# A Reliability Engineering Approach for Managing Risks in CubeSats
## 5 Conclusions  
The proposed approach details a method for performing reliability analysis during the preliminary design of a CubeSat. The approach aims at developing a systematic formulation for the FMECA coefficients in order to generate indexes capable of prioritizing failure modes, attempting to fill some open gaps in current FMECA-based literature. As confirmed by a recent literature review in the field, there are currently four classes of open issues about FMECA, confirmed as well in the space domain:  
* applicability, mainly in terms of excessive subjectivity of the approach, difficulty for information management; and ineffective timeliness of the application itself;
* cause-effect, in terms of cause-effect chains identification and level of details for the description of failure modes;
* risk analysis, in terms of lack of specific criteria for quantifying risks and ambiguous definitions leading again to potential subjectivity in the assessment;
* problem solving, in terms of lack of specific guidelines for defining the most suitable mitigating solutions. The proposed method offers some operational improvements both for the applicability and the risk analysis class. In particular, the suggested coding system to explore different functional levels of the system plays a contribution to the information exchange, maintaining a user-friendly systematic perspective. The development of indicators based on CubeSat-related evidences (where possible) allows for exploring risks more properly, contributing to manage problems related to the risk analysis class. Consequently, the approach has further additional indirect effects on all the classes, in terms of subjectivity reduction. Nevertheless, it is important to note the potential criticalities due to subjectivity, even if reduced, remain an open issue in the formulation of the FMECA, especially in the estimation of severity. Based on this observation, it is important to highlight that each model of the world relies on a number of assumptions, whose validity conditions the results of the application of model itself. The assumptions discussed in the paper and the respective numerical values represent deterministic model abstractions, i.e., implying static values for the coefficients, based on deterministic variables (such as for detectability) or estimated parameters (severity, occurrence). All these parameters are themselves imprecisely known, and thus inherently uncertain, implying the need to evaluate how precise our state of knowledge is about the model and its parameters, paving the way to the notion of epistemic uncertainty. Even if this paper proposes a high-level approach with a pragmatical perspective, there are several possible extensions and formalizations. Further research should thus focus on epistemic uncertainty of the proposed coefficients, e.g., relying on the Bayesian theory of probability, or Petri nets [43]. However, as recently proved by a wide literature on the usability of the FMECA, it is important to note that these kinds of advanced mathematical methods are almost exclusively used in academia, while they are practically ignored by industry. The challenge in this sense consists thus of developing user-friendly tools to support more advanced analyses [42], even in combination with other reliability engineering methods, such as Fault Tree Analysis (FTA) or Event Tree Analysis (ETA) to further explore the failure modes and find respectively the minimal cut sets, or the critical chains of failures. Furthermore, future research should follow recent trends already discussed in other high-risk domains, (e.g.,) aviation, healthcare, nuclear power plants. On this path, it appears necessary to focus not only on a techno-centric perspective for analyzing system reliability, but also on a socio-technical analysis of system resilience, intended as the ability to bounce back to normal operation following a disruption.