Modern RAMI Method Capabilities

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Modern RAMI Method Capabilities

ARIES 2011 Quarter #2 Review27-28 July, 2011; Gaithersburg, Maryland

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

Page 2 of 14ARIES Quarterly ReviewGaithersburg, MD – 27, 28 July 2011

Introduction

• 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• Locate the shortest troubleshooting path• Determine the most operationally efficient path• Find the path that minimizes overall maintenance cost

Objectives

MaintenanceAction

Status=“A” Status=“NA”

FF NFF

1 2

pF 1- pF

IF 1(1- pF)

StatusMissing

1-1-2

FF NFF

0 1

0 2

FF NFF

pF 1-pF

cF (1-c)(1-1-2)pF

(1-1-2)(1-pF)

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

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

Empirical data subjective input

Non-InducedFailure

InducedFailure

1-IF

cF(1-IF)1 pF

IF 1 pF

Non-InducedFailure

InducedFailure

IF(1-1-2)pF

1-IF IF

ConfirmedFailure

UnconfirmedFailure

UnconfirmedFailure

ConfirmedFailure

1-cFcF

cF1-cF

(1-cF)(1-IF)1 pF (1-cF) (1-c)(1-1-2)pF


FMEA/FMECA

n

iiiii t

1


FMEA Risk Elements - Severity

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

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

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 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

Failure Data by Part Serial Number

Trend Tests

FailureCategor

y

Analytical process to focus on logistics and repair quality

Renewal ProcessBy External Factors

Non-Technical

Technical

Reliability Analysis on TTUR, NFF, & TTF

Design Evaluation and Modification

Monitoring & Alerting Impact Analysis of

Preventive Maintenance

Constant Trend

Improving Trend

Policy Evaluation & Cost Optimization

Hazard Rates

Rogue Units Identification

Constant Hazard Rate

Decreasing Hazard Rate

Increasing Hazard Rate

Design Modification & Optimal

Inspection Interval

RAT, MHT, LTT tests


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

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

72152

5.041

rrr

rrRz


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.

r

i i

end

T

TT

1

ln2


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:

where:

T = total operating time (termination time)

Xi = age of the system at the successive failure

N = total number of failures

N

T

TN

xN

i

i

12

21

The test statistic is approximately a standard normal random variable


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Modern RAMI Method Capabilities