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

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.

Keywords: anomaly prediction; assembly processes; sensor data; smart manufacturing; time series analysis.

Figure 4

Comparison of F1 score with the LSTM model, Transformer, and the method of moments using Noisy-OR. (A1:A5, A9—see Table 3).

Figure 5

Comparison of F1 score with the LSTM, Transformer, and the method of moments using Noisy-MAX. (A1:A5, A9—see Table 3).

Figure 6

Loss/error comparison of different function approximator choices and combining rule predictions.

Figure A1

Feature importance scores using XGBoost Cover measure for all the features.

Figure A2

Feature importance scores of XGBoost Cover measure for top 20 features.

Figure A3

XGBoost tree.

Figure A4

Some images from FF Cell: R01—Robot 1, R02—Robot 2, R03—Robot 3, R04—Robot 4.

Figure 1

Shows an abstract illustration of the RI2AP method proposed in this work. Sensor measurements correspond to the health of different rocket parts. Several function approximations are then used to predict anomalous occurrences from the sensor measurements, and their outputs are combined using combining rules. The combining rules allow natural aggregation mechanisms, e.g., NOISY-OR and NOISY-MAX, as shown in the illustration.

Figure 2

Illustrates the RI2AP method. (a,b) correspond to Equations (1) and (2), respectively.

Figure 3

Detailed illustration of RI2AP.

Figure 7

Deployment architecture of forecasting model.

Figure 8

Deployment Result 1—Potentiometer R02 Sensor and Anomaly type: Body2Removed.

Figure 9

Deployment Result 2—Potentiometer R03 Sensor and Anomaly type: R04 crashed nose.