In industrial settings, traditional centralized ap-proaches for training AI models can be insufficient due to limited training data. Industrial Federated Learning (IFL) offers a promising solution by enabling collaborative training across multiple industrial devices, while keeping data on-premises. In this paper, we propose a novel approach for supporting the development, deployment, integration and execution of IFL solutions. The proposed method provides a lifecycle management of FL artifacts and supports FL as a Service (FlaaS). This enables the extensibility and customizability of FL-based edge applications in industrial settings. Additionally, we introduce a federated clustering algorithm that we have integrated into a condition monitoring app running on client locations to evaluate the proposed lifecycle management. We run two scenarios with four and 33 clients using real-world time series data from industrial pumps. Our results show the applicability of the implemented lifecycle management and demonstrates that privacy-preserving approaches compete well with privacy-disclosing ones.

Artificial Intelligence (AI) is increasingly explored in various domains and industries. Many companies experiment with AI, but too often those experiments are one-off analyses based on outdated data and the resulting models never make it into production. This paper proposes a framework for building and operating AI models at the industrial edge. The center of this framework is the model artifact, a model-generating entity. We analyze three AI model use-cases and user roles involved in industrial AI applications to illustrate the challenges in deploying and operating AI applications in industrial edge scenarios. We propose to structure the AI models into predefined artifacts that enable deployments with only a few clicks. The edge device links sensor data with the model input and returns the model output as feedback back into the industrial process. Model training, deployment, and management can be carried out in a scalable manner even by a non-expert. Several models can be managed in parallel, and data can be linked to the respective sensor or machine.

The digital transformation provides industries with unparalleled opportunities for value creation. AI and Machine learning (AI/ML)-driven approaches for data analysis applied to the massive amounts of data steaming from industrial processes can lead to enhanced operation, costs reduction, and powerful decision-making strategies. In this paper we address the problem of Quality Assurance (QA) in industrial manufacturing. We propose Smart Active Sampling (SAS), a new QA sampling strategy for quality inspection outside the production line. Based on the principles of active learning, an AI/ML model trained for quality prediction decides which produced pieces or samples are sent to quality inspection, to further improve its own prediction accuracy. SAS reduces the production of scrap parts due to earlier detection of quality violations. By inspecting a much lower number of samples as compared to traditional random sampling approaches, SAS improves QA efficiency and cuts down quality inspection costs, resulting in an overall smoother operation. We elaborate on some of the challenges faced in smart sampling strategies for quality inspection, describe the main concepts behind SAS, and showcase its application in a real-world manufacturing QA use case, training an AI/ML model for product defect prediction. Compared to a standard random sampling strategy, widely applied today in industrial QA applications, SAS improves model prediction accuracy requiring a significantly lower number of inspected samples, up to five time less samples in the analyzed dataset.

An increasing number of industrial assets are equipped with IoT sensor platforms and the industry now expects data-driven maintenance strategies with minimal deployment costs. However, gathering labeled training data for supervised tasks such as anomaly detection is costly and often difficult to implement in operational environments. Therefore, this work aims to design and implement a solution that reduces the required amount of data for training anomaly classification models on time series sensor data and thereby brings down the overall deployment effort of IoT anomaly detection sensors. We set up several in-lab experiments using three peristaltic pumps and investigated approaches for transferring trained anomaly detection models across assets of the same type. Our experiments achieved promising effectiveness and provide initial evidence that transfer learning could be a suitable strategy for using pre-trained anomaly classification models across industrial assets of the same type with minimal prior labeling and training effort. This work could serve as a starting point for more general, pre-trained sensor data embeddings, applicable to a wide range of assets.