Data-Driven Agile Risk Management for Mechatronic Systems via Markov Analysis of Failure Scenarios

Effective failure detection and diagnosis are crucial for ensuring that product requirements are met throughout the entire life cycle of mechatronic systems. An agile scenario analysis can help evaluate potential misbehavior and the fulfillment of product requirements, especially during its use phase. This paper aims to provide a comprehensive, data-driven risk analysis by networking individual failure chains derived from failure report databases. The proposed methodology uses Markov chains to model the network of failure scenarios and enables the calculation of the equilibrium state probabilities, which determine the failure states of the system in interaction with the complex network of influencing factors. This approach integrates analytical quantitative methods into agile product development frameworks to assess and mitigate risks. By incorporating iterative processes, continuous feedback, and adaptive planning, the framework enables agile risk management. It determines the probabilities of observed failure chains and evaluates potential, previously unobserved failure scenarios. The resulting risk analysis supports the development of specific mitigation strategies, enhancing the reliability and robustness of complex mechatronic systems. The methodology contributes to improving product quality across all life cycle phases through dynamic, systematic, and data-driven risk management.