Patent Abstract:
A process for developing machine classification systems includes using human experts to associate expected operations with various machine states including drawbar pull, tool position, tool commands, gear, and ground speed, among others, to create a classification system that can be used in a particular machine. The classification system operates in real time to infer operations such as dig, dump, travel, and push from machine state inputs and logs the operations for use in operational analysis and maintenance of the machine.

Full Description:
TECHNICAL FIELD 
       [0001]    The present disclosure relates generally to developing a system that uses raw machine data to classify operations of a machine, such as a bulldozer, loader, excavator, etc. 
       BACKGROUND  
       [0002]    It is useful to know what operations a machine is performing for many reasons, including scheduling preventive maintenance, providing operator training, and suggesting supplemental equipment purchases, to name a few. However, short of asking an operator to specifically log every operation, which is impractical, machines used for construction, mining, logging, and others functions, do not report their activities, only the state of the machine. 
         [0003]    U.S. Publication 2102041910 (the &#39;910 publication) discloses a method of establishing a process decision support system that combines expert analysis and operational data to be determined if a given process is good or bad. The &#39;910 publication fails to teach developing an operation classifier that determines a current operation of a machine based on expected operations of the machine and associated machine states. 
       SUMMARY  
       [0004]    In an aspect of the disclosure, a method of developing a machine operation classifier includes i) identifying, via a user interface of a computer, an operation of a machine, ii) compiling, at the computer, a list of conditions that are associated with the operation of the machine, and repeating, at the computer, steps i and ii for one or more operations that the machine is expected to perform. The method may also include generating, via the computer, a classifier algorithm, wherein the classifier algorithm outputs the operation of the machine selected from the identified operations of the machine in response to identification of conditions in the associated list of conditions when the classifier algorithm is executed on a processor of the machine. The operation of the machine may includes the operation of one of a construction machine, a mining machine, or an earthmoving machine. 
         [0005]    In another aspect of the disclosure, a method of creating an operation classification algorithm for a machine may include developing a catalog of operations performed by the machine, cataloging events associated with each of the operations, wherein the events include tool events, direction events, gear events, and load/power events and for each event, document one or more machine conditions associated with the event. The method may include, for each machine condition, developing a calculation used to determine the one or more conditions from one of a current machine state or a combination of current and previous machine states. The method may continue by generating a classification algorithm that monitors the one or more machine conditions and outputs a current operation of the machine using the one or more conditions to identity events associated with the operation. 
         [0006]    In yet another aspect of the disclosure, a computer for creating a classification algorithm for a machine may include a processor, a user interface coupled to the processor, and a memory storing instructions for execution on the processor. When the instructions are executed on the processor, the computer may receive, via the user interface, information about the machine. The information may include a catalog of operations performed by the machine, one or more events associated with each of the operations, and information for determining when each of the one or more of events has occurred. Further instructions may be executed by the processor that cause the computer to generate the classification algorithm that determines when one or more events has occurred in the machine and matches the one or more events to an operation from the catalog of operations. 
         [0007]    These and other benefits will become apparent from the specification, the drawings and the claims. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0008]      FIG. 1  is an illustration of a first off-road machine; 
           [0009]      FIG. 2  is an illustration of a second off-road machine; 
           [0010]      FIG. 3  is an illustration of a third off-road machine; 
           [0011]      FIG. 4  is a block diagram of an exemplary controller for an off-road machine; 
           [0012]      FIG. 5  is a block diagram of an exemplary computer adapted to generate machine operation classifier algorithms; 
           [0013]      FIG. 6  illustrates an exemplary input chart for identification of events and operations; and 
           [0014]      FIG. 7  is a flow chart of an exemplary method of generating machine operation classifier algorithms for a particular machine. 
       
    
    
     DESCRIPTION  
       [0015]    A machine operation classifier observes characteristics of the operation of a machine and decides what operation is being performed. The classifier then logs the operations for later use in analyzing performance, operator training, maintenance scheduling, and more. However, the development of the operation classifier is complicated. Dozens of measurements are available from the machine, from direct measurements such as engine RPM and hydraulic cylinder pressures, to indirect measurements such as drawbar pull or tool position. 
         [0016]      FIGS. 1-3  illustrate different machines with exemplary operations that each perform and some of the characteristics that may be observed to determine an operation. 
         [0017]      FIG. 1  is an illustration of an off-road machine, specifically, an excavator  100 . The excavator  100  may have an engine  102 , tracks  104  or wheels for propulsion, and an implement  106  for use in performing a work function, in this case digging. The implement  106  may include a boom  108  and a boom cylinder  110  used to raise and lower the boom  108 . The implement  106  may also include a stick  112  that extends and retracts using a stick cylinder  114  and may further include a tool, such as a bucket  116 , the that rotates using a bucket cylinder  118 . In operation, the excavator  100  may use combinations of cylinder positions to engage the bucket  116  into a dig site to remove material and then to maneuver the bucket  116  to dump the material away from the dig site or into a dump truck or the like. 
         [0018]    At a high level, the basic operations of the excavator  100  may include ‘travel’ using the tracks  104 , ‘dig,’ and ‘dump.’ At a lower level, the excavator  100  may also perform functions including boom raise and lower, stick reach and pull, as well as bucket rotate in and bucket rotate out. Each of these operations may be accomplished by one or a combination of events, including tool events, direction events, gear events, and power events. The identification of events may include “raw channel” information, such as tool commands, ground speed, gear settings, engine speed, fuel burn rate, or tool position. This raw channel information may be supplemented by “derived channel” information such as developing power (e.g. drawbar pull) using, for example, a gear setting and engine speed. Derived channel information may also be used when certain raw channel information is not available such as tool position. For example, tool position may be derived by integrating raw tool commands over time and incorporating upper and lower limits for tool position. That is, a boom up command can be integrated over time to follow movement of the boom. However, because the boom up command may be held beyond the time the boom  108  reaches its maximum height, a saturation limit must be applied so that the calculated position does not track beyond the actual position. 
         [0019]    Information from the raw channel or derived channel data may be used to determine certain true/false events, such as whether the tool is active or inactive or whether a tool is engaged or disengaged. To determine if a particular operation is being carried out, several true/false events such as “stick extended=true” and “tool active =true” may be evaluated in combination. 
         [0020]    Each event may have its own condition and tolerances. For example, to determine whether a tool active event is true or false a current tool command may be compared against a predetermined active value in view of a tolerance for the tool command. 
         [0021]    Other events can be developed using information such as cylinder position for the various hydraulic cylinders, cylinder pressures can be used to determine whether a tool is loaded or unloaded, and groundspeed calculations using engine revolutions per minute (RPM) and corresponding transmission or torque converter settings may be used to determine power events. Other observable conditions may be associated with these true/false events such as change of engine RPM, change in hydraulic cylinder position, change in cylinder pressure, etc. 
         [0022]    Exemplary operations and events are illustrated in Table 1 and Table 2 below. 
         [0023]    For example, when the excavator  100  is performing a traveling operation the associated conditions may include: the boom  108  up, the stick  112  in, implement controls  107  neutral, and the transmission in a high gear, such as gear  3  or above. In another example, when the excavator  100  is performing a dig operation a more complex set of conditions may be evaluated to automatically determine the dig operation. Several true/false events may be defined for the operation. For example, “tool active =true,” “tool engaged =true,” and “high load/power =true” may be sufficient to infer that a dig operation is occurring. Negative events, such as “direction forward =false” may also be used to infer operations. 
         [0024]    In order to define an event as being true or false, a value for a condition may be defined, with a given tolerance for the condition. The tolerance may provide for some hysteresis so that the state changes are damped and so that when alternate conditions, such as tool engaged and tool disengaged, are both true, the machine operation classifier algorithm may be able to infer that the machine is in a transition state. For example, a tool active event may be identified when the boom  108  may be down more than 20% from its fully up position with a 5% tolerance, the stick  112  may be out more than 40% from its fully in position with a 5% tolerance, and the bucket  116  may be rotated more than 40% from its fully in position with a 10% tolerance. Once some defined set of initial conditions are met, subsequent changes in conditions may be evaluated to see if the boom  108  is adjusted up or down (i.e., tool active), the stick  112  is drawn in, or the bucket  116  is rotated in, that is, using a derivative of respective cylinder positions. Hydraulic pressures for the stick cylinder  114  and the bucket cylinder  118  may be monitored to watch for pressure increases associated with engaging a work surface  119 . 
         [0025]    Once instantaneous conditions for an operation are developed for a machine, such as the excavator  100 , a computer programmed for the special function of developing a classification algorithm may be operated to generate the classification algorithm for a particular machine that classifies operations of the machine by evaluating the instantaneous conditions or the time series of conditions associated with that operation. The computer program that generates the algorithm is discussed in more detail below. One goal of the process is to select the minimum set of conditions and/or events required to identify an operation. 
         [0026]      FIG. 2  illustrates a grader  120  having a motor  122 , a steering wheel  124 , blade control  126 , a blade  130 , a blade angle cylinder  132  and a height cylinder  134 . The grader  120  may include steerable wheels  136 . The grader  120  is configured to scrape and level a worksite  138  using the blade  130 . 
         [0027]    As with the excavator  100  above, the grader  120  may operate in several modes including a transport mode and a grading mode. The transport mode may be identified by conditions including groundspeed being above a certain threshold and the position of the height cylinder  134  being retracted beyond a threshold position. The grading mode may be identified by characteristics including blade position and drawbar pull, for example measured by strain gauges on the drawbar  140 . 
         [0028]      FIG. 3  illustrates a wheel loader  150  with a motor  152 , operator control  154 , a boom  156 , boom cylinder  158 , and bucket  160 . The bucket  160  may be rotated between a load position and dump position via a bucket arm  162  and a corresponding bucket cylinder  164 . 
         [0029]    Operations of the wheel loader  150  may include moving to a load point, loading, moving to a dump point, dumping and scraping. Each may be characterized by events associated with that operation. For example, loading may be characterized by lowering the boom  156  beyond a percentage of full height, such as  50 %, rotating the bucket  160  back within a percentage of fully up (or racked), such as  40 %, and being engaged in forward motion. When this action is followed by retracting the bucket  160  to fully up and raising the boom  156 , the load operation may be confirmed. 
         [0030]    Dumping may also have a sequence of events that characterize the operation, such as raising the boom  156  and fully dumping the bucket  160 , either at once or in stages. The ability to measure the bucket load improves the ability to identify operations in that a bucket full of material is indicative of having completed a load operation and an empty bucket is indicative of having completed a dump operation. Bucket load may be directly determined from a raw condition of the loader  150 , such as a mass sensor (not depicted) on the boom  156 . Alternatively, the bucket load determination may use a derived condition from other measurements such as boom cylinder pressure and bucket cylinder pressure. 
         [0031]    One scraping operation may involve fully lowering the bucket  160  and lowering the boom  156  so that the bottom edge of the bucket  160  is nearly vertical to the work surface and then moving backward to level the work surface. By recognizing these conditions, the scraping operation may be identified. 
         [0032]      FIG. 4  illustrates a controller  200  that may be used in a machine, such as excavator  100  or any of the other machines discussed above, to execute a classification algorithm that identifies a machine operation based on observed conditions in the machine  100 . Controller  200  may include a processor  202  coupled to a memory  204  via a data bus  206 . Also connected to the data bus  206  may be a number of sensor inputs, that may include but is not limited to, a torque or drawbar pull sensor  208 , a groundspeed sensor  210 , a track speed sensor  212 , a slope sensor  214 , or a gear sensor  216 . Also connected to the data bus  206  may be outputs such as a driver to provide information to an operator display  218  or an interface  220  to provide log data to an local device, such as a memory card, or via a network connection (not depicted) to an external device. 
         [0033]    The memory  204  may be any of a number of physical hardware memories including separately or in combination hard disk drive, a solid-state memory, flash memory, removable storage media, or the like, but does not include propagated media such as carrier waves. The memory  204  may include an operating system  222  and associated utilities  224  used, for example, for set up and diagnostics. The memory  204  may also include the classification algorithm  226  that is executed by the processor  202  to collect data from the various inputs and generate a log of operations. The classification algorithm  226  may include performance calculations  228  such as those discussed above to identify certain events based on characteristics of the machine  100 . The classification algorithm  226  may also include, among other routines, operating data and/or lookup tables  230  used to store available operations, events associated with each of the operations, and conditions associated with the various events. 
         [0034]      FIG. 5  illustrates a computer  250  that may include a processor  252  and a memory  254  coupled by a data bus  256 . The computer  250  may include a variety of user interface elements including, but not limited to, a display or touch screen  258 , a keyboard and/or mouse  260 , a microphone  262 , a camera  264 , and speakers  266 . The computer  250  may also include a network interface  268  used to communicate via a local or wide area network (not depicted). As above, the memory  254  may be any of a number of physical hardware memories including separately or in combination hard disk drive, a solid-state memory, flash memory, removable storage media, or the like, but does not include propagated media such as carrier waves. 
         [0035]    The memory  254  may include an operating system  272  and utilities  274 . The memory may also include an algorithm program  276  that receives input about operations of a machine as well as various events and associated conditions. The operation of the algorithm program  276  as discussed in more detail with respect to  FIG. 6 . Briefly, while the algorithm program  276  is executed by the processor  252  various inputs are received including a catalog of operations  278 , a catalog of events  280  and their associated conditions, and a corresponding classification algorithm  282  is output in stored for use in a particular machine, such as machine  100 . The algorithm program  276  may be used to generate multiple classification algorithms for various machines as illustrated by a second operations catalog  284 , a second event catalog  286 , and a second classification algorithm  288 . 
         [0036]      FIG. 6  illustrates an exemplary input chart  300  for identification of events and operations. The chart  300  illustrates how a user may interface with the algorithm program  276  to identify characteristics associated with various operations so that the algorithm can generate the code necessary to identify and log the operations of interest. The chart  300  shows exemplary data collected over time for a bulldozer displayed in vertically arranged set. Other data sets may be used depending on the piece of equipment and the exact operations being characterized. 
         [0037]      FIG. 6  shows four representative data series: ground speed, power (i.e., drawbar pull), blade raise (i.e., blade tool active), and blade angle (i.e., blade angle active). 
         [0038]    A user may create a drop down list of the possible operations/segments for the particular machine, in this case, Load, Carry, Spread, and Reverse. Other machines may have different lists of possible operations. Next, the user may select one of the operations in the drop down list to let the system know which operation logic will be created. The user may then specify whether the parameter that is being specified should be a minimum or a maximum. Finally, the user may move the cursor over one of the time series plots and sees a dynamic horizontal line, e.g., line  302  for ground speed, line  304  for power, line  306  for blade raise, and line  308  for blade position. Each line  302 ,  304 ,  306 ,  308  may be separately selected and adjusted by dragging with a cursor. When the user has decided on the threshold value for a given channel, the user may click an input button to accept the location. The system may then record, for example, “Carry if PWR_alg&gt;0.4” assuming minimum was previously selected. More interface options allow selection of “and” or “or” criteria to compose multiple logic conditions. 
         [0039]    The user may also add labels  310  and  312  via the user interface that allow correlation of operations to the data series for ease of identification. Sample pseudo-code output associated with the completed process are shown below. 
       INDUSTRIAL APPLICABILITY  
       [0040]    The ability to generate classification algorithms for various machines by capturing the operations the machine performs and events associated with each operation reduces the time and effort required to create classification algorithms and may also improve the quality of the classification algorithms by generating consistent code from a human readable set of inputs. 
         [0041]      FIG. 7  is a flow chart of an exemplary method  330  of generating machine operation classified algorithms for a particular machine  100 . At block  332  an operation performed by the machine  100  may be identified. A list of exemplary operations is depicted in Table  1  below. For example, dig, carry, and spread are typical of bulldozer operations where the blade digs material, carries the material to a point at the worksite, and spreads the material at the new location. 
         [0042]    At block  334 , events associated with the operation may be identified. The events may include tool events, direction events, gear events and load or power events. As discussed above, events may be true/false evaluations related to conditions in the machine  100 . At block  336 , for each event one or more conditions associated with that event may be identified. The condition may be defined as a function of raw data (see, e.g. Table  4  below) or may be a function of derived information, an example of which is shown in Table  3  below. Derived information may be calculated using one or more raw data elements. 
         [0043]    At block  338  for each condition a calculation is developed that evaluates conditions in the machine for current and/or past machine conditions, whether raw or derived, and generates an output corresponding to the inferred operation being performed. In one embodiment, the calculation may be an actual calculation or may be a programmatic device such as case statements known in some programming languages. 
         [0044]    For example, forward travel may be described in pseudo-code as 
         [0000]    
       
         
               
               
             
           
               
                   
                   
               
             
             
               
                   
                 blade_tool_active == 0 
               
               
                   
                 AND 
               
               
                   
                 PWR &lt; 0.2 OR steer_avg &gt; 0.2 
               
               
                   
                 AND 
               
               
                   
                 ground_speed_mps &gt; 0 
               
               
                   
                   
               
             
          
         
       
     
         [0045]    where PWR is drawbar pull, steer_avg is steering average displacement, and groundspeed is in meters per second. 
         [0046]    Similarly, the load operation of a bulldozer may be expressed in pseudo-code as: 
         [0000]    
       
         
               
               
             
           
               
                   
                   
               
             
             
               
                   
                 ALWAYS (the following must always be true for the operation) 
               
               
                   
                 gear &gt; 0 AND steer &lt; 0.3 AND 0 &lt; ground_speed_mps &lt; 1.5 
               
               
                   
                 BEGIN IF (the following triggers the operation to start): 
               
               
                   
                 PWR_deriv &gt; 0.02 OR eng_spd_deriv &lt; −25 
               
               
                   
                 AND 
               
               
                   
                 PWR &gt; 0.1 AND 
               
               
                   
                 blade_lower_flag &gt; 0 AND 
               
               
                   
                 blade_tool_active == 1 AND 
               
               
                   
                 gear &gt; 0 AND 
               
               
                   
                 steer_avg &lt; 0.3 
               
               
                   
                 END IF (the following triggers the operation to stop) 
               
               
                   
                 PWR_deriv &gt; 0.075 
               
               
                   
                   
               
             
          
         
       
     
         [0047]    where steer is the steering angle and PWR_deriv is the first derivative of drawbar pull. 
         [0048]    If additional operations are available to be included in the classification algorithm execution returned to block  332  and the process is repeated for the additional operation. If no more operations are to be included in the classification algorithm, execution may continue at block  340  where the classification algorithm  226  used for installation into the controller  200  of the machine  100  may be generated. The classification algorithm  226  may be stored in memory  254  of the computer  250  and transmitted to the machine  100  via the network interface  268  or may be transferred using a known removable memory, such as a flash drive. 
         [0049]    Tables 1-4 illustrate representative values. The actual values for a particular machine may be less than shown or may have values not specifically illustrated here. 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 1 
               
               
                   
               
               
                 Operations 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 Idle 
               
               
                   
                 Travel 
               
               
                   
                 Reverse 
               
               
                   
                 Dig/Load 
               
               
                   
                 Carry/Haul 
               
               
                   
                 Dump/Spread 
               
               
                   
                 Compact 
               
               
                   
                 Grade 
               
               
                   
                 Ditch 
               
               
                   
                   
               
             
          
         
       
     
         [0000]    
       
         
               
               
               
               
             
           
               
                 TABLE 2 
               
               
                   
               
               
                   
                 Direction 
                   
                   
               
               
                 Tool Events 
                 Events 
                 Gear Events 
                 Load/Power Events 
               
               
                   
               
             
             
               
                 Tool Active 
                 Forward 
                 High Gear 
                 Low Load/Power 
               
               
                 Tool Inactive 
                 Stopped 
                 Low Gear 
                 High Load/Power 
               
               
                 Tool Engaged 
                 Reverse 
                 Forward Gear 
                 High Fuel Burn 
               
               
                 Tool Disengaged 
                   
                 Reverse Gear 
                 Low Fuel Burn 
               
               
                 Specific Tool 
                   
                 Neutral 
               
               
                 Position 
               
               
                 Tool High Pressure 
               
               
                 Tool Low Pressure 
               
               
                   
               
               
                 Note: 
               
               
                 Events are True or False 
               
             
          
         
       
     
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 3 
               
               
                   
               
               
                 Derived 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 Integrated Tool Command Gives Tool/Cyl 
               
               
                   
                 Position 
               
               
                   
                 Derivative of Cylinder Position 
               
               
                   
                 Derivative of Motor Position 
               
               
                   
                 Frequency of Tool Command 
               
               
                   
                 Drawbar-Pull 
               
               
                   
                 Pull-Weight Ratio 
               
               
                   
                 Normalized Drawbar-Pull 
               
               
                   
                 Normalized Tool Command (−1 to 1) 
               
               
                   
                 Machine Power 
               
               
                   
                 Tool Force 
               
               
                   
                   
               
             
          
         
       
     
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 4 
               
               
                   
               
               
                 Raw 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 Cylinder Position 
               
               
                   
                 Motor Position 
               
               
                   
                 Tool Position 
               
               
                   
                 Tool Positve Command 
               
               
                   
                 Tool Negative Command 
               
               
                   
                 Tool Signed Command 
               
               
                   
                 Fuel Burn Rate 
               
               
                   
                 Tool Pressure 
               
               
                   
                 Signed Ground Speed 
               
               
                   
                 Ground Speed 
               
               
                   
                 Engine Speed 
               
               
                   
                 Transmission Input Speed 
               
               
                   
                 Gear 
               
               
                   
                 Transmission Gear Ratios 
               
               
                   
                 Fixed Drivetrain Ratios 
               
               
                   
                   
               
             
          
         
       
     
         [0050]    In accordance with the provisions of the patent statutes and jurisprudence, exemplary configurations described above are considered to represent a preferred embodiment of the invention. However, it should be noted that the invention can be practiced otherwise than as specifically illustrated and described without departing from its spirit or scope.

Technology Classification (CPC): 4