{"id":16750,"date":"2016-11-18T07:53:01","date_gmt":"2016-11-18T07:53:01","guid":{"rendered":"http:\/\/www.safetyangel.com\/recdev\/?page_id=16750"},"modified":"2016-11-18T07:54:54","modified_gmt":"2016-11-18T07:54:54","slug":"weibull-example-9-parametric-recurrent-event-data-analysis-example","status":"publish","type":"page","link":"http:\/\/www.safetyangel.com\/recdev\/weibull-example-9-parametric-recurrent-event-data-analysis-example\/","title":{"rendered":"Weibull++ Example 9 – Parametric Recurrent Event Data Analysis Example"},"content":{"rendered":"
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Weibull++ Example 9 - Parametric Recurrent Event Data Analysis Example<\/h2><\/div><\/div><\/div><\/div><\/div>
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Examples provided here are purely for illustrating software features and functionality.<\/span><\/h3>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div>
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Weibull++ Example 9 – Parametric Recurrent Event Data Analysis Example<\/span><\/h3>\n

Download Example File for Version 10 (*.rsgz10)<\/a> or Version 9 (*.rsr9)<\/a><\/b><\/p>\n

Background<\/span><\/h3>\n

The repairs made to a system are tracked over a period of time. Each repair is capable of addressing any damage accumulated up to the time of the current failure. The objective is to determine how effective the repairs are.<\/p>\n

Experiment and Data<\/span><\/h3>\n

The table below displays the failure times for the system.<\/p>\n<\/div>

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\n\n\n\n\n<\/colgroup>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
0.7<\/td>\n121.9<\/td>\n285<\/td>\n<\/tr>\n
3.7<\/td>\n125.5<\/td>\n304<\/td>\n<\/tr>\n
13.2<\/td>\n133.4<\/td>\n315.4<\/td>\n<\/tr>\n
15<\/td>\n151<\/td>\n317.1<\/td>\n<\/tr>\n
17.6<\/td>\n163<\/td>\n320.6<\/td>\n<\/tr>\n
25.3<\/td>\n164.7<\/td>\n324.5<\/td>\n<\/tr>\n
47.5<\/td>\n174.5<\/td>\n324.9<\/td>\n<\/tr>\n
54<\/td>\n177.4<\/td>\n342<\/td>\n<\/tr>\n
54.5<\/td>\n191.6<\/td>\n350.2<\/td>\n<\/tr>\n
56.4<\/td>\n192.7<\/td>\n355.2<\/td>\n<\/tr>\n
63.6<\/td>\n213<\/td>\n364.6<\/td>\n<\/tr>\n
72.2<\/td>\n244.8<\/td>\n364.9<\/td>\n<\/tr>\n
99.2<\/td>\n249<\/td>\n366.3<\/td>\n<\/tr>\n
99.6<\/td>\n250.8<\/td>\n373<\/td>\n<\/tr>\n
100.3<\/td>\n260.1<\/td>\n379.4<\/td>\n<\/tr>\n
102.5<\/td>\n263.5<\/td>\n389<\/td>\n<\/tr>\n
112<\/td>\n273.1<\/td>\n394.9<\/td>\n<\/tr>\n
112.2<\/td>\n274.7<\/td>\n395.2<\/td>\n<\/tr>\n
120.9<\/td>\n282.8<\/td>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div><\/div>
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Analysis<\/span><\/h3>\n

Step 1:<\/b> Add a parametric recurrent event data analysis (RDA) folio to an existing project by choosing Insert > Folios > Parametric RDA<\/strong>.<\/p>\n

Step 2:<\/b> Enter the data into the data sheet. Note that you must enter the final observation time twice: once as a failure (F) and once as the end event (E).<\/p>\n

Step 3<\/b>: On the control panel, select the 3<\/b> parameters options and the Type II<\/b> virtual age model. Click Calculate<\/b> to calculate the parameters. The results are shown next.<\/p>\n<\/div>

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In ReliaSoft\u2019s BlockSim<\/a> software for system reliability, maintainability and availability analysis, the restoration factor, r, can be a number from 0 to 1, where 1 indicates that the block (component) is “as good as new” after the maintenance action (i.e., with a new age of 0) and 0 indicates that the block has not been improved at all by the maintenance action (i.e., with an age the same as the age before the maintenance was performed). A restoration factor of 0.5 implies a 50% improvement to the block and sets the age of the block to 50% of the age of the block at the time of the maintenance action. A restoration factor of 0.75 implies a 75% improvement to the block and sets the age of the block to 25% of the age of the block at the time of the maintenance action.<\/p>\n

When you are using the Type II virtual age model in Weibull++’s parametric RDA folio, the restoration factor is equal to 1 – the q parameter. Thus, the restoration factor here is 0.0684, which implies a 6.8% improvement to the system.<\/p>\n<\/div><\/div>