Datasets¶
Datasets
This file contains several datasets that are useful for testing and experimenting.
Please see the online documentation for detailed examples.

class
reliability.Datasets.
ALT_load
¶ This is a single stress accelerated life test (ALT) dataset conducted at 3 loads. This dataset contains 20 failure times and no censoring.
Sourced from Dr. Mohammad Modarres, University of Maryland
Returns:  failures (list) – The failure times
 failure_stresses (list) – The stress corresponding to each failure time
 info (dataframe) – Descriptive statistics about the dataset
Notes
When importing the dataset, ensure it is called using the brackets after the name. Example Usage:
from reliability.ALT_fitters import Fit_Weibull_Power from reliability.Datasets import ALT_load Fit_Weibull_Power(failures=ALT_load().failures, failure_stress=ALT_load().failure_stresses)

class
reliability.Datasets.
ALT_load2
¶ This is a single stress accelerated life test (ALT) dataset conducted at 3 loads. This dataset contains 18 values, 5 of which are censored.
Sourced from Dr. Mohammad Modarres, University of Maryland
Returns:  failures (list) – The failure times
 failure_stresses (list) – The stress corresponding to each failure time
 right_censored (list) – The right censored times
 right_censored_stresses (list) – The stress corresponding to each right censored time
 info (dataframe) – Descriptive statistics about the dataset
Notes
When importing the dataset, ensure it is called using the brackets after the name. Example Usage:
from reliability.ALT_fitters import Fit_Weibull_Power from reliability.Datasets import ALT_load2 Fit_Weibull_Power(failures=ALT_load2().failures, failure_stress=ALT_load2().failure_stresses, right_censored=ALT_load2().right_censored, right_censored_stress=ALT_load2().right_censored_stresses)

class
reliability.Datasets.
ALT_temperature
¶ This is a single stress accelerated life test (ALT) dataset conducted at 3 temperatures. The dataset contains mostly censored data but is easily fitted by several ALT models.
Sourced from Dr. Mohammad Modarres, University of Maryland.
Returns:  failures (list) – The failure times
 failure_stresses (list) – The stress corresponding to each failure time
 right_censored (list) – The right censored times
 right_censored_stresses (list) – The stress corresponding to each right censored time
 info (dataframe) – Descriptive statistics about the dataset
Notes
When importing the dataset, ensure it is called using the brackets after the name. Example Usage:
from reliability.ALT_fitters import Fit_Weibull_Exponential from reliability.Datasets import ALT_temperature Fit_Weibull_Exponential(failures=ALT_temperature().failures, failure_stress=ALT_temperature().failure_stresses, right_censored=ALT_temperature().right_censored, right_censored_stress=ALT_temperature().right_censored_stresses)

class
reliability.Datasets.
ALT_temperature2
¶ This is a single stress accelerated life test (ALT) dataset conducted at 4 temperatures. This dataset contains 40 values, 20 of which are censored.
Sourced from Dr. Mohammad Modarres, University of Maryland
Returns:  failures (list) – The failure times
 failure_stresses (list) – The stress corresponding to each failure time
 right_censored (list) – The right censored times
 right_censored_stresses (list) – The stress corresponding to each right censored time
 info (dataframe) – Descriptive statistics about the dataset
Notes
When importing the dataset, ensure it is called using the brackets after the name. Example Usage:
from reliability.ALT_fitters import Fit_Weibull_Exponential from reliability.Datasets import ALT_temperature2 Fit_Weibull_Exponential(failures=ALT_temperature2().failures, failure_stress=ALT_temperature2().failure_stresses, right_censored=ALT_temperature2().right_censored, right_censored_stress=ALT_temperature2().right_censored_stresses)

class
reliability.Datasets.
ALT_temperature3
¶ This is a single stress accelerated life test (ALT) dataset conducted at 3 temperatures. This dataset contains 30 values and no censoring.
Returns:  failures (list) – The failure times
 failure_stresses (list) – The stress corresponding to each failure time
 info (dataframe) – Descriptive statistics about the dataset
Notes
When importing the dataset, ensure it is called using the brackets after the name. Example Usage:
from reliability.ALT_fitters import Fit_Weibull_Exponential from reliability.Datasets import ALT_temperature3 Fit_Weibull_Exponential(failures=ALT_temperature3().failures, failure_stress=ALT_temperature3().failure_stresses)

class
reliability.Datasets.
ALT_temperature4
¶ This is a single stress accelerated life test (ALT) dataset conducted at 3 temperatures. This dataset contains 20 values and no censoring.
Returns:  failures (list) – The failure times
 failure_stresses (list) – The stress corresponding to each failure time
 info (dataframe) – Descriptive statistics about the dataset
Notes
When importing the dataset, ensure it is called using the brackets after the name. Example Usage:
from reliability.ALT_fitters import Fit_Weibull_Exponential from reliability.Datasets import ALT_temperature4 Fit_Weibull_Exponential(failures=ALT_temperature4().failures, failure_stress=ALT_temperature4().failure_stresses)

class
reliability.Datasets.
ALT_temperature_humidity
¶ This is a dual stress accelerated life test (ALT) dataset conducted at 2 different temperatures and 2 different humidities. The dataset contains 12 failures and no censoring.
Returns:  failures (list) – The failure times
 failure_stress_temp (list) – The temperature stress corresponding to each failure time
 failure_stress_humidity (list) – The humidity stress corresponding to each failure time
 info (dataframe) – Descriptive statistics about the dataset
Notes
When importing the dataset, ensure it is called using the brackets after the name. Example Usage:
from reliability.Datasets import ALT_temperature_humidity from reliability.ALT_fitters import Fit_Normal_Dual_Exponential data = ALT_temperature_humidity() Fit_Normal_Dual_Exponential(failures=data.failures, failure_stress_1=data.failure_stress_temp, failure_stress_2=data.failure_stress_humidity)

class
reliability.Datasets.
ALT_temperature_voltage
¶ This is a dual stress accelerated life test (ALT) dataset conducted at 2 different temperatures and 2 different voltages. The dataset is contains 12 failures and no censoring.
Returns:  failures (list) – The failure times
 failure_stress_temp (list) – The temperature stress corresponding to each failure time
 failure_stress_voltage (list) – The voltage stress corresponding to each failure time
 info (dataframe) – Descriptive statistics about the dataset
Notes
When importing the dataset, ensure it is called using the brackets after the name. Example Usage:
from reliability.Datasets import ALT_temperature_voltage from reliability.ALT_fitters import Fit_Normal_Dual_Exponential data = ALT_temperature_voltage() Fit_Normal_Dual_Exponential(failures=data.failures, failure_stress_1=data.failure_stress_temp, failure_stress_2=data.failure_stress_voltage)

class
reliability.Datasets.
ALT_temperature_voltage2
¶ This is a dual stress accelerated life test (ALT) dataset conducted at 3 different temperatures and 2 different voltages. There are 18 failures and no censoring. Note that there is stresspair that contains only a single failure.
Returns:  failures (list) – The failure times
 failure_stress_temp (list) – The temperature stress corresponding to each failure time
 failure_stress_voltage (list) – The voltage stress corresponding to each failure time
 info (dataframe) – Descriptive statistics about the dataset
Notes
When importing the dataset, ensure it is called using the brackets after the name. Example Usage:
from reliability.Datasets import ALT_temperature_voltage2 from reliability.ALT_fitters import Fit_Normal_Dual_Exponential data = ALT_temperature_voltage2() Fit_Normal_Dual_Exponential(failures=data.failures, failure_stress_1=data.failure_stress_temp, failure_stress_2=data.failure_stress_voltage)

class
reliability.Datasets.
MCF_1
¶ This dataset is formatted for use with the Mean Cumulative Function (MCF_parametric or MCF_nonparametric). It consists of failure times for five systems. It exhibits a fairly constant failure rate, appearing to be slightly increasing (beta > 1).
Returns:  times (list) – A list of lists. Each sublist contains the failure times for each system.
 number_of_systems (int) – The number of systems in the dataset (len(times))
Notes
When importing the dataset, ensure it is called using the brackets after the name. Example Usage:
from reliability.Repairable_systems import MCF_nonparametric from reliability.Datasets import MCF_1 MCF_nonparametric(data=MCF_1().times)

class
reliability.Datasets.
MCF_2
¶ This dataset is formatted for use with the Mean Cumulative Function (MCF_parametric or MCF_nonparametric). It consists of failure times for 56 systems. It exhibits an increasing failure rate at the start and a decreasing failure rate near the end. Due to this shape it is not fitted well by the power law model used in MCF parametric.
Returns:  times (list) – A list of lists. Each sublist contains the failure times for each system.
 number_of_systems (int) – The number of systems in the dataset (len(times))
Notes
When importing the dataset, ensure it is called using the brackets after the name. Example Usage:
from reliability.Repairable_systems import MCF_nonparametric from reliability.Datasets import MCF_2 MCF_nonparametric(data=MCF_2().times)

class
reliability.Datasets.
automotive
¶ This dataset is relatively small and a challenging task to fit with any distribution due to its size and shape. It also includes mostly right censored data which makes fitting more difficult.
Sourced (with permission) from: V.V. Krivtsov and J. W. Case (1999), Peculiarities of Censored Data Analysis in Automotive Industry Applications  SAE Technical Paper Series, # 1999013220
Returns:  failures (list) – The failure times
 right_censored (list) – The right censored times
 info (dataframe) – Descriptive statistics about the dataset
Notes
When importing the dataset, ensure it is called using the brackets after the name. Example Usage:
from reliability.Datasets import automotive from reliability.Fitters import Fit_Weibull_2P Fit_Weibull_2P(failures=automotive().failures, right_censored=automotive().right_censored)

class
reliability.Datasets.
defective_sample
¶ This dataset is heavily right censored with intermixed multiply censored data (not all censored values are greater than the largest failure). It exhibits the behavior of a defective sample (aka. Limited fraction defective). Thanks to Alexander Davis for providing this dataset.
Returns:  failures (list) – The failure times
 right_censored (list) – The right censored times
 info (dataframe) – Descriptive statistics about the dataset
Notes
When importing the dataset, ensure it is called using the brackets after the name. Example Usage:
from reliability.Datasets import defective_sample from reliability.Fitters import Fit_Weibull_DS Fit_Weibull_DS(failures=defective_sample().failures, right_censored=defective_sample().right_censored)

class
reliability.Datasets.
electronics
¶ This dataset is heavily right censored without intermixed censoring (all censored values are greater than the largest failure). Thanks to Jiwon Cha for providing this dataset.
Returns:  dataframe (dataframe) – A dataframe with columns of time, quantity, category (this is data in XCN format).
 failures (list) – The failure times
 right_censored (list) – The right censored times
 info (dataframe) – Descriptive statistics about the dataset
Notes
This dataset is in the correct format for use in Fit_Weibull_2P_grouped. When importing the dataset, ensure it is called using the brackets after the name. Example Usage:
from reliability.Datasets import electronics from reliability.Fitters import Fit_Weibull_2P_grouped Fit_Weibull_2P_grouped(dataframe=electronics().dataframe)

class
reliability.Datasets.
mileage
¶ This dataset is simple to fit. It contains 100 values with no right censoring. The data appears to be from a Normal Distribution.
Sourced from Example 2.31 (page 63) of Reliability Engineering and Risk analysis 3rd Edition by M. Modarres, M. Kaminskiy, and V.V. Krivtsov
Returns:  failures (list) – The failure times
 info (dataframe) – Descriptive statistics about the dataset
Notes
When importing the dataset, ensure it is called using the brackets after the name. Example Usage:
from reliability.Datasets import mileage from reliability.Fitters import Fit_Weibull_2P Fit_Weibull_2P(failures=mileage().failures)

class
reliability.Datasets.
mixture
¶ This dataset is from a mixture model with heavy censoring (97.90622% right censored). It is best modelled using a Weibull Mixture Model.
Returns:  failures (list) – The failure times
 right_censored (list) – The right censored times
 info (dataframe) – Descriptive statistics about the dataset
Notes
When importing the dataset, ensure it is called using the brackets after the name. Example Usage:
from reliability.Datasets import mixture from reliability.Fitters import Fit_Weibull_Mixture Fit_Weibull_Mixture(failures=automotive().failures, right_censored=automotive().right_censored)

class
reliability.Datasets.
system_growth
¶ This dataset is contains 22 values with no right censoring. The data is from a system that has an increasing MTBF.
Returns:  failures (list) – The failure times
 info (dataframe) – Descriptive statistics about the dataset
Notes
When importing the dataset, ensure it is called using the brackets after the name. Example Usage:
from reliability.Datasets import system_growth from reliability.Fitters import Fit_Weibull_2P Fit_Weibull_2P(failures=system_growth().failures)