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In this paper we propose a retrofitting methodology based on Design Thinking in a steel mill plant, and highlight how the retrofitting activity is important to allow even more companies to migrate to Industry 4.0, reducing the gap between SMEs and Large Industries for participation in the 4th Industrial Revolution.

In the paper we present the design and development of the Machine Learning (ML) modules for two case studies. In both cases we developed a ML model to learn the system’s normal behavior so to identify whichever abnormal condition may arise. Such a framework is usually referred to as Anomaly Detection (also known as Fault Detection or Novelty Detection). Our models succeeded at identifying the injected anomalies. In addition, no anomalies were observed when the model was fed with normal data. The results are discussed considering the trade-off between type of sensors, learning algorithm, training effort, computational demands.

As connectivity and flexibility requirements continue to drive performance demands in embedded edge computers and servers, industries are poised to take advantage. Factories and logistics firms are streamlining their production lines with greater precision using robotics. Hospitals are accelerating medical imaging appliances like ultrasound with AI capabilities.

Public sector and aerospace are also ramping up AI image recognition, event analysis, and security use cases to come up with exciting new efficiency models to improve quality of life in cities.

Performance is driving a new wave of creativity and innovation, and both computing modules and processors need to keep pace through enhanced flexibility.

Maximum degree of automation, more flexibility in production, significantly lower personnel resources: innovative industrial companies hope for a prosperous future through Artificial Intelligence and Machine Learning.
And that is not all: PwC‘s consultants have
found out that such technologies are an
obligatory exercise in staying competitive.

Keeping workers and the environment safe is a key priority when designing automated equipment and processes. Functional Safety (FuSa), a technology to remove unacceptable risks in the presence of a fault, is critical to applications in the industrial automation, agricultural, transportation, and healthcare industries, among others.

As FuSa has gained importance, Smart Mobility ARChitecture (SMARC) modules — low-power, high-performance modules that provide power, DRAM, and compute — have become a standard for many IoT edge applications. Now, SECO has created a new solution that simplifies the adoption of the latest available FuSa technologies
by combining the power of SMARC modules with a feature set focused on FuSa applications.

ATMs are an easy target for fraud attacks, like card skimming/trapping, cash trapping, malware and physical attacks. Attacks based on explosives are a rising problem in Europe and many other parts of the world. A report from the EAST association shows a rise of 80% of such attacks between the first six months of 2015 and 2016. This trend is particularly worrying, not only for the stolen cash, but also for the significant collateral damages to buildings and equipment.

According to Gartner, "Edge computing has moved from IoT-focused to a broadly considered complement to the more centralized hyperscale cloud. The edge role in distributed cloud and digital business ensures that despite the nascent market, technologies and architectures, edge computing is here to stay."

SECO is mentioned in the list of Industrial IoT Gateways Samples Vendors.