For inputs and outputs see the API reference.
There are a few datasets that have been included with reliability that users may find useful for testing and experimenting. Within reliability.Datasets the following datasets are available:
- automotive - 10 failures, 21 right censored. It is used in this example
- mileage - 100 failures with no right censoring. It is used in the examples for KStest and chi2test.
- system_growth - 22 failures with no right censoring. It is used in the example for reliability_growth.
- defective_sample - 1350 failures, 12296 right censored. It exhibits the behavior of a defective sample (also known as Limited failure population or Defective subpopulation).
- mixture - 71 failures, 3320 right censored. This is best modelled using a mixture model.
- electronics - 10 failures, 4072 right censored. It is used in this example.
- ALT_temperature - conducted at 3 temperatures. 35 failures, 102 right censored. For example usage of many of the ALT Datasets see the examples here.
- ALT_temperature2 - conducted at 4 temperatures. 40 failures, 20 right censored.
- ALT_temperature3 - conducted at 3 temperatures. 30 failures, 0 right censored.
- ALT_temperature4 - conducted at 3 temperatures. 20 failures, 0 right censored.
- ALT_load - conducted at 3 loads. 20 failures, 0 censored.
- ALT_load2 - conducted at 3 loads. 13 failures, 5 right censored.
- ALT_temperature_voltage - conducted at 2 different temperatures and 2 different voltages. 12 failures, 0 right censored.
- ALT_temperature_voltage2 - conducted at 3 different temperatures and 2 different voltages. 18 failures, 8 right censored.
- ALT_temperature_humidity - conducted at 2 different temperatures and 2 different humidities. 12 failures, 0 right censored.
- MCF_1 - this dataset contains failure and retirement times for 5 repairable systems. Exhibits a worsening repair rate.
- MCF_2 - this dataset contains failure and retirement times for 56 repairable systems. Exhibits a worsening then improving repair rate. Difficult to fit this dataset.
All datasets are functions which create objects and every dataset object has several attributes.
For the standard datasets, these attributes are:
- info - a dataframe of statistics about the dataset
- failures - a list of the failure data
- right_censored - a list of the right_censored data
- right_censored_stress - a list of the right_censored stresses (ALT datasets only)
For the ALT datasets, these attributes are similar to the above standard attributes, just with some variation for the specific dataset. These include things like:
- other similarly named attributes based on the dataset
For the MCF datasets these attributes are:
If you would like more information on a dataset, you can type the name of the dataset in the help function (after importing it).
from reliability.Datasets import automotive print(help(automotive))
If you would like the statistics about a dataset you can access the info dataframe as shown below.
from reliability.Datasets import defective_sample print(defective_sample().info) ''' Stat Value Name defective_sample Total Values 13645 Failures 1350 (9.89%) Right Censored 12295 (90.11%) '''
The following example shows how to import a dataset and use it. Note that we must use brackets () to call the dataset (since it is a class) before accessing the failures and right_censored values.
from reliability.Datasets import automotive from reliability.Fitters import Fit_Weibull_2P Fit_Weibull_2P(failures=automotive().failures,right_censored=automotive().right_censored,show_probability_plot=False) ''' Results from Fit_Weibull_2P (95% CI): Analysis method: Maximum Likelihood Estimation (MLE) Failures / Right censored: 10/21 (67.74194% right censored) Parameter Point Estimate Standard Error Lower CI Upper CI Alpha 134243 42371.1 72314.7 249204 Beta 1.15586 0.295842 0.699905 1.90884 Goodness of fit Value Log-likelihood -128.974 AICc 262.376 BIC 264.816 AD 35.6075 '''
If you have an interesting dataset, please email me (email@example.com) and I may include it in this database.
If you would like to use any of these datasets in you own work, you are permitted to do so under the LGPLv3 license. Under this license you must acknowledge the source of the datasets.