Multifault

Multifault is a term used in reliability engineering to describe the occurrence of two or more faults within a system or its environment, either simultaneously or in close succession. It contrasts with single-fault models that assume only one fault at a time. Multifault analysis seeks to understand how fault interactions, common-cause failures, and cascading effects influence safety, availability, and performance.

Modeling approaches include probabilistic risk assessment, fault tree analysis extended for multiple faults, Markov and Bayesian networks, and Monte Carlo simulation. Analysts use concepts such as minimal cut sets and maximal fault combinations to identify critical fault interactions and estimate system reliability under multifault conditions. Key challenges include dependencies among components and external stressors that can create surprise interactions.

Diagnosis and mitigation for multifault scenarios rely on model-based diagnosis, sensor fusion, and adaptive redundancy management. The goal is to detect and localize multiple concurrent faults, reconfigure systems to maintain essential functions, and minimize disruption through graceful degradation and safe shutdown when necessary.

Applications appear in aerospace, automotive, energy, industrial control, and software reliability, where multiple latent or triggered faults can occur. Designers address multifault risk through diverse redundancy, fault isolation, and robust control laws, as well as scenario-based testing and simulation to validate resilience.

Challenges include the combinatorial explosion of possible fault combinations, data scarcity for rare event interactions, and accurately capturing dependencies such as common-cause failures. Ongoing research focuses on scalable modeling techniques, improved diagnostic algorithms, and integrated fault-tolerant architectures.