Transparent and Interpretable Failure Prediction of Sensor Time Series Data with Convolutional Neural Networks
Richard Meyes, Nils Hütten, Tobias Meisen · Procedia CIRP · 2021
The application of neural networks in production scenarios has been growing over the last years. However, their innate black-box character and the lack of transparency and interpretability of their decision making hinders their application in safety critical scenarios and scenarios with high failure costs. We present an approach to facilitate transparency of failure predictions performed by convolutional neural networks processing sensory time series data from a deep drawing manufacturing process for predictive quality purposes. Our approach enables domain experts to comprehend why a failure is predicted, enabling them to better assess whether to intervene into the production process or not.