Patent Publication Number: US-2021179120-A1

Title: Vehicle data classifying method and vehicle data classifying device

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is based upon and claims the benefit of priority from Japanese Patent Application No. 2019-227062 filed on Dec. 17, 2019, the contents of which are incorporated herein by reference. 
     BACKGROUND OF THE INVENTION 
     Field of the Invention 
     The present invention relates to a vehicle data classifying method and a vehicle data classifying device that acquire time-series data and classify vehicle data. 
     Description of the Related Art 
     Japanese Patent No. 4928532 discloses a failure diagnosis device that is connected to a vehicle including a data collecting device and performs a diagnosis of a failure of the vehicle. The data collecting device on the vehicle side sequentially collects vehicle data (velocity, engine rotational velocity, and the like) detected by each sensor provided in each section of the vehicle, and stores the vehicle data in time series (time-series data). Furthermore, the failure diagnosis device acquires the time-series data from the data collecting devices of a plurality of vehicles and generates a reference value (normal value). The failure diagnosis device is connected to a vehicle that has experienced a failure, and performs the failure diagnosis by acquiring the vehicle data from the data collecting device and comparing this vehicle data to the reference value that was generated in advance. 
     When generating the reference value (normal value), the failure diagnosis device sequentially divides the time series data at prescribed time intervals (3 sec). For example, the failure diagnosis device divides the time-series data into vehicle data of a detection time period from T 1  to T 2 , vehicle data of a detection time period from T 2  to T 3 , . . . , and vehicle data of a detection time period from Tn−1 to Tn. For each individual piece of vehicle data resulting from this division, the failure diagnosis device determines the vehicle state (acceleration, deceleration, or the like) indicated by this piece of vehicle data. The failure diagnosis device then classifies each individual piece of vehicle data according to the determined vehicle state, and generates a reference value for each vehicle state based on the classified vehicle data. 
     SUMMARY OF THE INVENTION 
     When generating the reference values, the failure diagnosis device of Japanese Patent No. 4928532 sequentially divides the time-series data at prescribed time periods to create the plurality of pieces of vehicle data. This method has the following problem. As an example, there are cases where both vehicle data detected during acceleration and vehicle data detected during deceleration are included in one piece of vehicle data. The failure diagnosis device of Japanese Patent No. 4928532 classifies such a piece of vehicle data as one of acceleration or deceleration. If this piece of vehicle data is classified as acceleration vehicle data, for example, then vehicle data detected during deceleration is included in the vehicle data classified as acceleration data. In such a case, the accuracy of the acceleration reference value decreases. As a result, the accuracy of the failure diagnosis decreases. 
     The present invention has been devised taking into consideration the aforementioned problem, and has the object of providing a vehicle data classifying method and a vehicle data classifying device that can improve the accuracy of the vehicle data classification. 
     A first aspect of the present invention is: 
     a vehicle data classifying method for acquiring time-series data made up of one or more types of vehicle data detected every first time by one or more sensors of a vehicle and classifying the vehicle data, the vehicle data classifying method including: 
     a state determination step of repeatedly performing a process of selecting the vehicle data within a second time that is longer than the first time from the time-series data made up of a prescribed type of the vehicle data, while shifting a current selection range by a third time in a direction of passage of time relative to a previous selection range and causing a portion of the previous selection range and a portion of the current selection range to overlap, and determining the vehicle state indicated by the vehicle data included in each of the selection ranges; and a data classification step of selecting a detection time period of the vehicle data to be classified, selecting all of the selection ranges that include the vehicle data of the selected detection time period, identifying the vehicle state that is most numerous among one or more vehicle states indicated by the vehicle data included in the selected selection ranges, and classifying all of the types of vehicle data detected in the selected detection time period as data of the identified vehicle state. 
     A second aspect of the present invention is: 
     a vehicle data classifying device that acquires time-series data made up of one or more types of vehicle data detected every first time by one or more sensors of a vehicle and classifies the vehicle data, the vehicle data classifying device including: 
     a state determining section configured to repeatedly perform a process of selecting the vehicle data within a second time that is longer than the first time from the time-series data made up of a prescribed type of the vehicle data, while shifting a current selection range by a third time in a direction of passage of time relative to a previous selection range and causing a portion of the previous selection range and a portion of the current selection range to overlap, and determine the vehicle state indicated by the vehicle data included in each of the selection ranges; and a data classifying section configured to select a detection time period of the vehicle data to be classified, selects all of the selection ranges that include the vehicle data of the selected detection time period, identifies the vehicle state that is most numerous among one or more vehicle states indicated by the vehicle data included in the selected selection ranges, and classify all of the types of vehicle data detected in the selected detection time period as data of the identified vehicle state. 
     According to the present invention, it is possible to accurately classify vehicle data of each detection time period according to the vehicle state. 
     The above and other objects, features, and advantages of the present invention will become more apparent from the following description when taken in conjunction with the accompanying drawings, in which a preferred embodiment of the present invention is shown by way of illustrative example. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows a configuration of a vehicle and a failure diagnosis device (vehicle data classifying device) according to a first embodiment; 
         FIG. 2  shows an association between the velocity data and the vehicle state; 
         FIG. 3  shows a flow of a data collecting process performed by the vehicle; 
         FIG. 4  shows a flow of a data classifying process performed by the first embodiment; 
         FIG. 5  shows vehicle data transitions and selected velocity data selection ranges in the first embodiment; 
         FIG. 6  shows a flow of a state determining process using the velocity data and the engine rotation data; 
         FIG. 7  shows the configuration of a vehicle and a failure diagnosis device (vehicle data classifying device) according to a second embodiment; 
         FIG. 8  shows the flow of the data classifying process performed by the second embodiment; 
         FIG. 9  shows vehicle data transitions and selected velocity data selection ranges in the first embodiment; 
         FIG. 10  shows the flow of a state determining process using shift position data; and 
         FIG. 11  shows the flow of a state determining process using IG level data, engine rotation data, and accelerator position data. 
     
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     The following describes preferred embodiments of a vehicle data classifying method and a vehicle data classifying device according to the present invention, while referencing the accompanying drawings. 
     1. First Embodiment 
     [1.1. Configuration of the Failure Diagnosing System  10 ] 
     The failure diagnosis system  10  shown in  FIG. 1  includes a vehicle  20  and a failure diagnosis device  50 . The vehicle  20  is brought into a store that sells vehicles  20  when inspections or repairs are to be made. The failure diagnosis device  50  is provided in the sales store. In the first embodiment, the failure diagnosis device  50  functions as a vehicle data classifying device. 
     [1.2. Configuration of the Vehicle  20 ] 
     The vehicle  20  is a gasoline vehicle that includes an engine for driving. The vehicle  20  may be a hybrid vehicle that includes a motor for driving in addition to an engine. 
     The vehicle  20  includes a sensor group  22  and a data collecting ECU  24 . The sensor group  22  includes one or more sensors that are provided to each section of the vehicle  20  and detect various types of vehicle data. The sensor group  22  includes, for example, a velocity sensor  26  that detects the travel velocity of the vehicle  20  and an engine rotation sensor  28  that detects the rotational velocity of the engine. In addition to these, the sensor group  22  includes, for example, a sensor that detects the temperature of an engine coolant, a sensor that detects intake air pressure, a sensor that detects the throttle opening amount, and the like. Furthermore, the sensor group  22  may include a shift position sensor (including a gear position sensor, reverse switch, parking switch, and the like) that detects the shift position. The sensor group  22  may include a Hall sensor that detects the ignition level (ignition ON and ignition OFF) and an accelerator position sensor that detect an accelerator position. 
     The data collecting ECU  24  includes a communication interface  30 , a calculating section  32 , and a storage section  34 . The calculating section  32  includes a processor. The storage section  34  includes various storage devices (RAM, ROM, hard disk, and the like). 
     The calculating section  32  realizes various functions by having the processor execute a program stored in the storage section  34 . In the first embodiment, the calculating section  32  functions as a data processing section  36  and a failure judging section  38 . The functions of the data processing section  36  and the failure judging section  38  are described below in section [ 1 . 4 ]. 
     The storage section  34  stores the program and the like to be executed by the calculating section  32 . Furthermore, the storage section  34  stores detection data  40  and pre-failure data  42 . The detection data  40  is made up of one or more types of vehicle data detected by the sensor group  22 . The detection data  40  is data in which a detection time period Tn (n is a natural number) and vehicle data detected by each sensor in this detection time period Tn are associated with each other. The pre-failure data  42  is the detection data  40  from the failure occurrence timing to a prescribed time (e.g., 15 seconds) before the failure occurrence timing. The detection data  40  and the pre-failure data  42  are time-series data. 
     The sensor group  22 , the data collecting ECU  24 , and other ECUs described further below (not shown in the drawings) are connected via a communication bus  44 , and form a network  46  such as an F-CAN or B-CAN. The communication bus  44  includes a data link connector  48  (e.g., a USB connector) provided inside the vehicle cabin. The failure diagnosis device  50  can be connected to the network  46  via the data link connector  48 . 
     [1.3. Configuration of the Failure Diagnosis Device  50 ] 
     The failure diagnosis device  50  is formed by a computer (such as a personal computer, a tablet computer, a smartphone, or a specialized electronic device), for example. The failure diagnosis device  50  includes an input section  52 , a communication interface  54 , a display section  56 , a calculating section  58 , and a storage section  60 . The input section  52  includes a human-machine interface such as a touch panel, a keyboard, and a mouse. The display section  56  includes a display. The calculating section  58  includes a processor. The storage section  60  includes various storage devices (RAM, ROM, hard disk, and the like). 
     The calculating section  58  realizes various functions by having the processor execute programs stored in the storage section  60 . In the first embodiment, the calculating section  58  functions as a state determining section  62 , a data classifying section  64 , a reference value generating section  66 , and a diagnosing section  68 . The functions of the state determining section  62  and the data classifying section  64  are described further below in section [ 1 . 5 ]. The reference value generating section  66  reads classified data  70  from the storage section  60 , and performs a prescribed calculation to generate a normal value, i.e., a reference value  72 . The diagnosing section  68  reads the time-series data from the vehicle  20  that is the target of the failure diagnosis, determines the vehicle state indicated by the read time-series data, reads the reference value  72  corresponding to the determined vehicle state from the storage section  60 , and compares the time-series data to the reference value  72  to make the diagnosis. In this Specification, descriptions of a specific method for generating the reference value  72  and a specific method for diagnosing failure of the vehicle  20  are omitted. 
     The storage section  60  stores the programs and the like to be executed by the calculating section  58 . Furthermore, the storage section  60  stores the classified data  70  and the reference value  72 , as described above. The classified data  70  is a collection of a plurality of types of vehicle data acquired from each vehicle  20 . The plurality of types of vehicle data included in the classified data  70  are classified based on the vehicle state in the detection time period Tn of the vehicle data. As shown in  FIG. 2 , the five states of “engine stop”, “engine RUN”, “acceleration”, “deceleration”, and “constant velocity” are set as the vehicle states in the first embodiment.  FIG. 2  shows transitions of the velocity data V serving as the detection data  40 , and also shows the association (relationship) between the transition of the velocity data V, the velocity data V, and the vehicle state. The transition of the velocity data V is shown by a line in  FIG. 2 , but is actually a collection of pieces of point data (see  FIG. 5 ). The reference value  72  is generated for each vehicle state. 
     [1.4. Data Collecting Process Performed by the Vehicle  20 ] 
     The data collecting process performed by the vehicle  20  is described using  FIG. 3 . The process described below is performed every prescribed time (first time t 1 , e.g., 200 msec), from when the power supply of the vehicle  20  is turned ON to when the power supply of the vehicle  20  is turned OFF. 
     At step S 1 , the data processing section  36  acquires each type of vehicle data from each sensor included in the sensor group  22 . When step S 1  ends, the process moves to step S 2 . 
     At step S 2 , the data processing section  36  stores each type of vehicle data that has been acquired in the storage section  34 , as the detection data  40 . At this time, the data processing section  36  attaches the detection time period Tn to each type of vehicle data, and links pieces of vehicle data having the same detection time period Tn to each other. The amount of the detection data  40  is extremely large, and the capacity of the storage section  34  would be insufficient to store all of the pieces of vehicle data. Therefore, when storing new vehicle data in the storage section  34  as the detection data  40 , the data processing section  36  deletes the detection data  40  having the oldest detection time period Tn. When step S 2  ends, the process moves to step S 3 . 
     At step S 3 , the failure judging section  38  judges whether a failure has occurred. The failure judging section  38  detects a failure by receiving a failure code (DTC: Diagnostic Trouble Code) transmitted from another ECU (not shown in the drawings). The failure judging section  38  also detects a failure by receiving a failure trigger, such as engine stalling and startup problems. If a failure has occurred (step S 3 : YES), the process moves to step S 4 . On the other hand, if a failure has not occurred (step S 3 : NO), the current series of processes ends. 
     At step S 4 , the failure judging section  38  extracts the pre-failure data  42 , from the failure occurrence timing (DTC reception timing or the like) to a timing that is a prescribed time before this failure occurrence timing, from the detection data  40  and stores this pre-failure data  42  in the storage section  34 . When step S 4  ends, the current series of processes ends. 
     [1.5. Data Classifying Process Performed by the Failure Diagnosis Device  50 ] 
     The data classifying process performed by the failure diagnosis device  50  (vehicle data classifying device) is described using  FIGS. 4 to 6 . A salesperson connects a connector  76  of a data link cable  74 , which is connected to the failure diagnosis device  50 , to the data link connector  48  of the vehicle  20  that has been brought into the sales store. In this state, when the input section  52  of the failure diagnosis device  50  is manipulated, the pre-failure data  42  is forwarded from the storage section  34  of the vehicle  20  to the storage section  60  of the failure diagnosis device  50 . Furthermore, when the input section  52  is manipulated, the state determining section  62  reads the velocity data V included in the pre-failure data  42  from the storage section  60 , and the process shown in  FIG. 4  is performed. Steps S 11  to S 13  shown in  FIG. 4  are a state determination step. Step S 14  is a data classification step. The process shown in  FIG. 4  is started with n being equal to 1. 
     At step S 11 , the state determining section  62  selects the vehicle data in the detection time period Tn, which in this case is the velocity data V in a prescribed time (second time t 2 , e.g., 3 sec) starting from the detection time period for the velocity data V. The second time t 2  is a longer time than the first time t 1 . The process performed in step S 11  is described using  FIG. 5 . 
       FIG. 5  shows transitions of the velocity data V detected each first time t 1  by the velocity sensor  26  of the vehicle  20 , and the selection range Am of the velocity data V selected at step S 11 . The velocity data V includes the velocity data V detected in the detection time periods T 1  to T 12 . In the description using  FIG. 5 , for the sake of convenience, the numerical examples described above in which the first time t 1 =200 msec and the second time t 2 =3 sec are not used. 
     In the process of step S 11  performed first, the state determining section  62  selects velocity data V spanning the second time t 2 , which has a time width of the second time t 2  in the direction of the passage of time (a direction toward later detection time periods Tn) starting from the detection time period T 1 , which is the piece of velocity data V whose detection time period Tn is earliest. The time range of the second time t 2  is referred to as the selection range Am. The letter “m” appended to the end of the letter “A” indicates the number of times the velocity data V has been selected, i.e., the number of times the process of step S 11  has been performed. The range selected when the process of step S 11  is performed for the first time is A 1 . 
     The range selected when the process of step S 11  is performed for the second time is A 2 . The range selected when the process of step S 11  is performed for the m-th time is Am. The velocity data V of the detection time periods T 1  to T 5  is included in the initial selection range A 1 . When the selection of the velocity data V ends, the process moves to step S 12 . 
     At step S 12 , the state determining section  62  determines the vehicle state indicated by the velocity data V selected at step S 11 . This process is referred to as the state determining process. An example of the state determining process is described using  FIG. 6 . The specific numerical values used in the following description are merely examples, and the present invention is not limited to these values. 
     At step S 21 , the state determining section  62  calculates the average value of the velocity data V included in the selection range Am. For example, in the process of step S 21  performed for the first time, the state determining section  62  calculates the average value of each piece of velocity data V in the detection time periods T 1  to T 5  included in the selection range A 1 . The state determining section  62  then determines whether the average value (average velocity) is less than 3 km/h. If the average velocity is less than 3 km/h (step S 21 : YES), the process moves to step S 22 . On the other hand, if the average velocity is greater than or equal to 3 km/h (step S 21 : NO), the process moves to step S 25 . 
     At step S 22 , the state determining section  62  calculates the average value of the engine rotation data linked to the velocity data V included in the selection range Am. For example, in the process of step S 21  performed for the first time, the state determining section  62  calculates the average value of the engine rotation data of the detection time periods T 1  to T 5  included in the selection range A 1 . The state determining section  62  then determines whether the average value (engine rotation) is less than or equal to 200 rpm. If the engine rotation is less than or equal to 200 rpm (step S 22 : YES), the process moves to step S 23 . On the other hand, if the engine rotation is greater than 200 rpm (step S 22 : NO), the process moves to step S 24 . 
     At step S 23 , the state determining section  62  determines the vehicle state indicated by the velocity data V included in the selection range Am to be “engine stop”, from among the five vehicle states shown in  FIG. 2 . 
     At step S 24 , the state determining section  62  determines the vehicle state indicated by the velocity data V included in the selection range Am to be “engine RUN”, from among the five vehicle states shown in  FIG. 2 . 
     When moving from step S 21  to step S 25 , the state determining section  62  calculates an increase/decrease value of the velocity in the selection range Am. For example, the state determining section  62  calculates the velocity difference between a maximum value and a minimum value of the velocity data V included in the selection range Am. The state determining section  62  then determines whether the velocity increase/decrease value is greater than or equal to 10 km/h. If the velocity increase/decrease value is greater than or equal to 10 km/h (step S 25 : YES), the process moves to step S 26 . On the other hand, if the velocity increase/decrease value is less than 10 km/h (step S 25 : NO), the process moves to step S 29 . 
     At step S 26 , the state determining section  62  determines whether the velocity data V included in the selection range Am is a velocity increase or a velocity decrease. For example, the state determining section  62  compares the velocity data Va, whose detection time period Tn is earliest, to the velocity data Vb, whose detection time period Tn is latest, among the pieces of velocity data V included in the selection range Am. The state determining section  62  then determines the velocity data V to be a velocity increase if velocity data Va≤velocity data Vb, and determines the velocity data V to be a velocity decrease if velocity data Va&gt;velocity data Vb. If the velocity data V is a velocity increase (step S 26 : YES), the process moves to step S 27 . If the velocity data V is a velocity decrease (step S 26 : NO), the process moves to step S 28 . 
     At step S 27 , the state determining section  62  determines the vehicle state indicated by the velocity data V included in the selection range Am to be “acceleration”, among the five vehicle states shown in  FIG. 2 . 
     At step S 28 , the state determining section  62  determines the vehicle state indicated by the velocity data V included in the selection range Am to be “deceleration”, among the five vehicle states shown in  FIG. 2 . 
     When moving from step S 25  to step S 29 , the state determining section  62  determines the vehicle state indicated by the velocity data V included in the selection range Am to be “constant velocity”, among the five vehicle states shown in  FIG. 2 . 
     As described above, in the state determining process performed at step S 12  of  FIG. 4 , the state determining section  62  determines the vehicle state indicated by the velocity data V included in the selection range Am. In  FIG. 5 , the determination results of the vehicle state for these selection ranges Am are shown on the right side or left side of the selection ranges A 1  to A 8 . Here, the vehicle state indicated by the velocity data V included in the selection ranges A 1  to A 4  is “acceleration”, and the vehicle state indicated by the velocity data V included in the selection ranges A 5  to A 8  is “deceleration”. 
     At step S 13 , the state determining section  62  determines whether the selection of the velocity data V at step S 11  has ended. If the selection has ended (step S 13 : YES), the process moves to step S 14 . On the other hand, if the selection has not ended (step S 13 : NO), the process returns to step S 11 . 
     When returning from step S 13  to step S 11 , a value of 1 is added to n of the detection time period Tn and to m of the selection range Am. The state determining section  62  shifts the current selection range Am by a prescribed time (third time t 3 ) in the direction of the passage of time (direction toward later detection time periods Tn) from the previous selection range Am−1, and selects the velocity data V. The third time t 3  is a time that is greater than or equal to the first time t 1 . Here, the state determining section  62  causes a portion of the previous selection range Am−1 and a portion of the current selection range Am to overlap. For example, in a case where the previous selection range Am−1 is the selection range A 1  and the current selection range Am is the selection range A 2 , the state determining section  62  shifts the selection range A 2  by the third time t 3  in the direction of the passage of time from the selection range A 1 , and selects the velocity data V of the detection time periods T 2  to T 6 . The selections ranges A 1  and A 2  overlap in a range of the detection time periods T 2  to T 5 . 
     The process of step S 11  and the process of step S 12  are performed repeatedly until there are no more pieces of velocity data V to be selected. In the example of  FIG. 5 , after the selection ranges A 1  to A 8  have been sequentially selected, there is no more velocity data V following the detection time period T 12 , that is, there are no more pieces of velocity data V to be selected. At this timing, the state determining section  62  determines that the selection has ended. 
     At step S 14 , the data classifying section  64  classifies the pre-failure data  42  stored in the storage section  60  into one of the five vehicle states, based on the results of the state determining process of step S 12 . The process performed in step S 14  is described using  FIG. 5  again. 
     First, the data classifying section  64  selects the detection time period T 1 . The data classifying section  64  selects all of the selection ranges Am that include the velocity data V 1  of the detection time period T 1 , which is the selection range A 1  in the example shown in  FIG. 5 , and identifies the vehicle state indicated by the velocity data V included in this selection range A 1 , which is “acceleration” in this case. The data classifying section  64  then ultimately classifies the detected velocity data V 1  of the detection time period T 1  as data for “acceleration”. 
     Next, the data classifying section  64  selects the detection time period T 2 . The data classifying section  64  selects all of the selection ranges Am that include the velocity data V 2  of the detection time period T 2 , which are the selection ranges A 1  and A 2  in the example shown in  FIG. 5 . Furthermore, the data classifying section  64  identifies the vehicle state that is most numerous among the one or more vehicle states indicated by the pieces of velocity data V included in these selection ranges A 1  and A 2 , which is “acceleration” in this case. The data classifying section  64  then ultimately classifies the detected velocity data V 2  of the detection time period T 2  as data for “acceleration”. 
     The data classifying section  64  sequentially selects the detection time periods Tn and classifies the velocity data V of these detection time periods Tn, in the same manner as in the process for classifying the pieces of velocity data V 1  and V 2  of the detection time periods T 1  and T 2 . 
     The following describes a process in a case where the data classifying section  64  has selected the detection time period T 6 . The data classifying section  64  selects all of the selection ranges Am that include the velocity data V 6  of the detection time period T 6 , which are the selection ranges A 2  to A 6  in the example shown in  FIG. 5 . The vehicle state indicated by the velocity data V included in the selection ranges A 2  to A 4  is “acceleration”, and the vehicle state indicated by the velocity data V included in the selection ranges A 5  and A 6  is “deceleration”. The data classifying section  64  identifies the vehicle state that is most numerous among the one or more vehicle states indicated by the pieces of velocity data V included in these selection ranges A 2  to A 6 , which is “acceleration” in this case. The data classifying section  64  then ultimately classifies the detected velocity data V 6  of the detection time period T 6  as data for “acceleration”. 
     The following describes a process in a case where the data classifying section  64  has selected the detection time period T 7 . The data classifying section  64  selects all of the selection ranges Am that include the velocity data V 7  of the detection time period T 7 , which are the selection ranges A 3  to A 7  in the example shown in  FIG. 5 . The vehicle state indicated by the velocity data V included in the selection ranges A 3  and A 4  is “acceleration”, and the vehicle state indicated by the velocity data V included in the selection ranges A 5  to A 7  is “deceleration”. The data classifying section  64  identifies the vehicle state that is most numerous among the one or more vehicle states indicated by the pieces of velocity data V included in these selection ranges A 3  to A 7 , which is “deceleration” in this case. The data classifying section  64  then ultimately classifies the detected velocity data V 7  of the detection time period T 7  as data for “deceleration”. 
     As a result of the processes described above, the data classifying section  64  classifies the velocity data V of the detection time periods T 1  to T 6  as “acceleration”, and classifies the velocity data V of the detection time periods T 7  to T 12  as “deceleration”. The data classifying section  64  then stores the classified velocity data V in the storage section  60  as the classified data  70 . The data classifying section  64  sets a boundary between “acceleration” and “deceleration”, between the detection time period T 6  and the detection time period T 7 . 
     Furthermore, aside from the velocity data V, the data classifying section  64  classifies the vehicle data of the detection time periods T 1  to T 6  as “acceleration” and classifies the vehicle data of the detection time periods T 7  to T 12  as “deceleration”. 
     Due to the processes described above, all of the pre-failure data  42  is classified as “acceleration” or “deceleration”. Although a specific description is omitted, vehicle data is classified as “engine stop”, “engine RUN”, and “constant velocity” in the same manner. 
     2. Second Embodiment 
     In the first embodiment, as shown in  FIG. 5 , the velocity data V of the detection time periods T 5  to T 8  is included in five selection ranges Am, while the velocity data V of the detection time periods T 1  to T 4  and T 9  to T 12  is only included in four or fewer selection ranges Am. The state determination accuracy for the velocity data V of a detection time period Tn is higher when there are more selection ranges Am. In other words, the state determination accuracy for the velocity data V of the detection time periods T 5  to T 8  is relatively high, but the state determination accuracy for the velocity data V of the detection time periods T 1  to T 4  and T 9  to T 12  is relatively low. The second embodiment described below can make the state determination accuracy for the velocity data V of the detection time periods T 1  to T 4  and T 9  to T 12  be approximately the same as the state determination accuracy for the detection time periods T 5  to T 8 . 
     [2.1. Configuration of the Failure Diagnosing System  10 ] 
     As shown in  FIG. 7 , the failure diagnosis system  10  according to the second embodiment differs from the failure diagnosis system  10  of the first embodiment in that the calculating section  58  also functions as an initial period determining section  80  and a final period determining section  82 . 
     [2.2. Data Collecting Process Performed by the Vehicle  20 ] 
     The data collecting process performed by the vehicle  20  in the second embodiment is the same as the data collecting process performed by the vehicle  20  in the first embodiment. 
     [2.3. Data Classifying Process Performed by the Failure Diagnosis Device  50 ] 
     The data classifying process in the second embodiment is described using  FIGS. 8 and 9 . In the second embodiment, in the same manner as in the first embodiment, a sales person connects the failure diagnosis device  50  to the vehicle  20 , and when the input section  52  is manipulated, the process shown in  FIG. 8  is performed. Steps S 31  to S 34  shown in  FIG. 8  are an initial period determination step. Steps S 35  to S 37  are the state determination step. Steps S 38  to S 41  are a final period determination step. Step S 42  is the data classification step. The process shown in  FIG. 8  is started with n being equal to 1 (n=1). 
     At step S 31 , the initial period determining section  80  selects the vehicle data of the detection time period Tn, which in this case is the velocity data V that is within an initial period time t 1  that is shorter than the second time t 2  and includes the velocity data V 1  whose detection time period Tn is earliest among the pieces of velocity data V. In the example shown in  FIG. 9 , in step S 31  being performed for the first time, the initial period determining section  80  sets the initial period time t 1  to be two times the first time t 1  (t 1 ×2). In the same manner as in the first embodiment, the time range of the initial period time t 1  is referred to as the selection range Am. In  FIG. 9 , the selection range A 1  initially set by the initial period determining section  80  includes the pieces of velocity data V 1  and V 2  of the detection time periods T 1  and T 2 . When the selection of the velocity data V ends, the process moves to step S 32 . 
     At step S 32 , the initial period determining section  80  determines the vehicle state indicated by the velocity data V selected at step S 31 . Here, the initial period determining section  80  performs the state determining process shown in  FIG. 6 , instead of the state determining section  62 . When the state determining process ends, the process moves to step S 33 . 
     At step S 33 , the initial period determining section  80  adds an extension time te to the initial period time ti, to create a new initial period time ti. This means extending the selection range Am+1 (e.g., the selection range A 2 ) by the extension time te in the direction of the passage of time relative to the selection range Am (e.g., the selection range A 1 ). As an example, the extension time te is set to be the first time t 1 . When the extending of the selection range Am ends, the process moves to step S 34 . 
     At step S 34 , the initial period determining section  80  determines whether the new initial period time t 1  is greater than or equal to the second time t 2 . If the initial period time t 1  is greater than or equal to the second time t 2  (step S 34 : YES), the process moves to step S 35 . At this timing, the processing by the initial period determining section  80  ends and the processing by the state determining section  62  starts. On the other hand, if the initial period time t 1  is less than the second time t 2  (step S 34 : NO), the process returns to step S 31 . At this timing, the initial period determining section  80  performs the process of step S 31  on the new initial period time ti. In the example shown in  FIG. 9 , the velocity data V of the selection ranges A 1  to A 3  is selected before moving to step S 35 . 
     The processes of steps S 35  and S 36  are the same as the processes of steps S 11  and S 12  shown in  FIG. 4 . Therefore, descriptions of steps S 35  and S 36  are omitted. 
     At step S 37 , the state determining section  62  determines whether the velocity data V 12 , whose detection time period Tn is latest among the pieces of velocity data V, is included in the selection range Am. If the velocity data V 12  is included in the selection range Am (step S 37 : YES), the process moves to step S 38 . At this timing, the processing by the state determining section  62  ends and the processing by the final period determining section  82  starts. On the other hand, if the velocity data V 12  is not included in the selection range Am (step S 37 : NO), the process returns to step S 35 . In the example shown in  FIG. 9 , the velocity data V of the selection ranges A 4  to A 11  is selected before moving to step S 38 . 
     At step S 38 , the final period determining section  82  selects the velocity data V that is within a final period time tf that is shorter than the second time t 2  and includes the velocity data V 12  whose detection time period Tn is latest among the pieces of velocity data V. In the example shown in  FIG. 9 , in step S 38  being performed for the first time, the final period determining section  82  sets the final period time tf to be four times the first time t 1  (t 1 ×4). In the same manner as in the first embodiment, the time range of the final period time tf is referred to as the selection range Am. In  FIG. 9 , the selection range A 12  initially set by the final period determining section  82  includes the pieces of velocity data V 9  to V 12  of the detection time periods T 9  to T 12 . When the selection of the velocity data V ends, the process moves to step S 39 . 
     At step S 39 , the final period determining section  82  determines the vehicle state indicated by the velocity data V selected at step S 38 . Here, the final period determining section  82  performs the state determining process shown in  FIG. 6 , instead of the state determining section  62 . When the state determining process ends, the process moves to step S 40 . 
     At step S 40 , the final period determining section  82  subtracts a shortening time ts from the final period time tf, to create a new final period time tf. This means shortening the next selection range Am+1 (e.g., the selection range A 12 ) by the shortening time ts in the direction of the passage of time relative to the current selection range Am (e.g., the selection range A 11 ). As an example, the shortening time ts is set to be the first time t 1 . When the shortening of the selection range Am ends, the process moves to step S 41 . 
     At step S 41 , the final period determining section  82  determines whether the new final period time tf is greater than or equal to an end determination time tj. In the example shown in  FIG. 9 , the final period determining section  82  sets the end determination time tj to be two times the first time t 1  (t 1 ×2). If the final period time tf is less than or equal to the end determination time tj (step S 41 : YES), the process moves to step S 42 . On the other hand, if the final period time tf is greater than the end determination time tj (step S 41 : NO), the process returns to step S 38 . At this timing, the final period determining section  82  performs the process of step S 38  on the new final period time tf. 
     The process of step S 42  is the same as the process of step S 14  shown in  FIG. 4 . Here, the data classifying section  64  classifies the pre-failure data  42  stored in the storage section  60  as one of the five vehicle states, based on the results of the determining processes of steps S 32 , S 36 , and S 39 . 
     In order for the initial period determining section  80  and the final period determining section  82  to make the determinations concerning acceleration and deceleration, the velocity data V of at least two detection time periods Tn and Tn+1 is necessary. Therefore, in the examples described above, the shortest initial period time t 1  and end determination time tj are each set to be two times the first time t 1  (t 1 ×2). In a case of classification where the vehicle data of two detection time periods Tn and Tn+1 is not necessary, the shortest initial period time t 1  and end determination time tj may each be set to e the first time t 1 . 
     3. Modifications 
     In the embodiments described above, the vehicle  20  includes an engine. Instead, a vehicle  20  that does not include an engine may be used. For example, the vehicle  20  may be an electric vehicle (including a fuel cell vehicle) or the like that includes only a traction motor. In this case, the process of step S 22  shown in  FIG. 6  is replaced with a different process. 
     In the embodiments described above, the failure diagnosis device  50  temporarily stores the pre-failure data  42  in the storage section  60 , and classifies the vehicle data included in the pre-failure data  42 . Instead, the failure diagnosis device  50  may temporarily store the detection data  40  in the storage section  60 , and classify the vehicle data included in the pre-failure data  42 . In this case, the failure diagnosis device  50  may sequentially acquire the vehicle data of each vehicle  20 , via a wireless communication device. 
     The second time t 2  or the third time t 3  may be capable of being adjusted to be shorter or longer through a manipulation of the input section  52 . 
     In the embodiments described above, the various types of vehicle data are classified into the five vehicle states (acceleration, deceleration, and the like), based on the velocity data V and the engine rotation data included in the vehicle data. However, the present invention is not limited to these embodiments. The various types of vehicle data may be classified into other vehicle states, based on data other than the velocity data V and the engine rotation data. 
     As an example, the various types of vehicle data may be classified into vehicle states such as “parking”, “reverse”, “first velocity”, “second velocity”, etc. based on the shift position data included in the vehicle data. The shift position data is vehicle data indicating the detection result of the shift position sensor.  FIG. 10  shows the flow of the state determining process in this case. 
     At step S 51 , the state determining section  62 , the initial period determining section  80 , and the final period determining section  82  (referred to as the “state determining section  62  and the like”) determine the shift position based on the shift position data. The state determining section  62  and the like then classify the shift position data as any of “reverse” (step S 52 ), “parking” (step S 53 ), “first velocity” (step S 54 ), “second velocity” (step S 55 ), etc. 
     As another example, the various types of vehicle data may be classifies into vehicle states such as “IG OFF”, “IG ON”, “idling”, “low load”, and “high load”, based on ignition (IG) level data, the engine rotation data, and the accelerator position data included in the vehicle data. The IG level data is vehicle data indicating the detection result of the Hall sensor. The accelerator position data is vehicle data indicating the detection result of the accelerator position sensor.  FIG. 11  shows the flow of the state determining process in this case. 
     At steps S 60  to S 63 , the state determining section  62  and the like determine the IG level, whether there is engine rotation, and the manipulation amount of the acceleration pedal, based on the IG level data, the engine rotation data, and the accelerator position data. The state determining section  62  then classifies the IG level data, the engine rotation data, and the accelerator position data as any of “IG OFF” (step S 64 ), “IG ON” (step S 65 ), “idling” (step S 66 ), “low load” (step S 67 ), and “high load” (step S 68 ). 
     In the embodiments described above, the failure diagnosis device  50  classifies the pre-failure data  42  collected by the vehicle  20  into each of the prescribed vehicle states, and generates the reference value  72  of each type of vehicle data for each vehicle state. The reason for generating the reference value  72  using the pre-failure data  42  is that the vehicle data before failure is thought of as being vehicle data during normal operation. From the viewpoint of generating the reference value  72  based on normal vehicle data, the failure diagnosis device  50  may acquire the vehicle data collected at another timing from the vehicle  20 . 
     [4. Technical Concepts Obtainable from the Embodiments] 
     The following is a record of the technical concepts that can be understood from the embodiments and modifications described above. 
     A first aspect of the present invention is the vehicle data classifying method for acquiring time-series data (detection data  40 , pre-failure data  42 ) made up of one or more types of vehicle data detected every first time t 1  by one or more sensors (sensor group  22 ) of a vehicle  20  and classifying the vehicle data, the vehicle data classifying method including: 
     the state determination step (steps S 11  to S 13 , steps S 35  to S 37 ) of repeatedly performing a process of selecting the vehicle data within a second time t 2  that is longer than the first time t 1  from the time-series data made up of a prescribed type of the vehicle data (velocity data V), while shifting a current selection range Am by a third time t 3  in a direction of the passage of time relative to a previous selection range Am−1 and causing a portion of the previous selection range Am−1 and a portion of the current selection range Am to overlap, and determining the vehicle state indicated by the vehicle data included in each of the selection ranges Am; and 
     the data classification step (steps S 14 , S 42 ) of selecting a detection time period Tn of the vehicle data to be classified, selecting all of the selection ranges Am that include the vehicle data of the selected detection time period Tn, identifying the vehicle state that is most numerous among one or more vehicle states indicated by the vehicle data included in the selected selection ranges Am, and classifying all of the types of vehicle data detected in the selected detection time period Tn as data of the identified vehicle state. 
     According to the above configuration, the vehicle data of each detection time period Tn is included in the one or more types of vehicle data (velocity data V, engine rotation data, and the like), and all of the types of vehicle data detected in these detection time periods Tn is classified based on the vehicle state indicated by the one or more types of vehicle data. Therefore, according to the configuration described above, all of the types of vehicle data detected in each detection time period Tn can be accurately classified according to the vehicle state. 
     In the vehicle data classifying method according to the first aspect, the third time t 3  may be the same as the first time t 1 . 
     According to the above configuration, since the detection interval (first time t 1 ) for the vehicle data and the time interval (third time t 3 ) by which the selection range Am is shifted are the same, it is possible to clearly comprehend the boundary between vehicle states. 
     In the vehicle data classifying method according to the first aspect, the second time t 2  may be adjustable. 
     When generating the reference value  72  for failure determination using vehicle data (velocity data V) that fluctuates significantly, if the selection range Am (second time t 2 ) for the vehicle data is too long, there is a possibility that the fluctuation of the vehicle data (velocity data V) will become large in this selection range Am. When there is a large fluctuation in the vehicle data, the accuracy of the determination of the vehicle state indicated by the vehicle data becomes worse, and this causes the vehicle data classification accuracy to also become worse. According to the configuration described above, the selection range Am (second time t 2 ) can be shortened, and therefore it is possible to handle vehicle data (velocity data V) that has significant fluctuation. 
     The vehicle data classifying method according to the first aspect may further include the initial period determination step (steps S 31  to S 34 ) of repeatedly performing a process of selecting the vehicle data that is in an initial period time t 1  that is shorter than the second time t 2  and includes the vehicle data (velocity data V 1 ) whose detection time period Tn is earliest, from the time-series data (velocity data V) made up of the prescribed type of vehicle data, while extending the current selection Am range relative to the previous selection range Am−1 by a prescribed extension time to in the direction of the passage of time, and determining the vehicle state indicated by the vehicle data included in each selection range Am, wherein the initial period determination step may include, at a timing when the initial period time t 1  has reached the second time t 2 , ending the initial period determination step (step S 34 : YES) and starting the state determination step (steps S 35  to S 37 ). 
     The vehicle data classifying method according to the first aspect may further include the final period determination step (steps S 38  to S 41 ) of repeatedly performing a process of selecting the vehicle data that is in a final period time tf that is shorter than the second time t 2  and includes the vehicle data (velocity data V 12 ) whose detection time period Tn is latest, from the time-series data made up of the prescribed type of vehicle data (velocity data V), while shortening the current selection range Am relative to the previous selection range Am−1 by a prescribed shortening time is in the direction of the passage of time, and determining the vehicle state indicated by the vehicle data included in each selection range Am, wherein the state determination step (steps S 35  to S 37 ) may include, at a timing when the vehicle data (velocity data V 12 ) whose detection time period Tn is latest is included in the selection range Am, ending the state determination step (step S 37 : YES) and starting the final period determination step. 
     A second aspect of the present invention is the vehicle data classifying device (failure diagnosis device  50 ) that acquires time-series data (detection data  40 , pre-failure data  42 ) made up of one or more types of vehicle data detected every first time t 1  by one or more sensors (sensor group  22 ) of a vehicle  20  and classifies the vehicle data, the vehicle data classifying device including: 
     the state determining section  62  configured to repeatedly perform a process of selecting the vehicle data within a second time t 2  that is longer than the first time t 1  from the time-series data made up of a prescribed type of the vehicle data (velocity data V), while shifting a current selection range Am by a third time t 3  in a direction of the passage of time relative to a previous selection range Am−1 and causing a portion of the previous selection range Am−1 and a portion of the current selection range Am to overlap, and determine the vehicle state indicated by the vehicle data included in each of the selection ranges Am; and 
     the data classifying section  64  configured to select a detection time period Tn of the vehicle data to be classified, selects all of the selection ranges Am that include the vehicle data of the selected detection time period Tn, identifies the vehicle state that is most numerous among one or more vehicle states indicated by the vehicle data included in the selected selection ranges Am, and classify all of the types of vehicle data detected in the selected detection time period Tn as data of the identified vehicle state. 
     The vehicle data classifying device according to the second aspect may further includes the initial period determining section  80  configured to repeatedly perform a process of selecting the vehicle data that is in an initial period time t 1  that is shorter than the second time t 2  and includes the vehicle data (velocity data V 1 ) whose detection time period Tn is earliest, from the time-series data made up of the prescribed type of vehicle data (velocity data V), while extending the current selection range Am relative to the previous selection range Am−1 by a prescribed extension time to in the direction of the passage of time, and determine the vehicle state indicated by the vehicle data included in each selection range Am, wherein the initial period determining section  80  ends processing at a timing when the initial period time t 1  has reached the second time t 2 , and the state determining section  62  starts processing at a timing when the processing of the initial period determining section  80  ends. 
     The vehicle data classifying device according to the second aspect may further include the final period determining section  82  configured to repeatedly perform a process of selecting the vehicle data that is in a final period time tf that is shorter than the second time t 2  and includes the vehicle data (velocity data V 12 ) whose detection time period Tn is latest, from the time-series data made up of the prescribed type of vehicle data (velocity data V), while shortening the current selection range Am relative to the previous selection range Am−1 by a prescribed shortening time is in the direction of the passage of time, and determine the vehicle state indicated by the vehicle data included in each selection range Am; wherein the state determining section  62  ends processing at a timing when the vehicle data (velocity data V 12 ) whose detection time period Tn is latest is included in the selection range Am, and 
     the final period determining section  82  starts processing at a timing when the processing by the state determining section  62  ends. 
     According to the second aspect, the same effects as the first aspect are realized. 
     The vehicle data classifying method and vehicle data classifying device according to the present invention are not limited to the above-described embodiments, and it goes without saying that various configurations could be adopted without departing from the scope of the present invention. 
     For example, the present invention is effective not only for the failure data stored by a vehicle, but also for real-time time-series data frames acquired in response to a request of the failure diagnosis device. Furthermore, the failure diagnosis device can also be used as a logger (e.g., a memorator). The present invention is also effective for time-series data frames obtained by directly reading communication with an ECU of the vehicle from a bus, and not from a storage section.