To gain deeper insights into a complex sensor system through the lens of causality, we present common and individual causal mechanism estimation (CICME), a novel threestep approach to inferring causal mechanisms from heterogeneous data collected across multiple domains. By leveraging the principle of Causal Transfer Learning (CTL), CICME is able to reliably detect domain-invariant causal mechanisms when provided with sufficient samples. The identified common causal mechanisms are further used to guide the estimation of the remaining causal mechanisms in each domain individually. The performance of CICME is evaluated on linear Gaussian models under scenarios inspired from a manufacturing process. Building upon existing continuous optimization-based causal discovery methods, we show that CICME leverages the benefits of applying causal discovery on the pooled data and repeatedly on data from individual domains, and it even outperforms both baseline methods under certain scenarios.

Data fusion deals with combining information from multiple sensors to support decision making. In such settings, machine learning methods, that principally only take correlation into account, have been applied widely due to their strong predictive and computational capabilities. In this paper, we investigate potential benefits of introducing causal knowledge in machine learning-based data fusion to address two common downstream tasks, namely, quality prediction and root cause analysis (RCA). To resemble the complex relationships typically associated with sensor data, we create simulation data with explicit modeling of latent confounding. The results of this study indicate that taking into account true causal knowledge significantly improves the performance of RCA, and leads to prediction models that are more robust to severe distribution shifts in the presence of latent confounding. Furthermore, if causal knowledge needs to be inferred from observational data using existing causal discovery methods, we propose a selection criterion to choose the best causal structure. We show that given a sufficient amount of data, the selected causal structure can be used as reliable input to solve the downstream tasks.

Manufacturing of car bodies heavily relies on demanding welding processes of joining body parts together that introduce thousands of joining welding spots in each car. Quality monitoring for these spots impacts production efficiency and cost. In this paper we develop an ML pipeline to predict the spot quality before the actual welding happens. This pipeline is based on a Feature Engineering~(FE) approach to manually design features using domain knowledge. We evaluated the pipeline with two datasets from industrial plants, achieving very promising results with prediction errors around 2%. Then, we develop an approach to semantically enhance FE pipelines in order to automate the ML process without compromising the prediction accuracy and to facilitate generalisation and transfer of FE-based models to other datasets and processes. Our ML pipeline has been deployed offline on various Bosch manufacturing datasets in a controlled environment since early 2019 and evaluated.

Machine learning (ML) based data analysis has attracted an increasing attention in the manufacturing industry, however, many challenges hamper their wide spread adoption. The main challenges are the high costs of labour-intensive data preparation from diverse sources and processes, the asymmetrical backgrounds of the experts involved in manufacturing analyses that impede efficient communication between them, and the lack of generalisability of ML models tailored to specific applications. Our semantically enhanced ML pipeline, SemFE, with feature engineering addresses these challenges, serving as a bridge to bring the endeavours of experts together, and making data science accessible to non-ML-experts. SemFE relies on ontologies for discrete manufacturing monitoring that encapsulate domain and ML knowledge; it has five novel semantic modules for automation of ML-pipeline development and user-friendly GUIs. The demo attendees will be able to use our system to build manufacturing monitoring ML pipelines, and to design their own pipelines with minimal prior knowledge of machine learning.