Real-Time Equipment Health Monitoring Using Unsupervised Learning Techniques

Research output: Chapter in Book/Report/Conference proceedingConference contribution to proceedingpeer-review

Abstract

Reducing unplanned downtime requires monitoring of equipment health. This may not be possible in many cases as traditional health monitoring systems often rely on the use of historical data and maintenance information which is not always available, especially for small and medium-sized enterprises. This paper presents a practical approach that uses sensor data for real-time equipment health indication. The methodology proposed consists of a set of steps. It starts with feature engineering which may include feature extraction to transform raw sensor data into a format more suitable for analysis. Anomaly detection follows next, where various techniques are employed to find any deviations in the engineered features indicating potential equipment deterioration or abrupt failures. Then comes the most important stages equipment health indication and alert generation. These stages provide timely information about the equipment’s condition and any necessary interventions. These steps make it possible for such an approach to be effective even when there is little or no historical data available. The applicability of this approach is validated through a lab-based case study.
Original languageEnglish
Title of host publicationProceedings of the 13th International Conference on Data Science, Technology and Applications (DATA 2024)
Number of pages8
PublisherSCITEPRESS Digital Library
Publication statusAccepted/In press - 2024
Event13th International Conference on Data Science, Technology and Applications - Dijon, France
Duration: 9 Jul 202411 Jul 2024
Conference number: 13
https://data.scitevents.org/

Conference

Conference13th International Conference on Data Science, Technology and Applications
Number13
Country/TerritoryFrance
CityDijon
Period09/07/2411/07/24
Otherhe purpose of the International Conference on Data Science, Technology and Applications (DATA) is to bring together researchers, engineers and practitioners interested on databases, big data, data mining, data management, data security and other aspects...
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