Reliability Growth Testing (RGT): Standards, Models, Methods, and Applications

Reliability Growth Testing (RGT) is a structured development-phase test program that exposes design weaknesses, drives corrective actions, and measurably improves product reliability metrics such as MTBF before production release. Guided by international standards including IEC 61014, IEC 61164, and MIL-HDBK-189, reliability growth testing applies proven statistical models such as the Duane and AMSAA/Crow models to track, project, and verify reliability improvement. This article explains the technical principles, sample types, test items, testing methods, and typical applications of reliability growth testing.

Technical Overview

Reliability Growth Testing (RGT) is a deliberate, iterative engineering process conducted during product development to increase the inherent reliability of a design. The core philosophy follows the TAAF cycle: Test the product under realistic or accelerated stress conditions, Analyze every failure to identify root causes, And Fix the underlying design or process weakness, then retest to confirm the effectiveness of the corrective action. Unlike a pass/fail qualification test, reliability growth testing is a dynamic program in which failures are expected and valuable, because each properly corrected failure permanently removes a failure mode from the population.

The quantitative foundation of RGT is the observation, first formalized by J.T. Duane in the early 1960s, that the cumulative failure rate of a well-managed development program decreases log-linearly with cumulative test time. This empirical relationship is expressed through the Duane model, where the cumulative mean time between failures (MTBF) grows as a power function of accumulated operating hours. The Military Handbook approach later refined by the AMSAA (Crow) model describes the failure process as a non-homogeneous Poisson process (NHPP) with a power-law intensity function, enabling rigorous statistical estimation, confidence intervals, and goodness-of-fit evaluation.

Reliability growth testing is governed by several recognized standards:

• IEC 61014 – Programmes for reliability growth, defining planning, monitoring, and management requirements for growth programs.
• IEC 61164 – Reliability growth: statistical test and estimation methods, providing procedures for assessing growth trends and estimating model parameters.
• MIL-HDBK-189 – Reliability Growth Management, widely referenced for planning curves, growth rate selection, and projection methodology.
• GB/T 15174 – Chinese national standard specifying reliability growth programme requirements, harmonized with IEC 61014 principles.

The primary output metrics of an RGT program include the demonstrated MTBF, the failure intensity at the end of test, the growth rate (management slope), the percentage of failure modes corrected, and the projected reliability of the mature production configuration.

Testing Samples

Electronic Assemblies and Circuit Card Modules

Printed circuit board assemblies, power supply modules, control electronics, and embedded controller units are the most common subjects of reliability growth testing. These samples are typically prototypes or early production units representing the intended design configuration, with sufficient units available to accumulate meaningful operating hours under combined environmental stress.

Mechanical and Electromechanical Equipment

Actuators, pumps, valves, gearboxes, relays, connectors, and servo mechanisms undergo growth testing to reveal wear-out mechanisms, fatigue weaknesses, lubrication deficiencies, and tolerance stack-up problems. Sample selection must represent actual production materials, processes, and workmanship, since reliability growth depends on realistic failure mode activation.

Aerospace and Defense Systems

Avionics, radar assemblies, flight control computers, guidance units, and communication equipment are classic candidates for formal reliability growth programs, because contractual MTBF requirements and mission criticality demand demonstrated improvement trajectories before deployment.

Automotive and Transportation Electronics

Engine control units, battery management systems, inverter modules, ADAS sensor units, and rail transit electronic equipment are subjected to growth testing to satisfy high mileage and lifecycle durability expectations under thermal cycling, vibration, and electrical stress.

Medical Devices and Industrial Control Products

Infusion pumps, patient monitors, programmable logic controllers, and industrial drives benefit from growth testing where regulatory expectations or critical service functions require evidence of design maturity and a quantified failure rate reduction.

Testing Items

Reliability growth testing evaluates and tracks the following measurable quantities and attributes:

• MTBF measurement and tracking – cumulative and instantaneous mean time between failures as a function of test time.
• Failure intensity (failure rate) – instantaneous rate of failure occurrence at the end of the growth program.
• Failure mode identification and classification – systematic categorization of failures into Type A modes (not economically correctable) and Type B modes (correctable design weaknesses).
• Growth rate estimation – the Duane management slope or AMSAA shape parameter that quantifies improvement speed.
• Corrective action effectiveness factor – the fractional reduction in failure mode intensity achieved by implemented fixes, typically assumed between 0.6 and 0.8 in projection models.
• Reliability growth trend testing – statistical hypothesis tests to confirm that a significant improvement trend exists rather than random fluctuation.
• Projected mature reliability – the reliability expected when all corrective actions are incorporated into production hardware.
• Environmental robustness indicators – performance margins under temperature, humidity, vibration, and combined stress conditions.

Testing Methods

Reliability Growth Program Planning

Planning per IEC 61014 and MIL-HDBK-189 begins by defining the initial reliability level observed at the start of testing, the target reliability requirement, and the growth rate. The planned growth curve describes how cumulative MTBF should increase with test time, allowing calculation of the total test duration required to reach the objective. A typical well-managed program assumes a Duane growth rate of approximately 0.2 to 0.4, with 0.3 serving as a common planning value for rigorous programs with dedicated failure analysis resources.

Test, Analyze, and Fix (TAAF) Execution

During execution, samples operate under stress profiles representative of the mission or field environment. Every failure is documented through a Failure Reporting, Analysis, and Corrective Action System (FRACAS), subjected to root cause analysis, and assigned a corrective action. Corrective actions are incorporated into the test articles or retained as deferred fixes, with the design change status carefully tracked so that demonstrated versus projected reliability can be separated.

Duane Model Tracking

The Duane method plots cumulative failure rate or cumulative MTBF against cumulative test time on log-log coordinates. A straight-line trend confirms log-linear growth, and the slope of the fitted line provides the growth rate. The cumulative MTBF relationship follows the form where the ratio of total test time to total failures grows as a power function of test time, and the instantaneous MTBF is derived by shifting the cumulative curve by the factor of the growth rate.

AMSAA / Crow Model Statistical Assessment

Per IEC 61164, the Crow-AMSAA model treats failures as an NHPP with mean value function expressed as lambda multiplied by test time raised to the beta power. The intensity function is lambda times beta times time raised to the quantity beta minus one, where a beta value below one indicates reliability growth, beta equal to one indicates no trend, and beta above one indicates deterioration. Maximum likelihood estimation determines the parameters, confidence intervals quantify uncertainty, and the Cramér-von Mises statistic verifies model adequacy for both time-truncated and failure-truncated tests.

Test Conditions and Stress Profiles

Stress levels are selected to activate realistic failure modes without introducing artificial mechanisms that would not occur in service:

• High and low temperature operation and thermal cycling derived from the mission profile
• Random vibration and mechanical shock representative of transport and operational exposure
• Humidity and temperature-humidity cycling for moisture-sensitive assemblies
• Electrical stress including power cycling, voltage variation, and transient surge conditions
• Combined environmental testing where multiple stresses act simultaneously to accelerate weak-point discovery

Corrective Action Verification and Projection

After each fix, continued testing verifies that the failure mode has been eliminated or reduced. At program conclusion, the projection methodology described in MIL-HDBK-189 combines the end-of-test demonstrated failure intensity with the effectiveness factors of pending corrective actions to estimate the reliability of the mature production configuration, providing the evidence needed for design release or reliability demonstration follow-on testing.

Applications

Reliability growth testing is applied across industries wherever design maturity and quantified reliability improvement carry contractual, regulatory, or commercial weight:

• Aerospace and defense procurement – demonstrating contractual MTBF growth milestones for avionics, ground vehicles, naval systems, and munitions before operational deployment.
• Automotive electronics development – validating durability improvements for ECUs, electrified powertrain components, and safety-related systems during pre-production maturation.
• Medical device engineering – building objective evidence of design reliability to support regulatory submissions and risk management files.
• Telecommunications and data center hardware – raising availability and reducing field replacement costs for high-volume, long-service-life equipment.
• Industrial automation and energy systems – improving uptime for drives, inverters, protection relays, and grid-connected power electronics.
• Consumer electronics cost reduction – eliminating early-life failure modes to reduce warranty claims and returns.

Accredited third-party laboratories frequently conduct or witness reliability growth programs, providing independent test execution, failure analysis support, and statistical assessment consistent with IEC 61014, IEC 61164, and MIL-HDBK-189 methodologies.

FAQ

What is reliability growth testing?

Reliability growth testing is a development-phase program in which a product is deliberately tested to induce failures, each failure is analyzed for root cause, and corrective design changes are implemented and verified. The process produces a measurable, statistically tracked improvement in reliability metrics such as MTBF over cumulative test time.

How does reliability growth testing differ from reliability demonstration testing?

Reliability growth testing aims to improve reliability and actively seeks out failures for correction, whereas reliability demonstration testing is a statistical pass/fail verification performed on a fixed design to confirm that a specified MTBF or failure rate requirement is met. Growth testing normally precedes demonstration testing and raises the probability of successful demonstration.

Which reliability growth models are most widely used?

The Duane model is the standard planning and graphical tracking tool, while the Crow-AMSAA (NHPP power-law) model is the primary method for statistical estimation, trend testing, and confidence bounding. Supplementary models such as the Gompertz and Lloyd-Lipow models are used for specific program structures.

How much test time does a reliability growth program require?

Required test time depends on the initial MTBF, the target MTBF, and the achievable growth rate. As a planning guideline, reaching a target that is several times the initial MTBF with a growth rate near 0.3 typically requires total accumulated test time on the order of five to twenty times the target MTBF, distributed across representative sample units.

When should reliability growth testing be performed?

Growth testing is most effective when started early, after functional design is stable enough to produce meaningful failures but while hardware and schedule flexibility still exist to incorporate corrective actions. Starting too late leaves insufficient time for the test-analyze-fix iterations that drive genuine improvement.

What data must be recorded during the program?

A compliant program records cumulative operating time for every unit, the exact time of each failure, failure symptoms and root cause analysis results, failure mode classification, corrective action descriptions and implementation dates, and the effectiveness factors used in projections. This dataset underpins FRACAS traceability and the statistical models of IEC 61164.