An average textile dyehouse is composed of 40 dyeing machines, whereby the processes in each of the machines are controlled simultaneously. One of the main control engineering problems of such processes is the temperature control where the temperature of the dye liquid is controlled in a desired manner to achieve right first time dyeing. To be able to analyse control behaviours of the machines and monitor how they affect each other, a container-based distributed simulation environment is created. This environment helps us not only to simulate the temperature control behaviour of the dyeing machines with different characteristics but also to develop and apply new control algorithms in a fast and secure manner.

Ability to predict a potential fault in wind turbines comes with considerable benefits. The data collected on the SCADA system monitoring the operational status of wind turbines contain information on wind turbine health. In our study, we analyse operational data from a wind farm in Turkey. This paper presents condition monitoring approaches that can be used to predict wind turbine failures before they happen. First, data points from five days prior to the faults are predicted using the glass-box classification model. Features and attribute combinations are discovered that are of the greatest importance to the success of this pre-fault prediction. Then, a condition monitoring method is proposed using LSTM Autoencoder model with significant features. For each model developed, a well-defined threshold value is determined through the analysis of the reconstruction error. The anomaly score exceeding the threshold value indicates a malfunction usually occurring within five days.

End-of-line (EOL) tests are an important step to detect and respond to reliability issues that electric motors face. In addition to conventional signal processing methods to establish automated test systems, Artificial Intelligence (AI) and Machine Learning (ML) based methods in recent years, managed to become a major enabler for smart manufacturing thanks to advancements in hardware and software components. Inevitably, the importance of quality data made its way into considerations and requirements of automated fault detection and condition monitoring systems. In this regard, this study proposes an AI-based testing framework for electric motors. We provide information on the reasons of faults observed and a test procedure to detect them. We also give detailed specifications on hardware (sensors and data collection equipment), and provide a data architecture and analysis on properties of ML models that make sense to be used in such scenarios.

Building on many discoveries and inventions, electronics started affecting people’s everyday lives in a significant fashion following the invention of the first solid state transistor in late 1940s. The miniaturization paved way for the mass electronics production and later the digital revolution, the outcomes of which are visible to all members of the public today. After about a two-decade-long swing around 2000s from hardware towards software regarding what affects lives more, a point has now been reached where electronics is more important to all and its use is more ubiquitous and crucial than ever before. In most if not all of end user or industrial applications, the capability and quality of electronics hardware are the key determining factors.The European electronics components and systems (ECS) industry has traditionally had a high base line for electronics innovation. However, the industry is now compelled, partly due to competition and partly due customer demand, to manufacture even more reliable electronics products than before. Guaranteeing the reliability of electronics hardware entails the entire ECS value chain to undergo a paradigm shift to holistically address reliability as a key issue. The European ECS industry previously adopted overseas outsourcing considerably, however it is now taking steps to reshape itself into a more coherent value chain with the aim of having not only the electronics designs but also the electronics manufacturing made in Europe.H2020-ECSEL programme successfully funds highly competitive projects in the area of electronics components and systems. We present here a prologue to a similarly funded project entitled Intelligent Reliability 4.0 ("iRel40"), by providing a background to the topic of ECS, project objectives, and the methodologies and implementations we plan to undertake during the 36-month period of this ongoing project.

The remote maintenance of home appliances, like washing machines, air conditioning, and heating system is a complex problem, but with the help of the ongoing developments on Internet of Things, Data Analysis and Artificial Intelligence, the problem can now be tackled with success. This paper mostly focus in presenting the architecture developed within the aim of the SMART-PDM project for the acquisition of data on the operation of home appliances and then it also shows some preliminary results for washing machines, which give some hints on how to fine tune the system to achieve predictive maintenance and condition monitoring.

Manufacturing is undergoing an immense change triggered with widespread sensorisation, volumes of data being generated, and advanced machine learning technologies. Problems once solvable via simpler approaches considering more monolithic paradigms have evolved to become larger systems (Cyber Physical Systems; CPS) and Systems of Systems. The scaling, manageability, security, data handling requirements of such systems, as well as the industry’s common goal to reusability have led to several outcomes at the broader European level, Arrowhead and Mimosa being two of those so far. In this study, we consider an Industry 4.0 "Predictive Maintenance" problem. Instead of a rushing with straight data analysis approach as defined under CRISP-DM, we first delve into creating a more widely consumable and reusable set of building blocks by implementing an Arrowhead and Mimosa framework, which together form the route to the machine learning steps that finally lead to the solution.