Taking a dynamic approach to safety
- Trustworthiness within the collaborative infrastructure along the value chain is a prerequisite for stable operations.
- Effective safety and security are key challenges as this can build trust with asset owners and operators.
- Optimal operation depends on the quality, design and condition of the gear set; lubricants that fit the operating conditions; and regular evaluations and maintenance.
Machine safety insights
- Industry 4.0 and digital twins offer manufacturers an opportunity to improve safety by enhancing situational awareness.
- Asset administration shells (AAS) exchange asset-related data between assets and production orchestration systems or engineering tools and acts as a link for Industry 4.0 objects, which can give the manufacturer better insights on how to improve safety.
The convergence of enterprise information technology (IT) and operational technology (OT) is enabling systems and devices to exchange and interpret shared data on a global scale. By combining the strengths of the physical and virtual worlds, cyber-physical systems have the potential to significantly enhance industry performance, facilitate new products and spark innovative business models as the real systems can be modeled using digital twins in multiple ways.
Today, digital twins operate in parallel to real-world factories, where thousands of sensors constantly collect and process data, either locally or on a larger scale. A digital twin receives continuous, real-time data from a product or asset to create a virtual representation of that physical object. As the object can be virtually monitored 24/7 this enhances situational awareness.
Asset administration shell (AAS) is a term coined by Plattform Industrie 4.0 (I.40) in Germany. Every I.40 asset is allocated an AAS, which exchanges asset-related data between assets and production orchestration systems or engineering tools. As the AAS contains all of the information and functionalities of an asset, it acts as a link between I4.0 objects, allowing for the use of many different communication channels and applications.
The AAS can be used for:
Non-intelligent and intelligent products
Covering the complete lifecycle of products, devices, machines and facilities
Allowing for integrated value chains
Serving as the digital basis for the development of autonomous systems and AI.
For systems that incorporate adaptive and smart features, resilience becomes vital and is added to the list of trustworthiness requirements. Trustworthiness within the collaborative infrastructure along the value chain is a prerequisite for stable operations.
Risk management and machine safety
While I4.0 sees reduced risk in several areas, the range and flexibility of connected interfaces introduce a new set of risk issues. As production facilities become more complex, operators must manage a rapidly evolving system that incorporates multiple interdependencies, while minimizing downtime. It is therefore vital to consider the shifting landscape of risk, which is why I4.0 requires a new risk management approach that is customized to each individual actual use case.
As the increased flexibility created by these interdependent and dynamically changing I4.0 systems introduces new complexities and challenges, there is a shift from static risk assessment to one of dynamic risk. Analyzing and assessing the underlying physical and cyber risks to humans, property, and the environment is therefore a challenging task.
Machinery safety standards define a set of general physical hazards that are used during type certification. However, current standards, such as ISO 12100 – Safety of machinery – General principles for design – Risk assessment and risk reduction, have not been designed around the concept of machine connectivity and interoperability. Conventional safety concepts do not consider the sources and effects of cyber threats that could create new hazards.
In practice, when a machine operates in an application-specific context, its limits and applicable hazardous situations may differ significantly from those considered under worst-case and stand-alone scenarios. Additional hazardous situations may also arise from machine-to-machine interaction.
To give an example, an automated guided vehicle (AGV) making its final approach to a machine for docking may pose a collision risk between two industrial assets. This unsafe docking event risk may be mitigated by using two safety measures incorporated in AGV design – A speed control system and a parking braking system control.
Although there is no risk for humans in a confined area, these measures are necessary to protect industrial assets from damage. The use of a context-sensitive safety approach could achieve the goal of property protection combined with higher system efficiency.
A third scenario example looks at process optimization, where operational downtime and bottlenecks may not pose a risk to humans, property and the environment, but they can affect system performance. AGVs with different maximum rated speeds, navigating in line, one after the other, are limited by the maximum speed of the first in line. If lane width and clearance distances from adjacent obstacles are deemed safe – for example, no human can step into the AGV’s path without being detected – then the system can change to parallel navigation. Such context-sensitive safety can enable higher speeds, improved navigation flexibility, and increased efficiency.
The above scenarios demonstrate the need for adaptive production systems capable of monitoring and recognizing hazardous situations during runtime, to ensure that residual risks are handled within current practices. In addition to the limitations of the conventional worst-case approach, system operators should also be aware of real-world situations where safety installations may be either consciously manipulated or inadvertently modified, as these can cause serious accidents.
To meet the new needs of I4.0, a new event-triggered, dynamic risk assessment and automated validation of safety measures approach is required. This would assist system designers and operators to navigate complex risk landscapes, in both virtual simulations and real-world applications. This requires a continuous and holistic risk assessment to ensure stable operations, increased productivity and reduce downtime in a smart manufacturing environment. This necessitates a digital or virtual representation of the physical manufacturing system, using digital twins and asset administration shells. These so-called cyber-physical systems combine the strengths of the physical and virtual worlds and have the potential to significantly enhance industrial performance as the systems can be modeled using the digital twin in multiple ways.
While digital twins and AAS help manufacturers optimize performance and accurately predict business obstacles, they are also faced with the challenge of navigating a complex new risk landscape. Effective safety and security are key challenges as this can build trust with asset owners and operators, but it is becoming increasingly impossible to apply existing risk assessment criteria to a dynamic I4.0 operating environment that is characterized by multiple interactions and data flows.
It is vital the digital twins have customized safety and security profiles. A safety profile should be modeled to describe asset safety from a general and an application-specific perspective. These profiles should then be processed by an inference engine against actual application constraints to define limits and risk-mitigation capabilities in a real-world application, thereby providing automated risk evaluations at runtime.
Darren Hugheston-Roberts is head of machinery safety at TÜV SÜD. This originally appeared on Control Engineering Europe’s website. Edited by Chris Vavra, web content manager, Control Engineering, CFE Media and Technology, [email protected].
Keywords: machine safety, operations management
See additional machine safety stories at https://www.controleng.com/mechatronics-motion-control/machine-safety/
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