Reliability Prediction is the primary analytical method used during system design to calculate expected failure rates (λ), Mean Time Between Failures (MTBF), and operational availability. By evaluating component stress, environmental factors, and historical failure data, reliability prediction allows engineers to assess design feasibility, evaluate trade-offs, and establish baseline RAMS parameters early in the product life cycle.
Reliability Prediction is a structured methodology for estimating the probability that an electronic, mechanical, or electromechanical system will perform its required function under stated conditions for a specified period of time.
Performed during early design stages—before physical prototypes are manufactured or field testing begins—predictions rely on mathematical models published in commercial, industrial, and military standards (such as MIL-HDBK-217, Telcordia SR-332, and FIDES).
The resulting MTBF and failure rate figures serve as vital inputs to downstream safety and maintainability analyses, including FMECA, Fault Tree Analysis (FTA), Reliability Block Diagrams (RBD), and Life Cycle Costing (LCC).
- Failure Rate (λ): Failures per million hours (FPMH) or FITs (failures in 109 hours).
- MTBF: Mean Time Between Failures (1 / λ) for repairable systems.
- MTTF: Mean Time To Failure for non-repairable assemblies.
- Mission Reliability R(t): Probability of survival over operational duration t.
Reliability prediction is not merely a compliance reporting exercise; it is an active engineering tool used across product development phases:
Determines whether proposed design concepts can meet required operational MTBF targets before freezing architecture or committing to tooling.
Evaluates alternative design options, component selections, cooling mechanisms, and derating levels to optimize system life versus cost.
Pinpoints "reliability drivers"—components or sub-assemblies contributing disproportionately to overall failure rates—to prioritize redesign or redundancy.
Provides base failure rate parameters required for Maintainability Analysis, FMECA, RBD, System Safety Assessment, and Spare Parts Logistics.
Standard reliability prediction codes distinguish between two primary analytical approaches based on design maturity:
Applied during conceptual and proposal development phases when detailed schematic diagrams and operating stress levels are not yet fully defined.
- Requires generic component quantities and global environment selection.
- Provides a rapid baseline failure rate calculation assuming nominal 50% stress limits.
Executed during detailed design and PCB layout stages when full schematic information, operating voltages, currents, and thermal junction temperatures are known.
- Incorporates exact electrical stress ratios, Arrhenius thermal models, and quality factors.
- Delivers high precision to guide component derating and cooling system design.
Component failure rates increase exponentially with operating temperature according to Arrhenius behavior.
10°C Junction Drop ≈ 50% Failure Rate Reduction
Operating components well below peak electrical capacity prevents dielectric breakdown and micro-cracking.
Standard Practice: Operate Capacitors at ≤50% Rated V
Component procurement levels (πQ factors) adjust baseline predictions based on supplier testing and screening.
MIL-SPEC Screening Multipliers Reduce Baseline λ
Calculate junction temperatures, voltage stress factors, and quality derating margins across entire system BOMs in minutes.
ALD's RAM Commander software supports all major international electronic, mechanical, and industrial reliability standards:
Import or build the system Bill of Materials (BOM) hierarchy into a structured Product Tree.
Select applicable prediction standards and define environmental conditions (e.g., Ground Benign, Airborne Inhabited, Naval Sheltered).
Enter operating temperatures, thermal derating factors, applied voltages, and quality levels for each component.
Execute automated calculation models to aggregate component failure rates into assembly, module, and system-level MTBF.
Generate formal prediction reports, identify critical failure drivers, and feed parameters to FMECA and RBD tools.
Test ALD software capabilities on your own projects or request a private online walk-through with ALD reliability engineers.