Analytics-driven embedded systems, an introduction
Analytics-driven embedded systems are here. The ability to create analytics that process massive amounts of business and engineering data is enabling designers in many industries to develop intelligent products and services. Designers can use analytics to describe and predict a system’s behavior, and further combine analytics with embedded control systems to automate actions and decisions.
In some implementations, the analytics are performed in the cloud to improve embedded system performance. Borislav Savkovic, a controls systems engineer by training, led a team at BuildingIQ to design a building climate control system that uses analytics to reduce energy consumption. The system starts with gigabytes of engineering and business data. The engineering data comes from power meters, thermometers, pressure sensors, and other HVAC sensors. The business data comes from weather forecasts, real-time energy prices, and demand response data. In the analytics-driven system, the team uses signal processing to remove noise, machine learning to detect spikes, control theory to account for heating and cooling dynamics, and multi-objective optimization with hundreds of parameters. The analytics running in BuildingIQ’s cloud service tune the building’s HVAC embedded systems. The result: an analytics-driven system that reduces energy consumption up to 25 percent in commercial buildings.
In other cases, the analytics run directly in the embedded systems themselves. The design team at Scania, the Swedish truck manufacturer, embeds analytics into their emergency braking systems to provide real-time crash avoidance to reduce accidents and meet stringent EU regulations. Engineering data from cameras and radar are processed in real time for object detection and road marking detection, and subsequently fused to signal collision warning alerts and automatic brake request. System safety and reliability are ensured with exhaustive test and verification, including test scenario creation, system modeling with simulated and recorded data, and hardware-in-the-loop (HIL) testing.
These two examples highlight the steps designers use in developing analytics-driven systems:
We’ll cover pre-processing in this installment, and the remaining steps in parts two and three.
The first step in developing analytics is to access the wealth of available data to explore patterns and develop deeper insights. The datasets are not only large in size, but can also come from many different sources and represent many different attributes.
