Vibration and Acoustic Signal Analysis for Early Fault Detection in Clean-Room Robotics
Abstract
Clean-room robotic systems play a crucial role in semiconductor manufacturing, pharmaceutical assembly, and precision laboratory settings. These robots must operate reliably to avoid costly production delays and product contamination. Early detection of mechanical faults through vibration and acoustic signal analysis can improve reliability, reduce maintenance costs, and prevent unplanned downtime. This paper reviews key methods for extracting vibration and acoustic features, signal processing techniques, diagnostic models, experimental results, and future research directions. Results show that frequency domain analysis, machine learning classifiers, and sensor fusion improve fault detection accuracy in robotic joints, motors, and bearings.
Fields of Study & Subjects
Machine Fault Diagnosis Techniques
Fault Detection and Control Systems
Mechanical Engineering Research and Applications
Publication details
- Journal / source
- International Journal of Artificial Intelligence Data Science and Machine Learning
- Volume
- 7
- Issue
- 1
- Pages
- 26–28
- Publication date
- 1 January 2026
- Language
- EN