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AFD Facts
AFD and Other Techniques
AFD Software

AFD CASE STUDY:
 Walking Bearing Problem

"…Finding 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 failure analysis…."

– Dave Harrold in "New school of thought produces safer process control designs," Technology Update, May 1999

 
 

AFD Facts

What is Anticipatory Failure Determination  (AFD)?

Anticipatory Failure Determination is an application of I-TRIZ specifically designed for:

  • Failure Analysis -- A systematic procedure for identifying the root causes of a failure or other undesired phenomenon in a system, and for making corrections in a timely manner.
  • Failure Prediction -- A systematic procedure for identifying beforehand, and then preventing, all dangerous or harmful events that might be associated with a system.

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.

Without adequate 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:

STEP 1: INVERT THE PROBLEM

For Failure Analysis: Instead of asking "Why did the failure happen?" ask instead: "How can I make it happen?"

For 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 materials present?
  • Is the necessary energy available or producible?
  • 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 GUESSING


AFD 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 more complex.

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 countermeasures.

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.

Comparative Criteria

Traditional (FMEA)

AFD

Purpose of the technique
  • Identify potential failure modes and to rate the severity of their effects
  • Identify Critical and Significant Characteristics
  • Rank order potential design and process deficiencies
  • 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, process design
  • System design, product design, process design
Analytical tools
  • 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 Environmental Resources
Process for completion
  • Generally linear following design intent
  • Iterative and "inverted" or subversive by probing how failures can be deliberately created.
Thoroughness of the analysis
  • Fair to good, depending on the rigor of application and the knowledge level of the team/individual
  • 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

FMEA Step

Integral AFD Component

Potential Failure Mode Failure Prediction mode of AFD:
  • Cause-effect diagrams for the system (subsystem, component)
  • Automatic Inverted Problem formulation
  • Automatic access to AFD knowledge base (Checklists and Operators)
Potential Effects of Failure 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
Potential Causes/Mechanisms of Failure Application of the Failure Analysis mode of AFD, in particular:
  • Cause-effect diagrams for the system (subsystem, component)
  • Localizing the failure
  • Automatic Inverted Problem formulation
  • 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
Recommended Actions Application of Prevention and/or Elimination of the Failure mode of the AFD, in particular:

1. Automatic problem formulation

2. Automatic access to AFD knowledge base, in particular the following Operators:

  • Eliminating the causes of the failure
  • Removing the source of harm or change its properties
  • Modifying the harmful effect
  • Counteracting the harmful effect
  • Isolating the system from the harmful effect
  • 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 effect
  • 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

FMEA Step

AFD Notes

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 Modes The AFD process is more robust because:
  • There is an extensive knowledge base from which potential failures can be identified.

  • The failure modes are designated as failures of cause or a failure of an effect (Note: a cause at one level may be an effect at another level).

  • The recommendations relative to failures are derived from an automatic identification of where they reside in the cause/effect hierarchy.

  • The recommended treatment of the failure is a function of its designation as either a cause or an effect.

  • 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 be caused.
  • 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 possible.
4a. Severity
  • 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 hazardous.
  • This step in AFD also incorporates the logic of the FMEA Step 5a, "Likelihood of Occurrence."
  • The integration of severity with likelihood at this point in the process focuses downstream analysis on the most hazardous scenarios.
4b. Classification
  • 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 happen?"
  • 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 missed.
  • 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 Psychological Inertia.
  • 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 Occurrence
  • 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 methods.
  • It is also possible to apply the AFD process to detection methods to understand all possible ways failures can occur in the detection systems.
6a. Detection Effectiveness
  • Same as in FMEA.
7. Risk Priority Number
  • Calculate same as in FMEA.
8/8a. Recommended Actions
  • 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 AFD

FPDS Milestone

AFD Notes

PS2

  • With AFD, it is possible to do failure prediction at any level (the vehicle, systems, subsystems, and components).

S1

  • 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 applicable.
  • If root causes of problems are not known, the Failure Analysis mode of AFD is used to reveal root causes.
  • 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.

PA

  • See the comparison to FMEA above.

PT

  • 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

Anticipatory Failure Determination (AFD®) is implemented in two software programs:

  • 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 the AFD software?

AFD software can aid the following individuals:

  • process and design engineers
  • quality engineers
  • engineering and environmental consultants
  • manufacturing and environmental engineers
  • reliability engineers
  • students
  • 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 environmental emissions
  • furnishing a systematic approach to overcoming potential design flaws
  • minimizing liability costs and concerns
  • providing results that can lead to competitive advantage
  • development of improved problem-solving skills

How does the software implement the AFD method?

The AFD® Failure Analysis software guides the user through the following process:

1. Document and analyze the system and failure using the Failure Analysis Questionnaire.

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 Questionnaire.

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 relationships.

4. Develop a set of failure scenarios (multi-stage failure hypotheses) using checklists and I-TRIZ operators.

5. For 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 deemed significant.

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.

Software modules

Both AFD® 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.

An example of a Problem Formulator model:

  • Navigator – context-sensitive Help for the Problem Formulator.

A 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.

A 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.

A 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 user-generated analogies.

An 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 engineering applications.

Screen shot from the Innovation Guide module of the Ideation Failure Analysis software:

 

System requirements

  • 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.


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