MediaWiki API result

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            "*": "Subscribe to the mediawiki-api-announce mailing list at <https://lists.wikimedia.org/postorius/lists/mediawiki-api-announce.lists.wikimedia.org/> for notice of API deprecations and breaking changes."
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        "pages": {
            "3895": {
                "pageid": 3895,
                "ns": 0,
                "title": "Recurrent Event Data Analysis",
                "revisions": [
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                        "*": "{{template:LDABOOK|20|Non-Parametric Recurrent Events Data Analysis}}\nRecurrent Event Data Analysis (RDA) is used in various applied fields such as reliability, medicine, social sciences, economics, business and criminology. Whereas in life data analysis (LDA) it was assumed that events (failures) were independent and identically distributed (iid), there are many cases where events are dependent and not identically distributed (such as repairable system data) or where the analyst is interested in modeling the number of occurrences of events over time rather than the length of time prior to the first event, as in LDA. \n\nWeibull++ provides both parametric and non-parametric approaches to analyze such data. \n\n*The [[Recurrent_Event_Data_Analysis#Non-Parametric_Recurrent_Event_Data_Analysis|non-parametric approach]] is based on the well-known Mean Cumulative Function (MCF). The Weibull++ module for this type of analysis builds upon the work of Dr. Wayne Nelson, who has written extensively on the calculation and applications of MCF [[Appendix:_Life_Data_Analysis_References|[31]]].\n\n*The [[Recurrent_Event_Data_Analysis#Parametric_Recurrent_Event_Data_Analysis|parametric approach]] is based on the General Renewal Process (GRP) model, which is particularly useful in understanding the effects of the repairs on the age of a system. Traditionally, the commonly used models for analyzing repairable systems data are the perfect renewal processes (PRP), which corresponds to perfect repairs, and the nonhomogeneous Poisson processes (NHPP), which corresponds to minimal repairs. However, most repair activities may realistically not result in such extreme situations but in a complicated intermediate one (general repair or imperfect repair/maintenance), which are well treated with the GRP model.\n\n\n= Non-Parametric Recurrent Event Data Analysis  = <!-- THIS SECTION HEADER IS LINKED FROM ANOTHER LOCATION IN THIS DOCUMENT. IF YOU RENAME THE SECTION, YOU MUST UPDATE THE LINK(S). -->\n<div class=\"noprint\">\n{{:Non-Parametric_Recurrent_Event_Data_Analysis}}\n</div>\n\n= Parametric Recurrent Event Data Analysis  = <!-- THIS SECTION HEADER IS LINKED FROM ANOTHER LOCATION IN THIS DOCUMENT. IF YOU RENAME THE SECTION, YOU MUST UPDATE THE LINK(S). -->\n{{:Parametric_Recurrent_Event_Data_Analysis}}"
                    }
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            "9542": {
                "pageid": 9542,
                "ns": 0,
                "title": "Redundant Systems RBD",
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                        "*": "{{Reference Example|{{Banner BlockSim Reference Examples}}}}\n\nThis example validates the results for redundant systems in BlockSim's analytical and simulation diagrams.\n\n\n{{Reference_Example_Heading1}}\n\nThe data set is from example 4.7 on page 81 in the book ''Life Cycle Reliability Engineering'' by Dr. Guangbin Yang, John Wiley & Sons, 2007.\n\n\n{{Reference_Example_Heading2}}\n\nA small power plant is equipped with two identical generators in a cold standby system configuration. Hence, the standby generator starts working as soon as the active generator fails. The assumptions are that there are no interruptions between switches and that the switching system is 100% reliable. Each generator follows an exponential life distribution with a failure rate (lambda) of 3.6 \u00d7 10<sup>-5</sup> failures per hour. The reliability of the power plant at 5000 hours and the mean time to failure (MMTF) are calculated.\n\n\n{{Reference_Example_Heading3}}\n\nSubstituting the lambda and time into Equation 4.31 on page 81, the reliability of the system is calculated as 98.56% at time 5000 hours.\n\n:<math>R(t) = (1 + \\lambda t)e^{-\\lambda t}  \\,\\!</math>\n\n:<math>R(5000) = (1 + (3.6 \\times 10^{-5}) \\times 5000)e^{-(3.6 \\times 10^{-5}) \\times 5000} = 0.9856\\,\\!</math>\n\n\nBy setting n = 2 in Equation 4.30 on page 81, the mean time to failure is calculated as 5.56 x 10<sup>4</sup> hours.\n\n<math>MTTF = \\frac{n}{\\lambda}=\\frac{2}{3.6 \\times 10^{-5}}= 5.56 \\times 10^{4} \\,\\!</math>\n\n\n{{Reference_Example_Heading4|BlockSim}}\n\nIn BlockSim, the generator system RBD is configured as shown below.\n\n[[Image:Redundant_rbd.png|center]]\n\n\nEach generator is modeled using an exponential distribution with a failure rate (lambda) of 3.6 x 10<sup>-5</sup> failures per hour.\n\n\n'''Analytical Proof'''\n\nThe reliability of the system at 5,000 hours is calculated in the QCP as 98.56%\n\n[[Image:Redundant_qcp.png|center|500px]]\n\n\nThe mean time to failure (MTTF) is calculated as 5.55 x 10<sup>4</sup> hours.\n\n[[Image:Redundant_qcpmttf.png|center|500px]]\n\n\n'''Simulation Proof'''\n\nWe can also estimate the results by using the simulation tool in BlockSim. The simulation settings are shown below. \n\n[[Image:Redundant_sim.png|center|500px]]\n\n\nThe point reliability of the system at 5,000 hours is calculated in the QCP as 98.55%\n\n[[Image:Redundant_simqcp.png|center|500px]]\n\n\nThe mean time to failure (MTTF) is calculated as 5.84 x 10<sup>4</sup> hours.\n\n[[Image:Redundant_simqcpmttf.png|center|500px]]"
                    }
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