Multi-Modal Sensor Fusion and Ensemble Learning Predictive Maintenance in Heavy Construction Equipment: A Stochastic Failure Prognostics Framework

Author(s)	Sai Kothapalli
Country	United States
Abstract	This paper presents a comprehensive framework for implementing predictive analytics and machine learning techniques to optimize maintenance schedules for construction equipment. Through the integration of IoT sensors, historical maintenance records, and environmental data, this research developed a multi-modal predictive model achieving 89.3% accuracy in failure prediction. The research case study involving a fleet of 150 excavators demonstrated a 34% reduction in unplanned downtime and 28% decrease in maintenance costs over 18 months. The proposed system combines condition monitoring, predictive modeling, and decision support systems to enable proactive maintenance strategies in the construction industry.
Field	Engineering
Published In	Volume 5, Issue 5, September-October 2023
Published On	2023-09-08
DOI	https://doi.org/10.36948/ijfmr.2023.v05i05.47567
Short DOI	https://doi.org/g9q3z5

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research