Predictive Equipment State Based on Hidden Markov Model and Production Plan
Journal
IEEE International Symposium on Semiconductor Manufacturing Conference Proceedings
Part Of
IEEE International Symposium on Semiconductor Manufacturing Conference Proceedings
Start Page
1
End Page
3
Date Issued
2024-12-09
Author(s)
Abstract
The conventional approach in Total Productive Maintenance (TPM) relies on Overall Equipment Effectiveness (OEE) as a key metric for assessing manufacturing productivity, despite its limitations as a lagging indicator due to reliance on historical data. Predictive Overall Equipment Effectiveness (POEE) addresses this limitation by utilizing Virtual Metrology (VM) to deliver real-time quality insights for process optimization. However, predicting long-term equipment conditions and OEE remains a challenge due to the need for real-time equipment data. To address this, a two-phased Predictive Equipment Health with Hidden Markov Models (PEHMM) is proposed. The offline phase uses historical manufacturing data to build behavioral models that capture equipment dynamics through Hidden Markov Models (HMMs). The online phase leverages these models, integrating production plans and optionally real-time sensor data, to predict short- or long-term equipment health and its impact on production yield. Validation using datasets like NASA IMS bearings shows promising results, but current applications are limited to diagnostics.
Event(s)
30th International Symposium on Semiconductor Manufacturing, ISSM 2024, Hybrid, Tokyo, 9 December 2024 through 10 December 2024. Code 206944
Subjects
advance process control
equipment prognostics
fault detection and classification
Gaussian mixture model
hidden Markov model
Publisher
IEEE
Type
conference paper