ReliabilityBookScripts
In this repository you will find scripts, datasets, examples developped in the book "System Reliability Theory; Models, Statistical Methods and Applications" by M. Rausand, A. Barros, A. Høyland (Wiley Edition).
Install / Use
/learn @RausandBarros/ReliabilityBookScriptsREADME
System Reliability Theory - Models, Statistical Methods, and Applications 3rd edition.
In this repository you will find scripts, datasets, examples developped in the book "System Reliability Theory; Models, Statistical Methods and Applications" by M. Rausand, A. Barros, A. Høyland.
Table of Contents
1. Repository description
2. Get the repository
3. Get the environment
4. Bug reports
5. License
1. Repository description
To facilitate the understanding of notebooks, we tried to minimize the call to external subfunctions. The advantage is that notebooks are independent and subfunctions are directly defined at the notebook level and are rather simple. The inconvenient is that several notebooks have the same subfunctions and that they might slightly differe from one to another... The reader should be aware of that.
Below is a tree view of the repository relying on the book's sections numbering:
.
├── 05_ProbabilityDistributionsInReliabilityAnalysis
│ ├── 05_04_SomeTimeToFailureDistributions --> R Code
│ │ ├── 05_04_02_TheGammaDistribution.ipynb --> R Code
│ │ ├── 05_04_03_TheWeibullDistribution.ipynb --> R Code
│ │ ├── 05_04_04_TheNormalDistribution.ipynb --> R Code
│ │ └── 05_04_05_TheLognormalDistribution.ipynb --> R Code
│ └── 05_07_AdditionalContinuousDistributions
│ └── 05_07_02_TheBetaDistribution.ipynb --> R Code
├── 06_SystemReliabilityAnalysis
│ └── 06_03_NonRepairableSystems
│ └── 06_03_02_NonrepairableParallelStructures.ipynb --> R Code
├── 10_CountingProcesses
│ └── 10_07_Problems
│ ├── 10_07_Dataset_Problem_10_13.txt --> Dataset
│ └── TestDataLoading.ipynb --> R Code
├── 11_MarkovAnalysis
│ ├── 11_09_TimeDependentSolution.ipynb --> Python
│ ├── 11_11_MultiphaseMarkovProcesses
│ │ ├── 11_11_01_ChangingTheTransitionRates.ipynb --> Python
│ │ └── 11_11_02_ChangingTheInitialState.ipynb --> Python
│ ├── 11_12_PiecewiseDeterministicMarkovProcesses
│ │ └── 11_12_03_ASpecificCase.ipynb --> Python
│ └── 11_13_SimulationOfAMarkovProcess.ipynb --> Python
├── 12_PreventiveMaintenance
│ ├── 12_04_DegradationModels
│ │ ├── 12_04_02_TrendModelsRegressionBasedModels.ipynb --> Python
│ │ └── 12_04_03_ModelsWithIncrements.ipynb --> Python
│ └── 12_05_ConditionBasedMaintenance
│ ├── 12_05_02_ContinuousMonitoringAndFiniteDiscreteStateSpace1.ipynb --> Python
│ ├── 12_05_02_ContinuousMonitoringAndFiniteDiscreteStateSpace2.ipynb --> Python
│ ├── 12_05_02_ContinuousMonitoringAndFiniteDiscreteStateSpace3.ipynb --> Python
│ ├── 12_05_04_InspectionBasedMonitoringAndFiniteDiscreteStateSpace.ipynb --> Python
│ └── 12_05_05_InspectionBasedMonitoringAndContinuousStateSpace.ipynb --> Python
├── 14_ReliabilityDataAnalysis
│ ├── 14_02_SomeBasicConcepts
│ │ └── 14_02_02_SurvivalTimes.ipynb --> R Code
│ ├── 14_03_ExploratoryDataAnalysis
│ │ ├── 14_03_01_ACompleteDataset.ipynb --> R Code
│ │ ├── 14_03_02_SampleMetrics.ipynb --> R Code
│ │ ├── 14_03_03_Histogram.ipynb --> R Code
│ │ ├── 14_03_04_DensityPlot.ipynb --> R Code
│ │ ├── 14_03_05_EmpiricalSurvivorFunction.ipynb --> R Code
│ │ ├── 14_03_06_QQPlot.ipynb --> R Code
│ │ └── 14_03_dataset.txt --> Dataset
│ ├── 14_04_ParameterEstimation
│ │ ├── 14_04_03_MethodOfMomentsEstimation.ipynb --> R Code
│ │ ├── 14_04_04_MaximumLikelihoodEstimation.ipynb --> R Code
│ │ ├── 14_04_06_WeibullDistributedLifetimes.ipynb --> R Code
│ │ └── 14_04_dataset.txt --> Dataset
│ ├── 14_05_TheKaplanMeierEstimate
│ │ └── 14_05_02_TheKaplanMeierEstimatorForACensoredDataset.ipynb --> R Code
│ ├── 14_06_CumulativeFailureRatePlots
│ │ └── 14_06_01_TheNelsonAalenEstimateOfTheCumulativeFailureRate.ipynb --> R Code
│ ├── 14_07_TotalTimeOnTestPlotting
│ │ └── 14_07_01_TotalTimeOnTestPlotForCompleteDatasets.ipynb --> R Code
│ └── 14_09_Problems
│ ├── 14_09_Dataset_Problem_14_01.txt --> Dataset
│ ├── 14_09_Dataset_Problem_14_02.txt --> Dataset
│ ├── 14_09_Dataset_Problem_14_03.txt --> Dataset
│ ├── 14_09_Dataset_Problem_14_11.txt --> Dataset
│ ├── 14_09_Dataset_Problem_14_12.txt --> Dataset
│ ├── 14_09_Dataset_Problem_14_13.txt --> Dataset
│ ├── 14_09_Dataset_Problem_14_16.txt --> Dataset
│ ├── 14_09_Dataset_Problem_14_17.txt --> Dataset
│ ├── 14_09_Dataset_Problem_14_18.txt --> Dataset
│ └── TestDataLoading.ipynb --> R Code
├── 15_BayesianReliabilityAnalysis
│ └── 15_03_SelectionOfPriorDistribution
│ └── 15_03_01_BinomialModel.ipynb --> R Code
├── ReliabilityBookEnv.yml --> Environment definition
├── LICENSE.txt --> License
└── README.md
2. Get the repository
To get the repository, run:
git clone https://github.com/RausandBarros/ReliabilityBookScripts.git
or simply download it as ZIP file from the GitHub page.
The link with the repository may be removed by deleting the \.git folder.
3. Python installation
Anaconda is a package and environment manager. It is a common practice to dedicate a specific environment to each project to ensure its consistency and limit potential interdependence with other projects. This is especially needed when several people share/work on the same project. In the following, we assume that Anaconda’s latest version (Python 3.7) https://www.anaconda.com/distribution/ is installed on your computer (Windows, macOS or Linux) and is up to date.
3.1. Environment installation
Use command line interface:
- Windows: click Start, search or select Anaconda Prompt from the menu
- macOS or Linux: open a terminal window
Run command with the path to the file ReliabilityBookEnv.yml correctly set:
conda env create -f ./MY/PATH/TO/ReliabilityBookEnv.yml
You should be able to check the installation by running the following command that list installed environments (i.e. ReliabilityBookEnv):
conda env list
3.2. Run Jupyter and notebooks
Still from the command line interface, start the ReliabilityBookEnv environment by running the command:
conda activate ReliabilityBookEnv
Then, launch Jupyter with the command:
jupyter notebook
Then use Jupyter's interface to move to the folder where the repository has been cloned or copied.
Here are the instructions: - to install the python's environment according to this repository Requirements; - to run Jupyter and therefore the notebooks provided within this repository.
At the end of the session, deactivate the environment with command:
conda deactivate
The environment might be deleted with command:
conda remove --name ReliabilityBookEnv --all
4. Bug reports
Bug reports would be appreciated and may be filed through the issue tracker. Pull requests won't be accepted except by invitation. Please file an issue first through the former link.
5. License
This project is licensed under the terms of the MIT license (see file License.txt).
