Contact Us
Journal Statistics & Approval Details
Recent Published Paper
Our Author's Feedback
 •  SJIF Impact Factor: 8.067       •  ISRA Impact Factor 7.894       •  Hard Copy of Certificates to All Authors       •  DOI by Crossref for all Published Papers       •  Soft Copy of Certificates- Within 04 Hours       •  Authors helpline No: +91-8813907089(Whatsapp)       •  No Publication Fee for Paper Submission       •  Hard Copy of Certificates to all Authors       •  UGC Approved Journal: IJRASET- Click here to Check       •  Conference on 18th and 19th Dec at Sridevi Womens Engineering College     
ISSN: 2321-9653
Estd : 2013

Ijraset Journal For Research in Applied Science and Engineering Technology

Machine Learning Based Predictive Maintenance Framework for Rotating Machinery - A Case Study Using Vibration and Temperature Data

Authors: Aswin Ram A P, Hari Kesavan M, Harshavardhini D

DOI Link: https://doi.org/10.22214/ijraset.2025.72786

Certificate: View Certificate

Abstract

Predictive maintenance is essential for ensuring the reliability and efficiency of mechanical systems, particularly in industries where unexpected equipment failures can cause costly downtimes. Traditional approaches are often reactive or based on fixed schedules and lack real-time insights into system health. This study presents an interdisciplinary framework that integrates mechanical system diagnostics with machine learning to implement predictive maintenance. Sensor data, such as vibration and temperature, which are key indicators of mechanical condition, are analyzed using supervised learning algorithms including Support Vector Machines (SVM), Random Forest (RF), and Artificial Neural Networks (ANN). These models are trained to classify equipment states and predict potential failures. The proposed method demonstrates that combining mechanical domain knowledge with computational intelligence enables early fault detection and improves maintenance planning. The results highlight the effectiveness of this hybrid approach in delivering scalable, data-driven solutions for condition-based monitoring, emphasizing the synergy between mechanical engineering and computer science in solving real-world industrial challenges.

Introduction

v

Conclusion

This study presents an integrated machine learning-based framework for the predictive maintenance of mechanical systems using vibration data. By combining mechanical feature extraction techniques with advanced supervised learning models such as support vector machines, random forest, and artificial neural networks, the proposed system can accurately classify machine conditions and detect early signs of failure.

References

[1] Jardine, A. K. S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510 [2] Zhang, W., Yang, D., & Wang, H. (2019). Data-driven methods for predictive maintenance of industrial equipment: A survey. IEEE Systems Journal, 13(3), 2213–2227. [3] Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2020). Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, 104761. [4] Smith, W. A., & Randall, R. B. (2015). Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study. Mechanical Systems and Signal Processing, 64–65, 100–131. [5] Tsai, C.-Y., Huang, W.-L., & Su, C.-Y. (2020). Intelligent predictive maintenance system using artificial neural networks and sensor fusion for rotating machinery. Sensors, 20(17), 4771.

Download Paper

Paper Id : IJRASET72786

Publish Date : 2025-06-25

ISSN : 2321-9653

Publisher Name : IJRASET

DOI Link : Click Here

Whatsapp Submit Paper Online