Precision Maintenance With PARM and Augmented Reality for Asset Optimization DOI

D. Dhinakaran,

N. Jagadish Kumar,

A. Raja Brundha

et al.

Advances in computer and electrical engineering book series, Journal Year: 2024, Volume and Issue: unknown, P. 21 - 48

Published: June 30, 2024

The domain of equipment maintenance and asset management faces challenges related to minimizing downtime, optimizing performance, reducing costs. Traditional approaches often rely on reactive strategies, leading costly downtime suboptimal performance. Moreover, the complexity modern necessitates innovative solutions for efficient management. This study aims revolutionize by introducing precision maintenance, a proactive approach that integrates predictive AR (PARM) with augmented reality (AR) technology. authors propose PARM framework, which leverages real-time data from IoT sensors, analytics, immersive interfaces enable technicians perform tasks unprecedented efficiency. By predicting potential failures providing guidance through interfaces, Precision Maintenance empowers organizations optimize performance minimize downtime.

Language: Английский

Integrating BWM, ISM, and MICMAC: Key Performance Indicators for Circular-Ambidexterity Supply Chain Management in Palm Oil Industry DOI
Rangga Primadasa, Dina Tauhida, Elisa Kusrini

et al.

Process Integration and Optimization for Sustainability, Journal Year: 2025, Volume and Issue: unknown

Published: March 26, 2025

Language: Английский

Citations

0
Predictors of Successful Maintenance Practices in Companies Using Fluid Power Systems: A Model-Agnostic Interpretation DOI Creative Commons
Marko Orošnjak,

Ivan Beker,

Nebojša Brkljač

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(13), P. 5921 - 5921

Published: July 6, 2024

The study identifies critical factors influencing companies’ operational and sustainability performance utilising fluid power systems. Firstly, the performs Machine Learning (ML) modelling using variables extracted from survey instruments in West Balkan region. dataset comprises 115 companies (38.75% response rate). data consist of 22 predictors, including meta-data three target variables. K-Nearest Neighbours algorithm offers highest predictive accuracy compared to other seven ML models, Ridge Regression, Support Vector ElasticNet Regression. Next, a model-agnostic interpretation, we assess feature importance mean dropout loss. After extracting most essential features, test hypotheses understand individual variables’ local global interpretation maintenance metrics. findings suggest that Failure Analysis Personnel, analytics, usage advanced technological solutions significantly impact availability these

Language: Английский

Citations

1
Precision Maintenance With PARM and Augmented Reality for Asset Optimization DOI

D. Dhinakaran,

N. Jagadish Kumar,

A. Raja Brundha

et al.

Advances in computer and electrical engineering book series, Journal Year: 2024, Volume and Issue: unknown, P. 21 - 48

Published: June 30, 2024

The domain of equipment maintenance and asset management faces challenges related to minimizing downtime, optimizing performance, reducing costs. Traditional approaches often rely on reactive strategies, leading costly downtime suboptimal performance. Moreover, the complexity modern necessitates innovative solutions for efficient management. This study aims revolutionize by introducing precision maintenance, a proactive approach that integrates predictive AR (PARM) with augmented reality (AR) technology. authors propose PARM framework, which leverages real-time data from IoT sensors, analytics, immersive interfaces enable technicians perform tasks unprecedented efficiency. By predicting potential failures providing guidance through interfaces, Precision Maintenance empowers organizations optimize performance minimize downtime.

Language: Английский

Citations

1