Modern RAMI Method Capabilities
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Modern RAMI Method Capabilities. Tom Weaver Counter CBRNE DEW Platform Systems Technology Boeing Research and Technology. I-Li Lu, Ph.D. Applied Statistics Platform Performance Technology Boeing Research and Technology. ARIES 2011 Quarter #2 Review

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Modern rami method capabilities

Modern RAMI Method Capabilities

Tom Weaver


Platform Systems Technology

Boeing Research and Technology

I-Li Lu, Ph.D.

Applied Statistics

Platform Performance Technology

Boeing Research and Technology

ARIES 2011 Quarter #2 Review

27-28 July, 2011; Gaithersburg, Maryland


  • Proposed ARIES Reliability Estimation Tool is the most immediately needed tool

  • Making the tool will draw on a range of techniques and processes that will fit within the tool or will allow for future additional capabilities

  • Some examples are:

    • Root Causes Identification

    • Failure Mode and Effects/Criticality Analyses

    • Rogue Unit Identification and Tracing

    • Trend Monitoring and Evaluation

Root causes identification


Time between Failures: Empirical distributions and the “remaining lifetimes”

Time between Events: Empirical distributions and the “remaining lifetimes”



Empirical data subjective input











1- pF











1(1- pF)

















IF 1 pF











cF(1-IF)1 pF

cF (1-c)(1-1-2)pF

(1-cF)(1-IF)1 pF

(1-cF) (1-c)(1-1-2)pF

Root Causes Identification

Fmea risk elements severity
FMEA Risk Elements - Severity


Determine all failure modes based on the functional requirements and their effects. Examples of failure modes are: Electrical short-circuiting, corrosion or deformation. A failure mode in one component can lead to a failure mode in another component, therefore each failure mode should be listed in technical terms and for function. Hereafter the ultimate effect of each failure mode needs to be considered. A failure effect is defined as the result of a failure mode on the function of the system as perceived by the user. In this way it is convenient to write these effects down in terms of what the user might see or experience. Examples of failure effects are: degraded performance, noise or even injury to a user. Each effect is given a severity number (S) from 1 (no danger) to 10 (critical). These numbers help an engineer to prioritize the failure modes and their effects. If the severity of an effect has a number 9 or 10, actions are considered to change the design by eliminating the failure mode, if possible, or protecting the user from the effect. A severity rating of 9 or 10 is generally reserved for those effects which would cause injury to a user or otherwise result in litigation.

Fmea risk elements occurrence
FMEA Risk Elements - Occurrence


Determine the cause of a failure mode and how many times it occurs. This can be done by looking at similar products or processes and the failure modes that have been documented for them. A failure cause is looked upon as a design weakness. All the potential causes for a failure mode should be identified and documented. Examples are: erroneous algorithms, excessive voltage or improper operating conditions. A failure mode is given an occurrence ranking (O), again 1–10. Actions need to be determined if the occurrence is high (meaning > 4 for non-safety failure modes and > 1 when the severity-number from step 1 is 9 or 10). This step is called the detailed development section of the FMEA process. Occurrence also can be defined as %. If a non-safety issue happened less than 1%, we can give 1 to it. It is based on your product and customer specification

Fmea risk elements detectability
FMEA Risk Elements - Detectability


Determine appropriate actions and their efficiency. In addition, design verification is needed. The proper inspection methods need to be chosen. Start from the current controls of the system, that prevent failure modes from occurring or which detect the failure before it reaches the critical stage. Then identify testing, analysis, monitoring and other techniques that can be or have been used on similar systems to detect failures. From these controls one can learn how likely it is for a failure to be identified or detected. Each combination from the previous 2 steps receives a detection number (D). This ranks the ability of planned tests and inspections to remove defects or detect failure modes in time. The assigned detection number measures the risk that the failure will escape detection. A high detection number indicates that the chances are high that the failure will escape detection, or in other words, that the chances of detection are low.

Fmeca process
FMECA Process

FMECA extends FMEA by including a criticality analysis, which is used to chart the probability of failure modes against the severity of their consequences. With the following logical steps:

  • Define the system

  • Define ground rules and assumptions in order to help drive the design

  • Construct system block diagrams

  • Identify failure modes (piece part level or functional)

  • Analyze failure effects/causes

  • Feed results back into design process

  • Classify the failure effects by severity

  • Perform criticality calculations

  • Rank failure mode criticality

  • Determine critical items

  • Feed results back into design process

  • Identify the means of failure detection, isolation and compensation

  • Perform maintainability analysis

  • Document the analysis, summarize uncorrectable design areas, identify special controls necessary to reduce failure risk

  • Make recommendations

  • Follow up on corrective action implementation/effectiveness

Rogue unit id and tracing
Rogue Unit ID and Tracing

Failure Data by Part Serial Number

Trend Tests

Monitoring & Alerting

Impact Analysis of Preventive Maintenance

RAT, MHT, LTT tests

Constant Trend

Improving Trend



Design Evaluation and Modification

Policy Evaluation & Cost Optimization

Decreasing Hazard Rate



Hazard Rates

Constant Hazard Rate

Analytical process to focus on logistics and repair quality

Renewal Process

By External Factors

Increasing Hazard Rate

Reliability Analysis on TTUR, NFF, & TTF

Design Modification & Optimal Inspection Interval

Rogue Units Identification

Basic trend monitoring
Basic Trend Monitoring

  • The Reverse Arrangement Test (a simple and useful test that has the advantage of making no assumptions about a model for the possible trend)

  • The Military Handbook Test (optimal for distinguishing between "no trend' and a trend following the NHPP Power Law or Duane model)

  • The Laplace Trend Test (optimal for distinguishing between "no trend' and a trend following the NHPP Exponential Law model)

Reverse arrangement test
Reverse Arrangement Test

Given r repairs, T1, T2, ...., Tr, the interarrival times I2=R2-T1, I3=T3-T2, ...., Ir=Tr-Tr-1 and the censoring time Tend > Tr, we calculate how many instances we have of a later interarrival time being strictly greater than an earlier interarrival time. These are called reversals. Too many reversals indicates a significant improving trend and too few reversals indicates a significant degradation trend. More formally,

  • Count a reversal every time Ij < Ik for some j and k with j < k.

  • Compute the total number of reversals, R.

  • For r repair times, the maximum possible number of reversals is r(r-1)/2.

  • If there are no trends, the expected number of reversals is r(r-1)/4.

  • For r > 12, the following approximation can be used to determine if the number of reversals is statistically significant.

  • The advantage of this test is that it is simple and it makes no assumptions about a model for the possible trend

Military handbook test
Military Handbook Test

  • Given r repairs, T1, T2, ...., Tr and the censoring time Tend > Tr, we calculate the test statistic

  • This test statistic follows a chi-square distribution with 2*r degrees of freedom.

  • This test is recommended for the case when the choice is between no trend and a non-homogeneous Poisson process (NHPP) power law (Duane) model.

Trend monitoring metrics
Trend Monitoring Metrics

The Laplace Trend Test tests the hypothesis that a trend does not exist within the data. The Laplace Trend Test can serve as a preliminary metric to determine whether the system is deteriorating, improving, or if there is no trend at all. Calculate the test statistic, using the following equation:


T = total operating time (termination time)

Xi = age of the system at the successive failure

N = total number of failures

The test statistic is approximately a standard normal random variable