Evaluating Modern Data Integration Approaches with a Focus on eXpressML

Across contemporary engineering programmes encompassing systems engineering, diagnostics, testing, supportability and long-term sustainment, a persistent and increasingly critical challenge has emerged. 

Tools employ different data structures, formats, and assumptions. Terminology varies across domains, with identical words often carrying entirely different meanings. As organisations accelerate their digital transformation, the volume and complexity of engineering data continue to grow alongside the difficulty of interpreting, exchanging and reusing it with consistency.

The consequences are significant: miscommunication between teams, redundant work, incompatible models, and design knowledge that becomes difficult if not impossible to leverage across the system lifecycle or revive for future programmes.

In response, engineering organisations have explored multiple approaches to digital integration. Each offers distinct advantages and limitations, yet all aim to address the same overarching question:

This strategy creates a single, authoritative repository often rooted in MBSE that contains all project data. Every tool translates to and from this central model.

The master-model approach tries to eliminate fragmentation, but semantic gaps across engineering disciplines still make integration difficult.

Instead of centralising everything, this model allows each discipline to maintain its own independent data and tools. Integration happens selectively only where it delivers value using APIs or translators.

This approach is flexible but struggles with the same fundamental challenge: shared meaning across disciplines.

This emerging strategy distributes models across disciplines as in the PLM-style approach but uses non-proprietary, standardised, well-defined formats to link them.

Here, the “authoritative source of truth” is the chain of standardised exchanges, not a single repository.

Even with these challenges, a standards-based digital thread is increasingly seen as the most scalable and sustainable approach.

A demonstration presented between 2021 and 2022 showcased a closed-loop digital thread linking 11 engineering tools and activities from MBSE to diagnostics to sustainment analysis.

The flow includes:

1. MBSE defines the system architecture.
2. Diagnostic engineering generates diagnostic procedures and identifies test needs.
3. Test engineering maps tests to signals and generates automatic test-program code.
4. Run-time diagnostics interpret test results, guide troubleshooting, and feed empirical data back into the models.
5. Sustainment simulations analyse historical diagnostic performance to refine maintenance strategies.

The key to making this possible: every activity reads and writes to standardised formats, ensuring continuity of meaning and traceability across the lifecycle.

At the heart of diagnostic integration lies eXpressML, a non-proprietary format that documents the full diagnostic model developed in eXpress™.

What eXpressML Captures

• Component topology and connectivity
• Functional dependencies
• States and behavioural information
• Reliability data (failure modes, failure rates)
• Test definitions and coverage rules
• Custom attributes for project-specific extensions
• Global identifiers linking data across tools

Why eXpressML Matters

• High interoperability: Easily ingests MBSE, CAD, and reliability data before modelling diagnostic logic.
• Semantic clarity: Ensures consistent interpretation of diagnostic knowledge across teams.
• Long-term durability: Once enhanced in eXpress™, the model is exported back to eXpressML, forming a lasting, tool-agnostic representation of diagnostic engineering work.

In the case study, MBSE data is imported into eXpress™, enriched with functional dependencies and diagnostic rules, and then re-exported to eXpressML allowing downstream tools to consume it reliably. 

 

At the heart of diagnostic integration lies eXpressML, a non-proprietary format that documents the full diagnostic model developed in eXpress™.

What eXpressML Captures

• Component topology and connectivity
• Functional dependencies
• States and behavioural information
• Reliability data (failure modes, failure rates)
• Test definitions and coverage rules
• Custom attributes for project-specific extensions
• Global identifiers linking data across tools

Why eXpressML Matters

• High interoperability: Easily ingests MBSE, CAD, and reliability data before modelling diagnostic logic.
• Semantic clarity: Ensures consistent interpretation of diagnostic knowledge across teams.
• Long-term durability: Once enhanced in eXpress™, the model is exported back to eXpressML, forming a lasting, tool-agnostic representation of diagnostic engineering work.

In the case study, MBSE data is imported into eXpress™, enriched with functional dependencies and diagnostic rules, and then re-exported to eXpressML allowing downstream tools to consume it reliably.

As organisations adopt standards-based digital threads and tools such as eXpress, many rely on Spherea for specialised expertise in testability engineering, diagnostics, and the development of optimised support strategies. We combine deep technical knowledge with practical programme experience to help organisations ensure system availability, reduce lifecycle costs, and strengthen engineering decision-making.

Our capabilities span the full spectrum of testability and supportability engineering. We support customers in validating system availability metrics such as detection rates and fault-location performance using advanced diagnostic modelling in eXpress™ and complementary RAMS methodologies.