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With an ongoing digital transformation towards industry 4.0 and the corresponding growth of collected sensor data based on cyberphysical systems, the need for automatic data analysis in industrial production lines has increased drastically. One relevant application scenario is the usage of intelligent approaches to anticipate upcoming failures for maintenance. In this paper, we present a novel approach for anomaly detection regarding predictive maintenance in an industrial data-intensive environment. In particular, we are focusing on historical sensor data from a real reflow oven that is used for soldering surface mount electronic components to printed circuit boards. The sensor data, which is provided within the scope of the EU-Project COMPOSITION (under grant no. 723145), comprises information about the heat and the power consumption of individual fans inside a reflow oven. The data set contains timeannotated sensor measurements in combination with additional process information over a period of more than seven years.

Administrators and operators of next generation cities will likely be required to exhibit a good understanding of technical features, data issues, and complex information that, up to few years ago, were quite far from day-to-day administration tasks. In the smart city era, the increased attention to data harvested from the city fosters a more informed approach to city administration, requiring involved operators to drive, direct and orient technological processes in the city, more effectively. Such an increasing need, requires tools and platforms that can easily and effectively be controlled by non-technical people. In this paper, an approach for enabling easier composition of real-time data processing pipelines in smart-cities is presented, exploiting a block-based design app roach, similar to the one adopted in the Scratch programming language for elementary school students. Language primitives and corresponding REST representations are discussed, showing the viability of the approach. Future works will include experimentation of the proposed concepts in the context of a smart city pilot in Turin, Italy

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Current paradigms such as the Internet of Things (IoT) and cyber-physical systems are transforming production environments, where related processes are not only faster and with higher standards, but also more flexible and adaptable to changes in the environment. To address the ever-increasing flexibility requirements while keeping current production standards, a new set of technologies is needed. This paper presents an IoT machine learning and orchestration framework, applied to detection of failures of surface mount devices during production. The paper shows how to build a scalable and flexible system for real-time, online machine learning. Furthermore, the approach is evaluated by using a novel and realistic simulation of a production line for electronic devices as a case study. The system evaluation is done in a holistic manner by analyzing various aspects involving the software architecture, computational scalability, model accuracy, production performance, among others.