Aircraft Certification - Qualitative and Quantitative Analysis

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Sofema Online (SOL) considers the role and differences related to Qualitative and Quantitative Analysis


Qualitative and quantitative analysis provides a comprehensive and robust understanding of the subject matter.

By combining both approaches, we can gain a more comprehensive understanding of the type, including its functional aspects, user experience, and performance metrics

Qualitative analysis helps capture subjective aspects and provides a deeper understanding of the context and nuances surrounding the type certification process.

>> It involves gathering and interpreting non-numerical data, such as expert opinions, observations, and feedback.

Quantitative analysis involves numerical data and objective measurements, providing a quantitative representation of the performance and characteristics of the type being certified.

Qualitative Analysis

>> A qualitative analysis is a review of all factors affecting the safety of a product, system, operation, or person. It involves an examination of the design against a predetermined set of acceptability parameters.
>> All possible conditions and events and their consequences are considered to determine whether they could cause or contribute to injury or damage.
>> A qualitative analysis always precedes a quantitative one.
>> Qualitative analysis verifies the proper interpretation and application of the safety design criteria established by the preliminary hazard study.
>> It also verifies that the system will operate within the safety goals and parameters established by the Operational Safety Assessment (OSA).
>> It ensures that the search for design weaknesses is approached in a methodical, focused way.

Quantitative Analysis

>> Quantitative analysis takes the qualitative analysis one logical step further. It evaluates more precisely the probability that an accident might occur.
>> This is accomplished by calculating probabilities.
>> In a quantitative analysis, the risk probability is expressed using a number or rate. The objective is to achieve maximum safety by minimizing, eliminating, or establishing control over significant risks.
>> Significant risks are identified through engineering estimations, experience, and documented history of similar equipment.
>> A probability is the expectation that an event will occur a certain number of times in a specific number of trials. (Reliability engineering uses similar techniques to predict the likelihood (probability) that a system will operate successfully for a specified mission time.
>> Reliability is the probability of success. It is calculated from the probability of failure, in turn, calculated from failure rates (failures/unit of time) of hardware (electronic or mechanical).
>> An estimate of the system failure probability or unreliability can be obtained from reliability data using the formula:

o P = 1-e-lt
o Where is the probability of failure, is the natural logarithm, l is the failure rate in failures per hour, and is the number of hours operated.

Note: System safety analyses predict the probability of a broader definition of failure than does reliability.

This SSA definition includes:

>> A failure must equate to a specific hazard
>> Hardware failures that are hazards
>> Software malfunctions
>> Mechanically correct but functionally unsafe system operation
>> due to human or procedural errors
>> Human error in design
>> Unanticipated operation due to an unplanned sequence of
>> events, actions or operating conditions.
>> Adverse environment.

It is important to note that the likelihood of damage or injury reflects a broader range of events or possibilities than reliability.

>> Many situations exist in which equipment can fail and no damage or injury occurs because systems can be designed to fail safely.
>> Conversely, many situations exist in which personnel are injured using equipment that functioned reliably (the way it was designed) but at the wrong time because of an unsafe design or procedure. A simple example is an electrical shock received by a repair technician working in an area where power has not failed.

Likelihood of occurrence

Working with likelihood requires an understanding of the following concepts.

>> A probability indicates that a failure, error, or accident is possible even though it may occur rarely over a period of time or during a considerable number of operations.
>> A probability cannot indicate exactly when, during which operation, or to which person an accident will occur.
>> It may occur during the first, last, or any intermediate operation in a series without altering the analysis results.

o Consider an example of when the likelihood of an aircraft engine failing is accurately predicted to be one in 100,000.

Example: The first time the first engine is tried it fails. One might expect the probability of the second one failing to be less. But, because these are independent events, the probability of the second one is still one in 100,000.

The classic example demonstrating this principle is that of flipping a coin.

The probability of it landing "heads-up" is 1 chance in 2 or 0.5. This is true every time the coin is flipped even if the last 10 trials experienced a "heads-up" result.

Important Note: Do not change the prediction to match limited data.

Probabilities are statistical projections that can be based on specific past experiences.

>> Even if the equipment is expected to perform the same operations as those used in the historical data source, the circumstances under which it will be operated can be expected to be different.
>> Additional variations in production, maintenance, handling, and similar processes generally preclude two or more pieces of equipment from being exactly alike. Minor changes in equipment have been known to cause failures and accidents when the item was used.
>> If an accident or failure occurs, correcting it by changing the design, material, procedures, or production process immediately nullifies certain portions of the data.

Best Practice: Consider the statistical nature of probabilities when formulating a conclusion.

>> Sometimes data are valid only in special circumstances. For instance, a statistical source may indicate that a specific number of aircraft accidents due to birdstrikes take place every 100,000 or million hours.
>> One may conclude from this data, that the probability of a bird strike is comparatively low. Hidden by the data analysis approach, is the fact that at certain airfields, where birds abound, the probability of a bird strike accident is much higher than the average.

This example demonstrates that generalized probabilities will not serve well for specific, localized areas. This applies to other environmental hazards such as lightning, fog, rain, snow, and hurricanes.

Best Practice: Look for important variables that may affect conclusions based on statistics.

Next Steps

Sofema Aviation Services ( offers training to cover CS 25 System Safety Assessments – please see the following: Type Certification System Safety Assessment – 5 Days. For additional questions or comments, email us at [email protected]


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