Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine. - M. Saimurugan; K. I. Ramachandran; V. Sugumaran; N. R. Sakthivel (Expert Syst. Appl., 2011)

A comparative study of sound and vibration signals in detection of rotating machine faults using support vector machine and independent component analysis. - M. Saimurugan; K. I. Ramachandran (Int. J. Data Anal. Tech. Strateg., 2014)

Establishing Statistical Correlation Between Sensor Signature Features and Lubricant Solid Particle Contamination in a Spur Gearbox. - K. Rameshkumar; R. Sriram; M. Saimurugan; P. Krishnakumar (IEEE Access, 2022)

Machine Learning Approach for Predicting the Solid Particle Lubricant Contamination in a Spherical Roller Bearing. - K. Rameshkumar; Kaviarasu Nataraj; P. Krishnakumar; M. Saimurugan (IEEE Access, 2024)

Rotational Machine Fault Diagnosis Using Artificial Intelligence (AI) Strategies for the Operational Challenges Under Variable Speed Condition: A Review. - S. Sowmya; M. Saimurugan; Immanuel Edinbarough (IEEE Access, 2024)

Distinctive Contribution of Sound Spectral Features in Enhancing Vibration-Based Multi-Component Fault Classification Under Non-Stationary Speed Conditions. - S. Sowmya; M. Saimurugan; Naveen Venkatesh (IEEE Access, 2025)