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render = function () {var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div',[_c('v-toolbar',{staticClass:\"rounded-t font-weight-medium\",attrs:{\"dense\":\"\",\"dark\":\"\",\"flat\":\"\",\"color\":\"primary lighten-1\"},scopedSlots:_vm._u([{key:\"extension\",fn:function(){return [_c('v-tabs',{attrs:{\"background-color\":\"primary lighten-1\",\"show-arrows\":\"\"},model:{value:(_vm.tab),callback:function ($$v) {_vm.tab=$$v},expression:\"tab\"}},[_c('v-tab',{attrs:{\"href\":\"#Overview\",\"active-class\":\"selected-tab rounded-t\"}},[_vm._v(\" Overview \")]),_c('v-tab',{attrs:{\"href\":\"#DetailedView\",\"active-class\":\"selected-tab rounded-t\"}},[_vm._v(\" Detailed view \")]),_c('v-tab',{attrs:{\"href\":\"#Data\",\"active-class\":\"selected-tab rounded-t\"}},[_vm._v(\" Data \")]),_c('v-tab',{attrs:{\"href\":\"#PerformancePeriod\",\"active-class\":\"selected-tab rounded-t\"}},[_vm._v(\" Performance period \")]),_c('v-tab',{attrs:{\"href\":\"#Statistics\",\"active-class\":\"selected-tab rounded-t\"}},[_vm._v(\" Statistics \")])],1)]},proxy:true}])},[_c('v-toolbar-title',[_vm._v(\"Diagnostics\")])],1),_c('v-tabs-items',{staticClass:\"pa-5\",model:{value:(_vm.tab),callback:function ($$v) {_vm.tab=$$v},expression:\"tab\"}},[_c('v-tab-item',{attrs:{\"value\":\"Overview\"}},[_c('p',[_vm._v(\" The \"),_c('b',[_vm._v(\"Diagnostics\")]),_vm._v(\" page shows, in detail, the data used to calculate the performance statuses. \")]),_c('p',[_vm._v(\" More details of the calculations can be found in the \"),_c('b',[_vm._v(\"DATA\")]),_vm._v(\" tab. \")]),_c('p',[_vm._v(\" The first overview page shows the different performance indicators monitored for this vessel. \")]),_c('p',[_vm._v(\" For each performance indicator, a chart shows all the long trend data points (1 per day) with trendlines and trend events overlaid. \")]),_c('p',[_c('img',{staticClass:\"image elevation-4\",attrs:{\"src\":require(\"@/assets/images/help/diagnostics/figure-1.png\")}})]),_c('p',[_vm._v(\" Trend events are automatically received from the vessel but additional ones may be created using Danelec Fleet Insights or existing ones edited. \")]),_c('p',[_c('img',{staticClass:\"image elevation-4\",attrs:{\"src\":require(\"@/assets/images/help/diagnostics/figure-2.png\")}})]),_c('p',[_vm._v(\" In the figure above, one trend event (Finish docking) marks the end of one trend period and the start of another. \")]),_c('p',[_vm._v(\" You can easily find the benefit resulting from this docking event by expanding the \"),_c('b',[_vm._v(\"Benefit\")]),_vm._v(\" area. \")]),_c('p',[_c('img',{staticClass:\"image elevation-4\",attrs:{\"src\":require(\"@/assets/images/help/diagnostics/figure-3.png\")}})]),_c('p',[_vm._v(\" The table shows that after the docking, when sailing at 50% MCR, the vessel is sailing 2.51 knots faster than before the docking. This translates to a saving of 26.56 MT of fuel oil per day. \")]),_c('p',[_vm._v(\" The first part of any new trend period (by default 120 days) is used to calculate a benchmark level of the vessel performance under optimal conditions (i.e. straight out from the dock). However, if preferred, it is also possible to set the benchmark level manually; for example, at -4 % compared to the baseline. \")]),_c('p',[_c('img',{staticClass:\"image elevation-4\",attrs:{\"src\":require(\"@/assets/images/help/diagnostics/figure-4.png\")}})]),_c('p',[_vm._v(\" The trend periods in the chart are clickable areas, \")]),_c('p',[_c('img',{staticClass:\"image elevation-4\",attrs:{\"src\":require(\"@/assets/images/help/diagnostics/figure-5.png\")}})]),_c('p',[_vm._v(\" that when clicked, open a more detailed view for that trend period. \")])]),_c('v-tab-item',{attrs:{\"value\":\"DetailedView\"}},[_c('p',[_c('img',{staticClass:\"image elevation-4\",attrs:{\"src\":require(\"@/assets/images/help/diagnostics/figure-6.png\")}})]),_c('p',[_vm._v(\" The data set can be exported to an Excel file for further analysis by clicking the \"),_c('b',[_vm._v(\"Export to Excel\")]),_vm._v(\" button in the top right corner. \")]),_c('dl',[_c('dt',[_c('b',[_vm._v(\"Result\")])]),_c('dd',[_c('p',[_vm._v(\" Shows the consequences of sailing with the vessel in its current condition, as opposed to its condition during the last benchmark period (i.e. after docking, cleaning, etc.). \")]),_c('p',[_vm._v(\" For example, when sailing at 50% MCR, the vessel above is sailing 1.62 knots slower than before. This will result in an additional cost of 17.88 MT of fuel oil per day. \")])]),_c('dt',[_c('b',[_vm._v(\"Benchmark\")])]),_c('dd',[_c('p',[_vm._v(\" Shows details about the benchmark period. The default is that the first 120 days of the trend period are used to set the benchmark, but the number of days can be changed to examine different results. \")])]),_c('dt',[_c('b',[_vm._v(\"Statistics\")])]),_c('dd',[_c('p',[_vm._v(\" Shows details and statistics of the data set. \")])]),_c('dt',[_c('b',[_vm._v(\"On board data collection limits\")])]),_c('dd',[_c('p',[_vm._v(\" Shows the conditions set up on the KSP onboard system to filter out any unrepresentative results due to bad weather. \")]),_c('p',[_vm._v(\" These conditions may be edited on board. In this case, a new record with an updated date will be added to the table. \")])])])]),_c('v-tab-item',{attrs:{\"value\":\"Data\"}},[_c('p',[_vm._v(\" The data used for the performance trend analysis are referred to as the Long Trend parameters. This is data averaged over a twenty-four hour period (midnight – midnight) and only includes data that was gathered under representative conditions. The diagnostic performance analysis is a statistical tool to detect changes of recorded performance over time. Changes of vessel performance due to wear, erosion or fouling may develop very slowly: Despite daily variations in the performance parameters, it can sometimes take years for significant permanent changes to become apparent. The Long Trend parameters are expressed as a \"),_c('em',[_vm._v(\"deviation in percent from baseline\")]),_vm._v(\". \")]),_c('p',[_vm._v(\" There are three parameters that are used for the diagnostic performance analysis: \")]),_c('ol',[_c('li',[_vm._v(\"Ship Speed loss\")]),_c('li',[_vm._v(\"Change of propeller RPM against the propeller curve\")]),_c('li',[_vm._v(\"Change of machinery SFR relative to shop test data\")])]),_c('p',[_vm._v(\" On a twin-shaft vessel, there will usually be two sets of RPM data, each analysed individually. For a diesel-electric vessel there are likely to be multiple engines; up to five SFR variables can be defined. That means there might be up to eight sets of data available for analysis. \")]),_c('p',[_vm._v(\" For ship speed, the trend parameter is calculated using the expression: \")]),_c('p',[_c('img',{staticClass:\"image elevation-4\",attrs:{\"alt\":\"ShipSpeed = (w - wref)/wref * 100%\",\"src\":require(\"@/assets/images/help/ShipSpeedDevFormula.png\")}})]),_vm._v(\" Where: \"),_c('ol',[_c('li',[_vm._v(\"w = \"),_c('em',[_vm._v(\"measured ship speed at the measured shaft power.\")]),_vm._v(\" See figure below\")]),_c('li',[_vm._v(\"w\"),_c('sub',[_vm._v(\"ref\")]),_vm._v(\" = \"),_c('em',[_vm._v(\"baseline ship speed at the measured shaft power\")])])]),_c('p',[_c('img',{staticClass:\"image elevation-4\",attrs:{\"alt\":\"Power-Speed ref curve\",\"src\":require(\"@/assets/images/help/PowerSpeedRefCurve.png\")}})])]),_c('v-tab-item',{attrs:{\"value\":\"PerformancePeriod\"}},[_c('p',[_vm._v(\" The collected data is divided into Performance Periods, which are analysed separately. To be relevant, an analysis should be performed over a period where the configuration of the ship is relatively constant. It makes sense to start a new performance evaluation after, for example, the ship’s hull is cleaned, the propeller polished or the engine overhauled. It is then possible to see the performance effect of the action. In addition, starting from a known ‘good’ condition, it is possible to get an indication of when the operation should be performed again. \")]),_c('p',[_vm._v(\" The Performance Periods are defined by setting trend events. Trend events can be set either using the KSP system on board the vessel or using the Danelec Fleet Insights web application. It is important to note that events set in the office are not copied to the vessel. In addition, at the current time, where events are already set up for the vessel in the office application, new events set up onboard are not copied to the office. Events are added and edited on the diagnostic page. \")])]),_c('v-tab-item',{attrs:{\"value\":\"Statistics\"}},[_c('h3',[_c('a',[_vm._v(\"Correlation \")])]),_c('p',[_vm._v(\" Correlation (from \"),_c('em',[_vm._v(\"Co-\")]),_vm._v(\" (meaning \\\"together\\\"), and \"),_c('em',[_vm._v(\"Relation\")]),_vm._v(\") is a statistical measure of how closely two sets of data relate to each other and whether higher values in one data set are related to higher or lower values in the other. \")]),_c('ol',[_c('li',[_vm._v(\"Correlation is Positive when the values increase together, and\")]),_c('li',[_vm._v(\"Correlation is Negative when one value decreases as the other increases\")])]),_c('p',[_vm._v(\" It is possible to calculate a value, the \"),_c('em',[_vm._v(\"correlation coefficient\")]),_vm._v(\", which indicates how strong the relationship between the data sets is. \")]),_c('ol',[_c('li',[_vm._v(\"1 is a perfect positive correlation\")]),_c('li',[_vm._v(\"0 is no correlation (the values don't seem linked at all)\")]),_c('li',[_vm._v(\"-1 is a perfect negative correlation\")])]),_c('img',{staticClass:\"image elevation-4\",attrs:{\"alt\":\"Correlation\",\"src\":require(\"@/assets/images/help/Correlation.png\")}}),_c('p',[_vm._v(\" Strictly speaking, a high correlation coefficient does not mean that the changes in one data set are the cause of the changes in the other, only that they change together. However, in the KSP diagnostic system, we know that there is a relationship between the values that we are studying and we can say that when the correlation is close to +/- 1, it is a very good indication that the trend is “steady” and is likely to continue in the future. \")]),_c('h3',[_c('a',{attrs:{\"id\":\"StdDev\"}},[_vm._v(\"Standard deviation\")])]),_c('p',[_vm._v(\" Standard deviation is another parameter that says something about how “narrow” the trend is over time and is a measure of how spread out the values are. A high value for the standard deviation means that the dots/points have a “wide corridor” and vary a lot from day to day. A low number for the standard deviation means that the daily measurements are quite steady and consistent. The value of the standard deviation can therefore also be used to evaluate how reliable the trend line is. \")]),_c('p',[_vm._v(\" Examples of low and high standard deviations: \")]),_c('img',{staticClass:\"image elevation-4\",attrs:{\"alt\":\"LowStandardDeviation\",\"src\":require(\"@/assets/images/help/LowStandardDeviation.png\")}}),_c('img',{staticClass:\"image elevation-4\",attrs:{\"alt\":\"HighStandardDeviation\",\"src\":require(\"@/assets/images/help/HighStandardDeviation.png\")}}),_c('h3',[_c('a',{attrs:{\"id\":\"Average\"}},[_vm._v(\"Average (Mean)\")])]),_c('p',[_vm._v(\" The Mean is simply the normal “average” that we meet in everyday life (as opposed to the mode, median or other ‘averages’ used in statistics). In other words, it is the sum of all the points divided by the number of points. The average/mean value can be used to estimate the overall consequences for the Vessel ( With an average of “ -0.18 %” it shows that the overall impact since last event have been a reduction of -0.18% compared to benchmark, even though the current status might show a 0.9% change from benchmark). \")]),_c('h3',[_c('a',{attrs:{\"id\":\"TrendFunc\"}},[_vm._v(\"The Trend function\")])]),_c('p',[_vm._v(\" The Trend function shows the actual formula used to draw the current trend-line. This may be useful if you want to use any of the data in other software (for example in Microsoft Excel etc.). \")])])],1)],1)}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nimport { Component, Vue } from \"vue-property-decorator\";\n\n@Component\nexport default class DiagnosticsHelp extends Vue {\n  tab = null;\n}\n","import mod from \"-!../../../node_modules/cache-loader/dist/cjs.js??ref--15-0!../../../node_modules/thread-loader/dist/cjs.js!../../../node_modules/babel-loader/lib/index.js!../../../node_modules/ts-loader/index.js??ref--15-3!../../../node_modules/cache-loader/dist/cjs.js??ref--1-0!../../../node_modules/vue-loader/lib/index.js??vue-loader-options!./Diagnostics.vue?vue&type=script&lang=ts&\"; export default mod; export * from \"-!../../../node_modules/cache-loader/dist/cjs.js??ref--15-0!../../../node_modules/thread-loader/dist/cjs.js!../../../node_modules/babel-loader/lib/index.js!../../../node_modules/ts-loader/index.js??ref--15-3!../../../node_modules/cache-loader/dist/cjs.js??ref--1-0!../../../node_modules/vue-loader/lib/index.js??vue-loader-options!./Diagnostics.vue?vue&type=script&lang=ts&\"","import { render, staticRenderFns } from \"./Diagnostics.vue?vue&type=template&id=95671512&scoped=true&\"\nimport script from \"./Diagnostics.vue?vue&type=script&lang=ts&\"\nexport * from \"./Diagnostics.vue?vue&type=script&lang=ts&\"\nimport style0 from \"./Diagnostics.vue?vue&type=style&index=0&id=95671512&scoped=true&lang=scss&\"\n\n\n/* normalize component */\nimport normalizer from \"!../../../node_modules/vue-loader/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n  script,\n  render,\n  staticRenderFns,\n  false,\n  null,\n  \"95671512\",\n  null\n  \n)\n\nexport default component.exports\n\n/* vuetify-loader */\nimport installComponents from \"!../../../node_modules/vuetify-loader/lib/runtime/installComponents.js\"\nimport { VTab } from 'vuetify/lib/components/VTabs';\nimport { VTabItem } from 'vuetify/lib/components/VTabs';\nimport { VTabs } from 'vuetify/lib/components/VTabs';\nimport { VTabsItems } from 'vuetify/lib/components/VTabs';\nimport { VToolbar } from 'vuetify/lib/components/VToolbar';\nimport { VToolbarTitle } from 'vuetify/lib/components/VToolbar';\ninstallComponents(component, {VTab,VTabItem,VTabs,VTabsItems,VToolbar,VToolbarTitle})\n","module.exports = __webpack_public_path__ + \"img/figure-3.1e2f8701.png\";","module.exports = __webpack_public_path__ + \"img/figure-4.616b3c53.png\";","module.exports = __webpack_public_path__ + \"img/figure-2.d7e9ed61.png\";","module.exports = __webpack_public_path__ + \"img/HighStandardDeviation.ce62ca72.png\";","module.exports = __webpack_public_path__ + \"img/LowStandardDeviation.1c9d5b5f.png\";","module.exports = __webpack_public_path__ + \"img/figure-1.878ec8ab.png\";","module.exports = __webpack_public_path__ + \"img/figure-6.34832d8c.png\";","module.exports = __webpack_public_path__ + \"img/figure-5.124306b1.png\";","module.exports = __webpack_public_path__ + \"img/Correlation.86459f74.png\";","module.exports = 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= __webpack_public_path__ + \"img/PowerSpeedRefCurve.3f8359dd.png\";","export * from \"-!../../../node_modules/mini-css-extract-plugin/dist/loader.js??ref--9-oneOf-1-0!../../../node_modules/css-loader/dist/cjs.js??ref--9-oneOf-1-1!../../../node_modules/vue-loader/lib/loaders/stylePostLoader.js!../../../node_modules/postcss-loader/src/index.js??ref--9-oneOf-1-2!../../../node_modules/sass-loader/dist/cjs.js??ref--9-oneOf-1-3!../../../node_modules/cache-loader/dist/cjs.js??ref--1-0!../../../node_modules/vue-loader/lib/index.js??vue-loader-options!./Diagnostics.vue?vue&type=style&index=0&id=95671512&scoped=true&lang=scss&\""],"sourceRoot":""}