Predictive Maintenance and Asset Management Using Motion Analytics

Title

Predictive Maintenance and Asset Management Using Motion Analytics

Creator

Pradeepa, S.V.; Gokilavani, R.; Deepan, A.; Sridevi, J.; Selvi, K.

Description

In this chapter, the researcher studies how motion analytics enabled by smart sensing and data modeling provides an effective way of predictive maintenance and managing assets within industrial systems. It talks about how time based diagnostics and anomaly detection algorithms in the past time along with the history trends could be used to predict machine failures even before it has happened. The combination of machine learning and Internet of Things (IoT) platforms is stressed as one of the enablers of real- time decision making and low rates of downtimes. Such implementation issues as data quality, sensor calibration, system scalability are also tackled in this chapter. It lays stress on the importance of motion analytics with respect to reliable and efficient planning of lifecycle management and key assets. 2026, IGI Global Scientific Publishing. All rights reserved.

Source

Methodologies and Applications of Intelligent Motion Control Systems;pp.75-103

Date

01-01-2025

Publisher

IGI Global

Coverage

Pradeepa S.V., JSPM University, Pune, India; Gokilavani R., Chirst University, Banglore, India; Deepan A., Sambhram University, Uzbekistan; Sridevi J., Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology, India; Selvi K., Saveetha Engineering College, Chennai, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-836933638-0; 979-836933637-3;

Format

online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.4018/979-8-3693-3637-3.ch004

https://www.scopus.com/pages/publications/105025077369?origin=resultslist