TTControl: Janosch Fauster, Director Application Development Center, and Markus Plankensteiner, Vice President Sales & Marketing

IoT Solutions Offer Huge Potential to responses provided by (left to right) Janosch Fauster, Director Application Development Improve Machine Center, and Markus Plankensteiner, Vice President Sales & Marketing, TTControl and Process Management

Data collection at the machine will increase and enable deeper analysis in the cloud to improve efficiency and processes.

Data, IoT & Connectivity

What will be the best use cases for the Internet of Things (IoT) and data in the coming years?

We see huge potential to improve machine and process management with IoT solutions in the coming years. The collection of data at the point of the machine will increase and enable deeper analysis in the cloud with technologies such as streaming analytics and big data. This will enable machine builders, owners and operators to vastly improve the economic efficiency of their processes.

What potential do you see in the use of artificial intelligence (AI) and augmented/virtual reality (AR/VR) –both for the design and manufacture of products, as well as within equipment?

Emerging technologies will advance machinery development and operator function in the future. On development projects, we already benefit from fine-tuning machine processes, as well as quality control on the equipment. A perception algorithm, for instance, can observe harvested grain quality and automatically optimize the harvesting process.

Sophisticated assistance functions allow less skilled workers to be more efficient, and increase the safety of operators and bystanders by advanced perception systems. Augmented and virtual reality can be of value for operating equipment in remote locations or hazardous environments. A combination of automated propulsion and augmented reality can support the operator in low-visibility scenarios.

What advancements do you see on the horizon for connectivity and machine learning in the next decade?

We expect machine learning to have a positive impact on product costs. Also, some spillover effects from automotive can lead to reduced chip and sensor costs. That will be necessary, since multiple Central Processing Unit (CPU) cores, accelerators and Graphics Processing Units (GPUs) will have to execute the growing number of advanced assistance functions, resulting in an exponential growth in the amount of software. A short time to market can still be ensured, despite facing massive complexity, by relying on certified chips, ECUs and robust software frameworks.

What challenges remain for the continued adoption of data, IoT and connectivity-related technologies or systems?

Technology in emerging technologies is progressing a great deal. The current innovation cycle for some key components is about 2 years, where the new generation outperforms the previous generation by hundreds of percent in every aspect of performance. This fuels challenges relating to obsolescence handling; remember, for example, the switch to 5G on one hand and sunsetting 3G on the other hand.

The automotive industry postponed its plans for level 4 and level 5 autonomy as a result of the advancements in computational powers and deep neural networks that remained necessary. We think the heavy equipment industry can learn from that experience by setting realistic targets and by better appraising the investments needed to implement our use cases.

Automation & Smart Systems

What further benefits will automation bring to the heavy vehicle and equipment industries?

Many heavy vehicles are expensive, high-tech pieces of equipment used in labor-intensive industries. Skilled operators are scarce, but the highest level of machine performance and safety is still expected. Automated quality control and the automated fine-tuning of operational parameters enable also less experienced operators to achieve high levels of productivity. |

Head to the Web

Listent to our podcast interview with Janosch Fauster to learn about more emerging trends at oemoh.co/Podcast_TTControl.