JP2004268633A - Remote damage prediction system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To enhance damage prediction accuracy in a remote failure prediction system. <P>SOLUTION: According to the remote failure prediction system, a failure prediction server 100 is connected to a failure prediction object vehicle 110 through a network and predicts whether or not the failure is generated on the failure prediction object vehicle 110 in the near future using vehicle data before generation of the failure previously obtained from the failed vehicle 111. Specifically, the vehicle data are received from the failure prediction object vehicle 110 and the received vehicle data and the vehicle data of the failed vehicle are correlatively operated. If a constant correlative relationship exists in both, A similar failure to that of the failed vehicle is generated. Since the prediction result is reported to a user of the failure prediction object vehicle 110, attention can be drawn to the user before the failure. <P>COPYRIGHT: (C)2004,JPO&NCIPI

Description

[0001]
TECHNICAL FIELD OF THE INVENTION
The present invention relates to a remote failure prediction system for predicting a failure connected to a vehicle remotely and a failure prediction system for predicting a failure using inter-vehicle communication.
[0002]
[Prior art]
2. Description of the Related Art Conventionally, a failure diagnosis system that connects to a failed vehicle via a network and diagnoses the location of the failure has been proposed (for example, Patent Document 1). According to this conventional technique, by remotely connecting the failure diagnosis system and the vehicle, it is possible to grasp whether the vehicle has failed and the location of the failure without the customer having to go to the service factory. Met.
[0003]
However, it is even more convenient for the customer if it is possible to predict the occurrence of a failure before the failure, rather than grasping the location of the failure after the failure. Because if a failure can be predicted, the vehicle can be returned to the service factory before the failure and the problem parts can be replaced, thereby avoiding the worst case of sudden failure of the vehicle and getting stuck Because you can.
[0004]
Conventionally, as a remote system for predicting a failure, there is an invention described in Patent Document 2, for example. In this conventional technique, the output of a sensor mounted on each vehicle is learned, and if the learning value obtained by learning is not within a normal range, it is determined that there is an abnormality.
[0005]
[Patent Document 1]
JP 2002-228552 A
[Patent Document 2]
JP-A-2002-202003.
[0006]
[Problems to be solved by the invention]
However, in the above-described conventional technique, a learning value is acquired “for each vehicle”, and the acquired learning value is compared with a normal range. That is, the failure is diagnosed using only the vehicle data obtained from the own vehicle.
[0007]
Such a determination method does not require vehicle data of another vehicle, and therefore can be executed in the vehicle without having to perform remote diagnosis via a network. Therefore, if a network is formed as in the prior art, it cannot be said that it is advantageous in terms of cost. Use of the network requires added value commensurate with the cost.
[0008]
Therefore, in the present invention, by utilizing data of other vehicles (for example, vehicle data of the same type of vehicle) collected via a network, the advantage of network connection is utilized and the reliability of failure prediction is improved. The purpose is to:
[0009]
As described above, in the present invention, the advantages of networking are fully utilized, but in some cases, even simpler predictions may be sufficient.
[0010]
Therefore, still another invention provides a failure prediction system that predicts a failure by exchanging vehicle data using a plurality of vehicles of the same type using inter-vehicle communication.
[0011]
[Means for Solving the Problems]
In order to achieve the above object, a remote failure prediction system according to the present invention has the following configuration.
[0012]
A first invention of the present application is a remote failure prediction system for remotely predicting the occurrence of a failure in a failure prediction target vehicle and transmitting a failure prediction result to a customer, the failure prediction target being connected via a network. Receiving means for receiving vehicle data from the vehicle, and vehicle data from one or more other vehicles of the same type as the failure prediction target vehicle, and the received vehicle data from the failure prediction target vehicle; Storage means for storing vehicle data from the vehicle; reading means for reading the stored vehicle data from the failure prediction target vehicle and vehicle data from the other vehicle; and a vehicle from the failure prediction target vehicle Failure prediction means for comparing data with vehicle data from the other vehicle and predicting the occurrence of a failure based on the comparison result; and And a sending means for sending to the customer of the prediction target vehicle.
[0013]
Further, among the other vehicles, there is further provided extraction means for extracting vehicle data of another vehicle which is in the same or similar driving condition as the failure prediction target vehicle, and wherein the failure predicting device has the same or similar driving condition. , A remote failure prediction system for predicting the occurrence of a failure using vehicle data of another vehicle located in the vehicle.
[0014]
Here, the driving situation may include, for example, at least one of acceleration / deceleration type, road type, road curvature, road gradient, temperature, weather, day of the week, time, time zone, and traffic congestion. Good.
[0015]
Further, of the other vehicles, the vehicle further includes extraction means for extracting vehicle data of vehicles having the same or similar model or mileage as the failure prediction target vehicle, and the failure prediction means includes a vehicle having the same or similar year. A remote failure prediction system is provided that predicts the occurrence of a failure by using vehicle data of another vehicle that is located within a mileage or a formula.
[0016]
Further, in any one of the remote failure prediction systems described above, based on information on a traveling position included in the vehicle data, the vehicle data of another vehicle traveling in a place close to the failure prediction target vehicle is obtained. A remote failure prediction system for predicting the occurrence of a failure using the system is provided.
[0017]
Further, if the other vehicle is further traveling in the same lane as the failure prediction target vehicle or traveling in the same direction, the prediction accuracy will be further improved.
[0018]
Further, based on the information on the traveling position included in the vehicle data, the other vehicle that has traveled in the past in the same or close to the location of the failure prediction target vehicle is specified, and the other vehicle and the failure prediction target vehicle By predicting the occurrence of a failure using vehicle data acquired when traveling in the same or a nearby place, other vehicle data to be compared increases, and the prediction accuracy will be further improved. As described above, since it is considered that there are many vehicles of the same type that have traveled in the place in the past, the failure prediction is performed even if there is no vehicle currently traveling in the same or close place as the failure prediction target vehicle. I can do it.
[0019]
The above object is also achieved by a remote failure prediction method corresponding to the remote failure prediction system. The above object is also achieved by a program for realizing each means or each step included in the remote failure prediction system by a computer, and a computer-readable storage medium storing the program code.
[0020]
According to a second aspect of the present invention, there is provided a vehicle whose failure is predicted by any of the remote failure prediction systems described above.
[0021]
That is, the vehicle according to the present invention includes a means for detecting information relating to the operation state of the vehicle, a means for storing the information relating to the detected operation state of the vehicle as vehicle data, and a method for storing the stored vehicle data in the remote failure mode. It has means for transmitting to a prediction system, means for receiving a failure prediction result from the remote failure prediction system, and means for outputting the received failure prediction result.
[0022]
According to the third aspect of the present invention, there is provided a failure prediction system for predicting the occurrence of a failure in a vehicle storing vehicle data relating to an operation state.
[0023]
That is, the failure prediction system according to the present invention is an inter-vehicle communication unit for communicating with another vehicle, and another vehicle communicable by the inter-vehicle communication unit, and is a target of the failure occurrence prediction. Search means for searching for another vehicle of the same type as the failure prediction target vehicle, vehicle data received from one or more other vehicles found by the search means, and storage for storing vehicle data of the failure prediction target vehicle Means, reading means for reading the stored vehicle data received from one or more other vehicles, and vehicle data of the failure prediction target vehicle, and receiving from the read one or more other vehicles Failure prediction means for comparing vehicle data with vehicle data of the failure prediction target vehicle and predicting occurrence of a failure based on the comparison result; and output means for outputting a prediction result by the failure prediction means Characterized in that it has a.
[0024]
Further, in the present application, the vehicle further includes extraction means for extracting vehicle data of another vehicle in the same or similar driving situation as the failure prediction target vehicle among the other vehicles, wherein the failure prediction means is the same or similar. A failure prediction system is also provided that predicts the occurrence of a failure using vehicle data of another vehicle having the same traveling state.
[0025]
Further, the traveling situation may include, for example, at least one of acceleration / deceleration type, road type, road curvature, road gradient, temperature, weather, day of the week, time, time zone, and traffic congestion.
[0026]
Further, according to the present application, among the other vehicles, further includes extraction means for extracting vehicle data of a vehicle having the same or similar model or mileage as the failure prediction target vehicle, the failure prediction means, There is also provided a failure prediction system that predicts the occurrence of a failure using vehicle data of another vehicle having the same or similar age or mileage.
[0027]
Further, according to the present application, the occurrence of a failure is predicted using vehicle data of another vehicle traveling in a place close to the failure prediction target vehicle, based on the information on the traveling position included in the vehicle data. A failure prediction system is also provided.
[0028]
Further, if the other vehicle is further traveling in the same lane as the failure prediction target vehicle or traveling in the same direction, the prediction accuracy will be further improved.
[0029]
The above object is also achieved by a failure prediction method corresponding to the failure prediction system. The above object is also achieved by a program for realizing each means or each step included in the failure prediction system by a computer, and a computer-readable storage medium storing the program code.
[0030]
According to a fourth aspect of the present invention, there is provided a vehicle equipped with any one of the above-described failure prediction systems.
[0031]
【The invention's effect】
According to the present invention, a remote failure prediction system, a remote failure prediction method, a remote failure prediction program, and the like that can improve the reliability of failure prediction as compared with the related art are realized.
[0032]
That is, according to the first or sixteenth aspect of the present invention, taking advantage of the network connection, a failure is predicted based on the vehicle data of the failure prediction target vehicle and the vehicle data of another vehicle of the same type as the failure prediction target vehicle. By predicting the occurrence of a failure in the prediction target vehicle, the reliability of the failure prediction can be improved. That is, if the vehicle data of the same type of vehicle that is supposed to show the same tendency does not show the statistically similar tendency, it is considered that some abnormality has occurred in the failure prediction target vehicle. This anomaly may depend on the failure itself or a sign of failure. Therefore, a failure can be predicted by detecting this anomaly.
[0033]
According to the second, third, tenth, or eleventh aspect, vehicle data is compared between vehicles having the same or similar running conditions, so that the reliability of failure prediction can be further improved. If the other vehicle to be compared and the vehicle to be subjected to failure prediction are of the same type, both vehicle data should naturally be the same or similar. However, if one is vehicle data when traveling on a straight road and the other is vehicle data when turning a curve, it is expected that the data will be different even though the vehicle is the same type. You. In such a case, there is a possibility that it is determined that there is a failure in one of the data because the two data do not match. Therefore, the failure prediction is further improved by predicting the failure in consideration of the traveling conditions of the other vehicle to be compared and the vehicle to be predicted.
[0034]
According to the invention described in claim 4 or 12, since the failure is predicted using the vehicle data of the vehicle having the same or similar model year or mileage as the failure prediction target vehicle, the reliability of the failure prediction is improved. be able to. That is, if both vehicles are of the same or similar age or mileage, the progress of aging is considered to be almost the same, so if both vehicle data do not match, a failure will occur in one of the vehicles You may have.
[0035]
According to the fifth or thirteenth aspect of the present invention, if both vehicles are traveling in a place close to each other, the traveling conditions are more similar, so that a minor abnormality can be detected, and the failure prediction is performed. Accuracy can be improved.
[0036]
According to the invention of claim 6 or 14, by using vehicle data between vehicles traveling in the same traveling lane or traveling in the same direction, the traveling conditions are more approximated, Even a small abnormality can be detected, and the accuracy of failure prediction can be improved. For example, assuming that a plurality of vehicles are traveling close to each other, if the vehicle is at an intersection, the traveling directions are different, and sometimes the traveling conditions may be significantly different. Therefore, by imposing such additional conditions, the accuracy of failure prediction can be improved.
[0037]
According to the invention as set forth in claim 7, the occurrence of a failure is predicted using vehicle data of another vehicle that has traveled in the past at the same or a similar location as the failure prediction target vehicle, so that another Vehicle data will increase and the accuracy of predictions will improve.
[0038]
According to the invention described in claim 8 or claim 15, by linking with the remote failure prediction system described above, a failure prediction result can be reported to a customer such as a driver.
[0039]
According to the ninth, seventeenth, or eighteenth aspect, vehicle data can be exchanged with the same type of vehicle by inter-vehicle communication, so that the occurrence of a failure can be easily predicted without requiring a large-scale network. It becomes possible.
[0040]
BEST MODE FOR CARRYING OUT THE INVENTION
Hereinafter, an embodiment of a remote failure prediction system according to the present invention will be described in detail with reference to the drawings. In the following, there may be embodiments that seem at first glance not to be described in the claims, but these are not intentionally excluded, and the invention described in the claims is not described. It is to be understood that they are not set forth in the appended claims because they are equivalent.
[0041]
[First Embodiment]
FIG. 1 is a diagram illustrating a configuration example of a system according to the present embodiment. The information center 100 is a computer group that accumulates various types of information transmitted from the customer's vehicles 110 and 111 and the service factory 120 and provides a failure prediction result. The information center 100 is connected to customer's vehicles 110 and 111 via the Internet 130, a mobile communication line, or the like. The customers' vehicles 110 and 111 acquire data necessary for failure prediction and transmit the data to the information center 100.
[0042]
Here, the vehicle 110 is a vehicle to be subjected to failure prediction. The same-type vehicle 111 is a vehicle of the same type as the vehicle 110, and is a comparison target when a failure is predicted. In FIG. 1, only one homogenous vehicle 111 is shown. However, in the case of performing statistical processing or improving the accuracy of failure prediction, it is desirable that the number of vehicles be plural or large.
[0043]
The information center 100 includes a failure prediction server 101 for executing a failure prediction process, a customer information database 102 storing information of customers (all customers or contractors of a failure prediction service), and diagnostic conditions for failure prediction diagnosis. A diagnostic condition database 103 to be stored, a vehicle information database 104 to store vehicle data transmitted from the customers' vehicles 110 and 111, a map database 106 to store map data, and various programs for diagnosing or predicting failures And a diagnostic program database 107 storing the diagnostic program database. The failure prediction server 101 is a kind of computer, and includes a CPU, a memory, a hard disk drive, a communication interface, and the like. In the following description, various processes are executed by the CPU executing a program corresponding to the flowchart.
[0044]
For example, according to the failure prediction server 101 described above, vehicle data from the failure prediction target vehicle (vehicle 110) connected via a network, and at least one other vehicle (vehicle 111) of the same type as the failure prediction target vehicle Receiving means (communication interface) for receiving vehicle data from the vehicle, and the received vehicle data from the failure prediction target vehicle and vehicle data from one or more other vehicles having the same type as the failure prediction target vehicle. Storage means for storing (hard disk drive or memory); reading means (CPU, failure prediction program) for reading out the stored vehicle data from the failure prediction target vehicle and the vehicle data from the one or more other vehicles And comparing vehicle data from the failure prediction target vehicle with vehicle data from the other vehicle, and determining whether a failure has occurred based on the comparison result. A remote failure prediction system comprising a failure prediction unit (CPU, failure prediction program) for predicting, and a transmission unit (communication interface) for transmitting a failure prediction result by the failure prediction unit to a customer of the failure prediction target vehicle can do.
[0045]
FIG. 2 is a block diagram illustrating a configuration example of the vehicle according to the present embodiment. The remote vehicle client device 200 is a main controller including a CPU, a memory, and the like, acquires control data and sensor outputs from various control units and sensors, accumulates the acquired data in a memory, and stores the accumulated information in a mobile terminal (mobile communication interface). ) 250 or the short-range wireless communication interface 255 to transmit to the information center 100 or receive a failure prediction result from the information center 100. The short-range wireless communication interface 225 can employ a wireless standard for wireless LAN, Bluetooth, and ETC.
[0046]
The body system 210 includes a power window unit 211, a headlight unit 212, an audio unit 213, an air conditioner unit 214, a wiper unit 215, a door lock unit 216, and the like, and transmits control results and data such as current and voltage to a vehicle client device. Output to 200.
[0047]
The control system system 220 includes an ABS (antilock brake system) 221 for controlling the brake not to lock, a DSC (dynamic stability controller) 222 for controlling the behavior of the vehicle, and an EGI for controlling the fuel supply. (Electric Gas Injection) 223, EAT (Electric Automatic Transmission) 224 for controlling the automatic transmission, ICCW (Intelligent Cruise Control & Warning) 225 for assisting driver's driving such as automatic driving, etc. The control result is notified to the client device 220 for use.
[0048]
Various sensors are mounted on the vehicle. For example, a GPS sensor 231 that receives a radio wave from a GPS (Global Positioning System) satellite to calculate the current position of the vehicle, a VICS information receiver 232 that receives traffic congestion information and the like, and a vehicle speed sensor 233 that detects the speed of the vehicle An inter-vehicle distance sensor 234 for measuring a distance from another vehicle such as a preceding vehicle, a yaw rate sensor 235 for detecting a yaw rate of the vehicle, an acceleration sensor for detecting acceleration of the vehicle, a steering angle sensor 237 for detecting a steering angle of the vehicle, A throttle opening sensor 238 for detecting the opening of the throttle, a brake pressure sensor 239 for detecting the pressure applied to the brake, a blinker SW sensor 240 for detecting the operating state of the blinker switch, an outside air temperature sensor 241 for measuring the outside air temperature, and Rain sensor 24 that measures whether or not it is raining and the amount of rain Such as is mounted on the vehicle.
[0049]
The navigation controller 260 is a device that searches map information stored in a hard disk drive or the like based on positioning data from the GPS sensor 231 and displays a map of the current position. The input / output device 270 for the driver serving as a customer includes a display device such as a liquid crystal display and a voice main unit, and outputs map data from the navigation controller 260 and outputs a failure prediction result from the information center 100. Or
[0050]
As described above, in the present embodiment, a vehicle (for example, a GPS sensor 231 or the like) for detecting information on an operation state of a vehicle, which is a vehicle whose failure is predicted by the above-described remote failure prediction system, Means for storing information on the operation state of the vehicle as vehicle data (eg, a memory, etc.), and means for transmitting the stored vehicle data to the remote failure prediction system (eg, a mobile communication interface 250 or a short-range wireless communication interface) 255), means for receiving a failure prediction result from the remote failure prediction system (eg, the mobile communication interface 250 or the short-range wireless communication interface 255), and means for outputting the received failure prediction result (eg, The input / output device 270) is provided.
[0051]
FIG. 3 is a flowchart relating to collection of vehicle data according to the present embodiment. In step S300, the CPU mounted on the vehicle client device 200 samples outputs from various sensors and units at appropriate timings and stores them in a memory.
[0052]
In step S302, the CPU mounted on the vehicular client device 200 performs predetermined timing (for example, periodically, at the time of engine start, after a predetermined time has elapsed since the engine start, and when an explicit instruction is input by the driver, etc.). ), The vehicle data stored in the memory is read, information for specifying the vehicle or the driver is added, and the information is transmitted to the information center 100 via the mobile communication interface 250 or the short-range wireless communication interface 255.
[0053]
In step S310, the failure prediction server 101 of the information center 100 receives the vehicle data transmitted from the vehicle via a mobile communication interface or the like. In step S312, the failure prediction server 101 stores the received vehicle data in the vehicle information database 104 in association with the information specifying the corresponding vehicle or driver. By the above procedure, the vehicle data of each vehicle is stored in the vehicle database 104.
[0054]
FIG. 4 is a flowchart of the remote failure prediction process according to the present embodiment. In step S400, a failure prediction is triggered on the vehicle side. For example, the vehicle client device 200 receives a request for a failure prediction by operating the input / output device 270 by a passenger of the vehicle. In step S402, the vehicle client device 200 reads vehicle data for a predetermined period stored in the memory, and transmits the vehicle data to the failure prediction server 101.
[0055]
In step S404, the failure prediction server 101 receives the vehicle data via the Internet 130. In step S406, the failure prediction server 101 performs a failure occurrence prediction process using the received vehicle data. In step S408, the failure prediction server 101 transmits a prediction result to the vehicle that has requested the failure prediction.
[0056]
In step S410, the vehicle client device 200 receives the failure prediction result. In step S412, the vehicle client device 200 causes the input / output device 270 to output the received failure prediction result. The failure prediction is executed according to the above flow.
[0057]
FIG. 5 is a flowchart relating to the failure prediction processing according to the present embodiment. A failure prediction program corresponding to this flowchart is stored in the storage device of the failure prediction server 101. FIG. 6 is a diagram conceptually illustrating the processing of this flowchart.
[0058]
In step S500, the failure prediction server 101 reads out the i-th diagnostic condition from the diagnostic condition database 103, and extracts the vehicle data of the prediction target vehicle 110 that satisfies the read-out i-th diagnostic condition from the vehicle information database 104 by searching. I do. The time width of the vehicle data extracted here is T. If this T is too long, it takes a long time for the prediction process. Therefore, it is desirable to set the time width to be as short as possible and suitable for maintaining sufficient failure prediction accuracy.
[0059]
Alternatively, considering that the time width required for each diagnostic condition may be different, the time width T may be made variable for each diagnostic condition and set to an appropriate value. In this case, the time width T required for each diagnostic condition may be empirically determined.
[0060]
The diagnosis condition may be a condition that the vehicle is the same type of vehicle, or may be an additional condition that the vehicle is in the same or similar traveling situation. As the driving situation, for example, the type of acceleration / deceleration, the type of road, the curvature of the road, the gradient of the road, the temperature, the weather, the day of the week, the time of day, the time of day, the traffic congestion state, and the like can be considered. Of course, other conditions are used. You may.
[0061]
These pieces of information are all directly included in the vehicle data, or indirectly derived from the information included in the vehicle data. For example, by searching the map database 106 based on the position information acquired by the GPS sensor 231 and included in the vehicle data, whether the road on which the vehicle is traveling is an expressway or a general road, Alternatively, it is possible to know whether the curve is a straight line or a curve, and the gradient. In addition, it is possible to determine whether the vehicle is accelerating or decelerating by obtaining the time change of the vehicle speed data included in the vehicle data. The temperature, weather, and the like can also be determined based on vehicle data from the outside temperature sensor 241 and the rain sensor 242 mounted on the vehicle. In addition, the traffic congestion state can be determined based on the data of the VICS information receiver 232 included in the vehicle data.
[0062]
The additional condition may be that the vehicle is in the same or similar model year or mileage. The data of the model year and the mileage are also read from the ROM or the like by the vehicle client device 200 and stored in the vehicle data. Incidentally, the failure prediction server 101 may read and acquire the year registered in the customer database 102.
[0063]
Further, traveling in a nearby place may be an additional condition. Whether it is located in a close place can be determined based on the position information acquired by the GPS sensor 231 and stored in the vehicle data. Note that the proximity means a distance relationship to the extent that both vehicle data show the same or similar tendency if both the failure prediction target vehicle 110 and the same type vehicle 111 are normal.
[0064]
It may be an additional condition that the vehicle is traveling in the same traveling lane or traveling in the same direction. Whether the vehicle is traveling in the same traveling lane or traveling in the same direction is determined by the position information acquired by the GPS sensor 231 and stored in the vehicle data, the time change of the position information, and the position information. It can be determined based on the road information extracted from the map database 106 based on the information. In addition, if the road has three lanes on one side, it is included in the same lane of any lane, but the opposite lane in the same road will be a different lane.
[0065]
Note that the vehicle to be compared may be a vehicle that is currently traveling, or may be another vehicle that has traveled at the same location or a location around the same location in the past. For example, while the number of other vehicles currently traveling will not be very large, there are many other vehicles that have traveled in the past at or near the same location. Therefore, when performing the failure prediction by the statistical processing, the prediction accuracy of the failure will be improved as the parameter is larger.
[0066]
The above exemplified conditions may be used alone, or an appropriate combination may be selected from all possible combinations.
[0067]
The diagnostic condition may correspond to a specific failure. That is, a diagnostic condition that can detect a specific failure is empirically specified as appropriate, and registered in a database. For example, the diagnostic conditions for detecting tire wear include rainy weather, running on a 130R curve, and occurrence of a skidding phenomenon.
[0068]
In the example of FIG. 6, diagnostic conditions such as the same type of vehicle (model GF-FD3S), sunny weather and running on a highway are extracted as the first diagnostic condition, and a failure prediction target vehicle that satisfies the first diagnostic condition Is extracted.
[0069]
In step S502, the failure prediction server 101 determines whether or not vehicle data of the failure prediction target vehicle 110 that satisfies the i-th diagnostic condition has been extracted. If extracted, the process proceeds to step S504. If not extracted, the process proceeds to step S520, where i is incremented, and then returns to step S500.
[0070]
In step S <b> 504, the failure prediction server 101 retrieves, from the vehicle information database 104, vehicle data of another identical vehicle 111 that satisfies the i-th diagnostic condition by searching. Here, one vehicle data may be extracted, but it is desirable that a plurality or a plurality of vehicle data be extracted in order to improve the prediction accuracy.
[0071]
In the example of FIG. 6, diagnostic conditions, such as the same type of vehicle (model GF-FD3S), sunny weather, and running on a highway, are extracted as the first diagnostic conditions, and a plurality of other diagnostic conditions that satisfy the first diagnostic conditions are extracted. Data of the same type of vehicle is extracted.
[0072]
In step S506, the failure prediction server 101 performs statistical processing on the extracted vehicle data of the other same-type vehicles 111 (other vehicle groups). For example, an average value and a standard deviation are obtained. In the example of FIG. 6, the average value of the air-fuel ratio data for T minutes for two other vehicles of the same type is obtained.
[0073]
In step S508, the failure prediction server 101 determines whether the vehicle data of the prediction target vehicle 110 and the vehicle data of the other vehicle group subjected to the statistical processing are statistically the same or similar. For example, it is determined whether the vehicle data of the prediction target vehicle 110 is the same or similar to the average value of the vehicle data of another vehicle group, or is within an error range.
[0074]
In the example of FIG. 6, the air-fuel ratio data of the failure prediction target vehicle 110 is compared with the air-fuel ratio data of another vehicle group that has been statistically processed, and it is determined whether the data has the same or similar tendency. If there is a statistically identical or similar tendency, the process proceeds to step S510; otherwise, the process proceeds to step S512.
[0075]
In step S510, the failure prediction server 101 adds the i-th diagnostic condition to the failure list. The failure list is stored in a storage unit such as a memory or a hard disk drive. Note that the fault identification code corresponding to the i-th diagnostic condition may be stored in the fault list. The correspondence between the diagnosis condition and the failure identification code may be stored in the diagnosis condition database 103 or may be registered in another database.
[0076]
In step S512, the failure prediction server 101 determines whether the failure prediction has been completed for all the failure prediction servers. If not, the process proceeds to step S520, where the value of i is incremented and the process returns to step S500, where a failure is predicted for the next diagnostic condition.
[0077]
In step S514, the failure prediction server 101 reads the failure list stored in the storage unit, and determines which diagnostic condition or failure identification code has been registered. If any of the diagnostic conditions is registered, there is a possibility that some failure has occurred or some failure will occur in the future. Further, if any one of the failure identification codes is registered, it is predicted that a failure corresponding to the failure identification code will occur in the near future. If no failure is predicted, the process proceeds to step S518. If the occurrence of a failure is predicted, the process proceeds to step S516.
[0078]
In step S516, the failure prediction server 101 creates a failure prediction result (prediction report) for reporting to the driver. For example, a failure prediction result may be created by sorting the diagnostic conditions or failure identification codes registered in the failure list for each urgency. The failure prediction result may include, for example, all predicted failures, or only failures that may hinder operation or failures with a high degree of urgency that are directly related to safety. You may. This is because there is a possibility that some drivers may be overly anxious if a low urgency level is notified, so that only a high urgency level is notified.
[0079]
When the degree of urgency of a failure is high, the degree of urgency may be reflected to create a failure prediction result that makes it easy for the driver to understand the degree of urgency.
[0080]
In step S518, the failure prediction server 101 creates a prediction result indicating no failure and stores it in the storage unit. For example, the prediction result is "No failure was predicted. Enjoy comfortable driving." Or "No failure was predicted at this time. Or "The failure was not predicted at this time. However, failures may occur unexpectedly, so be sure to perform pre-operation inspections and periodic inspections." It would be a good message.
[0081]
As described above, according to the present embodiment, the failure is predicted based on the vehicle data of the actual failed vehicle, so that the accuracy of the failure prediction can be increased as compared with the related art.
[0082]
[Second embodiment]
A second embodiment based on the remote failure prediction system according to the first embodiment will be described. In the following description, the same configuration as that of the first embodiment will not be described repeatedly, and will be described focusing on the characteristic portions of the present embodiment.
[0083]
In the second embodiment, the failure prediction process is also performed when normal vehicle data is registered in the vehicle information database 103.
[0084]
FIG. 7 is a flowchart related to failure prediction according to the second embodiment. Steps common to FIGS. 3 and 4 are denoted by the same reference numerals to simplify the description. In the present embodiment, after Steps S300 to S312 are executed, Step S714 and subsequent steps are added.
[0085]
In step S714, the failure prediction server 101 determines whether a predetermined time has elapsed since the previous prediction processing. The reason why the prediction result is provided when the predetermined period has elapsed is as follows. Although the failure of the vehicle does not occur so frequently, if the failure prediction processing is executed every time the vehicle data arrives, the load on the system is increased uselessly. Therefore, it is sufficient to execute only at an appropriate timing for predicting a failure.
[0086]
More specifically, the failure prediction server 101 reads, from the customer database 102, the time at which the previous failure prediction processing was performed for the vehicle that transmitted the vehicle data, while acquiring the current time from a timer. Take the difference of If the difference exceeds a predetermined time, the process proceeds to step S406, and a failure occurrence prediction process is executed.
[0087]
On the other hand, if the difference does not exceed the predetermined time, that is, if sufficient time has not elapsed since the previous prediction processing, the processing is terminated without executing the prediction processing.
[0088]
If the failure is not predicted as a result of the failure prediction obtained in step S406, the process may be terminated without notifying the driver of the prediction result.
[0089]
In the present embodiment, the failure prediction is performed at the timing of transmitting the vehicle data. Therefore, even when the driver has forgotten to execute the failure prediction, the failure prediction result can be automatically reported.
[0090]
Even when the vehicle data is transmitted, if the time has not passed sufficiently since the previous prediction processing, the processing is terminated without executing the prediction processing. Can be suppressed.
[0091]
In addition, when a failure is not predicted, the process ends without notifying the driver of the prediction result, so that transmission of useless information to the driver can be suppressed. In the present embodiment, since the failure prediction is executed without the driver being conscious, if the occurrence of a failure is not predicted, it may be preferable to keep the driver unaware as it is possible to concentrate on driving. .
[0092]
[Third Embodiment]
In the above-described embodiment, the vehicle data is collected at the information center 100 using a large-scale network such as the Internet 130. This is because even if there is no other similar vehicle 110 traveling in a place close to the same date and time, the vehicle data of the other similar vehicle 110 traveling in the same place in the past is stored in the vehicle information database 104. If present, failure could be predicted.
[0093]
Further, since data of a large number of same-type vehicles is stored in the vehicle information database 104 and statistical processing is executed, the accuracy of failure prediction is relatively high.
[0094]
However, in the above-described embodiment, although the failure prediction accuracy is high, it is necessary to install and manage the failure prediction server 101 and various databases, which is disadvantageous in terms of cost. Further, although the fault accuracy may be inferior, a relatively simple fault prediction may be sufficient.
[0095]
Therefore, in the third embodiment, a failure prediction system is more easily realized by applying inter-vehicle communication. That is, a large-scale network such as the Internet 130 is not indispensable, and failure prediction can be performed only by forming an inter-vehicle network.
[0096]
In the third embodiment, the inter-vehicle communication is used, but the communicable range of the inter-vehicle communication is about several meters to about 100 m. Therefore, if the failure prediction target vehicle 110 and the other same-type vehicle 111 can communicate with each other, it means that both vehicles are approaching within a radius or a position of 100 m or less. It can be said that there is. For example, it will be true that the same weather condition is present in a range of a radius of 100 m. Therefore, in the present embodiment, there is an advantage that both vehicle data are data acquired in the same or similar driving situation by themselves without extracting vehicle data in a similar driving situation.
[0097]
Further, since the vehicle data can be obtained in real time, it is not necessary to hold the vehicle data for a long time, and the vehicle data used for the failure prediction processing may be immediately discarded. As a result, the memory and the hard disk drive can be used effectively.
[0098]
FIG. 8 is a diagram illustrating the concept of the third embodiment. In the above-described embodiment, the vehicle data acquired by the vehicle is uploaded to the vehicle information database 104. However, in the present embodiment, the vehicle data is acquired from another identical car 111 by inter-vehicle communication. Then, the failure is predicted by the vehicle client mounted on the failure prediction target vehicle 110. In this sense, the vehicle client can be thought of as a failure prediction system or a failure prediction device.
[0099]
FIG. 9 is a block diagram illustrating a configuration example of vehicles 110 and 111 according to the third embodiment. 2 are denoted by the same reference numerals, and description thereof will be omitted. Here, the vehicle client mounted on the failure prediction target vehicle 110 functions as the failure prediction device 900. On the other hand, the vehicle client or the failure prediction device 900 mounted on the same type vehicle 111 collects vehicle data, and transmits the collected vehicle data to the failure prediction device 900 mounted on the failure prediction target vehicle 110. It will function as a data collection / transmission device. Of course, the vehicle client 200 may include both a function as a failure prediction device and a function as a vehicle data collection / transmission device.
[0100]
The failure prediction device 900 has a failure prediction program mounted on the vehicle client 200. The CPU 201 forms the core of the failure prediction device 900 and executes failure prediction according to the failure prediction program 204 stored in the ROM 202. The ROM 202 stores vehicle type data 203 serving as reference information for determining whether the vehicle is the same type of vehicle, and the above-described failure prediction program 204. The RAM 205 stores the vehicle data 206, the vehicle data 208 of the own vehicle, and the failure prediction list 209 received from the other same type vehicle 111. Also, an inter-vehicle communication unit 256 is mounted on each vehicle.
[0101]
FIG. 10 is a flowchart of a failure occurrence prediction program according to the third embodiment. The same processes as those described with reference to FIG. 5 are denoted by the same reference numerals, and description thereof is omitted.
[0102]
In step S1000, failure prediction device 900 searches for a vehicle of the same type in the communicable range of the failure prediction target vehicle. For example, the CPU 201 controls the inter-vehicle communication unit 256 to receive a beacon transmitted by a vehicle located in the vicinity, and determines whether or not the vehicle is of the same type from vehicle type data included in the beacon. Then, when it is determined that the other surrounding vehicles are the same type of vehicle, the CPU 201 controls the inter-vehicle communication unit 256 to request more detailed vehicle data. The same type vehicle 111 transmits vehicle data in response to this request. CPU 201 stores the vehicle data received by inter-vehicle communication unit 256 in RAM 205. When a plurality of corresponding vehicles exist, vehicle data is received from as many vehicles of the same type as possible and stored in the RAM 205.
[0103]
In step S1002, the CPU 201 determines whether the vehicle data stored in the RAM 205 satisfies the i-th diagnostic condition. If there is no vehicle that satisfies the condition, the process proceeds to step S1022, and a message indicating unpredictability is created and output from the input / output device 270. If there is one satisfying the condition, the process proceeds to step S506.
[0104]
Thereafter, when the processing from S506 to S518 is completed and the prediction result is obtained, the prediction result is output in step S412.
[0105]
Note that, for example, if the vehicle data for T minutes is used in the failure prediction, after acquiring the vehicle data for T minutes in advance, steps S1002 and subsequent steps may be executed, or t seconds shorter than T may be executed. Is received in real time from the other same-type vehicle 111, the vehicle data is also acquired in real time in the failure target predicted vehicle 110, and these are compared. The result of the comparison may be statistically processed, and a determination may be made for the entire T section to predict a failure.
[0106]
Thus, in the third embodiment, the occurrence of a failure can be predicted with a simpler configuration than in the other embodiments. Moreover, since other vehicle data is used, the prediction accuracy is improved as compared with the related art.
[0107]
[Other embodiments]
In the above-described embodiment, the occurrence of a failure is predicted by comparing vehicle data between a plurality of vehicles of the same type. However, if vehicle data that does not depend on the type of vehicle is used, the above-described implementation can be performed between different types of vehicles. The form can be applied.
[0108]
In the statistical processing described above, a failure is determined when the vehicle data of the target vehicle 110 statistically deviates from the vehicle data of another identical vehicle 111. However, in this case, even when the vehicle data of the failure target vehicle 110 is relatively superior to the vehicle data of the other same-type vehicles 111 and thus does not statistically match, there is a possibility that the failure may be determined. Therefore, when the vehicle deviates in such an excellent direction, the failure prediction server 101 or the failure prediction device 900 executes control that does not determine a failure. For example, if the fuel efficiency or the like deviates in an excellent direction, it is satisfactory for the user, and thus it is not determined that a failure has occurred. It should be noted that “correspondence” means showing the same or similar tendency, and does not mean perfect match.
[0109]
The failure prediction result may be directly transmitted from the failure prediction server 110 to the vehicle client device 200, or may be transmitted to the customer as an e-mail. For example, the failure prediction server 110 searches the customer database 102 based on the vehicle number of the failure prediction target vehicle 110, and extracts the customer's e-mail address. Then, the failure prediction server 110 transmits a prediction result to the extracted e-mail address.
[0110]
If not only the driver's e-mail address but also a plurality of e-mail addresses such as the e-mail address of the company to which the driver belongs are registered in the customer database, the failure prediction server 110 Since the prediction result can be distributed to the company to which the driver belongs, there is an advantage that the whole company can be used for safety management.
[Brief description of the drawings]
FIG. 1 is a diagram illustrating a configuration example of a system according to an embodiment;
FIG. 2 is a block diagram showing a configuration example of a vehicle according to the embodiment.
FIG. 3 is a sequence relating to collection of vehicle data according to the embodiment;
FIG. 4 is a sequence of a remote failure prediction process according to the embodiment.
FIG. 5 is a flowchart of a remote failure prediction process according to the embodiment.
FIG. 6 is a diagram illustrating a concept of a remote failure prediction process according to the present embodiment.
FIG. 7 is a sequence of a remote failure prediction process according to the second embodiment.
FIG. 8 is a diagram illustrating a configuration example of a system according to a third embodiment;
FIG. 9 is a block diagram showing a configuration example of a vehicle according to a third embodiment.
FIG. 10 is a flowchart relating to a remote failure prediction process according to the third embodiment.
[Explanation of symbols]
100… Information center
101: Failure prediction server
110: Failure prediction target vehicle
111 ... other similar vehicles

Claims (18)

In a remote failure prediction system that remotely performs a failure prediction on a failure prediction target vehicle and transmits a failure prediction result to a customer,
Via a network, receiving means for receiving vehicle data from the failure prediction target vehicle, and vehicle data from one or more other vehicles of the same type as the failure prediction target vehicle,
Vehicle data from the received failure prediction target vehicle, storage means for storing vehicle data from the other vehicle,
Reading means for reading the stored vehicle data from the failure prediction target vehicle and vehicle data from the other vehicle,
Failure prediction means for comparing vehicle data from the failure prediction target vehicle with vehicle data from the other vehicle, and predicting the occurrence of a failure based on the comparison result;
A transmission means for transmitting a failure prediction result by the failure prediction means to a customer of the failure prediction target vehicle. Among the other vehicles, further includes extraction means for extracting vehicle data of other vehicles in the same or similar running conditions as the failure prediction target vehicle,
2. The remote failure prediction system according to claim 1, wherein the failure prediction unit predicts the occurrence of a failure using vehicle data of another vehicle having the same or similar driving situation. The driving condition includes at least one of acceleration / deceleration type, road type, road curvature, road gradient, temperature, weather, day of the week, time, time zone, and traffic congestion. The remote failure prediction system according to claim 2. Extraction means for extracting vehicle data of vehicles having the same or similar model year or mileage as the failure prediction target vehicle among the other vehicles,
2. The remote failure prediction system according to claim 1, wherein the failure prediction unit predicts occurrence of a failure using vehicle data of another vehicle having the same or similar model year or mileage. 3. Based on the information on the traveling position included in the vehicle data, another vehicle traveling in a place close to the failure prediction target vehicle is specified, and the occurrence of a failure is predicted using the vehicle data of the other vehicle. The remote failure prediction system according to any one of claims 1 to 4, wherein: The remote failure prediction system according to claim 5, wherein the other vehicle is a vehicle traveling in the same lane as the failure prediction target vehicle or traveling in the same direction. On the basis of the information on the traveling position included in the vehicle data, another vehicle that has traveled in the past at the same or close place as the failure prediction target vehicle is specified, and the occurrence of the failure is performed using the vehicle data of the other vehicle. The remote failure prediction system according to any one of claims 1 to 4, wherein A vehicle whose failure is predicted by the remote failure prediction system according to any one of claims 1 to 7,
Means for detecting information about the operating state of the vehicle;
Means for storing information on the detected operating state of the vehicle as vehicle data,
Means for transmitting the stored vehicle data to the remote failure prediction system;
Means for receiving a failure prediction result from the remote failure prediction system;
Means for outputting the received failure prediction result. In a failure prediction system that predicts the occurrence of a failure for a vehicle that stores vehicle data related to an operation state,
An inter-vehicle communication means for communicating with another vehicle,
Search means for searching for another vehicle that is the same vehicle as the failure prediction target vehicle that is the target of the failure occurrence prediction, which is another vehicle communicable by the inter-vehicle communication means,
Storage means for storing vehicle data received from one or more other vehicles discovered by the search means, and vehicle data of the failure prediction target vehicle,
Reading means for reading the stored vehicle data received from the at least one other vehicle and the vehicle data of the failure prediction target vehicle,
Failure prediction means for comparing vehicle data received from the read one or more other vehicles with vehicle data of the failure prediction target vehicle, and predicting the occurrence of a failure based on the comparison result,
Output means for outputting a failure prediction result by the failure prediction means. Among the other vehicles, further includes extraction means for extracting vehicle data of other vehicles in the same or similar running conditions as the failure prediction target vehicle,
The failure prediction system according to claim 9, wherein the failure prediction unit predicts the occurrence of a failure using vehicle data of another vehicle having the same or similar driving situation. The driving condition includes at least one of acceleration / deceleration type, road type, road curvature, road gradient, temperature, weather, day of the week, time, time zone, and traffic congestion. The failure prediction system according to claim 10. Extraction means for extracting vehicle data of vehicles having the same or similar model year or mileage as the failure prediction target vehicle among the other vehicles,
The failure prediction system according to claim 9, wherein the failure prediction unit predicts the occurrence of a failure using vehicle data of another vehicle having the same or similar model year or mileage. Based on the information on the traveling position included in the vehicle data, another vehicle traveling in a place close to the failure prediction target vehicle is extracted, and the occurrence of a failure is predicted using the vehicle data of the other vehicle. The failure prediction system according to any one of claims 9 to 12, wherein: 14. The failure prediction system according to claim 13, wherein the other vehicle is traveling in the same traveling lane as the failure prediction target vehicle or traveling in the same direction. A vehicle equipped with the failure prediction system according to any one of claims 9 to 14. In a remote failure prediction method for remotely predicting the failure of a failure prediction target vehicle and transmitting a failure prediction result to a customer,
Receiving vehicle data from the failure prediction target vehicle via a network, and vehicle data from one or more other vehicles of the same type as the failure prediction target vehicle;
Storing the received vehicle data of the failure prediction target vehicle and vehicle data from the other vehicle,
Reading the vehicle data from the stored failure prediction target vehicle and vehicle data from the other vehicle,
Comparing vehicle data from the failure prediction target vehicle with vehicle data from the other vehicle, and predicting the occurrence of a failure based on the comparison result;
Transmitting a prediction result by the failure prediction means to a customer of the failure prediction target vehicle. In a failure prediction method for predicting the occurrence of a failure for a vehicle that stores vehicle data related to an operation state,
Searching for another vehicle of the same type as the failure prediction target vehicle to be subjected to the failure occurrence prediction by inter-vehicle communication,
Receiving vehicle data by inter-vehicle communication from one or more other vehicles discovered by the search,
Storing vehicle data received from one or more other vehicles discovered by the search means, and vehicle data of the failure prediction target vehicle;
Reading the vehicle data received from the one or more other stored vehicles and the vehicle data of the failure prediction target vehicle;
Comparing the vehicle data received from the one or more other vehicles that have been read with the vehicle data of the failure prediction target vehicle, and predicting the occurrence of a failure based on the comparison result;
Outputting the result of the prediction. In a failure prediction program for predicting the occurrence of a failure for a vehicle that stores vehicle data related to an operation state,
Searching for another vehicle of the same type as the failure prediction target vehicle to be subjected to the failure occurrence prediction by inter-vehicle communication,
Receiving vehicle data by inter-vehicle communication from one or more other vehicles discovered by the search,
Storing vehicle data received from one or more other vehicles discovered by the search means, and vehicle data of the failure prediction target vehicle;
Reading the vehicle data received from the one or more other stored vehicles and the vehicle data of the failure prediction target vehicle;
Comparing the vehicle data received from the one or more other vehicles that have been read with the vehicle data of the failure prediction target vehicle, and predicting the occurrence of a failure based on the comparison result;
And a step of outputting a result of the prediction.

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