Matches in SemOpenAlex for { <https://semopenalex.org/work/W4380360458> ?p ?o ?g. }
Showing items 1 to 75 of
75
with 100 items per page.
- W4380360458 abstract "Nowadays, sensors play a vital role in monitoring many appliances in various sectors, including the medical field. The electrocardiogram (ECG) is a test used to check the heart’s electric activity, using the ECG sensors. These sensors can get faulty and cause serious havoc. In this paper, a real-time classification and prognostics system is proposed that could classify between normal and faulty ECG signals and could even predict whether the sensor will get faulty based on previous data. Drift and bias fault are considered in this study. Four different machine learning algorithms, the Support Vector Machine, K-Nearest Neighbour, Decision Tree and Naive Bayes, are used and compared for classification purposes. Three statistical time-domain features, mean of the signal, energy of the signal and the peak-to-peak value of the signal, are used as the feature set in this study. The autoregression algorithm is used for fault prognosis. To replicate a practical scenario in an industrial system, an unbalanced dataset is generated having a larger number of normal signals than faulty signals. The model is implemented on raspberry pi for low resource hardware implementation. The performance of the classifiers is evaluated using different metrics. From the results, we can see that the KNN algorithm achieves the best accuracy of 95% which is suitable for real-time deployment. We also see that a very good result in the case of fault prognosis is achieved owing to the fact that the faults introduced are linear in nature. Different Root Mean Squared Error-values are presented in the paper for both faults." @default.
- W4380360458 created "2023-06-13" @default.
- W4380360458 creator A5052512842 @default.
- W4380360458 creator A5068887717 @default.
- W4380360458 creator A5071720739 @default.
- W4380360458 date "2022-12-12" @default.
- W4380360458 modified "2023-09-25" @default.
- W4380360458 title "FauDigPro: A Machine Learning based Fault Diagnosis and Prognosis System for Electrocardiogram Sensors" @default.
- W4380360458 cites W1653347237 @default.
- W4380360458 cites W1975477163 @default.
- W4380360458 cites W2004039783 @default.
- W4380360458 cites W2012450123 @default.
- W4380360458 cites W2532883992 @default.
- W4380360458 cites W2767507316 @default.
- W4380360458 cites W2894771803 @default.
- W4380360458 cites W3022757405 @default.
- W4380360458 cites W3031502194 @default.
- W4380360458 doi "https://doi.org/10.1109/icmiam56779.2022.10146898" @default.
- W4380360458 hasPublicationYear "2022" @default.
- W4380360458 type Work @default.
- W4380360458 citedByCount "0" @default.
- W4380360458 crossrefType "proceedings-article" @default.
- W4380360458 hasAuthorship W4380360458A5052512842 @default.
- W4380360458 hasAuthorship W4380360458A5068887717 @default.
- W4380360458 hasAuthorship W4380360458A5071720739 @default.
- W4380360458 hasConcept C110083411 @default.
- W4380360458 hasConcept C119857082 @default.
- W4380360458 hasConcept C12267149 @default.
- W4380360458 hasConcept C124101348 @default.
- W4380360458 hasConcept C127313418 @default.
- W4380360458 hasConcept C138885662 @default.
- W4380360458 hasConcept C153180895 @default.
- W4380360458 hasConcept C154945302 @default.
- W4380360458 hasConcept C165205528 @default.
- W4380360458 hasConcept C175551986 @default.
- W4380360458 hasConcept C2776401178 @default.
- W4380360458 hasConcept C2780150128 @default.
- W4380360458 hasConcept C41008148 @default.
- W4380360458 hasConcept C41895202 @default.
- W4380360458 hasConcept C50644808 @default.
- W4380360458 hasConcept C52001869 @default.
- W4380360458 hasConcept C84525736 @default.
- W4380360458 hasConceptScore W4380360458C110083411 @default.
- W4380360458 hasConceptScore W4380360458C119857082 @default.
- W4380360458 hasConceptScore W4380360458C12267149 @default.
- W4380360458 hasConceptScore W4380360458C124101348 @default.
- W4380360458 hasConceptScore W4380360458C127313418 @default.
- W4380360458 hasConceptScore W4380360458C138885662 @default.
- W4380360458 hasConceptScore W4380360458C153180895 @default.
- W4380360458 hasConceptScore W4380360458C154945302 @default.
- W4380360458 hasConceptScore W4380360458C165205528 @default.
- W4380360458 hasConceptScore W4380360458C175551986 @default.
- W4380360458 hasConceptScore W4380360458C2776401178 @default.
- W4380360458 hasConceptScore W4380360458C2780150128 @default.
- W4380360458 hasConceptScore W4380360458C41008148 @default.
- W4380360458 hasConceptScore W4380360458C41895202 @default.
- W4380360458 hasConceptScore W4380360458C50644808 @default.
- W4380360458 hasConceptScore W4380360458C52001869 @default.
- W4380360458 hasConceptScore W4380360458C84525736 @default.
- W4380360458 hasLocation W43803604581 @default.
- W4380360458 hasOpenAccess W4380360458 @default.
- W4380360458 hasPrimaryLocation W43803604581 @default.
- W4380360458 hasRelatedWork W1470425429 @default.
- W4380360458 hasRelatedWork W2952523812 @default.
- W4380360458 hasRelatedWork W3022791929 @default.
- W4380360458 hasRelatedWork W3185179407 @default.
- W4380360458 hasRelatedWork W3186233728 @default.
- W4380360458 hasRelatedWork W3213308033 @default.
- W4380360458 hasRelatedWork W4200057378 @default.
- W4380360458 hasRelatedWork W4291177832 @default.
- W4380360458 hasRelatedWork W4377964522 @default.
- W4380360458 hasRelatedWork W4384345534 @default.
- W4380360458 isParatext "false" @default.
- W4380360458 isRetracted "false" @default.
- W4380360458 workType "article" @default.