AFD and Other Techniques
AFD CASE STUDY:
Walking Bearing Problem
solutions to complex technical problems often
requires thinking outside our 'knowledge-based box.'
Anticipatory Failure Determination® is a methodology
to develop creative solutions to complex technical
problems and is effective in conducting system
Dave Harrold in "New school of thought produces
safer process control designs," Technology
Update, May 1999
What is Anticipatory
Failure Determination (AFD)?
Anticipatory Failure Determination is an application
of I-TRIZ specifically designed for:
How is does AFD differ from other failure analysis methods?
Systems in which failures have occurred
-- or might occur -- are zones of "poor information." The reason?
Little information is published about negative effects with unknown
causes, or about the causes of dangerous or harmful failures. In fact, such information is often intentionally concealed.
information, it is very difficult to identify the root causes
(existing or possible) of a failure. One must rely on guesswork -- as is the case
with traditional failure methods.
AFD overcomes this obstacle
with a core 3-step model, providing unprecedented effectiveness:
INVERT THE PROBLEM
Failure Analysis: Instead of asking "Why
did the failure happen?" ask instead: "How can I make
Failure Prediction: Instead of asking "What failures might
happen?" ask instead: "How can I make
all possible dangerous or harmful failures happen?"
Now we can employ a wealth of available information based on what inventors have
profited from since the dawn of mankind: how to make something happen.
In other words, we have converted a failure problem into
an inventive problem.
STEP 2: IDENTIFY
FAILURE HYPOTHESES. Find a
method by which the known or potential failures can be intentionally produced.
STEP 3: UTILIZE
RESOURCES. Determine if all
the components necessary to realize each hypothesis are available in your
system, or if they can be derived from what is available:
- Are the required substances and
- Is the necessary energy available
- Is there time in
which the failure can "mechanize"?
- Is the space available
for the failure to take place?
- . . . and more
THE RESULT: NO MORE
and Other Techniques
What's the difference between
AFD and conventional failure prevention techniques?
The principle difference between AFD and
conventional techniques, such as Failure Mode and Effects Analysis (FMEA)
and Hazard and Operability Analysis (HAZOP), is the perspective from
which potential failures are determined. With conventional techniques,
the process of failure prediction proceeds linearly from an articulation
of the system's function(s) to what may occur if there is a failure
(absence) in delivering these functions. In other words, the
analytical line of reasoning follows design intent. Given a potential
failure, the effect of the failure, the probability that it will occur, and
the ability to detect it are determined. Once these parameters
are quantified (often very subjectively), a calculation of risk is
made. If the risk is determined to be unacceptably high, changes in
design or in detection capability can be suggested.
On the surface, the process sounds
logical. There are, however, serious structural weaknesses with these
traditional approaches. The first weakness stems from the process used
to determine failures. The process of failure determination is
essentially a brainstorming exercise initiated by probing what failures
"might" occur. This process suffers from the same syndrome
that the original product design process is subject to -- psychological
inertia. Also, because the analysis of potential
failures is accomplished within the same mental context that created the
design in the first place, there is a serious question of objectivity to
be raised with this approach. Engineers do not like to admit that their
designs are failure prone. A second shortcoming of traditional
approaches is that the analysis of failures is based on intended or
designed function. The issue of
"prohibited" functions is not considered. For example, the
function of a handgun is to shoot a bullet, and thus related failure analyses
proceed along the lines of the original design intent. The original
designers did not intend to design a weapon used by children
to shoot their classmates; this prohibited function is not a
part of conventional failure prevention techniques. Additionally, to be
more complete, functions must be analyzed not only from the
absence of intent, but also from the perspective of the function being
performed insufficiently or excessively.
The most serious drawback of traditional
approaches, however, is the absence of an integrated problem solving
mechanism to accurately pinpoint design deficiencies as a series of
"inventive" problems. An inventive problem is one
characterized by an inherent conflict. Traditional techniques do not
make provisions for solving difficult technological problems in an
inventive way. An inventive approach recognizes system conflicts and
attacks them head-on. In traditional approaches, if the design is deemed
to be too risky, correction of the problem is accomplished through a
number of design and redesign iterations or, as a stopgap -- redesign of
the detection systems. When the system deficiency is not defined as an
inventive problem, the results are often costly over designs, or the
addition of auxiliary compensating systems making the original design
All of the structural deficiencies noted
above have been designed out of AFD. First of all, the approach
to determining potential failures is the reverse of the one used in
conventional approaches. In AFD, the power of the technique comes from
the process of deliberately "inventing" failures. The engineer
has to transform himself or herself into a subversive. The idea is to
invent, cause and create failures. In the case of past failures, the
analytical process challenges one to invent a past failure. In future
failure prevention, the logic proceeds along the lines of inventing,
creating or devising the most catastrophic failures conceivable.
In both instances, the engineer inverts
the problem. The advantage to this approach is analogous to a defense
attorney becoming a prosecutor. The system's potential flaws are viewed
from a perspective that allows for full exploitation of a system's
weaknesses. It is obvious that, when all system deficiencies are made
explicit, the team or individual can take more effective
AFD also has an integrated problem
formulation engine to fully exploit the power of TRIZ. Failure
prevention is transformed from a defensive to an offensive
"inventive" exercise creating a seamless process for failure
determination and prevention.
The process is so effective that users
will sometimes become disenchanted with their system as having so many
drawbacks that it is a wonder it will work at all. This is normal as
these are potential failures. It is incumbent on the technical analyst
to prevent these from ever occurring.
|Purpose of the
- Identify potential failure modes
and to rate the severity of their effects
- Identify Critical and
- Rank order potential design and
- Help focus on elimination of
product and process deficiencies.
- Analyze previous failures and be
able to understand how to "invent" such failures
- Identify an exhaustive list of
potential failure scenarios as well as any negative, harmful
or undesired effects or phenomenon
- Transform the process of problem
analysis from asking why a failure occurred to how can a
failure be produced
- To incorporate the full
complement of TRIZ operators to develop innovative solutions
|Scope of applicability
- System design, product design,
- System design, product design,
- Previous FMEAs, subject matter
expertise, internal engineering and warranty data, logic of
the FMEA process
- Same as FMEA plus rigorous
problem formulation and inventive analogs utilizing: Inventive
Principles, Standard Solutions, incorporation of System and
|Process for completion
- Generally linear following
- Iterative and
"inverted" or subversive by probing how failures can
be deliberately created.
|Thoroughness of the
- Fair to good, depending on the
rigor of application and the knowledge level of the
- Good to excellent because of the
access to the AFD knowledge base, the TRIZ Inventive
Principles, Problem Formulation and analysis of all resources
AFD can be used as a stand-alone failure
prediction/prevention technique or as an enhancement to traditional
methodologies. For example ...
Synthesizing AFD into the FMEA process
|Potential Failure Mode
mode of AFD:
- Cause-effect diagrams for the
system (subsystem, component)
- Automatic Inverted Problem
- Automatic access to AFD
knowledge base (Checklists and Operators)
|Potential Effects of
||Access to the AFD
knowledge base, in particular the following checklists:
- Destroying the system's
resistance to a specific effect
- Making the system vulnerable
- Intensifying the failure
- Masking the failure
Causes/Mechanisms of Failure
the Failure Analysis mode of AFD, in particular:
- Cause-effect diagrams for the
system (subsystem, component)
- Localizing the failure
- Automatic Inverted Problem
- Identifying general methods of
providing the failure
- Identifying components necessary
for providing the failure
- Revealing components of the
failure among the system resources
- Automatic access to AFD
knowledge base, in particular the following checklists:
– Typical sources of high danger
– Transforming a harmless object into a source of danger
– Intensifying an available harmful effect
– Destroying the system's resistance to a specific effect
Prevention and/or Elimination of the Failure mode of the
AFD, in particular:
1. Automatic problem
2. Automatic access to AFD
knowledge base, in particular the following Operators:
- Eliminating the causes of the
- Removing the source of harm or
change its properties
- Modifying the harmful effect
- Counteracting the harmful effect
- Isolating the system from the
- Increasing the system's
resistance to the harmful effect
- Modifying or substituting the
object effected by harm
- Localizing the harmful effect
- Reducing the harmful effect
- "Blending in" defects
- Transient using of a harmful
- Facilitating detection
Comparison of FMEA,
AFD and FPDS
Figure 1, below, depicts a typical FMEA document with Steps 2 through 9 called out.
Reference this figure to follow the discussion below.
Figure 1. FMEA
|2. Define Functions
- In the AFD process, this step
starts with the definition of the Primary Useful Function (PUF)
for the "ideal" system.
- This creates a hierarchical
cause/effect function diagram that will be utilized
throughout the process to formulate failure prediction and
failure prevention scenarios.
|3. Identify Failure
||The AFD process is more
Because of its ability to
automatically identify causes from effects, coupled with the
extensive knowledge base, AFD reduces the analysis to
"focal points" of the system where the failures
against safety usually occur.
|4. Describe the Effect
- In AFD, there is an automatic
identification of effects and recommendations extrapolated
from the knowledge base on how to create them or how they can
- This generation of effects is
accomplished from a series of "directed" prompts
(corresponding checklists and Operators).
- The prompts enhance the
analytical (or brainstorming process) because the thinking
process is guided and not free form.
- The prompts reveal not only how
to manufacture a failure but also how to intensify, hide until
later, or how to create the most subtle version of the failure
- There is an option to use the
conventional FMEA classifications on severity.
- In AFD, failures are designated
in one of two ways, e.g., very hazardous or not very
- This step in AFD also
incorporates the logic of the FMEA Step 5a, "Likelihood
- The integration of severity with
likelihood at this point in the process focuses downstream
analysis on the most hazardous scenarios.
- Can use FMEA designations.
- In AFD, the most dangerous
components or subsystems termed the focal points are called
out for special attention.
|5. Determine Causes
- This is where traditional FMEA
and AFD are significantly different in their approach.
- In AFD, the determination of
causes is approached from an "inverted" perspective.
In other words, potential failures are invented. The question
essentially is "How can a failure be made to
- The process of inventing
failures is accomplished by taking inventory of all of the
available system and environmental "resources" that,
in some combination, could cause the failure. The analysis of
resources is not typically done in a traditional FMEA and, as
such, unusual combinations of causal factors are oftentimes
- The advantage of inventing
failures vis-à-vis resources is that it focuses the analysis
on the most critical aspects of the system and it breaks
- There is another subtle
advantage of inverted analysis -- that the analysis of
potential causes is transformed from an area with a dearth of
information (system where the failure happens) to one rich
with information (areas where the similar phenomenon is
provided intentionally, for some useful purpose).
|5a. Likelihood of
- In AFD, this issue is accounted
for simultaneously in step 4a – Severity.
|6. Detection Methods
- In AFD, the logic is similar to
FMEA -- how to prevent, eliminate or enhance detection
- It is also possible to apply the
AFD process to detection methods to understand all possible
ways failures can occur in the detection systems.
|7. Risk Priority Number
- Calculate same as in FMEA.
- This is another significant
departure from FMEA because the AFD software, through the
Problem Formulator*, can access a large knowledge base on
measures to take to prevent or eliminate a failure.
|9 a-d. Actions Taken
- AFD has the possibility of
predicting any harmful consequences that will be encountered
by implementing the concept into the existing system. AFD also
has the ability to enhance the conceptual solution to ease the
process of implementation.
*Patent Number 5581663
Figure 2 below should be used as a
guide for the notes that follow.
Figure 2. FPDS and
- With AFD, it is possible to do
failure prediction at any level (the vehicle, systems,
subsystems, and components).
- Reusability – If a component
is to be reused and all of the problems with it are known,
then the prevention and elimination aspects of AFD are
- If root causes of problems are
not known, the Failure Analysis mode of AFD is used to reveal
- It is also possible to utilize
the Ideation/TRIZ methodology to enhance the components to
make them more ideal or robust.
- With AFD, it is possible to
analyze new technology to determine any harmful side effects
the technology can have on the system by a complete or brief
variant of Failure Prediction aspect of AFD.
- See the comparison to FMEA
- With AFD, any local unexpected
problems can be solved quickly utilizing either the Failure
Prevention or Failure Analysis mode of AFD.
Anticipatory Failure Determination® (AFD) Software
Determination (AFD®) is implemented in two software
- Ideation Failure Analysis
-- for revealing the root causes of a failure or drawback in a system
(i.e., product or process), and developing solutions to eliminate them.
- Ideation Failure Prediction --
to predict all dangerous or harmful side effects that might be
associated with a system, and find means of preventing them.
Screen shot from the
Ideation Failure Analysis software:
Who should use
AFD software can aid the following individuals:
- process and design engineers
- quality engineers
- engineering and environmental
- manufacturing and environmental
- reliability engineers
- anyone interested in improving his/her
technical innovative skills and thought processes
What are the
benefits of using AFD software?
AFD software can help with the following:
- improving the quality and reliability
of a process of system
- reducing warranty costs
- reducing the potential for
- furnishing a systematic approach to
overcoming potential design flaws
- minimizing liability costs and
- providing results that can lead to
- development of improved
How does the software implement the AFD
The AFD® Failure Analysis
software guides the user through the following process:
1. Document and analyze the system and failure using the Failure
2. Use the Problem Formulator®
to create a graphic model of the system/failure, localize the
problem, and formulate the inverted problem statement.
3. Use the
I-TRIZ operators corresponding to the inverted problem statement to
generate failure hypotheses.
4. Categorize and validate the
failure hypotheses; select those deemed significant.
5. For each
selected hypothesis, use the Problem Formulator to create a graphic
model depicting the revealed root cause(s) of the failure; generate
a set of problem statements for each model.
6. Develop concepts
for preventing/eliminating the failure using the I-TRIZ operators
corresponding to the type of failure identified.
7. Evaluate each
concept; predict and resolve possible harmful consequences or
undesired drawbacks associated with each one.
The AFD Failure Prediction software guides the user through
the following process:
1. Document and analyze the system using the Failure Prediction
2. Use the Problem Formulator® to create
a graphic model of the system, identify the focal points by
evaluating the system against a set of checklists, describe the
system’s relationships to its environment, and formulate inverted
problem statements for each focal point.
3. Use the I-TRIZ
operators associated with each inverted problem statement to
generate failure hypotheses for the system and its external
4. Develop a set of failure scenarios (multi-stage
failure hypotheses) using checklists and I-TRIZ operators.
each scenario, identify the components required for it to be
realized and verify (using a set of checklists) whether the
necessary resources are present.
6. Categorize the failure
scenarios according to likelihood and consequences. Select those
7. Create a set of graphic models depicting
the relationships between each selected scenario and the functioning
of the system; generate a set of problem statements for each model.
8. For each selected scenario, develop concepts for failure
prevention/elimination using the I-TRIZ operators corresponding to
the type of failure identified.
9. Evaluate each concept; predict
and resolve possible harmful consequences or undesired drawbacks
associated with each one.
Failure Analysis and AFD® Failure Prediction software include the following modules:
- Failure Analysis Questionnaire –
a tool for identifying and documenting the root cause(s) of
a system or process failure.
- Problem Formulator – a
tool for creating a structured description of the technological
system under consideration by modeling its functional
characteristics and inherent weaknesses. Using this model, the
Problem Formulator generates a list of inverted problem statements
that guide the user in the development of multiple failure
hypotheses, and a list of direct problem statements that guide the
user toward multiple solution approaches.
example of a Problem Formulator model:
- Navigator –
context-sensitive Help for the Problem Formulator.
screen from the AFD System's Navigator:
- System of Operators –
a comprehensive knowledge base of over 100 Operators ("pathways
to innovation") embodying poven design change recommendations
for dealing with the prevention and elimination of failures.
screen from the System of Operators:
- Evaluating Results – a
tool that provides the user with benchmarking capabilities and
assists in the identification of the most effective solution
concepts. Helps reveals secondary problems (which are the possible
by-products of system improvement) and supports the prediction of
the undesirable consequences of implementing a solution.
screen from the Evaluating Results module:
- Innovation Illustration Library
– includes over 700 innovative design solutions used to stimulate
creativity and launch innovative solution concepts through
example of an AFD Illustration:
- Innovation Guide – a
compendium of over 150 articles describing physical, chemical and
geometric effects useful in solving technological problems
associated with failure mechanisms and for analyzing failures for
which these mechanisms are unknown. Inludes more than 1,000
Screen shot from the Innovation Guide module of the Ideation Failure
- Microsoft Windows™ 95 or higher
- VGA monitor (800x600x256 color)
- 16 MB RAM memory
- 10MB available hard disk space
AFD software is based on Windows™ and can be used without prior training. Ideation International is
available for consultation and to provide problem-solving assistance
related to the use of this and other Ideation software products.