RGA Data Types

Reliability growth analysis can be conducted using different data types. This chapter explores and examines the possible data schemes and outlines the available models for each data type. The data types for developmental testing (traditional reliability growth analysis) will be discussed first. Then we will discuss the data types that support the use of RGA models for analyzing fielded systems (either for repairable systems analysis or fleet data analysis).

Developmental Testing Data Types

Time-to-Failure Data

Time-to-failure (continuous) data is the most commonly observed type of reliability growth data. It involves recording the times-to-failure for the unit(s) under test. Time-to-failure data can be applied to a single unit or system or to multiple units or systems. There are multiple data entry schemes for this data type and each is presented next.

Failure Times Data

This data type is used for tests where the failure times are recorded. The data can be entered in a cumulative (where each row shows the total amount of test time) or non-cumulative (where each row shows the incremental test time) format, as shown next.

Failure Times data for a single system in cumulative format

Failure Times data for a single system in non-cumulative format

Grouped Failure Times

This data type is used for tests where the exact failure times are unknown and only the number of failures within a time interval are recorded (e.g., inspection data). For a single system, multiple failures can occur before the operator stops the test. In this case, X number of failures are found after Y hours of test time. Failures [math]\displaystyle{ X_{1}\,\! }[/math] , [math]\displaystyle{ X_{2}\,\! }[/math] , [math]\displaystyle{ X_{3}\,\! }[/math] , etc. could have occurred at any time period up to the termination time, thus exact times for each failure are not available. This is commonly called interval or grouped data.

When multiple units are tested, the units are inspected at predetermined time intervals and the number of failed units is recorded. When entering the time at which the failures occurred for grouped data, the time is equal to the total accumulated test time for all of the units being tested. The number of failed units is simply equal to the number of failures that occurred during the current interval. The next figure shows an example of data entry for grouped data.

Grouped Failure Times data

Multiple Systems (Known Operating Times)

This data type is used for tests where a number of systems are tested. If a failure occurs in any system, a corrective action is taken on the failed unit and any design changes are incorporated into all test systems. Once the corrective actions have been implemented, the test is resumed. The time-to-failure for the failed system, along with the current operating times of all other systems, are recorded. The data can be cumulative or non-cumulative. Consider the following table where the Failed Unit ID column indicates which unit failed. For example, if you enter 2 into the Failed Unit ID column, this indicates that the unit in the Time Unit 2 column is the one that failed. For the units that did not fail, you must enter the operating time at the time of the other unit's failure.

Multiple Systems(Known Operating Times) data.

In this table, two units are undergoing testing and the units do not accumulate age at the same rate. At 10 hours into the test, unit 1 fails and corrective action is taken on both units 1 and 2. By this time, both units have accumulated 10 hours of operation. At 17 hours, unit 2 fails and corrective action is again implemented on both units; however, unit 1 has accumulated 5 hours and unit 2 has accumulated 7 hours since the last event, and so forth.

Multiple Systems (Concurrent Operating Times)

This data type is used for tests where a number of systems are tested and the start, end of failure times are recorded. This data type assumes uniform time accumulation and that the systems are tested simultaneously. As an example, consider the data of two systems shown in the next figure. System 1 begins testing at time equals 0 (with a start event, S) and failures are encountered and corrected at 281, 312 and 776 (with failure events, F). Testing stops at 1000 hours (with an end event, E). System 2 begins testing at time equals 0 and failures are encountered and corrected at 40, 222 and 436. Testing stops at 500 hours. RGA has two options to view this type of data. The next two figures show the normal and advanced view. Both figures represent the same data. Note that when entering data within the normal view, each system must be initiated with a start event.

Normal view for Multiple Systems (Concurrent Operating Times) data

Advanced view for Multiple Systems (Concurrent Operating Times) data, data for one of two systems displayed

Multiple Systems (Concurrent Operating Times) - Using Dates

This is similar to the previous data type except that dates are utilized along with operating hours. This assumes noncontinuous usage and the software computes equivalent (average) usage rates. The next figure shows an example of this in RGA.

Normal view for Concurrent Operating Times for Multiple Systems data with dates

Multiple Systems with Event Codes

The Multiple Systems with Event Codes data type is used to analyze the failure data from a reliability growth test in which a number of systems are tested concurrently and the implemented fixes are tracked during the test phase. With this data type, all of the systems under test are assumed to have the same system hours at any given time. The next figure shows an example of this type of data.

Advanced view for Multiple Systems with Event Codes

Models for Time-to-Failure (Continuous) Data

The following models can be used to analyze time-to-failure (continuous) data sets. Models and examples using the different data types are discussed in later chapters.

1) Duane

2) Crow-AMSAA (NHPP)

3) Crow Extended

4) Crow Extended - Continuous Evaluation

Discrete Data

Discrete data is also referred to as success/failure or attribute data. It involves recording data from a test for a unit when there are only two possible outcomes: success or failure. An example of this is a missile that gets fired once and it either succeeds or fails. The data types available for analyzing discrete data with the RGA software are:

• Sequential

• Sequential with Mode

• Grouped per Configuration

• Mixed Data

Sequential Data

For Sequential data, an item is tested with only two possible outcomes: success or failure. This could be a one-shot item such as a missile or an entire system that either succeeds or fails. The item is then inspected after each trial and redesigned/repaired before the next trial. The figure below shows an example of this data type, where the row number in the data sheet represents the sequence of the trials. In this data set, trial #1 succeeded, trial #2 failed, and so on.

Sequential data

Sequential with Mode Data

Often after failure analysis you know the reason for failure during a particular trial. If this is the case, the reason for each failure can also be used in the analysis. This data entry is identical to the Sequential data with the exception that a failure code, mode or ID is added after each failure so that the analysis can take into account different failure modes. The next figure shows an example of this type of data.

Sequential with Mode data

Grouped per Configuration Data

This data type is used when multiple items, instead of a single item, are tested and the number of units that fail are recorded for each configuration. The row numbers that appear on the left side of the Data Entry Spreadsheet, shown in the figure below, represent the unique configurations. For example, row 1 indicates configuration 1 in which 10 missiles were fired and 5 failed, row 2 indicates configuration 2 in which 8 missiles were fired and 3 failed, etc. The data can be cumulative or non-cumulative.

Grouped per Configuration data

Mixed Data

The mixed data type can have input data that is either configuration in groups or individual trial by trial, or a mixed combination of individual trials and configurations of more than one trial. The following figure shows an example of this data type. For example the first row of this data sheet shows that three failures occurred in the first four trials, the second row shows that there was no failure in the next trial while the third row shows that three failures occured in the next four trials

Mixed data

Models for Discrete Data

The following models can be used to analyze discrete data. Models and examples using the different data types are discussed in later chapters.

1) Duane

2) Crow-AMSAA (NHPP)

3) Crow Extended

4) Lloyd-Lipow

5) Gompertz and Modified Gompertz

6) Logistic

Multi-Phase Data

Reliability data can be analyzed across multiple phases. This is useful when an overall reliability growth program is planned and involves multiple test phases.

(Multi-Phase) Failure Times Data

This data type can be used for tests that span multiple phases and the exact failure times are recorded. The figure below shows an example of multi-phase failure times data, where the different events signify failures (F), test phases (PH) or analysis points (AP).

Multi-phase failure times data

(Multi-Phase) Grouped Failure Times Data

This data type can be used for tests that span multiple phases and the exact failure times are unknown. Only the number of failures within a time interval are recorded, as shown in the figure below.

Multi-phase grouped failure times data

(Multi-Phase) Mixed Data

This data type can be used for tests that span multiple phases and it allows for configuration in groups, individual trial by trial, or a mixed combination of individual trials and configurations of more than one trial. An example of this data type can be seen in the figure below.

Multi-phase mixed data

Models for Multi-Phase Data

The Crow Extended - Continuous Evaluation model is used to analyze data across multiple phases.

Reliability Data

Reliability data consists of entering the reliability of the equipment at different times or stages. An example is shown in the figure below. In this case, the process is monitored at pre-defined time intervals and the reliability is recorded. The reliability can be computed by a simple ratio of the number of units still functioning vs. the number of units that entered the test stage or by using Life Data Analysis and related methods (e.g., Weibull analysis).

Reliability data.

Models for Reliability Data

The following models can be used to analyze reliability data sets. Models and examples using different data types are discussed in later chapters.

1) Lloyd-Lipow

2) Gompertz Models

3) Logistic

Fielded Systems

Fielded systems are systems that are used by customers in the field and for which failure information is not derived from an in-house test. This type of data is analogous to warranty data. The data types available for fielded systems data entry are:

• Repairable Systems

• Fleet

Repairable Systems

Repairable Systems data is identical in format to the Multiple Systems (Concurrent Operating Times) data. It also can be entered in the normal or advanced view. The figure below illustrates a sample data set. In repairable systems, the purpose of the analysis is not to evaluate reliability growth but rather to obtain reliability estimates for the system, including expected number of failures, reliability at a given time, and so forth.

Repairable systems data.

Models for Repairable Systems Data

The following models can be used to analyze repairable systems data. Models and examples using different data types are discussed in RGA Models for Repairable Systems Analysis.

1) Power Law

2) Crow Extended

Fleet Data

This data type is used to analyze the entire population (fleet). The data entry for this data type is similar to the data entry for repairable systems; however, the overall data analysis is again different. In repairable systems, the reliability of a single system can be tracked and quantified, whereas in a fleet analysis, data from the entire fleet as a whole is analyzed. The figure below presents an example of data entered for fleet analysis.

Fleet systems data.

Models for Fleet Data

The following models can be used to analyze fleet data. Models and examples using different data types are discussed in later chapters.

• Crow-AMSAA (NHPP)
• Crow Extended