Advanced Analytics for the Internet of Things, Services and People – ABB

Company ABB AG

Kallstadter Str. 1 | 68309 Mannheim | www.abb.de | Contact-center@de.abb.com Competitive

environment/

Driving factors

ABB is a leading global technology company in power and automation that enables utility, industry, and transport and infrastructure customers to improve their performance while lowering environmental impact. The ABB Group of companies operates in roughly 100 countries and employs about 135,000 people. Innovation and quality are the hallmarks of our offering, which ranges from switches to industrial robots to engineering and expert service, from power transmission and distribution networks to software that manages entire factories. ABB’s operations are globally balanced and distinguished by strong positions in all of the world’s principal markets.

Challenges ABB’s areas of business face two key global trends in our business. One is the shift towards renewables, which is accelerating despite the low oil price – 2015 was a strong year for investment in renewables, with 121 gigawatts of capacity added. This results in unprecedented demands to manage the complexity of the »digital grid« of the future. The other is what we call the industrial »Internet of Things, Services and People«, resulting for example in growing intelligence of machines that is driving quantum leap improvements in productivity and safety in industry. As a global leader in power and automation technologies, ABB is driving this change and supporting our customers to benefit from both of these shifts. We see them as key to our business, now and in the future, and essential to solving the underlying causes of key challenges that are affecting the world today, namely climate change and weak economic growth.

In power generation, renewables are transforming the energy mix, putting pressure on traditional producers to rethink their business models while lessening environmental impact and dramatically increasing grid complexity. The future grid will be far more complex with multiple feed-in points from traditional power plants to large-scale renewables on the supplside, and a coexistence of traditional demand patterns and microgrids and nanogrids on the demand side. Managing this complexity will require intelligently automated, digital power grids that can anticipate demand and supply patterns, while routing and transporting power to the ever-increasing number of consumption points of electricity.

On the automation side, advances in sensor technology, combined with ubiquitous connectivity and massive increases in our ability to process and store data, are enabling machines to become more and more intelligent, as well as to learn and to interact with humans in new ways. The basis for this is the industrial Internet of Things, Services and People. In time, this will enable the next stage of industrial automation, in which machines and entire process chains learn to reason and take decisions, making processes self-regulating and self-optimizing.

Raw data is useless, unless it is turned into knowledge. Data needs to be analyzed and applied ambitiously and innovatively – to the benefit of decision-makers, the individual industry, and the whole network. The answer is software-based analytics and decision tools suited for industrial operations management solutions. Big data expands the view of enterprises by increasing the range and variety of data that can be analyzed so that you have additional context and insight to enable better decision making. In addition, big data scales in a

predictable and straightforward way, both in size and speed, so that business analytics reporting solutions can grow with your business. Speed is also important. With decreased time to actionable results, big data can provide an advantage by adding a real-time view capability that can enable your personnel to be more responsive in day-to-day situations.

Big Data strategy/

initiative

Data collection and data analysis may increase knowledge and enable predictions, but unless someone acts on these, there will be no effect on the plant performance. Only when the knowledge is turned into actions and issues are resolved will there be a benefit from analyzing more data. In other words, knowing what is faulty is one part of the equation, but fixing it is another part. Providing remote access to data and analytics to service experts will close the loop of continued improvement. Online availability of support from a device or process expert is essential for a quick resolution of unwanted situations. Coupling remote access with the new technologies now available enables earlier detection and better diagnostics, and therefore facilitates faster service – resulting in better planning and an increase in plant and operational efficiency.

ABB follows a holistic approach in developing new service offerings based on advanced data analytic. Key objective is to leverage the available data to gain insights that will trigger specific actions in the industrial systems. To achieve this objective, Big Data technologies have been successfully applied in several research and development projects and pilot implementations. Examples are big data analytics of alarm management data from process plants, root cause isolation in large-scale industrial systems, or semantic search for faster problem resolution by case-based-reasoning.

Governmental R&D programs

ABB Corporate Research Germany is the coordinator of the cooperative project FEE funded by the Federal Ministry of Education and Research under the project call »Management and Analysis of Large Data Sets (Big Data)«. The goal of the BMBF research project FEE is therefore to detect critical situations in the plant at an early stage, and to develop assistance functions that support plant operators in decision making during critical situations. For this purpose appropriate real-time big data methods will be developed that will utilize the available heterogenous mass data from the plants. Early warnings will be provided to the operator in order to enable proactive instead of reactive actions. Furthermore, assistance functions will be developed that support the operators in deciding on their intervention strategy.

ABB is also partner of the Smart Data Innovation Lab (SDIL) hosted at the Karlsruhe Institute of Technology (KIT).

Innovation/

Knowledge transfer

ABB works with several research organizations like universities or the Fraunhofer-Gesellschaft.

Beside such bilateralactivities, ABB is an active contributor in Germanies committee work around Industrie 4.0 (Plattform Industrie 4.0, ZVEI, VDI/VDE, VDMA) and in the Industrial Internet Consortium. ABB, being a truly global company with strong representation in Europe and north america, has thereby the unique opportunity to transfer knowledge between the different approaches and innovation cultures, also bridging this gap within customer collaborations.

Innovation transfer is also e.g. taking care of in pilot customer projects, bringing together ABB’s research, development and business teams with the according teams from customers and/or partners.

Business model

Business models coming with Industrie 4.0 or the Industrial Internet are shifting from pure product business towards service business. This is new to both the suppliers and their customers, and the majority of the organization struggle to adapt to this new kind of business.

ABB’s concept of the Internet of Things, Services and People merges the changes in technology (things), business models (services) and organizations (people). The new service business models will focus on data-driven services, based on big data technologies. However, the first step towards the service world will be business models around product-service-systems (PSS).

In PSS, traditional products are being enhanced by data-driven services, enabled by cheaper sensor, communication and data management technologies, providing additional services such as predictive maintenance to the customer. It is important to understand that value in PSS (or cyber-physical-systems, CPS) is generated on the physical level, i.e. the product is still providing value to the customer, and on the cyber-services level, supporting the physical value

creation through data-driven services. However, the intermediate level of data storage (data layer), e.g. a digital twin, produces only costs and does not bring value per se, but enables the cyber-services.

With respect to big data in the industrial context, new business ecosystems will form, bringing IT companies and industrial partners closer together. One requirement to be successful in such ecosystems is that the business model of each partner needs to be successful, in order to allow the whole ecosystem to be successful. This requires an adaption of the innovation speed (IT companies are ususally faster than the industrial partners) and a stronger partnership between ecosystem participants, compared to todays supplier-customer relationsships.

Additional information

Project Web-Site FEE: http://www.fee-projekt.de/index_en.html Web Site Smart Data Innovation Lab: http://www.sdil.de/en/

ABB’s IoTSP Web-Site: http://new.abb.com/about/technology/iotsp

6.3.4 Event Monitoring in Value and Supply Chains – Siemens,