Fit_Exponential_Eyring¶

class
reliability.ALT_fitters.
Fit_Exponential_Eyring
(failures, failure_stress, right_censored=None, right_censored_stress=None, use_level_stress=None, CI=0.95, optimizer=None, show_probability_plot=True, show_life_stress_plot=True, print_results=True)¶ This function will Fit the ExponentialEyring lifestress model to the data provided. Please see the online documentation for the equations of this model. This model is most appropriate to model a lifestress relationship with temperature. It is recommended that you ensure your temperature data are in Kelvin.
Inputs: failures  an array or list of the failure times. failure_stress  an array or list of the corresponding stresses (such as temperature) at which each failure occurred. This must match the length of failures as each failure is tied to a failure stress. right_censored  an array or list of all the right censored failure times. right_censored_stress  an array or list of the corresponding stresses (such as temperature) at which each right_censored data point was obtained. This must match the length of right_censored as each right_censored value is tied to a right_censored stress. use_level_stress  The use level stress at which you want to know the mean life. Optional input. print_results  True/False. Default is True show_probability_plot  True/False. Default is True show_life_stress_plot  True/False. Default is True CI  confidence interval for estimating confidence limits on parameters. Must be between 0 and 1. Default is 0.95 for 95% CI. optimizer  ‘TNC’, ‘LBFGSB’, ‘powell’. Default is ‘TNC’. These are all bound constrained methods. If the bound constrained method fails, neldermead will be used. If neldermead fails the initial guess (using least squares) will be returned with a warning.
Outputs: a  fitted parameter from the Eyring model c  fitted parameter from the Eyring model loglik2  Log Likelihood*2 (as used in JMP Pro) loglik  Log Likelihood (as used in Minitab and Reliasoft) AICc  Akaike Information Criterion BIC  Bayesian Information Criterion a_SE  the standard error (sqrt(variance)) of the parameter c_SE  the standard error (sqrt(variance)) of the parameter a_upper  the upper CI estimate of the parameter a_lower  the lower CI estimate of the parameter c_upper  the upper CI estimate of the parameter c_lower  the lower CI estimate of the parameter results  a dataframe of the results (point estimate, standard error, Lower CI and Upper CI for each parameter) goodness_of_fit  a dataframe of the goodness of fit criterion (Loglikelihood, AICc, BIC) change_of_parameters  a dataframe showing the change of the parameters at each stress level mean_life  the mean life at the use_level_stress (only provided if use_level_stress is provided) Lambda_at_use_stress  the equivalent Exponential Lambda parameter at the use level stress (only provided if use_level_stress is provided) distribution_at_use_stress  the Exponential distribution at the use level stress (only provided if use_level_stress is provided) probability_plot  the axes handles for the figure object from the probability plot (only provided if show_probability_plot is True) life_stress_plot  the axes handles for the figure object from the lifestress plot (only provided if show_life_stress_plot is True)

static
LL
(params, t_f, t_rc, T_f, T_rc)¶

static
logR
(t, T, a, c)¶

static
logf
(t, T, a, c)¶

static