fig1 illustrates a process flow chart mapping steps for determining a recommended maintenance interval . as a first step 101 , failure mode models are created . in step 101 , models are created for each component of interest . the various failure mode models are then combined in step 102 to create a mathematical model , optionally illustrated as a graphical representation , of a particular operational metric of interest . after combining the various failure mode models generated in step 102 , a target operational metric is obtained in step 103 by calculating an operational metric at a target operational profile . with the target operational metric obtained in step 103 , the operational profile may then be incremented in step 104 . in the incrementation of step 104 , the maintenance interval may be adjusted such that each operational profile results in the same value of the operational metric . after the incrementation of step 104 , an operational metric may be calculated at the new operational profile in step 105 . after the new operational metric is calculated in step 105 , the operational metric is compared with a target operational metric in step 106 . if the operational metric calculated in step 105 is not equal to a target operational metric , the system may enter an optimization loop , whereby the maintenance interval is adjusted in step 107 followed by a new calculation of an operational metric at the newest operational profile in step 105 . if , on the other hand , the operational metric is found to be equal , within a desired tolerance ( for example , +/− 0 . 1 %), to the target operational metric in step 106 , the system may then consider whether other operational profiles need to be evaluated as part of the process of determining maintenance intervals in step 108 . if the system or an operator of the system determines that other operational profiles need to be evaluated , the operational profile may again be incremented as in step 104 . if the operational profile is again incremented in step 104 , a new operational metric is again calculated at the newest operational profile in step 105 , followed by the comparison in step 106 . if , on the other hand , it is determined that no other operational profiles need to be evaluated in step 108 , the system may then be evaluated within a new recommended maintenance interval using , e . g ., fmea procedures , in step 109 . after an evaluation of the system in step 109 , the system or an operator of the system determines whether any other failure modes need to be modeled due to extended operation in step 110 . if step 110 determines that yes , additional failure modes need to be modeled , the new additional models are combined with ail the other models already considered in step 102 . the system then proceeds through the steps outlined above with the new additional model ( s ) combined in step 102 . if , however , step 110 determines that no other failure modes need to be modeled or considered , the system is completed and a maintenance interval is determined in step 111 . fig2 illustrates a graphical comparison between conventional maintenance periods in curve 201 and a curve 202 based on the operational metric of the present application . in this figure , the vertical axis comprises a measure of factored fired starts 203 for a power generation unit . the horizontal axis comprises a measure of factored fired hours 204 for the power generation unit . curve 202 is illustrated as an iso - value curve for an operational metric . in this particular example , the operational metric is set equal to the value of the operational metric of the prior art ( conventional ) hours and starts maintenance interval , as indicated at point 205 . line 208 illustrates an example “ unit a ” being considered for when to take unit a offline for scheduled maintenance . point 209 illustrates the conventional point at which unit a would be taken offline , while point 210 illustrates a point according to the present technology when unit a would be taken offline . according to conventional methods , as soon as unit a had been started 1200 times , the unit must be taken offline , regardless of how many hours the unit had actually been in operation . line 211 illustrates , another example “ unit b ” being considered for when to take unit b offline for scheduled maintenance . according to conventional methods , as soon as unit b reached 32000 hours in service , regardless of the number of fired starts , unit b would be taken offline . similarly , point 212 illustrates a point at which unit b would be taken offline for maintenance according to conventional methods , while point 213 illustrates a point according to the present technology when unit b would be taken offline . as illustrated in fig2 , the area 206 , 207 between curve 201 and curve 202 represents additional unit operation that becomes possible using the system described herein . the dashed section of line 208 illustrates the added operational time of unit a as a result of the present technology . the dashed section of line 211 illustrates the additional operational time of unit b as a result of the present technology . fig3 illustrates a graphical comparison between conventional maintenance periods in curve 301 and a curve 302 based on the operational metric of the present application , similar to the curves illustrated in fig2 . in fig3 , the vertical axis comprises a measure of factored fired starts 303 for a power generation unit . the horizontal axis comprises a measure of factored fired hours 304 for the power generation unit . fig3 also illustrates a third curve 308 , which is also an iso - value curve for the operational metric , but with the operational metric set to a value below the operational metric that resulted in curve 302 . as indicated by arrow 309 , setting the operational metric to a lower value shifts the curve representing an improved maintenance interval determined herein by the present application . for example , curve 302 may represent an operational metric set to a likelihood of an unplanned outage or failure at approximately 30 %, while curve 308 may represent a likelihood of an unplanned outage or failure at approximately 25 %, which represents a lowered likelihood that a unit in a fleet of units will experience a failure . similar to fig2 , area 306 , 307 represents additional unit operation that becomes possible relative to the conventional maintenance interval determination . due to the shift in curve 308 , area 306 , 307 is smaller than area 206 , 207 illustrated in fig2 . curve 308 represents a recommended maintenance interval for the fleet with a reduced likelihood of failure as compared to curve 302 . fig4 ( a ) illustrates a graph of an operating metric as a function of an operational profile according to prior art . here , as an exemplary operating metric , the “ likelihood of unplanned outage ” is used . the exemplary operating profile comprises an “ n ratio ” defined as factored fired hours / factored starts . other operating metrics or operating profiles may be used . fig4 ( a ) has a vertical axis 403 comprising y , an operating metric . in fig4 ( a ) , the operating metric is illustrated as a likelihood of an unplanned outage , expressed as a percentage probability . fig4 ( a ) also has a horizontal axis 404 comprising x , an operational profile . the operational profile is illustrated as the n ratio defined above as factored fired hours / factored starts . curve 412 illustrates an example likelihood of an unplanned outage due to failure modes dependent on the number of hours a power generation unit has been in operation . curve 413 illustrates an example likelihood of an unplanned outage due to failure modes dependent on the number of factored starts for a power generation unit . curve 411 is a combined curve illustrating a total probability of an unplanned outage as a function of the operational profile . fig4 ( b ) illustrates a graph of an operating metric as a function of an operational profile according to the operating profile illustrated in fig2 . here , as in fig4 ( a ) , as an exemplary operating metric , the “ likelihood of unplanned outage ” is used . the exemplary operating profile comprises an “ n ratio ” defined as factored fired hours / factored starts . other operating metrics or operating profiles may be used . fig4 ( b ) has a vertical axis 403 comprising y , an operating metric . in fig4 ( b ) , the operating metric is illustrated as a likelihood of an unplanned outage , expressed as a percentage probability . fig4 ( b ) also has a horizontal axis 404 comprising x , an operational profile . the operational profile is illustrated as the n ratio defined above as factored fired hours / factored starts . curve 422 illustrates an example likelihood of an unplanned outage due to failure modes dependent on the number of factored fired hours a power generation unit has been in operation . curve 423 illustrates an example likelihood of an unplanned outage due to failure modes dependent on the number of factored starts for a power generation unit . curve 421 is a combined curve illustrating a total likelihood of an unplanned outage as a function of the operational profile . fig4 ( c ) illustrates a graph of an operating metric as a function of an operational profile according to the operating profile illustrated in fig3 . here , as in fig4 ( a ) , as an exemplary operating metric , the “ likelihood of unplanned outage ” is used . the exemplary operating profile comprises an “ n ratio ” defined as factored fired hours / factored starts . other operating metrics or operating profiles may be used . fig4 ( c ) has a vertical axis 403 comprising y , an operating metric . in fig4 ( c ) , the operating metric is illustrated as a likelihood of an unplanned outage , expressed as a percentage probability . fig4 ( c ) also has a horizontal axis 404 comprising x , an operational profile . the operational profile is illustrated as the n ratio defined above as factored fired hours / factored starts . curve 432 illustrates an example likelihood of an unplanned outage due to failure modes dependent on the number of factored fired hours a power generation unit has been in operation . curve 433 illustrates an example likelihood of an unplanned outage dependent on the number of factored starts for a power generation unit . curve 431 is a combined curve illustrating a total likelihood of an unplanned outage as a function of the operational profile . fig5 illustrates a graph of an operating metric as a function of an operational profile according to the operating profiles illustrated in fig2 - 3 . here , as in fig4 ( a ) , as an exemplary operating metric , the “ likelihood of unplanned outage ” is used . the exemplary operating profile comprises an “ n ratio ” defined as factored fired hours / factored starts . other operating metrics or operating profiles may be used . fig5 has a vertical axis 503 comprising y , an operating metric . in fig5 , the operating metric is illustrated as a likelihood of an unplanned outage , expressed as a percentage probability . fig5 also has a horizontal axis 504 comprising x , an operational profile . the operational profile is illustrated as the n ratio , defined above as factored fired hours / factored starts . fig5 juxtaposes total likelihood of an unplanned outage according to the prior art in curve 511 , according to the maintenance interval illustrated as line 202 in fig2 in curve 521 of fig5 , and according to the maintenance interval illustrated as line 308 in fig3 in curve 531 of fig5 . while the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment , it is to be understood that the invention is not to be limited to the disclosed embodiment , but on the contrary , is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims .