On AI and Data Technology Innovation in the Rail Industry. Interview with Gerhard Kress
“I think the biggest challenge is that in the rail business we have a very large set of old and country specific regulations that date back many decades. These regulations are meant to protect passengers, but some of them are not anymore fitting to the modern capabilities of technology and instead drive cost and slow innovation down dramatically.” –Gerhard Kress
Artificial intelligence acts as an enabler for many innovations in the rail industry.
In this interview, I have spoken with Gerhard Kress, who is heading Data Services globally for the Rail business, and is responsible for the Railigent ® solution at Siemens. We discussed innovation and the use of AI and Data-driven technologies in the transport sector, and specifically how the Siemens´ Railigent solution is implemented.
Railigent is cloud based, designed to help rail operators and rail asset owners, to improve fleet availability and improve operations, for example by enabling intelligent data gathering, monitoring, and analysis for prescriptive maintenance in the rail transport industry.
This interview is conducted in the context of a new EU funded project, called (LeMO (“Leveraging Big Data to Manage Transport Operations“). The LeMO project studies and analyses big data in the European transport domain, with focus to five transport dimensions: mode, sector, technology, policy and evaluation.
LeMO conducts a series of case studies, in order to provide recommendations on the prerequisites of effective big data implementation in the transport field. The LeMO project has selected Siemens´ Railigent as one of the main seven case studies in transport in Europe.
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Q1. What is your role at Siemens?
Gerhard Kress: At Siemens, I am heading Data Services globally for the Rail business. This means that I am heading all MindSphere Aplication Centers that focus on rail topics from the United States to Australia.
Q2. What are in your opinion the main challenges, barriers and limitations that transport researchers, engineers and policy makers today face as they work to build efficient, safe, and sustainable transportation systems?
Gerhard Kress: I think the biggest challenge is that in the rail business we have a very large set of old and country specific regulations that date back many decades. These regulations are meant to protect passengers, but some of them are not anymore fitting to the modern capabilities of technology and instead drive cost and slow innovation down dramatically.
Q3. You manage all the data analytics centers of Siemens for rail transport globally. What are the main challenges you face and how you solve them?
Gerhard Kress: There are a number of key challenges. First challenge is to develop offerings that are globally relevant for our customers. The rail industry is very different across the continents and with country specific legislation there is a very diverse landscape of requirements to address. Another important challenge is to manage the network of data analytics centers in such a way that they on leverage local specifics but at the same time learn from each other and act as a true global network.
The way we have addressed these issues is to set up in each MindSphere Application Center small agile teams that work very closely with customers to understand their issues and understand how they create tangible value. These teams create customer specific solutions, but use existing reusable analytics elements to build these solutions. In order to make this happen globally, we have created a simple set of tools and processes and have also centralized the product development function across all of the data analytics centers.
Q4. You are responsible for the Railigent Asset Management Solution at Siemens. What is it?
Gerhard Kress: Railigent is our solution to help customers manage their rail assets smarter and get more return from them. Therefore Railigent contains a cloud based platform layer to support ingest and storage of large and diverse data sets, high end data analytics and applications. This layer is open, both for customers and partners.
On top of this layer, Railigent provides a large set of applications for monitoring and analyzing rail assets. Also here applications and components can be provided by partners or customers. Target is to help customers improve fleet availability, maintenance and improve operations.
When I am talking about rail assets, this implies rolling stock / vehicles, signaling systems including field devices and also rail infrastructure.
Q5. Who are the customers for Railigent, and what benefits do they have in using Railigent?
Gerhard Kress: Customers for Railigent are for example rail operators and rail asset owners. The key benefits for them are that they can improve asset and system availability and therefore offer more services with the same fleet size. Railigent also helps these customers reduce lifecycle costs for their assets and improve their operations.
Q6. What are the main technological components of Railigent?
Gerhard Kress: Basically Railigent builds on technologies from Mindsphere, enlarged with rail specific elements like data models / semantics, rail specific format translators and of course our applications and data analytics models.
The foundation is a data lake in the cloud (AWS) in which we store the data in a loosely coupled format and create the use case specific structures on read.
Data gets ingested in batch or stream, depending on the source and during the data ingest we already apply the first analytics models to validate and augment the data.
For every step in the data lifecycle we use active notifications to move the data to the next stage and as much as it is possible we rely on platform services from AWS to build the applications.
Our applications consist out of micro services which we bundle in a common UI framework. And we have deployed a full CI/CD pipeline based on Jenkins.
Data analytics happens either in sand boxes, when the model is still in development or in the full platform.
We use mostly Python and pySpark, but are also using other technologies when needed (e.g. deep learning driven approaches).
Q7. MindSphere is Siemens´ cloud-based, open IoT operating system for the Industrial Internet of Things. What specific functionalities of MindSphere did you use when implementing Railigent and why?
Gerhard Kress: MindSphere and Railigent share a lot of core functions, especially in the way how the data connectivity and data handling is implemented and how IT security of the system is ensured. The key reason to use the same technology is that it is essential for our customers to have a secure and reliable platform. And the key differentiator we provide is generating the insight. Therefore the pure platform functionalities are not differentiating and therefore there is no rational for developing them all over again.
Q8. What other technologies did you use for implementing Railigent?
Gerhard Kress: The key elements of Railigent are not its platform components, but the reusable analytics elements as well as the rail specific applications.
For the analytics side, Railigent uses all types of analytics libraries, but also mathematical approaches newly developed by Siemens. Especially for the industrial data area, new mathematical approaches are often required and such approaches were then integrated into Railigent.
Q9. The foundation of Railigent is a data lake in the cloud (AWS) in which you store the data in a loosely coupled format and create the use case specific structures on read. Can you elaborate on how you handle batch and/ or stream of data?
Gerhard Kress: Railigent has to handle a large number of data formats, like diagnostic messages, sensor data, work orders, spare part movements, images, etc.
We receive data in all sorts of legacy formats, most of them are batch formats. These files we decrypt and then annotate them with specific information to enable us to quickly find the data back again and also to ensure it can be attributed to the right fleet and the right customer. Then we create a generic JSON file which we store in our data lake.
For stream data we use mostly MQTT as transfer protocol and then create the same JSON file format to persist this data in our data lake.
Q10. What data analytics do you perform?
Gerhard Kress: Most of the data analytics in Railigent is based on machine learning or deep learning. This can be classifiers to identify components which are already showing distress, or it can be prediction algorithms to identify the remaining useful life of a component. Most of the machine learning is supervised learning, but there are aso cases where unsupervised learning techniques are implemented.
Q11. Is there a difference in performing analytics when the model is still in development or in the full platform?
Gerhard Kress: We develop models usually in a type of sandbox environment so that we can quickly iterate the model on real data, validate the results and improve the model further. Once a certain quality is reached, we transfer the model into the operational environment of Railigent. This requires us to be much more formal in the deployment so that results are correct and the performance is predictable. And, of course, the model then needs to be integrated into the production data pipeline in order to be available 24/7
Q12. What are the lessons learned so far in using Railigent?
Gerhard Kress: So far we have quite a few lessons learnt from Railigent deployments and most of them deal with the value generation for our customers.
We have learned that we needed to be closer to our customers in creating applications. For this we have set up an agile “Accelerator” team, developing the first insights with the customer in the first week and making this all accessible through a first web application. These teams are often collocated with the customers so that we can jointly create the right solution for the customer problem.
In our customer activities, we have learned to see the customer value as the main driver of our activities. We try now to quickly deliver a first application which we then improve later, but we also focus on making the insights actionable so that the customer can immediately start implementing and gaining the promised value.
With regards to handling data, we have learned that in a complex big data world with many different types of data elements, we have to resort to a schema on read approach as an integrated and overarching logical data model would not be feasible.
These learnings we have implemented already and we can see the value which the changes helped create for our customers.
Q13. What is the roadmap ahead for Railigent?
Gerhard Kress: Railigent is just going to be released in Version 2.0 in July and we are aiming for Version 3 in December. On the roadmap we do not only have customer facing application features for rolling stock and signaling, but also technical building blocks, analytics components as well as platform topics. Our focus in V3 is on features to better integrate partners, capabilities to allow partners and customers easier access to analytics elements inside Railigent and handling of realtime data. Additionally we will improve the operations topics and deploy a new type of highly scalable and overarching analytical capabilities to be used by any application inside Railigent.
Our target is to become even more relevant for our customers and provide tangible value.
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Gerhard Kreß is responsible for Data services in the Siemens Mobility GmbH, aiming to build up new customer offerings enabled by data analytics for both rail vehicles and rail infrastructure.
Before that he was in Siemens Corporate Technology responsible for implementing the corporate big data initiative “Smart Data to Business” and he worked for 3 years in Siemens Corporate Strategy in the corporate program to refine the IT strategy for the Siemens businesses. There he was also responsible for setting up the Siemens big data initiative.
Prior to his work in Corporate Strategy he spent 8 years working in Siemens IT Solutions and Services (SIS), managing systems and technologies for the global service desks and in the project management of major IT outsourcing projects.
Gerhard Kreß started his professional career in McKinsey & Company, where he focused on growth initiatives and high tech industries.
He holds a German diploma in Theoretical Physics and a Master of Arts in International Relations and European Studies.
During his studies, Gerhard Kreß worked for the student NGO “AEGEE-Europe” where he was President and Member of the European board of the organisation.
Resources
– Railigent® – the application suite to manage your assets smarter – mov, (Link to YouTube Video), May 13, 2018
– Heading for Data-Driven Rail Systems, Siemens, 15 December 2017
– UNDERSTANDING AND MAPPING BIG DATA In Transport Sector, LeMo Project Deliverable D1.1, May 13, 2018 (Link to .PDF 78 pages)
– BIG DATA POLICIES In Transportation, LeMo Project Deliverable D1.2, May 31, 2018 (Link to .PDF 60 pages)
– BIG DATA METHODOLOGIES, TOOLS AND INFRASTRUCTURES in Transportation, LeMo Project Deliverable D1.3, July 16, 2018 (Link to .PDF 50 pages)
– LeMO Project Web site (LINK). The LeMO project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 770038.
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– On Data and Transportation. Q&A with Carlo Ratti, ODBMS.org, Apr. 11, 2018
– On Logistics and 3D printing. Q&A with Alan P. Amling, Vice President, UPS Corporate Strategy, ODBMS.org, Apr, 2018
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