Crow-AMSAA (NHPP)

In "Reliability Analysis for Complex, Repairable Systems" (1974), Dr. Larry H. Crow noted that the Duane model could be stochastically represented as a Weibull process, allowing for statistical procedures to be used in the application of this model in reliability growth. This statistical extension became what is known as the Crow-AMSAA (NHPP) model. This method was first developed at the U.S. Army Materiel Systems Analysis Activity (AMSAA). It is frequently used on systems when usage is measured on a continuous scale. It can also be applied for the analysis of one shot items when there is high reliability and large number of trials. Test programs are generally conducted on a phase by phase basis. The Crow-AMSAA model is designed for tracking the reliability within a test phase and not across test phases. A development testing program may consist of several separate test phases. If corrective actions are introduced during a particular test phase then this type of testing and the associated data are appropriate for analysis by the Crow-AMSAA model. The model analyzes the reliability growth progress within each test phase and can aid in determining the following:


 * Reliability of the configuration currently on test
 * Reliability of the configuration on test at the end of the test phase
 * Expected reliability if the test time for the phase is extended
 * Growth rate
 * Confidence intervals
 * Applicable goodness-of-fit tests

Crow-AMSAA (NHPP) Model
The reliability growth pattern for the Crow-AMSAA model is exactly the same pattern as for the Duane postulate discussed previously. That is, the cumulative number of failures is linear when plotted on ln-ln scale. Unlike the Duane postulate, the Crow-AMSAA model is statistically based. Under the Duane postulate, the failure rate is linear on ln-ln scale. However for the Crow-AMSAA model statistical structure, the failure intensity of the underlying non-homogeneous Poisson process (NHPP) is linear when plotted on ln-ln scale.

Let $$N(t)$$  be the cumulative number of failures observed in cumulative test time  $$t$$  and let  $$\rho (t)$$  be the failure intensity for the Crow-AMSAA model. Under the NHPP model, $$\rho (t)\Delta t$$  is approximately the probably of a failure occurring over the interval  $$[t,t+\Delta t]$$  for small  $$\Delta t$$. In addition, the expected number of failures experienced over the test interval $$[0,T]$$  under the Crow-AMSAA model is given by:


 * $$E[N(T)]=\mathop{}_{0}^{T}\rho (t)dt$$

The Crow-AMSAA model assumes that $$\rho (T)$$  may be approximated by the Weibull failure rate function:


 * $$\rho (T)=\frac{\beta }{{T}^{\beta -1}}$$

Therefore, if $$\lambda =\tfrac{1},$$  the intensity function,  $$\rho (T),$$  or the instantaneous failure intensity,  $${{\lambda }_{i}}(T)$$, is defined as:


 * $${{\lambda }_{i}}(T)=\lambda \beta {{T}^{\beta -1}},\text{with }T>0,\text{ }\lambda >0\text{ and }\beta >0$$

In the special case of exponential failure times there is no growth and the failure intensity, $$\rho (t)$$, is equal to  $$\lambda $$. In this case, the expected number of failures is given by:


 * $$\begin{align}

& E[N(T)]= & \mathop{}_{0}^{T}\rho (t)dt \\ & = & \lambda T \end{align}$$

In order for the plot to be linear when plotted on ln-ln scale under the general reliability growth case, the following must hold true where the expected number of failures is equal to:


 * $$\begin{align}

& E[N(T)]= & \mathop{}_{0}^{T}\rho (t)dt \\ & = & \lambda {{T}^{\beta }} \end{align}$$

To put a statistical structure on the reliability growth process, consider again the special case of no growth. In this case the number of failures, $$N(T),$$  experienced during the testing over  $$[0,T]$$  is random. The expected number of failures, $$N(T),$$  is said to follow the homogeneous (constant) Poisson process with mean  $$\lambda T$$  and is given by:


 * $$\underset{}{\overset{}{\mathop{\Pr }}}\,[N(T)=n]=\frac{n!};\text{ }n=0,1,2,\ldots $$

The Crow-AMSAA generalizes this no growth case to allow for reliability growth due to corrective actions. This generalization keeps the Poisson distribution for the number of failures but allows for the expected number of failures, $$E[N(T)],$$  to be linear when plotted on ln-ln scale. The Crow-AMSAA model lets $$E[N(T)]=\lambda {{T}^{\beta }}$$. The probability that the number of failures, $$N(T),$$  will be equal to  $$n$$  under growth is then given by the Poisson distribution:


 * $$\underset{}{\overset{}{\mathop{\Pr }}}\,[N(T)=n]=\frac{n!};\text{ }n=0,1,2,\ldots $$

This is the general growth situation and the number of failures, $$N(T)$$, follows a non-homogeneous Poisson process. The exponential, "no growth" homogeneous Poisson process is a special case of the non-homogeneous Crow-AMSAA model. This is reflected in the Crow-AMSAA model parameter where $$\beta =1$$. The cumulative failure rate, $${{\lambda }_{c}}$$, is:


 * $${{\lambda }_{c}}=\lambda {{T}^{\beta -1}}$$

The cumulative $$MTB{{F}_{c}}$$  is:


 * $$MTB{{F}_{c}}=\frac{1}{\lambda }{{T}^{1-\beta }}$$

As mentioned above, the local pattern for reliability growth within a test phase is the same as the growth pattern observed by Duane, discussed in the previous chapter. The Duane $$MTB{{F}_{c}}$$  is equal to:


 * $$MTB{{F}_}=b{{T}^{\alpha }}$$

And the Duane cumulative failure rate, $${{\lambda }_{c}}$$, is:


 * $${{\lambda }_}=\frac{1}{b}{{T}^{-\alpha }}$$

Thus a relationship between Crow-AMSAA parameters and Duane parameters can be developed, such that:


 * $$\begin{align}

& {{b}_{DUANE}}= & \frac{1} \\ & {{\alpha }_{DUANE}}= & 1-{{\beta }_{AMSAA}} \end{align}$$

Note that these relationships are not absolute. They change according to how the parameters (slopes, intercepts, etc.) are defined when the analysis of the data is performed. For the exponential case, $$\beta =1$$, then  $${{\lambda }_{i}}(T)=\lambda $$ , a constant. For $$\beta >1$$,  $${{\lambda }_{i}}(T)$$  is increasing. This indicates a deterioration in system reliability. For $$\beta <1$$,  $${{\lambda }_{i}}(T)$$  is decreasing. This is indicative of reliability growth. Note that the model assumes a Poisson process with Weibull intensity function, not the Weibull distribution. Therefore, statistical procedures for the Weibull distribution do not apply for this model. The parameter $$\lambda $$  is called a scale parameter because it depends upon the unit of measurement chosen for  $$T$$. $$\beta $$ is the shape parameter that characterizes the shape of the graph of the intensity function. The total number of failures, $$N(T)$$, is a random variable with Poisson distribution. Therefore, the probability that exactly $$n$$  failures occur by time  $$T$$  is:


 * $$P[N(T)=n]=\frac{n!}$$

The number of failures occurring in the interval from $${{T}_{1}}$$  to  $${{T}_{2}}$$  is a random variable having a Poisson distribution with mean:


 * $$\theta ({{T}_{2}})-\theta ({{T}_{1}})=\lambda (T_{2}^{\beta }-T_{1}^{\beta })$$

The number of failures in any interval is statistically independent of the number of failures in any interval that does not overlap the first interval. At time $${{T}_{0}}$$, the failure intensity is  $${{\lambda }_{i}}({{T}_{0}})=\lambda \beta T_{0}^{\beta -1}$$. If improvements are not made to the system after time $${{T}_{0}}$$, it is assumed that failures would continue to occur at the constant rate  $${{\lambda }_{i}}({{T}_{0}})=\lambda \beta T_{0}^{\beta -1}$$. Future failures would then follow an exponential distribution with mean $$m({{T}_{0}})=\tfrac{1}{\lambda \beta T_{0}^{\beta -1}}$$. The instantaneous $$MTBF$$  of the system at time  $$T$$  is:


 * $$m(T)=\frac{1}{\lambda \beta {{T}^{\beta -1}}}$$

Maximum Likelihood Estimators
The probability density function ( $$pdf$$ ) of the $${{i}^{th}}$$  event given that the  $${{(i-1)}^{th}}$$  event occurred at  $${{T}_{i-1}}$$  is:


 * $$f({{T}_{i}}|{{T}_{i-1}})=\frac{\beta }{\eta }{{\left( \frac{{{T}_{i}}}{\eta } \right)}^{\beta -1}}\cdot {{e}^{-\tfrac{1}\left( T_{i}^{\beta }-T_{i-1}^{\beta } \right)}}$$

The likelihood function is:


 * $$L={{\lambda }^{n}}{{\beta }^{n}}{{e}^{-\lambda {{T}^{*\beta }}}}\underset{i=1}{\overset{n}{\mathop \prod }}\,T_{i}^{\beta -1}$$

where $${{T}^{*}}$$  is the termination time and is given by:


 * $${{T}^{*}}=\left\{ \begin{matrix}

{{T}_{n}}\text{ if the test is failure terminated} \\ T>{{T}_{n}}\text{ if the test is time terminated} \\ \end{matrix} \right\}$$

Taking the natural log on both sides:


 * $$\Lambda =n\ln \lambda +n\ln \beta -\lambda {{T}^{*\beta }}+(\beta -1)\underset{i=1}{\overset{n}{\mathop \sum }}\,\ln {{T}_{i}}$$

And differentiating with respect to $$\lambda $$  yields:


 * $$\frac{\partial \Lambda }{\partial \lambda }=\frac{n}{\lambda }-{{T}^{*\beta }}$$

Set equal to zero and solve for $$\lambda $$ :


 * $$\widehat{\lambda }=\frac{n}$$

Now differentiate with respect to $$\beta $$ :


 * $$\frac{\partial \Lambda }{\partial \beta }=\frac{n}{\beta }-\lambda {{T}^{*\beta }}\ln {{T}^{*}}+\underset{i=1}{\overset{n}{\mathop \sum }}\,\ln {{T}_{i}}$$

Set equal to zero and solve for $$\beta $$ :


 * $$\widehat{\beta }=\frac{n}{n\ln {{T}^{*}}-\underset{i=1}{\overset{n}{\mathop{\sum }}}\,\ln {{T}_{i}}}$$

Biasing and Unbiasing of Beta
The equation above returns the biased estimate of $$\beta $$. The unbiased estimate of $$\beta $$  can be calculated by using the following relationships. For time terminated data (meaning that the test ends after a specified number of failures):


 * $$\bar{\beta }=\frac{N-1}{N}\hat{\beta }$$

For failure terminated data (meaning that the test ends after a specified test time):


 * $$\bar{\beta }=\frac{N-2}{N}\hat{\beta }$$

Example: Crow-AMSAA Analysis with MLE
Two prototypes of a system were tested simultaneously with design changes incorporated during the test. The following table presents the data collected over the entire test. Find the Crow-AMSAA parameters and the intensity function using maximum likelihood estimators. Developmental test data for two identical systems Solution For the failure terminated test, $${\beta}$$ is:


 * $$\widehat{\beta }=\frac{22}{22\ln 620-\underset{i=1}{\overset{22}{\mathop{\sum }}}\,\ln {{T}_{i}}}$$


 * where:


 * $$\underset{i=1}{\overset{22}{\mathop \sum }}\,\ln {{T}_{i}}=105.6355$$


 * Then:


 * $$\widehat{\beta }=\frac{22}{22\ln 620-105.6355}=0.6142$$

And for $${\lambda}$$ :


 * $$\widehat{\lambda }=\frac{22}=0.4239$$

Therefore, $${{\lambda }_{i}}(T)$$  becomes:


 * $$\begin{align}

& {{\widehat{\lambda }}_{i}}(T)= & 0.4239\cdot 0.6142\cdot {{620}^{-0.3858}} \\ & = & 0.0217906\frac{\text{failures}}{\text{hr}} \end{align}$$

The next figure shows the plot of the failure rate. If no further changes are made, the estimated MTBF is $$\tfrac{1}{0.0217906}$$  or 46 hr.

Grouped Data
For analyzing grouped data, we follow the same logic described previously for the Duane model. If Eqn. (amsaa2a) is linearized:


 * $$\ln [E(N(T))]=\ln \lambda +\beta \ln T$$

According to Crow [9], the likelihood function for the grouped data case, (where $${{n}_{1}},$$   $${{n}_{2}},$$   $${{n}_{3}},\ldots ,$$   $${{n}_{k}}$$  failures are observed and  $$k$$  is the number of groups), is:


 * $$\underset{i=1}{\overset{k}{\mathop \prod }}\,\underset{}{\overset{}{\mathop{\Pr }}}\,({{N}_{i}}={{n}_{i}})=\underset{i=1}{\overset{k}{\mathop \prod }}\,\frac{{{(\lambda T_{i}^{\beta }-\lambda T_{i-1}^{\beta })}^{{{n}_{i}}}}\cdot {{e}^{-(\lambda T_{i}^{\beta }-\lambda T_{i-1}^{\beta })}}}{{{n}_{i}}!}$$

And the MLE of $$\lambda $$  based on this relationship is:


 * $$\widehat{\lambda }=\frac{n}{T_{k}^{\widehat{\beta }}}$$

And the estimate of $$\beta $$  is the value  $$\widehat{\beta }$$  that satisfies:


 * $$\underset{i=1}{\overset{k}{\mathop \sum }}\,{{n}_{i}}\left[ \frac{T_{i}^{\widehat{\beta }}\ln {{T}_{i}}-T_{i-1}^{\widehat{\beta }}\ln {{T}_{i-1}}}{T_{i}^{\widehat{\beta }}-T_{i-1}^{\widehat{\beta }}}-\ln {{T}_{k}} \right]=0$$

Example 4 Consider the grouped failure times data given in Table 5.2. Solve for the Crow-AMSAA parameters using MLE.

Solution To obtain the estimator of $$\beta $$, Eqn. (vv) must be solved numerically for $$\beta $$. Using RGA, the value of $$\widehat{\beta }$$  is  $$0.6315$$. Now plugging this value into Eqn. (vv1), the estimator of $$\lambda $$  is:


 * $$\begin{align}

& \widehat{\lambda }= & \frac{11}{3,{{000}^{0.6315}}} \\ & = & 0.0701 \end{align}$$

Therefore, the intensity function becomes:


 * $$\widehat{\rho }(T)=0.0701\cdot 0.6315\cdot {{T}^{-0.3685}}$$

Missing Data
Most of the reliability growth models used for estimating and tracking reliability growth based on test data assume that the data set represents all actual system failure times consistent with a uniform definition of failure (complete data). In practice, this may not always be the case and may result in too few or too many failures being reported over some interval of test time. This may result in distorted estimates of the growth rate and current system reliability. This section discusses a practical reliability growth estimation and analysis procedure based on the assumption that anomalies may exist within the data over some interval of the test period but the remaining failure data follows the Crow-AMSAA reliability growth model. In particular, it is assumed that the beginning and ending points in which the anomalies lie are generated independently of the underlying reliability growth process. The approach for estimating the parameters of the growth model with problem data over some interval of time is basically to not use this failure information. The analysis retains the contribution of the interval to the total test time, but no assumptions are made regarding the actual number of failures over the interval. This is often referred to as gap analysis. Consider the case where a system is tested for time $$T$$  and the actual failure times are recorded. The time $$T$$  may possibly be an observed failure time. Also, the end points of the gap interval may or may not correspond to a recorded failure time. The underlying assumption is that the data used in the maximum likelihood estimation follows the Crow-AMSAA model with a Weibull intensity function $$\lambda \beta {{t}^{\beta -1}}$$. It is not assumed that zero failures occurred during the gap interval. Rather, it is assumed that the actual number of failures is unknown, and hence no information at all regarding these failure is used to estimate $$\lambda $$  and  $$\beta $$. Let $${{S}_{1}}$$,  $${{S}_{2}}$$  denote the end points of the gap interval,  $${{S}_{1}}<{{S}_{2}}.$$  Let  $$0<{{X}_{1}}<{{X}_{2}}<\ldots <{{X}_}\le {{S}_{1}}$$  be the failure times over  $$(0,\,{{S}_{1}})$$  and let  $${{S}_{2}}<{{Y}_{1}}<{{Y}_{2}}<\ldots <{{Y}_}\le T$$  be the failure times over  $$({{S}_{2}},\,T)$$. The maximum likelihood estimates of $$\lambda $$  and  $$\beta $$  are values  $$\widehat{\lambda }$$  and  $$\widehat{\beta }$$  satisfying the following equations.


 * $$\widehat{\lambda }=\frac{{{N}_{1}}+{{N}_{2}}}{S\widehat{_{1}^{\beta }}+{{T}^{\widehat{\beta }}}-S_{2}^{\widehat{\beta }}}$$


 * $$\widehat{\beta }=\frac{{{N}_{1}}+{{N}_{2}}}{\widehat{\lambda }\left[ S\widehat{_{1}^{\beta }}\ln {{S}_{1}}+{{T}^{\widehat{\beta }}}\ln T-S_{2}^{\widehat{\beta }}\ln {{S}_{2}} \right]-\left[ \underset{i=1}{\overset{\mathop{\sum }}}\,\ln {{X}_{i}}+\underset{i=1}{\overset{\mathop{\sum }}}\,\ln {{Y}_{i}} \right]}$$

In general, these equations cannot be solved explicitly for $$\widehat{\lambda }$$  and  $$\widehat{\beta }$$, but must be solved by an iterative procedure.

Example
Consider a system under development that was subjected to a reliability growth test for $$T=1,000$$  hours. Each month, the successive failure times on a cumulative test time basis were reported. According to the test plan, 125 hours of test time were accumulated on each prototype system each month. The total reliability growth test program lasted for 7 months. One prototype was tested for each of the months 1, 3, 4, 5, 6 and 7 with 125 hours of test time. During the second month, two prototypes were tested for a total of 250 hours of test time. The next table shows the successive $$N=86$$  failure times that were reported for  $$T=1000$$  hours of testing.

$${{X}_{i}},$$  $$i=1,2,\ldots ,86$$, $$N = 86, T = 1000$$ The observed and cumulative number of failures for each month are:


 * 1)	Determine the maximum likelihood estimators for the Crow-AMSAA model.
 * 2)	Evaluate the goodness-of-fit for the model.
 * 3)	Consider $$(500,\ 625)$$  as the gap interval and determine the maximum likelihood estimates of  $$\lambda $$  and  $$\beta $$.

Solution
 * 1)	For the time terminated test:


 * $$\begin{align}

& \widehat{\beta }= & 0.7597 \\ & \widehat{\lambda }= & 0.4521 \end{align}$$
 * 2)	The Cramér-von Mises goodness-of-fit test for this data set yields:
 * $$C_{M}^{2}=\tfrac{1}{12M}+\underset{i=1}{\overset{M}{\mathop{\sum }}}\,{{\left[ (\tfrac{{{T}_{i}}}{T})\widehat{^{\beta }}-\tfrac{2i-1}{2M} \right]}^{2}}=$$  $$0.6989$$

The critical value at the 10% significance level is 0.173. Therefore, the test indicated that the analyst should reject the hypothesis that the data set follows the Crow-AMSAA reliability growth model. Figure 4fig814 is a plot of $$\ln N(t)$$  versus  $$\ln t$$  with the fitted line  $$\ln \hat{\lambda }+\hat{\beta }\ln t$$, where  $$\widehat{\lambda }=0.4521$$  and  $$\widehat{\beta }=0.7597$$  are the maximum likelihood estimates. Observing the data during the fourth month (between 500 and 625 hr), 38 failures were reported. This number is very high in comparison to the failures reported in the other months. A quick investigation found that a number of new data collectors were assigned to the project during this month. It was also discovered that extensive design changes were made during this period, which involved the removal of a large number of parts. It is possible that these removals, which were not failures, were incorrectly reported as failed parts. Based on knowledge of the system and the test program, it was clear that such a large number of actual system failures was extremely unlikely. The consensus was that this anomaly was due to the failure reporting. It was decided that the actual number of failures over this month would be assumed for this analysis to be unknown but consistent with the remaining data and the Crow-AMSAA reliability growth model.


 * 3)	Considering the problem interval $$(500,625)$$  as the gap interval, we will use the data over the interval  $$(0,500)$$  and over the interval  $$(625,1000).$$  Eqns. (gaplambda) and (gapbeta) are the appropriate equations to estimate  $$\lambda $$  and  $$\beta $$  since the failure times are known. In this case  $${{S}_{1}}=500,\,{{S}_{2}}=625$$  and  $$T=1000,\ {{N}_{1}}=35,\,{{N}_{2}}=13.$$  The maximum likelihood estimates of  $$\lambda $$  and  $$\beta $$  are:


 * $$\begin{align}

& \widehat{\beta }= & 0.5596 \\ & \widehat{\lambda }= & 1.1052 \end{align}$$

The next figure is a plot of the cumulative number of failures versus time. This plot is approximately linear, which also indicates a good fit of the model.



Crow Discrete Reliability Growth Model
The Crow-AMSAA model can be adapted for the analysis of success/failure data (also called "discrete" or "attribute" data).

Mixed Data
In the RGA software, the Discrete Data > Mixed Data option gives a data sheet that 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 calculations use the same mathematical methods described in section 5.3 for the Crow-AMSAA grouped data.

Example
Table 5.7 shows the number of fai $$\widehat{\beta }=0.7950$$ lures of each interval of trials and the cumulative number of trials in each interval for a reliability growth test. For example, the first row of Table 5.7 indicates that for an interval of 14 trials, 5 failures occurred.

Using RGA 7, the parameters of the Crow-AMSAA model are estimated as follows:


 * and:


 * $$\widehat{\lambda }=0.5588$$

As we have seen, the Crow-AMSAA instantaneous failure intensity, $${{\lambda }_{i}}(T)$$, is defined as:


 * $${{\lambda }_{i}}(T)=\lambda \beta {{T}^{\beta -1}},\text{with }T>0,\text{ }\lambda >0\text{ and }\beta >0$$

Using the above parameter estimates, we can calculate the or instantaneous unreliability at the end of the test, or $$T=68.$$


 * $${{R}_{i}}(68)=0.5588\cdot 0.7950\cdot {{68}^{0.7950-1}}=0.1871$$

This result that can be obtained from the Quick Calculation Pad (QCP), for $$T=68,$$  as seen in Figure Mixednst.FI. The instantaneous reliability can then be calculated as:


 * $${{R}_{inst}}=1-0.1871=0.8129$$

The average unreliability is calculated as:


 * $$\text{Average Unreliability }({{t}_{1,}}{{t}_{2}})=\frac{\lambda t_{2}^{\beta }-\lambda t_{1}^{\beta }}{{{t}_{2}}-{{t}_{1}}}$$

and the average reliability is calculated as:


 * $$\text{Average Reliability }({{t}_{1,}}{{t}_{2}})=1-\frac{\lambda t_{2}^{\beta }-\lambda t_{1}^{\beta }}{{{t}_{2}}-{{t}_{1}}}$$

Bounds on Average Failure Probability for Mixed Data
The process to calculate the average unreliability confidence bounds for mixed data is as follows:
 * 1)	Calculate the average failure probability.
 * 2)	There will exist a $${{t}^{*}}$$  between  $${{t}_{1}}$$  and  $${{t}_{2}}$$  such that the instantaneous unreliability at  $${{t}^{*}}$$  equals the average unreliability    . The confidence intervals for the instantaneous unreliability at  $${{t}^{*}}$$  are the confidence intervals for the average unreliability.

Bounds on Average Reliability for Mixed Data
The process to calculate the average reliability confidence bounds for mixed data is as follows:
 * 1)	Calculate confidence bounds for average unreliability    as described above.
 * 2)	The confidence bounds for reliability are 1 minus these confidence bounds for average unreliability.

Change of Slope
The assumption of the Crow-AMSAA (NHPP) model is that the failure intensity is monotonically increasing, decreasing or remaining constant over time. However, there might be cases in which the system design or the operational environment experiences major changes during the observation period and, therefore, a single model will not be appropriate to describe the failure behavior for the entire timeline. RGA incorporates a methodology that can be applied to scenarios where a major change occurs during a reliability growth test. The test data can be broken into two segments with a separate Crow-AMSAA (NHPP) model applied to each segment. Consider the data in Figure changeflopeisual that were obtained during a reliability growth test. As discussed above, the cumulative number of failures vs. the cumulative time should be linear on logarithmic scales. Figure changeflopeisualog shows the data plotted on logarithmic scales.

One can easily recognize that the failure behavior is not constant throughout the duration of the test. Just by observing the data, it can be asserted that a major change occurred at around $$140$$  hours that resulted in a change in the rate of failures. Therefore, using a single model to analyze this data set likely will not be appropriate. The Change of Slope methodology proposes to split the data into two segments and apply a Crow-AMSAA (NHPP) model to each segment. The time of change that will be used to split the data into the two segments (it will be referred to as $${{T}_{1}}$$ ) could be estimated just by observing the data but will most likely be dictated by engineering knowledge of the specific change to the system design or operating conditions. It is important to note that although two separate models will be applied to each segment, the information collected in the first segment (i.e. data up to $${{T}_{1}}$$ ) will be considered when creating the model for the second segment (i.e. data after  $${{T}_{1}}$$ ). The models presented next can be applied to the reliability growth analysis of a single system or multiple systems.

Model for First Segment
(Data up to $${{T}_{1}}$$ ) The data up to the point of the change that occurs at $${{T}_{1}}$$  will be analyzed using the Crow-AMSAA (NHPP) model. Based on Eqns. (amsaa5) and (amsaa6), the ML estimators of the model are:


 * $$\widehat=\frac{T_{1}^}$$


 * and


 * $${{\widehat{\beta }}_{1}}=\frac{{{n}_{1}}\ln {{T}_{1}}-\underset{i=1}{\overset{\mathop{\sum }}}\,\ln {{t}_{i}}}$$


 * where:

•	 $${{T}_{1}}$$ is the time when the change occurs. •	 $${{n}_{1}}$$ is the number of failures observed up to time $${{T}_{1}}.$$ •	 $${{t}_{i}}$$ is the time at which each corresponding failure was observed. Eqn. (beta1) can be rewritten as follows:


 * $$\begin{align}

& {{\widehat{\beta }}_{1}}= & \frac{{{n}_{1}}\ln {{T}_{1}}-\left( \ln {{t}_{1}}+\ln {{t}_{2}}+...+\ln {{t}_} \right)} \\ & = & \frac{(\ln {{T}_{1}}-\ln {{t}_{1}})+(\ln {{T}_{1}}-\ln {{t}_{2}})+(...)+(\ln {{T}_{1}}-\ln {{t}_})} \\ & = & \frac{\ln \tfrac+\ln \tfrac+...+\ln \tfrac} \end{align}$$


 * or


 * $${{\widehat{\beta }}_{1}}=\frac{\underset{i=1}{\overset{\mathop{\sum }}}\,\ln \tfrac}$$

Model for Second Segment
(Data after $${{T}_{1}}$$ ) The Crow-AMSAA (NHPP) model will be used again to analyze the data after $${{T}_{1}}$$. However, the information collected during the first segment will be used when creating the model for the second segment. Given that, the ML estimators of the model parameters in the second segment are:


 * $$\widehat=\frac{T_{2}^}$$

and similar to Eqn. (beta1mallq):


 * $${{\widehat{\beta }}_{2}}=\frac{{{n}_{1}}\ln \tfrac+\underset{i={{n}_{1}}+1}{\overset{n}{\mathop{\sum }}}\,\ln \tfrac}$$


 * where:

•	 $${{n}_{2}}$$ is the number of failures that were observed after  $${{T}_{1}}$$. •	 $$n={{n}_{1}}+{{n}_{2}}$$ is the total number of failures observed throughout the test. •	 $${{T}_{2}}$$ is the end time of the test. The test can either be failure terminated or time terminated.

Example
$$\begin{matrix} \text{7}\text{.8} & \text{99}\text{.2} & \text{151} & \text{260}\text{.1} & \text{342} & \text{430}\text{.2} \\ \text{17}\text{.6} & \text{99}\text{.6} & \text{163} & \text{273}\text{.1} & \text{350}\text{.2} & \text{445}\text{.7} \\ \text{25}\text{.3} & \text{100}\text{.3} & \text{174}\text{.5} & \text{274}\text{.7} & \text{355}\text{.2} & \text{475}\text{.9} \\ \text{15} & \text{102}\text{.5} & \text{177}\text{.4} & \text{282}\text{.8} & \text{364}\text{.6} & \text{490}\text{.1} \\ \text{47}\text{.5} & \text{112} & \text{191}\text{.6} & \text{285} & \text{364}\text{.9} & \text{535} \\ \text{54} & \text{112}\text{.2} & \text{192}\text{.7} & \text{315}\text{.4} & \text{366}\text{.3} & \text{580}\text{.3} \\ \text{54}\text{.5} & \text{120}\text{.9} & \text{213} & \text{317}\text{.1} & \text{379}\text{.4} & \text{610}\text{.6} \\ \text{56}\text{.4} & \text{121}\text{.9} & \text{244}\text{.8} & \text{320}\text{.6} & \text{389} & \text{640}\text{.5} \\ \text{63}\text{.6} & \text{125}\text{.5} & \text{249} & \text{324}\text{.5} & \text{394}\text{.9} & {} \\ \text{72}\text{.2} & \text{133}\text{.4} & \text{250}\text{.8} & \text{324}\text{.9} & \text{395}\text{.2} & {} \\ \end{matrix}$$

Table 5.8 - Failure times from a reliability growth test

The test has a duration of 660 hours. First, apply a single Crow-AMSAA (NHPP) model to all of the data. Figure Changeflopeingleodel shows the expected failures obtained from the model (the line) along with the observed failures (the points). As it can be seen from the plot, the model does not seem to accurately track the data. This is confirmed by performing the Cramér-von Mises goodness-of-fit test which checks the hypothesis that the data follows a non-homogeneous Poisson process with a power law failure intensity. The model fails the goodness-of-fit test because the test statistic (0.3309) is higher than the critical value (0.1729) at the 0.1 significance level. Figure Changeflopeingleodelesults shows a customized report that displays both the calculated parameters and the statistical test results.

Through further investigation, it is discovered that a significant design change occurred at 400 hours of test time. It is suspected that this modification is responsible for the change in the failure behavior.

In RGA 7 you have the option to perform a standard Crow-AMSAA (NHPP) analysis or to apply the Change of Slope, where you can specify a specific breakpoint, as shown in Figure changeflopereakoint. RGA 7 actually creates a grouped data set where the data in Segment 1 is included and defined by a single interval to calculate the Segment 2 parameters. However, these results are equivalent to the parameters estimated using the equations presented here.

Therefore, the Change of Slope methodology is applied to break the data into two segments for analysis. The first segment is set from 0 to 400 hours and the second segment is from 401 to 660 hours (which is the end time of the test). Based on Eqns. (lambda1) and (beta1mallq), the Crow-AMSAA (NHPP) parameters for the first segment (0-400 hours) are:


 * $$\widehat=\frac{T_{1}^}=\frac{50}=0.1008$$


 * and


 * $${{\widehat{\beta }}_{1}}=\frac{\underset{i=1}{\overset{\mathop{\sum }}}\,\ln \tfrac}=\frac{50}{\underset{i=1}{\overset{50}{\mathop{\sum }}}\,\ln \tfrac{400}}=1.0359$$

Based on Eqns. (lambda2) and (beta2mallq), the Crow-AMSAA (NHPP) parameters for the second segment (401-660 hours) are:


 * $$\widehat=\frac{T_{2}^}=\frac{58}=8.4304$$


 * $${{\widehat{\beta }}_{2}}=\frac{{{n}_{1}}\ln \tfrac+\underset{i={{n}_{1}}+1}{\overset{n}{\mathop{\sum }}}\,\ln \tfrac}=\frac{8}{50\ln \tfrac{660}{400}+\underset{i=51}{\overset{58}{\mathop{\sum }}}\,\ln \tfrac{660}}=0.2971$$

Figure changeflopelot shows a plot of the two-segment analysis along with the observed data. It is obvious that the "Change of Slope" method tracks the data more accurately.

This can also be verified by performing a Chi-Squared goodness-of-fit test. The Chi-Squared statistic is 1.2956, which is lower than the critical value of 12.017 at the 0.1 significance level; therefore, the analysis passes the test. Figure Changeflopereakodelesults shows a customized report that displays both the calculated parameters and the statistical test results.

When you have a model that fits the data, it can be used to make accurate predictions and calculations. Metrics such as the demonstrated MTBF at the end of the test or the expected number of failures at later times can be calculated. For example, Figure changeflopeTBF shows the instantaneous MTBF vs. time, together with the two-sided 90% confidence bounds. Note that confidence bounds are available for the second segment only. For times up to 400 hours, the parameters of the first segment were used to calculate the MTBF; while the parameters of the second segment were used for times after 400 hours. Also note that the number of failures at the end of segment 1 is not assumed to be equal to the number of failures at the start of segment 2. This can result in a visible jump in the plot, as in this example. Figure ChangeflopeCP shows the use of the Quick Calculation Pad (QCP) in RGA 7 to calculate the Demonstrated MTBF at the end of the test (instantaneous MTBF at time = 660), together with the two-sided 90% confidence bounds. All the calculations were based on the parameters of the second segment.



Estimating the Number of Failures if Testing Continues
Six systems were subjected to a reliability growth test and a total of 81 failures were observed. The following table presents the start and end times, along with the times-to-failure for each system. Do the following:
 * 1)	Estimate the parameters of the Crow-AMSAA model using maximum likelihood estimation.
 * 2)	How many additional failures would be generated if testing continues until 3000 hours?

Solution


 * 1)	The next figure shows the parameters estimated using RGA.




 * 2)	The number of failures can be estimated using the Quick Calculation Pad as shown next. The estimated number of failures at 3000 hours is equal to $$83.2451$$  and 81 failures were observed during testing. Therefore, the number of additional failures generated if testing continues until 3000 hours is equal to  $$83.2451-81=2.2451\approx 3$$.



Determining Whether a Design Meets the MTBF Goal
A prototype of a system was tested at the end of one of its design stages. The test was run for a total of 300 hours and 27 failures were observed. The table below shows the collected data set. The prototype has a design specification of an MTBF equal to 10 hours with a 90% confidence level at 300 hours. Do the following:
 * 1)	Estimate the parameters of the Crow-AMSAA model using maximum likelihood estimation.
 * 2)	Does the prototype meet the specified goal?

Failure times data

Solution
 * 1)	The next figure shows the parameters estimated using RGA.




 * 2)	The instantaneous MTBF with one-sided 90% confidence bounds can be calculated using the Quick Calculation Pad (QCP) as shown next. From the QCP, it is estimated that the lower limit on the MTBF at 300 hours with a 90% confidence level is equal to 10.8170 hours. Therefore, the prototype has met the specified goal.



Analyzing Mixed Data for a One-Shot System
A one-shot system underwent reliability growth development for a total of 50 trials. The test was performed as a combination of configuration in groups and individual trial by trial. The table below shows the obtained test data set. The first column specifies the number of failures that occurred in each interval and the second column the cumulative number of trials in that interval. Do the following:
 * 1)	Estimate the parameters of the Crow-AMSAA model using maximum likelihood estimators.
 * 2)	What are the instantaneous reliability and the 2-sided 90% confidence bounds at the end of the test?
 * 3)	Plot the cumulative reliability with 2-sided 90% confidence bounds.
 * 4)	If the test was continued for another 25 trials what would the expected number of additional failures be?

Mixed data

Solution


 * 1)	The next figure shows the parameters estimated using RGA.




 * 2)	The figure below shows the calculation of the instantaneous reliability with the 2-sided 90% confidence bounds. From the QCP it is estimated that the instantaneous reliability at stage 50 (or at the end of the test) is 72.6971% with an upper and lower 2-sided 90% confidence bound of 82.3627% and 39.5926% respectively.




 * 3)	The following plot shows the cumulative reliability with the 2-sided 90% confidence bounds.




 * 4)	The last figure shows the calculation of the expected number of failures after 75 trials. From the QCP it is estimated that the cumulative number of failures after 75 trials is $$26.3770\approx 27$$ . Since 20 failures occurred in the first 50 trials, the estimated number of additional failures is 7.