6533b828fe1ef96bd12877d8
RESEARCH PRODUCT
Towards online bearing fault detection using envelope analysis of vibration signal and decision tree classification algorithm
Kjell G. RobbersmyrJagath Sri Lal SenanayakaHuynh Van Khangsubject
EngineeringBearing (mechanical)business.industry020208 electrical & electronic engineeringSIGNAL (programming language)Decision treePattern recognition02 engineering and technologyFault (power engineering)01 natural sciencesFault detection and isolationlaw.inventionStatistical classificationlaw0103 physical sciencesFault coverage0202 electrical engineering electronic engineering information engineeringArtificial intelligencebusiness010301 acousticsAlgorithmEnvelope (motion)description
Online bearing fault detection is an important method for monitoring the health status of bearings in critical machines. This work proposes a classification algorithm, which can be extended towards an online bearing fault detection. The objective is to detect and classify the bearing faults in early stages. The overall design aspects of the online bearing fault detection and classification system are discussed. The proposed method is validated using experimental data, and a high accuracy of the fault classification was observed. Therefore, the proposed method can be applied for an online early fault detection and classification system.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-08-01 | 2017 20th International Conference on Electrical Machines and Systems (ICEMS) |