoritma K-NN adalah mengukur perbedaan sampel berdasarkan jarak, dimana sampel normal dan sampel pelatihan serupa, tetapi sampel kesalahan dan sampel pelatihan berbeda secara signifikan [10]. Algoritma K-NN dapat dilihat pada Gambar 2 [11].…”

Section: K-nearest Neighbor (Knn)unclassified

Medicine Classification Based on Logo on Packaging Using K-Nearest Neighbor Algorithm Based on Digital Images

Trisakti Akbar,

Marwan Ramdhany Edy,

Nurul Amanda Pratiwi Hasbullah

et al. 2023

j. of deep. learning, comput. vis. and digital n.a. process.

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Obat adalah suatu zat yang dikonsumsi untuk menjaga kesehatan, menyembuhkan, dan mencegah penyakit. Penggunaan obat lazim kita temui dalam kehidupan sehari-hari, baik untuk keperluan penyembuhan, pencegahan penyakit maupun sebagai suplemen kesehatan. Meski begitu, kemudahan mendapatkan obat juga membawa dampak negatif. Misalnya, semakin banyak masyarakat yang menggunakan obat secara irasional. Pengetahuan masyarakat terkait penggolongan dan pola logo pada kemasan obat masih kurang sehingga obat yang diharapkan dapat menyembuhkan penyakit justru dapat membahayakan diri masyarakat yang mengkonsumsinya. Sebelumnya telah dilakukan penelitian untuk mengklasifikasikan jenis obat berbasis citra digital, namun proses cropping logo masih dilakukan secara manual. Oleh karena itu, penelitian ini mengusulkan metode baru untuk mengklasifikasikan jenis obat berdasarkan logo pada kemasannya menggunakan metode K-Nearest Neighbor (K-NN) dengan ekstraksi fitur warna. Tahapan metode yang diusulkan terdiri dari akuisisi citra, preprocessing, deteksi tepi, Circle Hough Transformation, segmentasi citra, cropping dan resize, ekstraksi fitur, dan klasifikasi. Berdasarkan hasil pengujian yang dilakukan, diperoleh tingkat akurasi sebesar 93,33%. Hasil pengujian tersebut menunjukkan bahwa metode yang diusulkan dapat mengklasifikasikan jenis obat dengan tingkat akurasi yang tinggi.

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“…Recently, many machine learning algorithms have applied identification characteristics to the detection of fault discrimination and achieved good results, such as K-nearest neighbor [18,19], support vector machine [20][21][22], random forest algorithm [23][24][25], fuzzy clustering algorithm [26,27], convolutional neural network [28,29], and so on.…”

Section: Introductionmentioning

confidence: 99%

Arc Grounding Fault Identification Using Integrated Characteristics in the Power Grid

Liu,

Zhang,

Dai

et al. 2024

ENERGY

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Arc grounding faults occur frequently in the power grid with small resistance grounding neutral points. The existing arc fault identification technology only uses the fault line signal characteristics to set the identification index, which leads to detection failure when the arc zero-off characteristic is short. To solve this problem, this paper presents an arc fault identification method by utilizing integrated signal characteristics of both the fault line and sound lines. Firstly, the waveform characteristics of the fault line and sound lines under an arc grounding fault are studied. After that, the convex hull, gradient product, and correlation coefficient index are used as the basic characteristic parameters to establish fault identification criteria. Then, the logistic regression algorithm is employed to deal with the reference samples, establish the machine discrimination model, and realize the discrimination of fault types. Finally, simulation test results and experimental results verify the accuracy of the proposed method. The comparison analysis shows that the proposed method has higher recognition accuracy, especially when the arc dissipation power is smaller than 2 × 10 3 W, the zero-off period is not obvious. In conclusion, the proposed method expands the arc fault identification theory.

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A Novel Fault Detection Scheme Based on Mutual k-Nearest Neighbor Method: Application on the Industrial Processes with Outliers

Cited by 14 publications

References 30 publications

Automatic and Early Detection of Parkinson’s Disease by Analyzing Acoustic Signals Using Classification Algorithms Based on Recursive Feature Elimination Method

Automatic and Early Detection of Parkinson’s Disease by Analyzing Acoustic Signals Using Classification Algorithms Based on Recursive Feature Elimination Method

Medicine Classification Based on Logo on Packaging Using K-Nearest Neighbor Algorithm Based on Digital Images

Arc Grounding Fault Identification Using Integrated Characteristics in the Power Grid

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