The theory in this topic is exercised through a toolkit of named analysis methods, each answering one question about the same system. This chapter is the map: what each method is for, when in the lifecycle it runs, how they hand data to one another, and where to go deeper. Every method here has (or will have) its own full RAMS Core book teaching it end to end; this page is the place to see them as one connected practice rather than nine separate documents.
The reliability toolkit
Nine methods carry the reliability workload of a typical programme. The first six are design-side: they run on paper, before hardware exists to fail. The last three are evidence-side: they run on test and field data, and they are what keeps the design-side honest.
| Method | The question it answers | Typical phase | Read more |
|---|---|---|---|
| Reliability Allocation | How does the system target split into budgets each designer can own? | Concept | Full topic published |
| Reliability Prediction | What failure rate does this design's parts, stresses, and environment imply? | Design | Full topic published |
| Derating Analysis | Is every component operating far enough inside its ratings? | Design | Topic planned |
| Reliability Block Diagram | What system reliability does this architecture deliver, given item rates? | Design | Topic planned |
| Fault Tree Analysis | Which combinations of failures produce this specific unwanted event, and how probable is it? | Design | Topic planned |
| Importance measures | Which components actually drive system risk, and where is improvement worth money? | Design | Computed within FTA and RBD analyses |
| Reliability Growth Analysis | Is test-and-fix actually improving the product, and will it reach the target in time? | Development test | Topic planned |
| Life Data (Weibull) Analysis | What distribution do the observed failures actually follow? | Test and field | Topic planned |
| FRACAS | Is every failure being reported, root-caused, and closed out? | Test and field | Topic planned |
Two entries deserve a note. Importance measures are not a standalone study but the decision layer of the modelling methods: once an RBD or fault tree is quantified, importance rankings (which item's improvement moves the system number most, which minimal cut sets carry the risk) convert the model from a score into an agenda. And FRACAS is less an analysis than an institution: the other eight methods each produce a result, while FRACAS produces the discipline that keeps results connected to reality.
How the methods chain together
The methods are usually taught separately and practised as a chain, and the chain is easiest to see as quantities in motion:
- Targets flow down. The system requirement enters allocation, which apportions it into subsystem and unit budgets. Those budgets are the acceptance criteria every later analysis reports against.
- Rates flow up. Prediction turns the parts list, stresses, and environment into item failure rates; RBD and FTA combine them through the system structure into mission reliability, availability, and top-event probabilities that can be compared with the allocated targets.
- Margins guard the rates. Derating verifies the stress assumptions the prediction relies on; a component outside its derating limits is a prediction line item that is quietly wrong.
- Rankings steer the iteration. Importance measures on the quantified models point to the components and cut sets where redesign, redundancy, or better parts buy the most system reliability per unit of effort.
- Evidence recalibrates everything. Growth analysis tracks whether test-and-fix is converging on the target; life data analysis replaces assumed distributions with fitted ones; FRACAS feeds the field record back into predictions, models, and the next allocation.
The chain is also the reason data management is a first-order reliability problem. Every arrow above is a handoff, and in document-driven programmes each handoff is a spreadsheet export, a retype, and a version-skew risk; audits of such programmes routinely find system models running on rates two revisions stale. Whatever toolset a programme uses, the arrows deserve as much engineering attention as the boxes.
Where RAMSynapse runs them
The built modules of the RAMSynapse platform cover the design-side chain on one shared system model, so the handoffs above are live links rather than exports: Reliability Allocation apportions targets down the product structure, Reliability Prediction computes part-stress and parts-count rates that flow directly into the Reliability Block Diagram and Fault Tree modules, Derating checks component stresses against approved profiles on the same records, and the fault tree module computes cut sets and importance measures on the shared rates. The live module ring on the home page shows this data flow as it exists in the product.
The evidence-side modules (FRACAS and Weibull life data analysis) are on the roadmap, designed to return field failure rates and fitted life distributions into the same shared records the design-side modules already read. Reliability growth analysis is covered here in RAMS Core as a discipline; a growth module is not part of the current registry.
Industry lenses
The toolkit is universal; the emphasis is not. Which methods dominate, which standards govern them, and what the deliverable is called varies by sector, and the industry pages describe how RAMSynapse is applied in each:
| Industry | What reliability work typically looks like |
|---|---|
| Defence and aerospace | Contractual reliability programmes in the MIL-STD-785 lineage: allocation, prediction, FMECA, and demonstration as deliverable data items |
| Space systems | One-shot and long-dormancy reliability, severe mass penalties on redundancy, heavy reliance on parts control and analysis over test |
| Railway | RAMS as a lifecycle discipline under the EN 50126 framework, with reliability targets tied to service availability commitments |
| Automotive | High-volume statistics: B-life requirements, warranty-driven life data analysis, and functional-safety interfaces per ISO 26262 |
| Energy and resources | Availability economics: reliability feeding maintenance strategy and production-loss models for long-life plant |
| Medical devices | Reliability evidence inside a regulated risk-management frame, with use-related and one-shot device statistics |
| Electronics and high tech | FIT-level component prediction, accelerated testing, and screening economics at consumer volumes |
| Nuclear | Redundancy and common-cause defence analysed to regulatory depth, with probabilistic risk assessment as the umbrella |
Read the toolkit table's methods through any one of these lenses and the same pattern holds: the mathematics of this topic is identical everywhere; what changes is which failure consequences the industry prices highest, and therefore which analyses its contracts demand.