RI2AP: Robust and Interpretable 2D Anomaly Prediction in Assembly Pipelines

Author(s)

Chathurangi Shyalika, University of South Carolina - ColumbiaFollow
Kaushik Roy, University of South Carolina - Columbia
Renjith Prasad, University of South Carolina - Columbia
Fadi El Kalach, University of South Carolina - Columbia
Yuxin Zi, University of South Carolina - Columbia
Priya Mittal
Vignesh Narayanan, University of South Carolina - ColumbiaFollow
Ramy Harik, University of South Carolina - ColumbiaFollow
Amit Sheth, University of South Carolina - ColumbiaFollow

Document Type

Article

Abstract

Predicting anomalies in manufacturing assembly lines is crucial for reducing time and labor costs and improving processes. For instance, in rocket assembly, premature part failures can lead to significant financial losses and labor inefficiencies. With the abundance of sensor data in the Industry 4.0 era, machine learning (ML) offers potential for early anomaly detection. However, current ML methods for anomaly prediction have limitations, with F1 measure scores of only 50% and 66% for prediction and detection, respectively. This is due to challenges like the rarity of anomalous events, scarcity of high-fidelity simulation data (actual data are expensive), and the complex relationships between anomalies not easily captured using traditional ML approaches. Specifically, these challenges relate to two dimensions of anomaly prediction: predicting when anomalies will occur and understanding the dependencies between them. This paper introduces a new method called Robust and Interpretable 2D Anomaly Prediction (RI2AP) designed to address both dimensions effectively. RI2AP is demonstrated on a rocket assembly simulation, showing up to a 30-point improvement in F1 measure compared to current ML methods. This highlights its potential to enhance automated anomaly prediction in manufacturing. Additionally, RI2AP includes a novel interpretation mechanism inspired by a causal-influence framework, providing domain experts with valuable insights into sensor readings and their impact on predictions. Finally, the RI2AP model was deployed in a real manufacturing setting for assembling rocket parts. Results and insights from this deployment demonstrate the promise of RI2AP for anomaly prediction in manufacturing assembly pipelines.

Digital Object Identifier (DOI)

https://doi.org/10.3390/s24103244

Publication Info

Published in Sensors, Volume 24, Issue 15, 2024.

© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

APA Citation

Chathurangi Shyalika, Roy, K., Prasad, R., Fadi El Kalach, Zi, Y., Mittal, P., Narayanan, V., Ramy Harik, & Sheth, A. (2024). RI2AP: Robust and Interpretable 2D Anomaly Prediction in Assembly Pipelines. Sensors, 24(10), 3244. https://doi.org/10.3390/s24103244