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Reliability growth

The reliability of a non-repairable component always decreases with time, but for repairable systems the term “reliability growth” refers to the process of gradual product improvement through the elimination of design deficiencies. In repairable systems, reliability growth is observable through an increase in the interarrival times of failures. Reliability growth is applicable to all levels of design decomposition from complete systems down to components. The maximum achieveable reliability is locked in by design, so reliability growth above the design reliability is only possible through design changes. It may be possible to achieve some reliability growth through other improvements (such as to the maintenance program) though these improvements will only help the system to achieve its design reliability.

The Duane method of modeling reliability growth involves the use of the total time on test [t] (we may also use distance, cycles, etc.) when the failure occurred and the sequence of the failure [N]. The cumulative mean time between failures (MTBF) is \(t/N\). By plotting \(ln(t)\) vs \(ln(t/N)\) we obtain a straight line which is used get the parameters lambda and beta. Using these parameters, we can model the instantaneous MTBF in the form \(\frac{t^{1-\beta}}{\lambda \times \beta}\). The function reliability_growth accepts the failure times and performs this model fitting to obtain the parameters lambda and beta, as well as produce the reliability growth plot. It is often of interest to know how much total time on test we need to meet a target MTBF. This can be found analytically by specifying the target_MTBF argument.

Inputs:

  • times - array or list of failure times
  • xmax - xlim to plot up to. Default is 1.5*max(times)
  • target_MTBF - specify the target MTBF to obtain the total time on test required to reach it.
  • show_plot - True/False. Defaults to True.
  • print_results - True/False. Defaults to True.
  • keyword arguments (such as color, title, etc) are accepted and used in the plot.

Outputs:

  • Lambda - the lambda parameter from the Duane model
  • Beta - the beta parameter from the Duane model
  • time_to_target - The time (from the start of the test) until the target MTBF is reached. time_to_target is only returned if target_MTBF is specified.
  • If show_plot is True, it will plot the reliability growth. Use plt.show() to show the plot.
  • If print_results is True, it will print a summary of the fitted parameters and time to target MTBF (if target is specified).

In the example below, we supply the total time on test when each failure occurred, and we also supply the target_MTBF as 50000 so that we can find out how much total time on test will be needed to reach the target MTBF.

from reliability.Repairable_systems import reliability_growth
import matplotlib.pyplot as plt
times = [10400,26900,43400,66400,89400,130400,163400,232000,242000,340700]
reliability_growth(times=times,target_MTBF=50000,label='Reliability growth curve',xmax=500000)
plt.legend()
plt.show()

'''
Reliability growth model parameters:
lambda: 0.002355878294089656
beta: 0.6638280053477188
Time to reach target MTBF: 428131.18039448344
'''
_images/reliability_growth.png