Emerging Challenges in Mechanical Systems: Leveraging Data Visualization for Predictive Maintenance

Deekshith Narsina; Krishna Devarapu; Arjun Kamisetty; Jaya Chandra Srikanth Gummadi; Nicholas Richardson; Aditya Manikyala

doi:10.18034/ajase.v10i1.124

Authors

Deekshith Narsina Senior Software Engineer, Capital One, 1600 Capital One Dr, Mclean, VA- 22102, USA
Krishna Devarapu Senior Data Solutions Architect, Mission Cloud Services Inc., Beverley Hills, CA, USA
Arjun Kamisetty Software Developer, Fannie Mae, 2000 Opportunity Wy, Reston, VA 20190, USA
Jaya Chandra Srikanth Gummadi Senior Software Engineer, Lowes Companies Inc., Charlotte, North Carolina, USA
Nicholas Richardson Software Engineer, JPMorgan Chase, 10 S Dearborn St, Chicago, IL 60603, USA
Aditya Manikyala Java Aws Developer, DPR Solutions Inc., 20130 Lakeview center plaza, Ashburn, VA 20147, USA

Keywords:

Predictive Maintenance (PdM)

Data Visualization

Mechanical Systems

Industrial IoT

Real-Time Monitoring

Failure Prediction

Abstract

This research addresses mechanical system issues and how data visualization might improve predictive maintenance (PdM). The primary goal is to examine how data visualization simplifies complicated maintenance data, improves prediction accuracy, and aids real-time decision-making. This research identifies PdM visualization trends, developments, and problems by synthesizing secondary data from academic publications, industry reports, and technical papers. Significant results show that visualization tools like real-time monitoring, AI integration, and immersive technologies like AR and VR may change PdM. These advances simplify complicated information, enable proactive maintenance, and boost mechanical system management efficiency. However, data integration, standards, and expensive implementation costs prevent wider use, especially for SMEs. The paper suggests standardization, labor training, and technology adoption incentives as governmental recommendations. Promoting PdM visualization via supporting policies would enable enterprises of all sizes to realize the full potential of predictive maintenance, boosting system dependability and operational efficiency.

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