Patent Publication Number: US-10769866-B2

Title: Generating estimates of failure risk for a vehicular component

Description:
FIELD OF THE INVENTION 
     Embodiments of the invention generally relate to information technology, and, more particularly, to vehicle monitoring and maintenance. 
     BACKGROUND 
     In condition-based monitoring, sensor readings are commonly used to determine the wear of vehicular components. Therefore, such readings often serve as predictors of wear related component failures. However, with respect to non-wear related component failures, such predictors are lacking. 
     Accordingly, a need exists for techniques for generating estimates of non-wear related failure risk for a vehicular component based on input sets of time series. 
     SUMMARY 
     In one aspect of the present invention, techniques for generating estimates of non-wear related failure risk for a vehicular component are provided. A first example computer-implemented method can include splitting an input time series pertaining to a vehicular component across a fleet of multiple vehicles into multiple sub-time series, wherein each of the multiple sub-time series comprises multiple data points of the input time series that correspond to one of multiple measurements derived from the vehicular component. Additionally, the method includes determining a weight applied to each of the multiple sub-time series based on a pre-determined weight associated with the input time series, wherein said determining comprises associating the pre-determined weight associated with the input time series with each of the multiple sub-time series; as well as applying one of two classification labels to each of the multiple sub-time series based on a relationship of the data points of the given sub-time series and a selected threshold value, wherein the classification labels comprise (i) associated with a failure of the vehicular component and (ii) not associated with a failure of the vehicular component. Further, the method also includes applying one of the two classification labels to the input time series to correspond to the label applied to a majority of the multiple sub-time series; and calculating a performance measure for the input time series based on (i) the weight applied to each of the multiple sub-time series, and (ii) a determination as to whether the correct classification label has been applied to each of the multiple sub-time series. The method additionally includes determining an updated weight associated with the input time series based on (i) the pre-determined weight associated with the input time series, (ii) the weight applied to each of the multiple sub-time series, and/or (iii) the performance measure; and generating an estimate of failure risk for the vehicular component based on (i) the classification label applied to each input time series and (ii) the updated weight associated with the input time series. 
     In another aspect of the invention, a second example computer-implemented method can include the steps of the first example method above, but wherein determining a weight applied to each of the multiple sub-time series comprises evenly distributing the pre-determined weight associated with the input time series across the multiple sub-time series. 
     In yet another aspect of the invention, a third example computer-implemented method can include the steps of the first example method above, but wherein determining a weight applied to each of the multiple sub-time series comprises distributing the pre-determined weight associated with the input time series to each of the multiple sub-time series inversely proportional to the length of each of the multiple sub-time series. 
     Additionally, in another aspect of the invention, a fourth example computer-implemented method can include the steps of the first example method above, but wherein determining a weight applied to each of the multiple sub-time series based on a pre-determined weight associated with the input time series comprises distributing the pre-determined weight associated with the input time series exclusively to the temporally last of the multiple sub-time series. 
     Another aspect of the invention or elements thereof can be implemented in the form of an article of manufacture tangibly embodying computer readable instructions which, when implemented, cause a computer to carry out a plurality of method steps, as described herein. Furthermore, another aspect of the invention or elements thereof can be implemented in the form of an apparatus including a memory and at least one processor that is coupled to the memory and configured to perform noted method steps. Yet further, another aspect of the invention or elements thereof can be implemented in the form of means for carrying out the method steps described herein, or elements thereof; the means can include hardware module(s) or a combination of hardware and software modules, wherein the software modules are stored in a tangible computer-readable storage medium (or multiple such media). 
     These and other objects, features and advantages of the present invention will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram illustrating system architecture, according to an example embodiment of the invention; 
         FIG. 2  is a diagram illustrating time series splitting, according to an example embodiment of the present invention; 
         FIG. 3  is a diagram illustrating left leaf and right leaf specifications, according to an example embodiment of the present invention; 
         FIG. 4  is a flow diagram illustrating techniques according to an embodiment of the invention; and 
         FIG. 5  is a system diagram of an exemplary computer system on which at least one embodiment of the invention can be implemented. 
     
    
    
     DETAILED DESCRIPTION 
     As described herein, an aspect of the present invention includes generating estimates of failure risk (for example, non-wear related failure risk) for a vehicular component. At least one embodiment of the invention includes obtaining and/or receiving, as input, a set of original time series (TS), each of which represents a collection of sample data points, wherein each sample data point includes a combination of measurements derived from a given vehicular component. 
       FIG. 1  is a diagram illustrating system architecture, according to an example embodiment of the present invention. By way of illustration,  FIG. 1  depicts a risk failure estimation system  110 , which receives input from sensors  102 A,  102 B and  102 C resident on and/or connected to vehicles  101 A,  101 B and  101 C, respectively. By way merely of example, the input from sensors  102 A,  102 B and  102 C can be transmitted wirelessly to the system  110  and/or can be transmitted to the system  110  via a direct electrical connection (for instance, via creating an electrical connection or interface between a given sensor and the system  110  upon detaching the sensor from a given vehicle). 
     As illustrated in  FIG. 1 , the system  110  includes a weight determination engine  112 , a classification engine  114 , a performance measure calculation engine  116 , a failure risk estimation generator engine  118 , a graphical user interface  120  and a display  122 . As further detailed herein, engines  112 ,  114 ,  116  and  118  process multiple forms of data to generate a failure risk estimate (for example, a non-wear related failure risk estimate) for one or more given vehicular components based on the input provided by sensors  102 A,  102 B and  102 C. As detailed herein, generated data, classifications and calculations can be stored by the system  110  in database  104 . The generated failure risk estimate for the given vehicular components are transmitted to the graphical user interface  120  and the display  122  for presentation and/or potential manipulation by a user. 
     As further detailed herein, given a weight for each of the original time series in the input set, at least one embodiment of the invention includes determining the weight of a given sub-time series by one of multiple methods selected based on considerations of preventing failure and allowing an increased number false alarms. Such methods, as additionally described herein, include (i) associating the weight of an original time series with each of the sub-time series of that original time series, (ii) evenly distributing the weight of each original time series across the sub-time series of that time series, (iii) distributing the weight of each original time series across the sub-time series of that original time series via inverse proportionality, and (iv) distributing the weight of each original time series to the last sub-time series of that original time series. 
     As noted, at least one embodiment of the invention includes obtaining input that includes a combination and/or collection of measurements derived from a given vehicular component, wherein one of the measurements indicates whether the given vehicular component failed or not. Accordingly, for all obtained and/or collected measurements, at least one embodiment of the invention includes selecting a particular value, and splitting, in connection with each selected value, each original time series into multiple sub-time series. The splitting occurs at each point wherein the measurement of the original time series crosses the selected value, such as illustrated in  FIG. 2 , which depicts a graph  202  illustrating time series splitting. In the example graph  202  in  FIG. 2 , the y-axis represents values of a collected measurement, wherein 0.5 has been selected as the value for splitting purposes. 
     Accordingly, and as noted above, given a weight for each original time series, the weight of each corresponding sub-time series (such as the three sub-time series illustrated in the  FIG. 2  graph) is determined via one of multiple alternative methods. In accordance with one or more embodiments of the invention, the particular method can be selected by a given user (for example, based on user preference between preventing a failure and limiting the number of false failure predictions). 
     As detailed above, the noted methods can include (i) associating the weight of an original time series with each of the sub-time series of that original time series (and subsequently, for example, divide each new weight by the total of all new weights), (ii) evenly distributing the weight of each original time series across the sub-time series of that original time series, (iii) distributing the weight of each original time series across the sub-time series of that original time series via inverse proportionality (for example, such that the distribution is inversely proportional to the length of each sub-time series), and (iv) distributing the weight of each original time series to the last sub-time series of that original time series. 
     By way of example, a user might select method (iv) if the user has a higher preference for limiting failure, and a user might select method (i) if the user has a higher preference for limiting false failure predictions. 
     Referring again to  FIG. 2  and the aspect of time series splitting, at least one embodiment of the invention includes selecting values and splitting, in connection with each selected value, each original time series into multiple sub-time series. Additionally, for each selected value, a discrimination score (for example, a log rank statistics) can be calculated and the value with the highest discrimination score can be chosen, together with the sub-time series corresponding to the chosen value. Also, in at least one embodiment of the invention, a node is created with a left leaf that contains all sub-time series with measurements above the selected value, and a right leaf that contains the sub-time series with measurements below or equal to the selected value, such as depicted in  FIG. 3 . 
     Accordingly,  FIG. 3  is a diagram illustrating left leaf and right leaf specifications, according to an example embodiment of the present invention. By way of illustration,  FIG. 3  depicts a selected value  302 , a left leaf  304  (which includes sub-time series (STS)  1  and sub-time series (STS)  3 ) and a right leaf  306  (which includes sub-time series (STS)  2 ). 
     If the right leaf  306  or the left leaf  304  contain a sub-time series with measurements that indicate a failure and a sub-time series with no measurements that indicate a failure, the above-noted procedures (in connection with  FIG. 3 ) are repeated with all sub-time series of that leaf now representing the original time series. Additionally, when a new node is created, the splitting criteria (that is, the measurement and the selected value) are stored in a database. Further, a class label is also stored for each leaf, wherein the class label represents a failure if the majority of the sub-time series in that leaf contain measurements indicating a failure, or a non-failure otherwise. 
     If no additional nodes are created, at least one embodiment of the invention includes splitting all of the original time series into two non-overlapping sets: one larger set of original time series and one smaller set of original time series. Such an embodiment subsequently performs the above-detailed steps detailed in connection with  FIG. 3  only for the larger set of the original time series and enters a loop over all nodes, starting from the bottom. 
     In an example iteration, the selected node is deleted and the remaining nodes are used for determining for each of the original time series contained in the small set of original time series if the selected node contains a measurement that indicates a failure. In at least one embodiment of the invention, this determination is carried out via the following procedure. Starting at the top node, a given time series from the small set of original time series is split into sub-time series (such as depicted, for example, in  FIG. 2 ). Each such sub-time series is then assigned either to the left leaf or the right leaf (such as detailed in  FIG. 3 , for example). After proceeding through all nodes, each sub-time series is given the class label of its corresponding leaf. Further, a classification error score is calculated based on the number of sub-time series that are given the wrong class label, multiplied by the weight associated with each such sub-time series. 
     After proceeding through all nodes and classifying the smaller set of original time series, the one or more nodes with the lowest classification error are selected. Starting from the bottom, individual nodes are deleted until the total number of nodes equals the number of nodes selected with the lowest classification error. A classification error (e) for the complete set of original time series is then calculated in the same manner as for the small set of original time series (as detailed above). Additionally, a performance measure (p) for the complete set of original time series is calculated via p=log((1−e)/e). 
     The weight (w) of each original input time series is then set in accordance with the particular sub-time series weight determination method (such as described above) that was utilized. For instance, if method (i) (that is, evenly distributing the weight of each original time series across the sub-time series of that original time series) was used: 
     
       
         
           
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     If method (ii) (that is, evenly distributing the weight of each original time series across the sub-time series of that time series) or method (iii) (that is, distributing the weight of each original time series across the sub-time series of that original time series via inverse proportionality) was used: 
     
       
         
           
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     Further, if method (iv) (that is, distributing the weight of each original time series to the last sub-time series of that original time series) was used: 
     
       
         
           
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     In at least one embodiment of the invention, the weight determining steps are iterated to a pre-specified condition, and in each iteration, the classifier as well as the performance are stored in a database. 
     Given a new time series, an example embodiment of the invention includes generating estimates for non-wear related failure risks for a given vehicular component by applying relevant classifiers (in the same manner as described for the small set of original time series) to the new time series, wherein the classification results are weighted by the corresponding performance measures of the relevant classifiers. 
       FIG. 4  is a flow diagram illustrating techniques according to an embodiment of the present invention. Step  402  includes splitting an input time series pertaining to a vehicular component across a fleet of multiple vehicles into multiple sub-time series, wherein each of the multiple sub-time series comprises multiple data points of the input time series that correspond to one of multiple measurements derived from the vehicular component. Step  404  includes determining a weight applied to each of the multiple sub-time series based on a pre-determined weight associated with the input time series, wherein said determining comprises associating the pre-determined weight associated with the input time series with each of the multiple sub-time series. The pre-determined weight can include, for example, a uniform weight for all input time series pertaining to the vehicular component. 
     Step  406  includes applying one of two classification labels to each of the multiple sub-time series based on a relationship of the data points of the given sub-time series and a selected threshold value, wherein the classification labels comprise (i) associated with a failure of the vehicular component and (ii) not associated with a failure of the vehicular component. Step  408  includes applying one of the two classification labels to the input time series to correspond to the label applied to a majority of the multiple sub-time series. 
     Step  410  includes calculating a performance measure for the input time series based on (i) the weight applied to each of the multiple sub-time series, and (ii) a determination as to whether the correct classification label has been applied to each of the multiple sub-time series. This determination can be based, for example, on whether the corresponding vehicular component experience a failure. Step  412  includes determining an updated weight associated with the input time series based on (i) the pre-determined weight associated with the input time series, (ii) the weight applied to each of the multiple sub-time series, and/or (iii) the performance measure. Determining an updated weight associated with the input time series can include calculating w*exp(p), if any of the one or more sub-time series is classified incorrectly, and reusing the pre-determined weight otherwise, wherein (w) represents the pre-determined weight and (p) represents the performance measure. 
     Step  414  includes generating an estimate of failure risk for the vehicular component based on (i) the classification label applied to each input time series and (ii) the updated weight associated with the input time series. Generating the estimate of failure risk can include, for example, calculating an average estimate of failure risk value based on (1) the classification label applied to each input time series and (2) the updated weight associated with input time series. 
     The techniques depicted in  FIG. 4  can also include obtaining the input time series pertaining to the vehicular component across the fleet of multiple vehicles, wherein the input time series includes multiple data points, and wherein the multiple data points comprise a collection of multiple measurements derived from the vehicular component across the fleet of multiple vehicles. Additionally, the techniques depicted in  FIG. 4  can include repeating all of the steps noted in  FIG. 4  for a given number of additional input time series pertaining to the vehicular component. 
     Also, at least one embodiment of the invention can include the steps detailed in  FIG. 4 , but wherein determining a weight applied to each of the multiple sub-time series includes evenly distributing the pre-determined weight associated with the input time series across the multiple sub-time series. Further, in such an embodiment, determining an updated weight (w) associated with the input time series includes calculating: 
             w   =       ∑     all   ⁢           ⁢   sub   ⁢     -     ⁢   time   ⁢           ⁢   series       ⁢     {             assigned   ⁢           ⁢   weight   ⁢           ⁢   to   ⁢           ⁢   the   ⁢           ⁢   sub   ⁢     -     ⁢   time   ⁢           ⁢   series   *     exp   (   p   )       ,               if   ⁢           ⁢   the   ⁢           ⁢   given   ⁢           ⁢   sub   ⁢     -     ⁢   time   ⁢           ⁢   series   ⁢           ⁢   is   ⁢           ⁢   classified   ⁢           ⁢   wrongly                     
and
 
             w   =       ∑     all   ⁢           ⁢   sub   ⁢     -     ⁢   time   ⁢           ⁢   series       ⁢     {               assigned   ⁢           ⁢   weight   ⁢           ⁢   to   ⁢           ⁢   the   ⁢           ⁢   sub   ⁢     -     ⁢   time   ⁢           ⁢   series     ,             otherwise         ,               
wherein (p) represents the performance measure.
 
     Additionally, at least one embodiment of the invention can include the steps detailed in  FIG. 4 , but wherein determining a weight applied to each of the multiple sub-time series based on a pre-determined weight associated with the input time series includes distributing the pre-determined weight associated with the input time series to each of the multiple sub-time series inversely proportional to the length of each of the multiple sub-time series. Further, in such an embodiment, determining an updated weight (w) associated with the input time series includes calculating: 
             w   =       ∑     all   ⁢           ⁢   sub   ⁢     -     ⁢   time   ⁢           ⁢   series       ⁢     {                 assigned   ⁢           ⁢   weight   ⁢           ⁢   to   ⁢           ⁢   a   ⁢           ⁢   given                 sub   ⁢     -     ⁢   time   ⁢           ⁢   series   *     exp   ⁡     (   p   )         ,                           if   ⁢           ⁢   the   ⁢           ⁢   given   ⁢           ⁢   sub   ⁢     -     ⁢   time   ⁢           ⁢   series               is   ⁢           ⁢   classified   ⁢           ⁢   wrongly                           
and
 
             w   =       ∑     all   ⁢           ⁢   sub   ⁢     -     ⁢   time   ⁢           ⁢   series       ⁢     {               assigned   ⁢           ⁢   weights   ⁢           ⁢   to   ⁢           ⁢   the   ⁢           ⁢   given   ⁢           ⁢   sub   ⁢     -     ⁢   time   ⁢           ⁢   series     ,             otherwise         ,               
wherein (p) represents the performance measure.
 
     One or more embodiments of the invention, additionally, can include the steps detailed in  FIG. 4 , but wherein determining a weight applied to each of the multiple sub-time series based on a pre-determined weight associated with the input time series includes distributing the pre-determined weight associated with the input time series exclusively to the temporally last of the multiple sub-time series. Further, in such an embodiment, determining an updated weight associated with the input time series includes calculating w*exp(p), if the last of the multiple sub-time series is classified incorrectly, and reusing the pre-determined weight otherwise, wherein (w) represents the pre-determined weight and (p) represents the performance measure. 
     The techniques depicted in  FIG. 4  can also, as described herein, include providing a system, wherein the system includes distinct software modules, each of the distinct software modules being embodied on a tangible computer-readable recordable storage medium. All of the modules (or any subset thereof) can be on the same medium, or each can be on a different medium, for example. The modules can include any or all of the components shown in the figures and/or described herein. In an aspect of the invention, the modules can run, for example, on a hardware processor. The method steps can then be carried out using the distinct software modules of the system, as described above, executing on a hardware processor. Further, a computer program product can include a tangible computer-readable recordable storage medium with code adapted to be executed to carry out at least one method step described herein, including the provision of the system with the distinct software modules. 
     Additionally, the techniques depicted in  FIG. 4  can be implemented via a computer program product that can include computer useable program code that is stored in a computer readable storage medium in a data processing system, and wherein the computer useable program code was downloaded over a network from a remote data processing system. Also, in an aspect of the invention, the computer program product can include computer useable program code that is stored in a computer readable storage medium in a server data processing system, and wherein the computer useable program code is downloaded over a network to a remote data processing system for use in a computer readable storage medium with the remote system. 
     An aspect of the invention or elements thereof can be implemented in the form of an apparatus including a memory and at least one processor that is coupled to the memory and configured to perform exemplary method steps. 
     Additionally, an aspect of the present invention can make use of software running on a general purpose computer or workstation. With reference to  FIG. 5 , such an implementation might employ, for example, a processor  502 , a memory  504 , and an input/output interface formed, for example, by a display  506  and a keyboard  508 . The term “processor” as used herein is intended to include any processing device, such as, for example, one that includes a CPU (central processing unit) and/or other forms of processing circuitry. Further, the term “processor” may refer to more than one individual processor. The term “memory” is intended to include memory associated with a processor or CPU, such as, for example, RAM (random access memory), ROM (read only memory), a fixed memory device (for example, hard drive), a removable memory device (for example, diskette), a flash memory and the like. In addition, the phrase “input/output interface” as used herein, is intended to include, for example, a mechanism for inputting data to the processing unit (for example, mouse), and a mechanism for providing results associated with the processing unit (for example, printer). The processor  502 , memory  504 , and input/output interface such as display  506  and keyboard  508  can be interconnected, for example, via bus  510  as part of a data processing unit  512 . Suitable interconnections, for example via bus  510 , can also be provided to a network interface  514 , such as a network card, which can be provided to interface with a computer network, and to a media interface  516 , such as a diskette or CD-ROM drive, which can be provided to interface with media  518 . 
     Accordingly, computer software including instructions or code for performing the methodologies of the invention, as described herein, may be stored in associated memory devices (for example, ROM, fixed or removable memory) and, when ready to be utilized, loaded in part or in whole (for example, into RAM) and implemented by a CPU. Such software could include, but is not limited to, firmware, resident software, microcode, and the like. 
     A data processing system suitable for storing and/or executing program code will include at least one processor  502  coupled directly or indirectly to memory elements  504  through a system bus  510 . The memory elements can include local memory employed during actual implementation of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during implementation. 
     Input/output or I/O devices (including but not limited to keyboards  508 , displays  506 , pointing devices, and the like) can be coupled to the system either directly (such as via bus  510 ) or through intervening I/O controllers (omitted for clarity). 
     Network adapters such as network interface  514  may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modems and Ethernet cards are just a few of the currently available types of network adapters. 
     As used herein, including the claims, a “server” includes a physical data processing system (for example, system  512  as shown in  FIG. 5 ) running a server program. It will be understood that such a physical server may or may not include a display and keyboard. 
     As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method and/or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, as noted herein, aspects of the present invention may take the form of a computer program product that may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (for example, light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     It should be noted that any of the methods described herein can include an additional step of providing a system comprising distinct software modules embodied on a computer readable storage medium; the modules can include, for example, any or all of the components detailed herein. The method steps can then be carried out using the distinct software modules and/or sub-modules of the system, as described above, executing on a hardware processor  502 . Further, a computer program product can include a computer-readable storage medium with code adapted to be implemented to carry out at least one method step described herein, including the provision of the system with the distinct software modules. 
     In any case, it should be understood that the components illustrated herein may be implemented in various forms of hardware, software, or combinations thereof, for example, application specific integrated circuit(s) (ASICS), functional circuitry, an appropriately programmed general purpose digital computer with associated memory, and the like. Given the teachings of the invention provided herein, one of ordinary skill in the related art will be able to contemplate other implementations of the components of the invention. 
     The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of another feature, integer, step, operation, element, component, and/or group thereof. 
     The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. 
     At least one aspect of the present invention may provide a beneficial effect such as, for example, generating estimates of failure risk for a vehicular component based on a set of time series derived from a given vehicular component. 
     The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.