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The concepts of Software-Defined Manufacturing (SDM) and Digital Twins emphasize the current trend in production system development. These systems are characterized by frequent software updates to address adjustable production processes and higher system flexibility. These software-intensive systems are safety-critical and require a thorough reliab...
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... sequencing of actions consists of connected actions and control nodes and shows the software flow of the system. This is illustrated in Figure 7. ControlNodes such as DecisionNode, MergeNode, JoinNode, and ForkNode can control the software flow. ...
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Digital Twins enable real-time monitoring through automatic model extraction of complex systems using data from sensors, meters and IoT devices to facilitate valuable decisions. In energy systems, Digital Twins can be integrated to improve system reliability by enabling adaptability to demand fluctuations and system disturbances. With the increasin...
Citations
... SysML v1 resilience profiles for reliability analysis were introduced in [14] and [15]. In our previous work [16], [17], [18] we used and extended the SysML v2 RiskMetadata package [19]. The RiskMetadata package allows the integration of reliability-related data, such as the failure probability of parts into SysML v2 models. ...
... To adequately describe and analyze flexible softwaredefined systems, M2M transformation methods that automatically generate risk models are indispensable. In our previous papers [18], [16], [17] we introduced a new method that includes a transformation method from SysML v2 to hybrid risk models. This enables the automated and continuous reliability assessment based on the SysML v2 [19] models of the digital twin. ...
In modern and complex production systems, the focus is shifted toward the software part. Software-Defined Manufacturing (SDM) and Cyber-Physical Production Systems (CPPS) characterize this trend. SDM and CPPS enable the concept of adaptive, flexible, and self-configuring production systems. These software-intensive robotic systems are safety- critical because they usually are applied in the same environ- ments as human workers. Therefore they require a continuous risk assessment. The uploading of a new software to the system can change its behavior drastically and therefore, the risk assessment needs to be redone. Key enabling technologies are digital twins, advanced and hybrid risk models, and Model-to- Model (M2M) transformation methods.
In this paper, we introduce a new approach to the automated and continuous risk assessment based on Robot Operating System (ROS) code of a software-defined robotic system. The approach pipelines four key elements: (i) a logger that logs the data of the digital twin, (ii) an adder algorithm that creates risk annotated code based on the given ROS code, the output of the logger, and the hardware description including risk data of robot parts, (iii) an M2M transformation algorithm that automatically generates hybrid risk models from risk-annotated code, and (iv) OpenPRA solvers for numerical evaluation of the generated hybrid risk models.
