Digital, IT, IT Security & Safety

We audited and certified the firstever reference architectures tailored specifically to the water industry (e.g. fresh water, wastewater, desalination) in April 2020. The blueprints were provided by Siemens and were certified to an international, industrial standard. Increased digitisation means a higher need for cybersecurity, and Siemens has been offering seamless cybersecurity concepts to many users and companies for a long time. However, as digitisation increases exponentially, this heightened threat has seen legislators across the world demand that operators of critical infrastructures all protect themselves. For industries such as water management, this will be the first time that cybersecurity is an issue on such a scale. This of course brings with it its own set of new criteria, meaning that IT service providers had the challenge of coming up with entirely new programmes suited to the industry. The certification confirms that both Siemens processes and the technical reference architecture adhere to the security requirements of the standards and is the first of its kind in the water industry and in a long tradition of IEC 62443 certifications from TÜV SÜD for Siemens.

Digitalisation has transformed the way we transact, work and live. In this world of increased connectivity, it is important to ensure the security, privacy and integrity of our data and digital infrastructures. As an industry thought leader for digital trust, TÜV SÜD’s aim is to increase awareness of cyber security.

Aligned with TÜV SÜD’s mission of inspiring trust, we are proud to be a member of the Charter of Trust. The global cyber security alliance was initiated by Siemens and the Munich Security Conference in 2018. Its 17 members are leading global organisations and key players in industries such as technology, aviation, advanced manufacturing and more. With a diverse range of partners, this initiative is a consolidation of leading experts with the latest technologies and sought after resources in the world.

Through our independence, deep domain and regulatory knowhow, we at TÜV SÜD aim to contribute to the Charter of Trust and its 10 key principles by protecting critical systems and sensitive data, helping our customers ensure their business continuity. Our service portfolio covers crucial aspects of cyber security in the context of our core business model covering testing, auditing, inspection, certification and training.

Find out more about the Charter of Trust at www.charteroftrust.com and how TÜV SÜD helps increase digital trust at www.tuvsud.com/en/themes/charter-of-trust.

Lifts perform a heavy duty – all day, every day. To predict optimal maintenance and prevent breakdowns at an early stage, TÜV SÜD Digital Service dove deep into the possibilities of the Internet of Things (IoT) and smart algorithms and developed an innovative solution. In Singapore, our team successfully implemented a smart lift testbed project for 118 lifts.

Tapping on their profound base of expertise in this field, our team designed and implemented tailormade algorithms for 24/7 operations monitoring, recording and anomaly detection based on sensor data. In a joint venture of lift experts and a sensor hardware provider, we developed a centralized, cloud based IoT solution to process almost realtime data on the physical condition, utilization, ride comfort, and anomaly detection of lifts. To make things even more demanding, the solution had to be applicable to different lifts, independent of the Original Equipment Manufacturer (OEM), age and type.

The project then moved into the handson phase: We equipped several lifts with laser and multi sensors on top of the cabin, which provide detailed information on the acceleration, position in the shaft, and more. Environmental variables such as pressure, temperature, and humidity are monitored as well.

The project proved to be an overall success: The testbed had a resounding 85% accuracy in failure prediction with the innovative analytics engine. The information collected provides a basis for automated predictive maintenance which will reduce costs and down time. And the story goes on: TÜV SÜD Digital Service already won another pilot project for implementing the innovative lift predictive maintenance solution on high-rise and industrial properties.

Making business fit for the future with Industry 4.0? Can do. For a leading industrial packaging product manufacturer TÜV SÜD Digital Service in Singapore developed a strategic roadmap and corresponding business case to create the path for Industry 4.0 transformation.

Guided by the vision and future targets of the client, the TÜV SÜD team worked to conceptualize and prioritize the I4.0 technology solutions that would be needed to realize the future state.

In a nine-week engagement we got busy to make the vision become reality. The current state of a factory was assessed, and our experts identified development opportunities in automation, connectivity, data analytics, as well as in the underlying IT and OT system architecture. Further analysis was conducted on the enterprise systems, databases, operations management, and strategic imperatives.

With defined transformational direction and targets, our experts crafted a high-level roadmap, and established the strategic priorities, investment requirements and cost benefit analysis. The Return on Investment (ROI) was projected for the entire Transformation program.

The final deliverables in the analysis and recommendations served as a basis for investment decisions of senior management at the global Head Office, direct the transformation journey at the local pilot site and serve as a blueprint for other plants.

In February 2021 we kicked off Drivers Club, an app by Division Mobility for their driver’s license applicants, with a remote design sprint.

The goal of the design sprint was to further develop the product within a short time span of three weeks, with the support of user input, by building a visual concept for user guidance.

By starting the design sprint with expert interviews, we collected as much relevant information as possible from every expert field, which also enabled us to scope out the project. Based on this input, all participants defined different features of the product and developed the user flows. These resources gave us enough foundation to start visual thinking, asking every participant to scribble suggestions for the visual implementation of the defined requirements. With these conceptual ideas in place, the whole design sprint team defined the direction for the visual prototype under the eye of the involved UX department. On the one hand, the resulting prototype was utilised for user testing, which enabled us to evaluate the product ideas with real users and therefore reduce the risk of expensive development errors.

On the other hand, it represented a solid foundation on which to define the requirements for a product that users will love.

At TÜV SÜD, we developed an AI-based solution to detect damage such as scratches and dents on cars. Pictures are taken of all sides of the car and then automatically analyzed to generate damage reports. As the setup works in near-real-time, the hardware, software, and architecture could be continuously improved, making the algorithms and services we employ as efficient as possible.

Our data engineers and cloud architects were responsible for designing, setting up, and constantly improving automated machine learning pipelines and a scalable cloud architecture that we used for data management. The key task for our data science team was to select, train, and retrain deep learning algorithms. As a result, we reached a high damage-detecting accuracy using region-based convolutional neural networks (R-CNN).

An active-learning approach also allowed us to integrate feedback on model retraining from our car experts to continuously improve the system. With the AI-based solution, we support our car experts and speed up the whole process.

This project is an excellent example of bringing the latest deep-learning-based approaches to life by integrating them into a scalable system for automated damage detection.