Reliability Prediction

Every reliability programme eventually has to answer one blunt question: how often will this design fail? The equipment does not exist yet, there is no fleet history, and yet the contract carries an MTBF number, the FMECA needs failure rates for its criticality arithmetic, the fault trees need basic-event probabilities, and the spares model needs demand rates. Reliability prediction is the analysis that produces those numbers before the hardware can produce them itself.

The method is old, standardised, and argued about, all three at once. At its core it is bookkeeping with physics sprinkled in: every part in the bill of materials is assigned a failure rate from a published model, the rate is adjusted for how hard the part is being worked and where it will serve, and the adjusted rates are summed up the product structure. The output, a system failure rate λ and its reciprocal MTBF, becomes the single most-consumed quantity in the entire RAMS workflow.

What a prediction is for is just as important as how it is computed. A handbook prediction is a consistent, comparable estimate of a mature design's inherent reliability, not a forecast of what the maintenance log will say. Programmes that understand this use predictions to compare architectures, expose the dominant contributors, and test compliance against allocated targets. Programmes that don't understand it end up defending a number that the field was never going to honour.

What it is and why it exists

Reliability prediction estimates the failure rate of equipment from the failure rates of its constituent parts, using standardised empirical models. The models were built by regression on large bodies of test and field data: for each part class, microcircuits, transistors, capacitors, connectors, relays, the data was fitted to a base failure rate plus a set of adjustment factors (the famous π factors) that capture how temperature, electrical stress, quality level, and application environment move the rate up or down.

The dominant reference is MIL-HDBK-217, "Reliability Prediction of Electronic Equipment", first published by the US military in the early 1960s and revised repeatedly as electronics evolved. The final revision is MIL-HDBK-217F (December 1991), amended by Notice 1 (July 1992, typographical corrections) and Notice 2 (28 February 1995), which revised the resistor, capacitor, connector, and interconnection models, including a surface-mount solder-joint model. Nothing has been issued since. An attempt to produce a revision G was launched in 2008 and abandoned; the handbook has been frozen for three decades while the components it models kept evolving. It also carries its own health warning on the cover: it is guidance, not a requirement, and its foreword says exactly that.

The handbook's own statement of purpose is worth internalising, because it defines the method's honest scope: to establish consistent and uniform methods for estimating the inherent reliability of mature designs, and to provide a common basis for comparing competing designs. Consistency and comparability: not clairvoyance.

Two framing points sharpen that scope. First, a handbook prediction estimates what the design will achieve at maturity, after production has settled and the early design weaknesses have been worked out of the build. That is a different quantity from what reliability growth testing measures during development, which is the reliability of the current build standard, defects included; a programme needs both numbers and should never confuse them. Second, a prediction adds no reliability by itself. Its entire value is as decision fuel: it forces a systematic walk through the design that surfaces requirement misreadings, unbalanced architectures, and unreliability hot-spots, and it does so early, when the biggest funding and architecture decisions are being made on the least information. That asymmetry (maximum decision leverage exactly when data is scarcest) is why prediction methods exist in a spectrum, from judgement-heavy comparison methods at concept stage to part-by-part stress models in detailed design.

The scope limitation that matters most is visible in the bathtub curve above. Handbook models assume a constant failure rate: the equipment is past its infant mortality (screened out by burn-in and manufacturing quality) and not yet into wear-out (managed by preventive replacement). Within that useful-life region the exponential distribution applies, the arithmetic becomes beautifully simple, rates add, MTBF is a reciprocal, and the model is blind, by construction, to anything that ages.

Electronic parts are only half the story. For mechanical equipment (springs, bearings, seals, gaskets, valves, actuators, pumps) the reference is the NSWC handbook of reliability prediction procedures for mechanical equipment (latest edition NSWC-11, May 2011, from the Naval Surface Warfare Center's Carderock Division). It works differently: instead of tabulated base rates, it computes failure rates from design and duty parameters, spring stress ratios, bearing loads, seal materials, through engineering equations. A programme with an electromechanical product typically runs both model families side by side, plus field-data compendia such as the NPRD/EPRD databooks for parts neither handbook models.

Where it sits in the programme

Prediction starts earlier than almost any other quantitative RAMS analysis and never really stops. At proposal stage, a parts count prediction produced from a preliminary parts list supports the bid's MTBF claim. Through detailed design, part stress predictions replace it, refreshed at every design revision as the BOM firms up and thermal and electrical analyses mature. After fielding, the prediction's job is to be progressively retired, replaced by observed rates from failure reporting and by life-data analysis, which are better evidence than any handbook.

The full ladder, stage by stage:

The analysis consumes three streams of input. The parts list comes from the design's bill of materials, with each part classified into a handbook category. The service profile declares where and how the equipment serves, environment category, ambient temperature, duty cycle, mission phases. The stress data comes from sibling engineering analyses: junction and hot-spot temperatures from thermal analysis, voltage and power stress ratios from the electrical design, the same ratios the derating analysis checks against its limits, which is why prediction and derating are natural companions: derating polices the stresses, prediction prices them.

Downstream, the prediction's outputs are the reliability programme's working currency:

• FMECA: the criticality calculation of MIL-STD-1629A multiplies part failure rate, failure-mode ratio, and conditional probability (Cm = β·α·λp·t). The λp comes straight from the prediction.
• RBD: reliability block diagrams take block failure rates from the prediction roll-up. The handbook itself is explicit that anything beyond a pure series structure (redundancy, duty cycles, mission profiles) is the modelling standard's territory, not the prediction's.
• FTA: quantitative fault trees need basic-event rates; handbook predictions are one of their standard pedigrees.
• Allocation: the prediction is the compliance test for allocated reliability budgets: allocation says what each block must achieve, prediction estimates what it will.
• Logistics: spares provisioning, maintenance planning, and lifecycle cost all key off predicted failure rates until field data takes over.