Description
The impact of digital technologies on industrial processes is widely discussed and often assumed as a "done deal". However, the industrial environment tends to move at its own pace, and the adoption of new digital technologies can represent a massive challenge in contexts dominated by electromechanics components.
Digitalization is penetrating the industry through sensors. Industrial machinery is monitored by an increasing number of sensors that collect a considerable amount of data that need to be processed to understand the health status of the machinery itself. The so-called "predictive maintenance" hides behind-the-scenes problems related to ultra-high-speed data sensing, data fusion, and machine learning.
The DSTREAMS project addresses the challenges that arise in the design of work methodologies associated with ultra-high-speed signals as input values to solutions understood as Industry 4.0. Under this paradigm, it is required to deploy acquisition, communication, visualization, and, importantly, the application of machine learning algorithms with high performance (precise and timely) to the problem-solving requirements, always guaranteeing the security of the information handled.
Proyecto RTC2019-006871-7 financiado por MCIN/AEI /10.13039/501100011033
