2022
DOI: 10.3390/s22197121
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A Novel Supervised Filter Feature Selection Method Based on Gaussian Probability Density for Fault Diagnosis of Permanent Magnet DC Motors

Abstract: For permanent magnet DC motors (PMDCMs), the amplitude of the current signals gradually decreases after the motor starts. In this work, the time domain features and time-frequency-domain features extracted from several successive segments of current signals make up a feature vector, which is adopted for fault diagnosis of PMDCMs. Many redundant features will lead to a decrease in diagnosis efficiency and increase the computation cost, so it is necessary to eliminate redundant features and features that have ne… Show more

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Cited by 5 publications
(4 citation statements)
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“…The number of instances in which the proposed model misinterprets the non-erroneous code blocks as erroneous code blocks are considered false negatives [34]. The sensitivity determined the ability of the model to correctly identify the erroneous code block, and the corresponding formula is shown in Equation (22).…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…The number of instances in which the proposed model misinterprets the non-erroneous code blocks as erroneous code blocks are considered false negatives [34]. The sensitivity determined the ability of the model to correctly identify the erroneous code block, and the corresponding formula is shown in Equation (22).…”
Section: Resultsmentioning
confidence: 99%
“…GPDF is widely used because it is the probability density function that emerges as a limit for the sum of random variables. It has been observed that, regardless of the probability density function of the individual variables, the probability density function of a combination of random variables that are independent resembles a Gaussian distribution as the total number of variables being summed increases [22]. The mathematical formulation of GPDF is shown in Equation (1).…”
Section: Feature Selection and Scalingmentioning
confidence: 99%
“…According to their interaction with classifiers, feature selection methods can be divided into four categories: filter, embedded, wrapper, and hybrid methods [7][8][9][10] . The filter method sorts genes according to the correlation of individual genes or the ability to distinguish target categories 11,12 . The embedded method automatically selects the feature gene according to the algorithm 13,14 .…”
Section: Accmentioning
confidence: 99%
“…Regarding engineering, the authors of [11] used Principal Component Analysis (PCA) in several predictors to remove noise from building energy consumption datasets. In another example, in fault diagnosis, FS was applied to select the best features extracted from magnet DC motors [12] or rotating machinery [13]. Furthermore, FR techniques are valuable in text mining tasks, such as document classification, e.g., in [14] PCA and Latent Semantic Indexing (LSI) were used to extract useful features for an SVM classifier.…”
Section: Introductionmentioning
confidence: 99%