Reliability Data Screening

Use this skill for public, educational reliability screening. From an ISO 14224 / OREDA style failure rate and a mean time to repair, it estimates mean time between failures (MTBF), steady-state availability with simple parallel redundancy, mission reliability, and expected failures, so an agent can scope a reliability/availability/maintainability (RAM) study before detailed analysis.

When to Use

• When a user asks for an MTBF, availability, or reliability figure from a failure rate and repair time.
• When an agent needs a quick parallel-redundancy availability estimate.
• When planned downtime must be folded into a screening availability number.
• When examples must run without proprietary OREDA datasets or vendor reliability data.

Inputs

• failure_rate_per_year: failure rate (lambda) in failures per year.
• mean_time_to_repair_h: mean time to repair in hours, default 24.
• redundancy: number of parallel units (k = 1 of n), integer, default 1.
• mission_time_years: mission duration in years for reliability, default 1.
• planned_downtime_h_per_year: planned downtime in hours per year, default 0.

Outputs

• mtbf_years: mean time between failures in years.
• mtbf_h: mean time between failures in hours.
• unit_availability: steady-state availability of a single unit.
• system_availability: availability of the redundant system including planned downtime.
• system_unavailability: one minus the system availability.
• reliability_over_mission: probability of no system failure over the mission time.
• expected_failures: expected single-unit failures over the mission time.
• availability_warning: ok, watch, or low-availability.
• assumptions: public assumptions used by the placeholder model.

Engineering Method

The Python class ReliabilityDataModel uses open reliability relations only:

• MTBF uses MTBF = 1 / lambda (years) and MTBF_h = 8760 / lambda (hours).
• single-unit availability uses A = MTBF_h / (MTBF_h + MTTR).
• redundant unavailability from failures uses (1 - A) ** n for n parallel units.
• planned downtime adds planned_downtime / 8760 to the unavailability, capped at 1.
• mission reliability uses R = exp(-lambda * t) for a unit and 1 - (1 - R) ** n for the parallel system.
• expected failures use lambda * mission_time for a single unit.

This is educational and screening-only logic. It assumes a constant failure rate (exponential lifetime), independent identical parallel units, and additive planned downtime. It is not a replacement for validated RAM analysis or a qualified reliability dataset.

Python Usage Pattern

from reliability_data_screening import ReliabilityDataModel

model = ReliabilityDataModel()
result = model.evaluate(
    failure_rate_per_year=0.5,
    mean_time_to_repair_h=48.0,
    redundancy=2,
    mission_time_years=1.0,
    planned_downtime_h_per_year=24.0,
)

print(result.mtbf_years)
print(result.system_availability)
print(result.availability_warning)

Related NeqSim Functionality

For validated reliability and risk modelling, redirect to NeqSim resources:

• proposed neqsim.process.reliability.ReliabilityDataset — ISO 14224 / OREDA dataset ingestion and availability roll-up; candidate NeqSim gap.
• neqsim.process.diagnostics — multi-source reliability priors for root cause analysis.
• the neqsim-process-safety skill — SIL and risk-matrix methods that consume availability figures.

This skill is a public triage layer that decides when to invoke a validated RAM analysis.

Validation Checklist

• Failure rate and mean time to repair are positive.
• Redundancy is an integer of at least 1.
• Planned downtime is in [0, 8760) hours per year.
• Tests cover MTBF, single-unit availability, redundancy improvement, planned downtime, and invalid input.
• Real RAM work is redirected to validated NeqSim resources and qualified datasets.

Common Mistakes

Limitations

• No proprietary OREDA datasets or vendor reliability data are included.
• A constant failure rate (exponential model) is assumed; no wear-out is modelled.
• Parallel units are assumed identical and independent (no common-cause factor).

References

• ISO 14224, Petroleum, petrochemical and natural gas industries — Collection and exchange of reliability and maintenance data for equipment.
• OREDA, Offshore and Onshore Reliability Data Handbook.
• IEC 61508, Functional safety of electrical/electronic/programmable electronic safety-related systems.
• NeqSim repository: https://github.com/equinor/neqsim