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Condition Monitoring of Roller Bearing by K-Star Classifier and K -Nearest Neighborhood Classifier Using Sound Signal.

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Titel: Condition Monitoring of Roller Bearing by K-Star Classifier and K -Nearest Neighborhood Classifier Using Sound Signal.
Autoren: Rahul Kumar Sharma, V. Sugumaran, Hemantha Kumar, Amarnath M
Quelle: Structural Durability & Health Monitoring ; ISSN: 1930-2983 (Print) ; ISSN: 1930-2991 (Online) ; Volume 11 ; Issue 1
Verlagsinformationen: Tech Science Press
Publikationsjahr: 2017
Schlagwörter: K-star, k-nearest neighborhood, k -NN, machine learning approach, condition monitoring, fault diagnosis, roller bearing, decision tree algorithm, J48, random tree algorithm, decision making, two layer feature selection, sound signal, statistical features
Beschreibung: Most of the machineries in small or large scale industry have rotating element supported by bearings for rigid support and accurate movement. For proper functioning of machinery, condition monitoring of the bearing is very important. In present study sound signal is used to continuously monitor bearing health as sound signals of rotating machineries carry dynamic information of components. There are numerous studies in literature that are reporting superiority of vibration signal of bearing fault diagnosis. However, there are very few studies done using sound signal. The cost associated with condition monitoring using sound signal (Microphone) is less than the cost of transducer used to acquire vibration signal (Accelerometer). This paper employs sound signal for condition monitoring of roller bearing by K-star classifier and k-nearest neighborhood classifier. The statistical feature extraction is performed from acquired sound signals. Then two layer feature selection is done using J48 decision tree algorithm and random tree algorithm. These selected features were classified using K-star classifier and k-nearest neighborhood classifier and parametric optimization is performed to achieve the maximum classification accuracy. The classification results for both K-star classifier and k-nearest neighborhood classifier for condition monitoring of roller bearing using sound signals were compared.
Publikationsart: article in journal/newspaper
Dateibeschreibung: application/pdf
Sprache: English
Relation: https://doi.org/10.3970/sdhm.2017.012.001
DOI: 10.3970/sdhm.2017.012.001
Verfügbarkeit: https://doi.org/10.3970/sdhm.2017.012.001
Rights: info:eu-repo/semantics/openAccess ; https://creativecommons.org/licenses/by/4.0/
Dokumentencode: edsbas.18FF5778
Datenbank: BASE
Beschreibung
Abstract:Most of the machineries in small or large scale industry have rotating element supported by bearings for rigid support and accurate movement. For proper functioning of machinery, condition monitoring of the bearing is very important. In present study sound signal is used to continuously monitor bearing health as sound signals of rotating machineries carry dynamic information of components. There are numerous studies in literature that are reporting superiority of vibration signal of bearing fault diagnosis. However, there are very few studies done using sound signal. The cost associated with condition monitoring using sound signal (Microphone) is less than the cost of transducer used to acquire vibration signal (Accelerometer). This paper employs sound signal for condition monitoring of roller bearing by K-star classifier and k-nearest neighborhood classifier. The statistical feature extraction is performed from acquired sound signals. Then two layer feature selection is done using J48 decision tree algorithm and random tree algorithm. These selected features were classified using K-star classifier and k-nearest neighborhood classifier and parametric optimization is performed to achieve the maximum classification accuracy. The classification results for both K-star classifier and k-nearest neighborhood classifier for condition monitoring of roller bearing using sound signals were compared.
DOI:10.3970/sdhm.2017.012.001