RAPTOR: A Reliability Analysis Tool Built Around Structured MIL-HDBK-217F Data
RAPTOR is an internal reliability analysis tool developed at Anzen to calculate electronic component failure rates according to MIL-HDBK-217F.
It originated during the CORSARIO project, where we needed a more efficient way to perform repeatable 217-based predictions and integrate the results into a broader MBSA workflow (ATICA). Existing tools either lacked flexibility, were difficult to integrate, or did not support the level of automation we required.
So we implemented our own.
The key idea is simple: reduce friction in 217-based analysis by structuring the data properly and making it reusable.
The Core Objective: A Pre-Classified MIL-HDBK-217F Component Database
In practice, the most time-consuming part of MIL-HDBK-217F analysis is not the equation itself. It is the classification step:
Selecting the correct part category
Applying the appropriate environment
Identifying quality levels
Ensuring consistent parameter interpretation
RAPTOR focuses on maintaining a database of electronic components already classified according to MIL-HDBK-217F. Once structured correctly, components can be reused across projects without reinterpreting the standard each time.
For LRUs with hundreds of components, this makes a measurable difference in efficiency and consistency. It also reduces variability between engineers performing similar analyses.
RAPTOR is therefore better understood as an analysis platform supported by a structured reliability database, rather than as a simple calculator.
RAPTOR Architecture Overview
The tool follows a JavaScript fullstack architecture:
Node.js as the server runtime
React for the user interface
Next.js to unify frontend and backend
GraphQL as the API layer
The choice of stack was driven by integration and maintainability concerns, not aesthetics.
From a RAMS perspective, the important point is separation of concerns:
The UI handles interaction only.
Business logic (including failure rate computation) runs server-side.
Data persistence is handled through structured models.
This allows the tool to evolve without tightly coupling calculation logic to presentation.
GraphQL as an Integration Layer
The GraphQL API is intentionally central to the architecture.
Instead of designing RAPTOR as a closed GUI tool, we exposed the data and operations through a structured API. This enables:
Script-based interaction
Automated population of component data
External querying of calculated results
Integration with other RAMS or MBSE tools
In the coming months, a proof of concept will explore integration with SysML v2 models, with the goal of linking reliability data directly to architectural elements.
For MBSE engineers, this is very important. Reliability prediction should not remain detached from the system model. If the architecture changes, the analysis should be able to follow.
GraphQL provides the technical foundation for that connection on the RAPTOR side.
Current Scope: LRU-Level Failure Rate Prediction
At the moment, RAPTOR calculates failure rates for the electronic components of an LRU using MIL-HDBK-217F.
It does not attempt to replace system-level modelling or safety assessment tools. Instead, it focuses on:
Component-level prediction
Structured aggregation
Data reuse across projects
The emphasis is on consistency and traceability of assumptions.
Planned Extensions
Several extensions are under consideration or in development.
FMEA Support
There is a natural link between failure rate prediction and Failure Modes and Effects Analysis. Integrating FMEA capabilities would allow better traceability between quantitative predictions and qualitative failure modelling.
Additional Methodologies (e.g., FIDES)
MIL-HDBK-217F is widely used but not always ideal, especially for modern electronics. Supporting methodologies such as FIDES would provide more flexibility depending on project requirements and domains.
Reliability Data Analytics
Beyond calculating λ values, there is value in analysing distribution of contributors, environmental sensitivities, and architectural concentration of risk. The idea is to extract design-relevant information from the analysis.
Closing Remarks
RAPTOR is not intended as a commercial off-the-shelf solution or a replacement for established enterprise tools. It is a focused reliability analysis tool built around three principles:
Structure the data properly.
Reduce repeated interpretation of the standard.
Enable integration with model-based engineering workflows.
For RAMS and MBSE engineers, the interesting part is not the UI or the stack. It is the possibility of treating reliability prediction as structured, reusable engineering data rather than a calculation exercise.
That shift, from isolated analysis to connected engineering data, is where the real improvement lies.
About the authors
César Munuera is a computer engineer specializing in Cloud and IT infrastructure at Anzen.
At Anzen, César’s work focuses on ATICA, our model-based tool for safety analysis. He contributes to the development and evolution of ATICA by implementing and maintaining key functionalities. His responsibilities include designing and managing API communication between the different ATICA applications, as well as deploying services both in the cloud and on-premises environments.
Samuel García is an aeronautical engineer with experience in Safety and Reliability engineering, focusing in recent years on the model-based safety analysis.
At Anzen, Samuel is divided into two main roles: one is to lead or support industrial projects as a consultant, and the other is to participate in the activities performed in the digital engineering department, leveraging those activities and ATICA development by feeding them with actual industrial needs.
Daniel Villafañe is an aerospace engineer with expertise in avionics, systems engineering and model-based design and analysis.
At Anzen, Daniel’s work is focused on ATICA, our model-based tool for safety analysis. Daniel is in charge of building system models and applying systems engineering processes while using ATICA to improve results on safety and reliability analyses for aerospace avionics projects.
