IoTI.com Content Director Brian Buntz wrote recently about the resources Siemens is throwing at software, and while that's significant, I'm more interested in Siemens' AI and machine learning work. For the past decade, Siemens' AI efforts have been focused on improving control of industrial processes using deep learning and reinforcement learning. An example of this technology is Siemens' "self-optimizing" gas turbines that leverage reinforcement learning. You can extend the classical control loop with a machine-learning loop using neural networks, making it dynamic and thus creating a new control policy.
Some of you may recall that back in October 2014, Hong Kong startup Ambi Labs unveiled its Ambi Climate as a gateway between your smartphone and your dumb air conditioner at home. But it isn't just about replacing your infrared remote control; what makes Ambi Climate unique is its machine learning capability, so that over time it learns your comfort preferences by way of various sensors, while also saving up to 20-percent energy according to user feedback. For instance, users in New York have a wider range of preferred temperatures, whereas users in Singapore peaked at around 77 degrees Fahrenheit (about 25 degrees Celsius) -- which is actually very close to what I prefer here in Hong Kong. On top of Comfort Mode, Ambi Climate also offers a Temperature Mode, an Away Mode (it only turns on the air conditioner to suit your settings) and a Manual Mode, as well as a timer and a scheduling feature.
The technology could also help human-driven and automated vehicles stay safe, for example by listening for emergency sirens or sounds indicating road surface quality. OtoSense has developed machine-learning software that can be trained to identify specific noises, including subtle changes in an engine or a vehicle's brakes. Under a project dubbed AudioHound, OtoSense has developed a prototype tablet app that a technician or even car owner could use to record audio for automated diagnosis, says Guillaume Catusseau, who works on vehicle noise in PSA's R&D department. Tests have shown that the system can identify unwanted noises from the engine, HVAC system, wheels, and other components.
More complex industrial equipment likely communicates over OPC or proprietary socket communications, but with all of their complexity, these machines still can't connect to the internet. Companies building IoT devices are solving this challenge by using gateways, also known as edge-based processing, to connect to cloud-based IoT platforms. However, connecting devices isn't as easy as updating software; instead, it's an investment in retrofitting old machines, replacing existing equipment, and enabling a workforce to leverage this equipment. After connecting devices and sending information to a data lake, we have to wait and observe what happens.
For example, the company's Software Environment for Neural Networks (SENN) is being continuously refined and adapted to new and evolving applications, including the optimization of gas turbines and wind turbines. Siemens Power Generation Services and CT have developed a system that continuously optimizes the operation and control of combustion in gas turbines. Based on AI from CT, the system, which is known as a Gas Turbine Autonomous Control Optimizer (GT-ACO), is currently being installed at a top customer in Asia. Improvements in overall gas turbine operation can be difficult to achieve because lower emissions characteristically result in shorter service life.
Falkner, Andreas (Siemens AG Austria) | Friedrich, Gerhard (University of Klagenfurt) | Haselböck, Alois (Siemens AG Austria) | Schenner, Gottfried (Siemens AG Austria) | Schreiner, Herwig (Siemens AG Austria)
The development of problem solvers for configuration tasks is one of the most successful and mature application areas of artificial intelligence. The provision of tailored products, services, and systems requires efficient engineering and design processes where configurators play a crucial role. For more than 25 years the application of constraint-based methods has proven to be a key technology in order to realize configurators at Siemens. This article summarizes the main aspects and insights we have gained looking back over this period.
Nodes represent entities and edges represent relationships between them, such as Customer-buys-Product. Promising-sounding or not, enterprises need more than cool technology and hype cycles to move to adoption: they need solutions for managing their data and metadata. Managing data vocabularies and mappings is crucial for instating Enterprise Knowledge Graphs, and the aptly named Semantic Web Company (SWC) presented its own solution in this space called PoolParty. IBM presented the research underpinnings of the Social Engagement Dashboard (SED), a solution for analytics in enterprise social networks.
BuildingIQ has responded by developing an energy management system driven by artificial intelligence (AI) that optimizes HVAC systems for maximum efficiency. Building automation is an example of a distributed control system (DCS). Not all smart or intelligent buildings use AI, but AI does enable smart control systems to learn about human habits and preferences without being programmed. The drive toward AI in building automation may also entice companies that make HVAC controls, including Azbil, Building Robotics, Delta Controls, Distech Controls, KMC Controls, Reliable Controls, and Trane.
Continuing our series of articles on Innovation we recently talked to Mike Zimmerman, Founder of BuildingIQ, about the approaching 3rd Step change in Building Energy Management (BEMS) technology. The system will combine an energy model of the building with external data such as weather forecasts and energy pricing signals to automatically write set points for the BEMS and execute Demand Response (DR) events. BuildingIQ was founded in 2009 from technology out of Australia National Labs has already developed software to intelligently assess and control HVAC energy for commercial building portfolios. Their SaaS (cloud based) software works with the buildings existing BEMS and utility demand response systems, incorporating weather forecasts, occupant comfort, utility prices and demand response signals into its optimization algorithms.
German engineering powerhouse Siemens today announced that it has set up an innovation unit named'next47' (as the company was founded back in 1847) to "foster disruptive ideas more vigorously and to accelerate the development of new technologies", more specifically in the fields of artificial intelligence, blockchain, autonomous machines and what it calls'decentralized electrification'. "With next47, we're living up to our company founder's ideals and creating an important basis for fostering innovation as we continue Siemens' development." It will be open to employees as well as to founders, external startups and established companies if they want to pursue business ideas in the company's strategic innovation fields. In its latest fiscal year, Siemens generated revenue of 75.6 billion and net income of 7.4 billion, with approximately 348,000 employees worldwide.