Application of dimension analysis and soft competitive tool to predict compound faults present in rotor-bearing systems

• Published in: Measurement : journal of the International Measurement Confederation 2022-04, Vol.193, p.110984, Article 110984
• Main Authors: Shinde, Prasad V., Desavale, Ramchandra G.
• Format: Article
• Language: English
• Subjects: Algorithms; Bearing; Bearings; Dimension analysis; Dimensional analysis; Experimentation; Fault diagnosis; High speed; Mathematical analysis; Matrix methods; Misalignment; Rotor-bearing systems; Support vector machine; Support vector machines; Unbalance; Vibration
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2022.110984

•A dynamic rotor-bearing model is proposed using the matrix method of dimensional analysis.•The proposed dimensional analysis method effectively predicts the vibration characteristics.•Vibration characteristics of the bearing with multiple defects are investigated.•The support vector machine (SVM) approach classifies multiple bearing faults from a rotor-bearing system. Localized and distributed faults in machinery may lead to catastrophic failures of the high-speed rotor bearings. Unbalance and misalignment present generate massive vibrations in the rotor-bearing system. This paper demonstrates duo of the matrix method of dimensional analysis (MMDA) and support vector machine (SVM) to investigate unbalance and misalignment present in the rotor-bearing system. Experimentation with different conditions is performed and compared with numerical results to reveal the effectiveness of the approach. The SVM algorithm is used to classify multiple fault classes based on vibration characteristics predicted by the MMDA model. The results comparison shows that the present duo predicts and classifies the misalignment and unbalance with acceptable error and can be useful for industrial high-speed machine diagnosis.