Abstract:
Embodiments are directed to receiving, by a computing device comprising a processor, an anticipated usage input, a load input and a strength input associated with a unit, calculating, by the computing device, a baseline reliability value based at least in part on the anticipated usage input, the load input and the strength input, receiving, by the computing device, usage data associated with use of the unit, generating, by the computing device, an updated reliability value based on at least some of the inputs used to calculate the baseline reliability value and the usage data, and determining, by the computing device, a lifetime for the unit based on the updated reliability value.

Description:
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT 
     This invention was made with Government support under DTFACT-11-D-00004 awarded by DOT/FAA William J. Hughes Technical Center. The Government has certain rights in this invention. 
    
    
     BACKGROUND 
     Certain rotorcraft component fatigue lifetimes are currently established by a deterministic damage calculation which may be used to provide approximately “6-9&#39;s” of reliability. The inputs to the calculation may be derived from past experience and engineering assumptions that may be used to incorporate design conservatism. Current health and usage monitoring systems (HUMS) methodologies provide measurements of loads and usage which can be used to remove excessive conservatisms from damage calculations, or provide additional safety to operators desiring additional margin. Methodologies are required to integrate these measurements into a process used to monitor operational lifetime while simultaneously and rigorously ensuring a desired level of reliability. 
     BRIEF SUMMARY 
     An embodiment of the disclosure is directed to a method comprising: receiving, by a computing device comprising a processor, an anticipated usage input, a load input and a strength input associated with a unit, calculating, by the computing device, a baseline reliability value based at least in part on the anticipated usage input, the load input and the strength input, receiving, by the computing device, usage data associated with use of the unit, generating, by the computing device, an updated reliability value based on at least some of the inputs used to calculate the baseline reliability value and the usage data, and determining, by the computing device, a lifetime for the unit based on the updated reliability value. 
     An embodiment of the disclosure is directed to an apparatus comprising: at least one processor, and memory having instructions stored thereon that, when executed by the at least one processor, cause the apparatus to: receive an anticipated usage input, a load input and a strength input associated with an aircraft, calculate a baseline reliability value based at least in part on the anticipated usage input, the load input and the strength input, receive usage data associated with use of the aircraft, generate an updated reliability value based on at least some of the inputs used to calculate the baseline reliability value and the usage data, and determine a lifetime for the aircraft based on the updated reliability value. 
     Additional embodiments are described below. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present disclosure is illustrated by way of example and not limited in the accompanying figures in which like reference numerals indicate similar elements. 
         FIG. 1  is a schematic block diagram illustrating an exemplary computing system in accordance with one or more embodiments of this disclosure; 
         FIG. 2  illustrates a flow chart of a damage calculation in accordance with the prior art; 
         FIG. 3  is a flow chart of an exemplary usage monitoring process in accordance with one or more embodiments of this disclosure; 
         FIG. 4  is an exemplary graph of reliability and lifetime in accordance with one or more embodiments of this disclosure; and 
         FIG. 5  is a flow chart of an exemplary usage monitoring process in accordance with one or more embodiments of this disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     It is noted that various connections are set forth between elements in the following description and in the drawings (the contents of which are included in this disclosure by way of reference). It is noted that these connections in general and, unless specified otherwise, may be direct or indirect and that this specification is not intended to be limiting in this respect. In this respect, a coupling between entities may refer to either a direct or an indirect connection. 
     Exemplary embodiments of apparatuses, systems, and methods are described for determining a lifetime (e.g., a reliable operational lifetime) associated with one or more components, devices, pieces of equipment, etc. In some embodiments, a lifetime may be extended (e.g., a credit may be awarded) relative to a baseline version or computation of the lifetime. In some embodiments, a lifetime for a unit may be determined based on data that is available for the unit, potentially as opposed to using data for an entire production run associated with a plurality of units. 
     Referring to  FIG. 1 , an exemplary computing system  100  is shown. The system  100  is shown as including a memory  102 . The memory  102  may store executable instructions. The executable instructions may be stored or organized in any manner and at any level of abstraction, such as in connection with one or more processes, routines, methods, etc. As an example, at least a portion of the instructions are shown in  FIG. 1  as being associated with a first program  104   a  and a second program  104   b.    
     The instructions stored in the memory  102  may be executed by one or more processors, such as a processor  106 . The processor  106  may be coupled to one or more input/output (I/O) devices  108 . In some embodiments, the I/O device(s)  108  may include one or more of a keyboard or keypad, a touchscreen or touch panel, a display screen, a microphone, a speaker, a mouse, a button, a remote control, a joystick, a printer, etc. The I/O device(s)  108  may be configured to provide an interface to allow a user to interact with the system  100 . 
     The system  100  is illustrative. In some embodiments, one or more of the entities may be optional. In some embodiments, additional entities not shown may be included. For example, in some embodiments the system  100  may be associated with one or more networks. In some embodiments, the entities may be arranged or organized in a manner different from what is shown in  FIG. 1 . One or more of the entities shown in  FIG. 1  may be associated with one or more of the devices or entities described herein. 
     Turning to  FIG. 2 , a flow chart  200  of a damage and lifetime calculation in accordance with the prior art is shown. As shown in  FIG. 2 , a number of inputs  202  may be associated with the calculation. The inputs may include a usage input  202   a, a  load input  202   b , and a strength input  202   c . The usage input  202   a  may be defined in terms of excepted or anticipated frequency or time of use (e.g., hours of operation) and/or operations undertaken. For example, in the context of a calculation associated with an aircraft, the usage input  202   a  may be at least partially defined in terms of aircraft maneuvers, such as take-off, climb, turn, land, etc. The load input  202   b  may be at least partially defined in terms of expected or anticipated weight of the unit or payload carried by the unit, maneuver loads (acceleration or deceleration), environmental conditions (e.g., wind, temperature, pressure), etc. The strength input  202   c  may correspond to expected or anticipated strength of the unit being analyzed and may be at least partially based on fatigue testing. 
     One or more variables (e.g., random variables) may be defined for each of the inputs  202   a ,  202   b , and  202   c . In some embodiments, composite worst case (CWC) line items for insignificant, marginal, or minimal inputs (e.g., input loads  202   b ) may be grouped into one variable. The variable(s) may be defined in accordance with various distribution types (e.g., a Weibull distribution, Normal distribution, or Lognormal distribution), and parameters may be chosen based on a selected distribution type or past experience. 
     The inputs  202  may be used to drive a calculation process  204 . For example, as shown in  FIG. 2 , the usage input  202   a  may drive a “1-9&#39;s” reliability  204   a - 1 , which in turn may drive a baseline usage definition CWC  204   a - 2 . The load input  202   b  may drive a “2-9&#39;s” reliability  204   b - 1 , which in turn may drive a baseline load definition  204   b - 2 . The strength input  204   c  may drive a “3-9&#39;s” reliability  204   c - 1 , which in turn may drive a baseline strength definition  204   c - 2 . The baseline usage definition CWC  204   a - 2 , the baseline load definition  204   b - 2 , and the baseline strength definition  204   c - 2  may serve as inputs to, or drive, a damage or lifetime calculation  204   d . The calculation  204   d  may generate one or more outputs indicative of different levels of reliability based on inputs from  204   a - 1 ,  204   b - 1 ,  204   c - 1  for various reliability allocation schemes. A first output level  204   e - 1  may be indicative of the so-called “6-9&#39;s” reliability. A second output level  204   e - 2  may be indicative of the so-called “5-9&#39;s” reliability. A third output level  204   e - 3  may be indicative of the so-called “7-9&#39;s” reliability. Which of the levels  204   e - 1  through  204   e - 3  is selected as an output to use in a particular embodiment may be a function of one or more inputs or conditions, such as environmental considerations, consequences of inoperability, costs for repairs or maintenance, etc. 
     The calculation  204  (e.g., the generation of output levels  204   e - 1 ,  204   e - 2 , and/or  204   e - 3 ) is typically based on engineering assumptions and past experience. The calculation  204  tends to err on the side of being conservative, such that a unit (e.g., an aircraft or component or device thereof) that is the subject of the calculation  204  tends to be assigned an operational lifetime that is less than is warranted. Moreover, the calculation  204  fails to take into consideration data that may be obtained based on actual field use of the unit. 
     Turning to  FIG. 3 , a flow chart of a usage monitoring process  300  in accordance with one or more embodiments is shown. The usage monitoring process  300  may be used to compute or assign a lifetime  302  to a unit that is the subject of the process  300 , as described further below. The usage monitoring process  300  may be implemented using a system such as that shown in  FIG. 1 . 
     The process  300  may be associated with a number of inputs. For example, the inputs  202  of  FIG. 2  may serve as inputs to the process  300 . A correlation matrix  304  may be defined for some or all of the variables associated with the inputs  202 . The correlation matrix  304  may be defined where dependencies between variables physically exist. The correlation matrix  304  may serve as an input to the process  300 . An assumption of CWC usage  306  may serve as an input to the process  300 . In some embodiments, the assumption of CWC usage  306  may correspond to continuous usage (e.g., 100% usage) of the unit that is the subject of the process  300 , or some other value or level of usage. A baseline or desirable reliability level  307  may be defined as input to the process  300 . 
     The inputs  202 ,  304 ,  306 , and  307  may drive, or be applied to, a calculation process  308 . The calculation process  308  may correspond to the calculation process  204  of  FIG. 2 . While the calculation process  308  is shown as defining a baseline reliability level of “6-9&#39;s” (e.g., level  204   e - 1  of  FIG. 2 ) in  FIG. 3 , another level (e.g.,  204   e - 2 ,  204   e - 3 , or any other level) may be provided by the process  308 . 
     The process  300  may provide for an updated or modified reliability calculation  310 . The updated reliability calculation  310  may be based on the baseline reliability level calculation process  308  inputs ( 202 ,  304 ) and measured regime usage data  312 . Assuming that the process  300  is being applied to determine a lifetime  302  associated with an aircraft, one or more regimes may be associated with usage of the aircraft. For example, regimes may be associated with maneuvers performed by the aircraft, such as take-off, climb, turn, land, etc. The example depicted in  FIG. 3  assumes a climb regime is being analyzed, and further assumes that the usage data  312  obtained in the field for the aircraft indicates that the aircraft experiences or undergoes a climb maneuver 7.4% of the time. The usage data  312  may also have a distribution associated with it, as described further below. 
     The distribution associated with the usage data  312  may represent or reflect a degree of uncertainty in the usage data  312 . The uncertainty in the usage data  312  may be classified in accordance with a number of types or categories. A first category may be based on the uncertainty being dependent on time spent in a regime. A second category may be based on the uncertainty being independent of time spent in a regime. A third category may be based on the uncertainty being dependent on a combination of time spent in a regime and percentage (e.g., measurement percentage) based uncertainties. In addition, measurement error associated with usage data, in terms of instrument limit or human error can also be addressed in connection with the usage data  312 . 
     Outputs (e.g., measured data) associated with a regime recognition process may be compared to so-called “truth data” that may be based on one or more inputs, such as one or more user inputs (e.g., pilot input). Based on the comparison of the outputs associated with the regime recognition process and the “truth data,” an entity (e.g., a computing device) or a user (e.g., an engineer) may determine a category of uncertainty for the usage data  312 . 
     In some embodiments, a single instance distribution may be generated. For example, statistical distributions may be fit to the uncertainty in the usage data  312 , where the form of the distributions may depend on the category of uncertainty in the usage data  312 . The distribution may be of the uncertainty (percentage based or time based) for, e.g., a single instance of a regime. 
     In some embodiments, data for a fielded aircraft may be collected. For example, the number of occurrences of a regime in question for an aircraft seeking a usage credit may be determined from the field or usage data  312 . 
     In some embodiments, a final distribution in the usage data  312  may be created or generated. The distribution may represent a random summation of N instances of a given regime. The N instances may be based on a collection and analysis of data, potentially as a function of time. 
     As shown in  FIG. 3 , in some embodiments the updated reliability calculation  310  may ignore any baseline assumptions made for the particular regime(s) that has/have been measured (e.g., climb in this example). The updated reliability calculation  310  may be based on, or apply, monte carlo techniques. The updated reliability calculation  310  may be based on, or apply, other structural reliability methods. Such other reliability methods may include one or more advanced reliability methods described in U.S. Pat. No. 8,200,442, entitled “Usage Monitor Reliability Factor Using An Advanced Fatigue Reliability Assessment Model”, issued on Jun. 12, 2012, the contents of which are incorporated herein by reference. 
     From the updated reliability calculation  310 , flow may proceed to a reliability versus (vs.) assigned life process or curve  314 . The reliability vs. assigned life process  314  may map a reliability level or value to one or more assigned lifetimes. Thus, based on the reliability calculation  310 , a lifetime may be assigned  302  using the process or curve  314 . An example of a graph  400  that may be used in connection with block  314  is described below in connection with  FIG. 4 . 
     In some embodiments, a continuous or repeated monitoring of the (regime of) usage may be performed, as reflected via the flow from the assignment of the lifetime  302  to the measurement of usage  312 . In some embodiments, the monitoring may occur for a specified time period (e.g., every ‘x’ days or every ‘y’ hours) or over one or more predetermined time intervals. In some embodiments, a determination of when to perform the monitoring may be based on a statistical analysis. The monitoring may be performed to update or modify the assigned lifetime  302 , in reference to an identified reliability level. In some embodiments, the reliability level may be adjusted or shifted in response to one or more events or conditions. 
     Turning now to  FIG. 4 , an exemplary reliability vs. assigned lifetime graph  400  is shown. As described above, the graph  400  may be used in connection with block  314  of  FIG. 3  to map a reliability value to one or more lifetime values associated with various usage monitoring scenarios. The values shown in connection with the graph  400  are illustrative. Different values may be used in some embodiments. 
     The graph  400  includes two exemplary curves. A first curve, CWC-based usage  402 , may correspond to a baseline curve that might not incorporate any field usage data. The curve  402  may be generated based on an application of the flowchart or process  200  of  FIG. 2 . A second curve, HUMS-based usage  404 , may correspond to a modification of the curve  402  when taking into consideration actual data (e.g., usage data) available for a particular unit. The curve  404  may be generated based on an application of the flowchart or process  300  of  FIG. 3 . 
     A review of the graph  400  indicates that for a given reliability value, the assigned lifetime will be increased using the curve  404  relative to the curve  402 . For example, using a reliability value of 0.9999932, the assigned lifetime using the curve  402  may be equal to 4872 hours, whereas the assigned lifetime using the curve  404  may be equal to 10655 hours. In other words, the lifetime will be enhanced or increased using the curve  404  relative to the curve  402 , while maintaining the same reliability. More generally, an availability of usage data  312  may be exploited to extend or adjust the lifetime of a subject unit. 
       FIG. 5  is a flow chart of an exemplary usage monitoring process  500  in accordance with one or more embodiments. The usage monitoring process  500  may be implemented using a system such as that shown in  FIG. 1 . The process  500  may include many of the blocks described above in connection with the process  300  of  FIG. 3 , and so a complete re-description of those blocks is omitted for the sake of brevity. The process  500  includes a usage monitoring and reliability factor (UMRF)  502 . The UMRF  502  may be used to weight the usage data  312 . For example, if the usage data  312  indicates that an aircraft engages in a climb operation 7.4% of the time, the UMRF  502  may adjust the value to, e.g., 15% of the time, for use in the updated reliability calculation  310 . Such an increase may be used to generate a more conservative assigned lifetime  302 . 
     As shown in  FIG. 5  via the flow from block  302  to block  502 , in some embodiments an iteration of the UMRF  502  may occur until a baseline reliability is obtained. Once the baseline reliability is obtained, the particular value of the UMRF  502  for that particular iteration may be used going forward or in future applications of the process  500 . The iteration may be performed in connection with a mapping or graph, such as the graph  400  of  FIG. 4 . 
     In some embodiments, in addition to, or as an alternative to, computing a lifetime for a unit, a probability of failure for the unit may be computed. Such a probability of failure computation may be based on a modification of one or more of the processes described herein. 
     Embodiments of this disclosure may be tied to one or more particular machines. For example, one or more computers, devices, or architectures may be configured to compute or adjust a lifetime based on a baseline calculation and data that may be obtained for a particular unit (e.g., a unit with a particular identification number or serial number) when the unit is implemented or used in the field. In some embodiments, a monitoring may take place to adjust the lifetime over time. 
     Some of the illustrative embodiments and examples described herein relate to lifetimes associated with an aircraft and components and devices thereof. Aspects of this disclosure may be applied in connection with other environments or contexts, such as marine applications, automotive applications, manufacturing activities, maintenance activities, etc. 
     As described herein, in some embodiments various functions or acts may take place at a given location and/or in connection with the operation of one or more apparatuses, systems, or devices. For example, in some embodiments, a portion of a given function or act may be performed at a first device or location, and the remainder of the function or act may be performed at one or more additional devices or locations. 
     Embodiments may be implemented using one or more technologies. In some embodiments, an apparatus or system may include one or more processors, and memory storing instructions that, when executed by the one or more processors, cause the apparatus or system to perform one or more methodological acts as described herein. Various mechanical components known to those of skill in the art may be used in some embodiments. 
     Embodiments may be implemented as one or more apparatuses, systems, and/or methods. In some embodiments, instructions may be stored on one or more computer-readable media, such as a transitory and/or non-transitory computer-readable medium. The instructions, when executed, may cause an entity (e.g., an apparatus or system) to perform one or more methodological acts as described herein. 
     Aspects of the disclosure have been described in terms of illustrative embodiments thereof. Numerous other embodiments, modifications and variations within the scope and spirit of the appended claims will occur to persons of ordinary skill in the art from a review of this disclosure. For example, one of ordinary skill in the art will appreciate that the steps described in conjunction with the illustrative figures may be performed in other than the recited order, and that one or more steps illustrated may be optional.