Moment-Based Reinforcement Learning for Ensemble Control

Yu, Yao-Chi; Narayanan, Vignesh; Li, Jr-Shin

doi:10.1109/tnnls.2023.3264151

Cited by 3 publications

(2 citation statements)

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“…Intuitively, employing sample means or averages helps alleviate the impact of significant differences in sample sizes. Narayanan et al [42] leverage ideas from Shohat and Tamarkin's book and generalize this idea to n th order moments, providing theoretical proof and experimental observations that validate the method's robustness to sample imbalances [43,44]. Let moments([m 1 l , ..., m t−1 l ]) denote the moments of the input list.…”

Section: Methods Of Momentsmentioning

confidence: 99%

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

Shyalika,

Roy,

Prasad

et al. 2024

Preprint

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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 is expensive), and the complex relationships between anomalies not easily captured by 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 is 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.

show abstract

Section: Methods Of Momentsmentioning

confidence: 99%

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

Shyalika,

Roy,

Prasad

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Intuitively, employing sample means or averages helps alleviate the impact of significant differences in sample sizes. Narayanan et al [42] leveraged ideas from Shohat and Tamarkin's book and generalized this idea to nth-order moments, providing theoretical proof and experimental observations that validate the method's robustness to sample imbalances [43,44]. Let moments([m 1 l , .…”

Section: Methods Of Momentsmentioning

confidence: 99%

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

Shyalika,

Roy,

Prasad

et al. 2024

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract