DIAGNOSIS APPARATUS

A diagnosis apparatus including: a first data acquisition part that acquires time series data from an apparatus to be diagnosed a first number of times every predetermined time period; a possibility determination part that determines whether there is a possibility of abnormality occurrence in the apparatus to be diagnosed during the predetermined time period on the basis of the time series data; a second data acquisition part that acquires the time series data a second number of times, which is greater than the first number of times, when it is determined that there is the possibility of abnormality occurrence; and an abnormality determination part that determines whether an abnormality has occurred in the apparatus to be diagnosed on the basis of changes in data indicated by the time series data acquired by the second data acquisition part.

TECHNICAL FIELD

The present invention relates to a diagnosis apparatus for diagnosing a state of an apparatus to be diagnosed.

BACKGROUND OF THE INVENTION

As an example of a diagnosis apparatus, there is a diagnosis apparatus that acquires, as time series data, various types of data accumulated during traveling of a vehicle and diagnoses a state of the vehicle (see Patent Document 1 below, for example). The diagnosis apparatus receives the time series data from a plurality of vehicles, serving as apparatuses to be diagnosed, in real time, and diagnoses the state of each vehicle.

PRIOR ART

Patent Document

BRIEF DESCRIPTION OF THE INVENTION

Problem to be Solved by the Invention

However, in a case where all of the time series data is received from the apparatuses to be diagnosed, the communication traffic becomes excessive, and the storage capacity of the time series data in the diagnosis apparatus becomes large, so that the processing load of the diagnosis apparatus is increased.

The present invention focuses on this point, and its object is to appropriately determine an occurrence of an abnormality in an apparatus to be diagnosed while suppressing the amount of time series data received.

Means for Solving the Problem

In a first aspect of the present invention, it is provided a diagnosis apparatus for performing data communication with an apparatus to be diagnosed, which is a diagnosis target, the diagnosis apparatus including: a first data acquisition part that acquires time series data for diagnosis from the apparatus to be diagnosed every predetermined time period, the first data acquisition part acquiring the time series data a first number of times in each period; a possibility determination part that determines whether there is a possibility of abnormality occurrence in the apparatus to be diagnosed during the predetermined time period on the basis of the time series data acquired by the first data acquisition part; a second data acquisition part that acquires the time series data a second number of times, which is greater than the first number of times, when the possibility determination part determines that there is the possibility of abnormality occurrence in consecutive predetermined time periods; and an abnormality determination part that determines whether an abnormality has occurred in the apparatus to be diagnosed on the basis of changes in data indicated by the time series data acquired by the second data acquisition part.

The first data acquisition part may acquire the time series data the first number of times in a first period within the predetermined time period, and the second data acquisition part may acquire the time series data the second number of times in a second period which is longer than the first period.

The second data acquisition part may acquire the time series data the second number of times in the second period which is more than twice the first period.

The first data acquisition part may acquire the time series data the first number of times in a first period, which is a period shorter than half of the predetermined time period.

The first data acquisition part may acquire, as the time series data, data indicating an operation state of the apparatus to be diagnosed.

The possibility determination part may determine that there is the possibility of abnormality occurrence when an evaluation index exceeds a predetermined threshold, the evaluation index being a time integral of an excess amount of data that changes over time, in the time series data, the excess amount being an amount of data exceeding a predetermined amount from when the data exceeds the predetermined amount to when the data falls below the predetermined amount.

The abnormality determination part may determine that an abnormality has occurred in the apparatus to be diagnosed when the evaluation index of the time series data indicates an increasing tendency.

Effect of the Invention

According to the present invention, it is possible to appropriately determine an occurrence of an abnormality in an apparatus to be diagnosed while suppressing the amount of time series data received.

DESCRIPTION OF EMBODIMENTS

<Configuration of a Diagnosis Apparatus>

A configuration of a diagnosis apparatus according to an embodiment of the present invention will be described with reference toFIGS.1and2.

FIG.1is a schematic diagram illustrating an outline of a diagnosis system1. The diagnosis system1is a system in which a diagnosis apparatus10and a plurality of vehicles2operate in cooperation with each other to diagnose states of the vehicles2. In the present embodiment, the vehicle2corresponds to an apparatus to be diagnosed, which is a diagnosis target.

The plurality of vehicles2are trucks, for example. The vehicles2are each equipped with a sensor or the like for measuring a state, and transmit measured data to the diagnosis apparatus10as time series data. For example, when the engine of the vehicle2is operating, the sensor measures a state of each unit such as a fuel injection system or an exhaust system. The sensor continuously performs measurement at predetermined intervals for a certain period of time. For example, the sensor performs measurement for 40 seconds at intervals of 12 minutes during operation of the engine of the vehicle2.

The diagnosis apparatus10can perform data communication with the plurality of vehicles2, and diagnoses states of the vehicles2. The diagnosis apparatus10is a server provided in a management center, for example. The diagnosis apparatus10receives the time series data from each vehicle2. The diagnosis apparatus10diagnoses the states of the vehicles2from the received time series data. The diagnosis apparatus10determines, based on a diagnosis result, whether a vehicle exhibits signs of a break down and needs maintenance. When it is determined that maintenance is needed, the diagnosis apparatus10provides a notification that prompts an administrator, a maintenance company, or the like of the vehicle2to perform maintenance.

The diagnosis apparatus10determines a vehicle2in which an abnormality has occurred as follows. Specifically, the diagnosis apparatus10acquires time series data of a short period of time (for example, 5 days) from a plurality of vehicles2to identify a vehicle2having a possibility of abnormality occurrence. Then, in order to confirm whether the abnormality actually has occurred in the specified vehicle2, the diagnosis apparatus10acquires time series data of a long period of time (for example, 20 days) from said vehicle2again, and performs an abnormality determination. By doing this, when the vehicle2is identified as a vehicle2having a possibility of abnormality occurrence, the amount of the time series data received can be suppressed by acquiring the time series data in a short time. On the other hand, the abnormality of the vehicle2can be determined with high accuracy by determining the abnormality by acquiring the long-term time series data from the vehicle2having a possibility of abnormality occurrence.

FIG.2is a block diagram illustrating the configuration of the diagnosis apparatus10. The diagnosis apparatus10is operated by an administrator of a management center, for example. As shown inFIG.2, the diagnosis apparatus10includes a communication part12, a storage14, and a control part16.

The communication part12communicates with the vehicles2. The communication part12transmits and receives data to and from the vehicles2. For example, the communication part12receives time series data indicating the states of the vehicles2from the vehicles2.

The storage14includes a read only memory (ROM) and a random access memory (RAM), for example. The storage14stores various types of data and a program to be executed by the control part16. The storage14stores various types of data. In the present embodiment, the storage14stores the time series data acquired from each of the plurality of vehicles2.

The control part16is a central processing unit (CPU), for example. The control part16controls reception of the time series data from the vehicles2by executing the program stored in the storage14. In the present embodiment, the control part16functions as a first data acquisition part162, a possibility determination part163, a second data acquisition part164, an abnormality determination part165, and a notification control part166.

The first data acquisition part162acquires time series data for diagnosis from the vehicles2every predetermined time period. For example, the first data acquisition part162acquires the time series data from each of the plurality of vehicles2every month, as the predetermined time period. The first data acquisition part162acquires the time series data received from the vehicles2by the communication part12. The first data acquisition part162stores the acquired time series data in the storage14.

The time series data is data indicating an operation state of a vehicle (for example, an engine) measured in the vehicles2. The time series data includes operation states of a combustion injection system, a valve operation system, and an exhaust system of the engine, a rotation speed of the engine, and the like, for example.

The first data acquisition part162acquires the time series data at predetermined intervals in each period. For example, the first data acquisition part162acquires the time series data from the vehicles2at intervals of 12 minutes. Therefore, the first data acquisition part162acquires the time series data a first number of times in each period.

FIG.3is a schematic diagram illustrating a period during which the first data acquisition part162acquires the time series data. The first data acquisition part162acquires the time series data for each period T1shown inFIG.3. At this time, the first data acquisition part162acquires the time series data the first number of times in a first period T2within the period T1. For example, the first data acquisition part162acquires the time series data the first number of times in the first period T2, which is a period shorter than half of the period T1. Here, the first period T2is the first five days in each period T1. Therefore, the first data acquisition part162acquires the time series data the first number of times at intervals of 12 minutes for 5 days.

The possibility determination part163determines whether there is a possibility of abnormality occurrence in a vehicle2. The possibility determination part163identifies a vehicle2having a possibility of abnormality occurrence from among the plurality of vehicles2. The possibility determination part163determines whether there is the possibility of abnormality occurrence in the vehicle2during the period T1on the basis of the time series data acquired by the first data acquisition part162. For example, when an evaluation index of the time series data exceeds a predetermined threshold value, the possibility determination part163determines that there is the possibility of abnormality occurrence. The evaluation index is an index indicating the degree of abnormality of a vehicle (e.g., an engine). For example, the evaluation index is represented by a time integral of an excessive amount of data that changes over time, in the time series data, the excess amount being an amount of data exceeding a predetermined amount from when the data exceeds the predetermined amount to when the data falls below the predetermined amount.

The second data acquisition part164acquires the time series data from a vehicle2having the possibility of abnormality occurrence. In the present embodiment, when the possibility determination part163determines that there is the possibility of abnormality occurrence in consecutive periods T1, the second data acquisition part164acquires the time series data a second number of times, which is greater than the first number of times. Specifically, when it is determined that periods T1in which the evaluation index exceeds the threshold value are continuous, the second data acquisition part164acquires the time series data the second number of times.

The time series data acquired by the second data acquisition part164is the same as the time series data acquired by the first data acquisition part164. However, the present invention is not limited thereto, and the time series data acquired by the second data acquisition part164may be different from the time series data acquired by the first data acquisition part164. The second data acquisition part164stores the acquired time series data in the storage14.

FIG.4is a schematic diagram illustrating a period during which the second data acquisition part164acquires the time series data. As shown inFIG.4, the second data acquisition part164acquires the time series data in a second period T3after the first period T2. The second data acquisition part164acquires the time series data the second number of times in the second period T3which is longer than the first period T2. Here, the second period T3is 20 days, which is more than twice the first period T2, which is 5 days. Therefore, the second data acquisition part164acquires the time series data the second number of times at intervals of 12 minutes for 20 days.

The abnormality determination part165determines whether an abnormality has occurred in the vehicle2. The abnormality determination part165determines whether the abnormality has occurred in the vehicle2on the basis of changes in data indicated by the time series data acquired by the second data acquisition part164. For example, the abnormality determination part165determines whether an abnormality has occurred in the vehicle2on the basis of changes in the evaluation index of the time series data. When the evaluation index shows a tendency to increase, the abnormality determination part165determines that the vehicle2has a tendency to deteriorate. The increasing tendency of the evaluation index can be determined by the inclination of the approximate line of the evaluation index, the magnitude of the accumulated value of the deviation per day, or the like, for example.

The notification control part166calls attention or prompts a desired operation by giving a notification. When the abnormality determination part165determines that an abnormality has occurred in the vehicle2, the notification control part166notifies the administrator of the diagnosis apparatus10. In addition, when the abnormality determination part165determines that an abnormality has occurred in the vehicle2, the notification control part166may give a notification that prompts an auto repair company or the like to perform maintenance.

<Abnormality Determination Process for a Vehicle>

An abnormality determination process for a vehicle will be described with reference toFIG.5

FIG.5is a flowchart illustrating an abnormality determination process for a vehicle. First, the first data acquisition part162acquires time series data from each vehicle2during the first period T2in each period T1(step S102). For example, the first data acquisition part162acquires the time series data for the first five days of a month.

Next, on the basis of the time series data acquired from the vehicles2by the first data acquisition part162, the possibility determination part163determines whether there is the possibility of abnormality occurrence in a vehicle2(step S104). For example, if the period T1in which the evaluation index of the time series data exceeds a predetermined threshold value continues, the possibility determination part163determines that there is the possibility of abnormality occurrence in the vehicle2.

When it is determined in step S104that there is a possibility of an abnormality occurrence (Yes), the second data acquisition part164acquires the time series data from the vehicle2having the possibility of abnormality occurrence in the second period T3which is longer than the first period T2(step S106). For example, the second data acquisition part164acquires the time series data for 20 days.

Next, the abnormality determination part165determines whether an abnormality has actually occurred in the vehicle2having the possibility of abnormality occurrence, on the basis of changes in the data indicated by the time series data acquired by the second data acquisition part164(step S108). For example, if the evaluation index of the time series data indicates an increasing tendency, the abnormality determination part165determines that an abnormality has occurred in the vehicle2.

When it is determined in step S108that an abnormality has occurred in the vehicle2(Yes), the notification control part166notifies that an abnormality has occurred in the vehicle2(step S110). For example, the notification control part166may provide a notification that prompts to perform maintenance since the vehicle2exhibits signs of a break down.

<Effects of the Present Embodiment>

The diagnosis apparatus10of the above-described embodiment determines whether there is the possibility of abnormality occurrence in a vehicle2on the basis of the time series data acquired during the period T2(for example, five days) in the period T1. When the diagnosis apparatus10determines that a vehicle2has the possibility of abnormality occurrence, the diagnosis apparatus10acquires time series data only for the period T3(for example, 20 days) from said vehicle2, and determines whether or not an abnormality has occurred in the vehicle2on the basis of changes in the acquired time series data. As a result, when determining the possibility of abnormality occurrence in each vehicle2, it is only necessary to acquire a small amount of time series data from each vehicle2, and therefore it is possible to narrow down the vehicles2that are the targets of the abnormality determination while suppressing the amount of the time series data received. On the other hand, by acquiring a large amount of time series data over the period T3from a vehicle2having the possibility of abnormality occurrence, it is possible to accurately perform the abnormality determination. Therefore, erroneous determinations can be prevented

The apparatus to be diagnosed, which is the diagnosis target, is the vehicle2in the above description, but the present invention is not limited thereto. The apparatus to be diagnosed may be an apparatus other than a vehicle.

The present disclosure is explained on the basis of the exemplary embodiments. The technical scope of the present disclosure is not limited to the scope explained in the above embodiments and it is possible to make various changes and modifications within the scope of the disclosure. For example, all or part the apparatus can be configured with any unit which is functionally or physically dispersed or integrated. Further, new exemplary embodiments generated by arbitrary combinations of them are included in the exemplary embodiments of the present disclosure. Further, effects of the new exemplary embodiments brought by the combinations also have the effects of the original exemplary embodiments.

DESCRIPTION OF SYMBOLS