Device and method for analyzing failure causes due to insulation breakdown based on big data analysis

Abstract

The present invention provides an apparatus and method for analyzing the cause of a failure caused by insulation breakdown based on big data analysis. When the measured insulation resistance value is below the minimum normal value, a failure inducing factor data set, a normal state data set, or a recovery state data set is generated, and the failure inducing factor data set, the normal state data set, or the recovery state data set is transmitted to a big data server. By receiving data corresponding to the data set from the big data server, an influence index is calculated for the failure inducing factor, and the failure inducing factor is selected based on the influence index to analyze the cause of the failure.

Description

Technical Field

[0001] The present disclosure relates to a system and method for determining a failure and analyzing a cause of the failure by using an insulation resistance value of a vehicle periodically collected, and more particularly, to an apparatus and method for analyzing a cause of a failure caused by dielectric breakdown based on big data analysis. Background Art

[0002] According to a conventional method of analyzing a cause of a failure, a device can be repaired by analyzing a cause of the failure only when the device is stored in a service center after a specific device included in a vehicle fails and when a failure situation occurs again in the vehicle. In addition, since it is necessary to disassemble and disassemble the device and a plurality of devices associated with the device to analyze the cause of the failure in the case where the same failure state occurs again, time and cost for analyzing the cause of the failure and repairing the device increase. Further, in the case where a failed device is a main component of a vehicle, if a cause of the failure cannot be accurately determined, all components must be replaced, resulting in incorrect or unnecessary repairs. Summary of the Invention

[0003] The present disclosure provides an apparatus and method for analyzing a cause of a failure caused by dielectric breakdown based on big data analysis. Through the apparatus and method, even in the case where failure symptoms occur intermittently, it is possible to calculate an influence index for a predicted failure inducing factor by analyzing driving information and insulation resistance value data collected through sensors attached to a vehicle, and select a failure inducing factor by calculating a cumulative number of influence indexes greater than or equal to a relative magnitude or a reference of the influence index to more accurately identify a cause of the failure.

[0004] According to an exemplary embodiment, an apparatus for analyzing a cause of a failure caused by dielectric breakdown based on big data analysis may include: a resistance value data monitoring unit configured to monitor whether an insulation resistance value of a vehicle decreases below a minimum normal value; a data set generation unit configured to set a failure state interval and a normal state interval according to a preset reference when the insulation resistance value is below the minimum normal value, and generate a failure inducing factor data set and a normal state data set including a plurality of failure inducing factor data for the failure state interval and the normal state interval; a data set transmission / reception unit configured to transmit the generated data set to a big data server and receive data corresponding to the data set from the big data server; an influence index calculation unit configured to calculate an influence index for a failure inducing factor by using the received data; and a result information generation unit configured to generate analysis result information by selecting a failure inducing factor based on the calculated influence index.

[0005] The device may further include an analysis result output unit configured to output analysis result information to a user. The dataset generation unit may be configured to, when the insulation resistance value is below the minimum normal value, set the period from the time point of measuring the insulation resistance value to a preset time before as a fault state period, and may be configured to set the period from the start time point of the fault state period to a preset time before as a normal state period.

[0006] The resistance value data monitoring unit may be configured to monitor whether the insulation resistance value received after the fault state period has recovered above the minimum normal value. The dataset generation unit may be configured to, when the insulation resistance value reaches above the minimum normal value again, set the period from the time point when the insulation resistance value reaches the minimum normal value to a preset time before as a recovery state period, and generate a recovery time point dataset including multiple fault inducing factor data for the recovery state period. The dataset transmission / reception unit may be configured to transmit the generated recovery time point dataset to a big data server and receive data corresponding to the dataset from the big data server.

[0007] In addition, the result information generation unit may be configured to select a fault inducing factor by reflecting the calculated impact index and the cumulative number of times according to the impact index. The impact index calculation unit may be configured to periodically receive a fault state dataset and a recovery state dataset, and calculate an impact index for the fault inducing factor by using data corresponding to the received fault state dataset and recovery state dataset. The result information generation unit may be configured to select a fault inducing factor based on the calculated impact index and reflect the selected fault inducing factor in the analysis result information.

[0008] The result information generation unit may be configured to generate a fault inducing factor analysis table according to the analysis result whenever the measured insulation resistance value of the vehicle is below the minimum normal value and a fault state is determined to occur. The fault inducing factor analysis table may include at least one of information for determining the relative magnitudes of the calculated impact index and the impact, fault inducing factor suspicion information, and cumulative number of times of selected fault inducing factor suspicion information.

[0009] According to an exemplary embodiment, the result information generation unit may be configured to determine that the impact cannot be judged and not accumulate the number of times to the cumulative number of times when the impact index is below a preset value. In particular, the analysis result information may include a reliability value, and the reliability value increases as the cumulative number of times increases. On the contrary, the reliability value decreases as the cumulative number of times decreases. The minimum normal value may be about 1000 kΩ. In addition, the device may include a big data server configured to receive a dataset from the dataset transmission / reception unit, extract data corresponding to the dataset, and transmit the extracted data to the dataset transmission / reception unit.

[0010] According to an exemplary embodiment, a method for analyzing the cause of a failure caused by insulation breakdown based on big data analysis may include: monitoring whether the insulation resistance value of a vehicle decreases below a minimum normal value; when the insulation resistance value is below the minimum normal value, setting a failure state interval and a normal state interval according to a preset criterion, and generating a failure inducing factor data set and a normal state data set including a plurality of failure inducing factor data for the failure state interval and the normal state interval; transmitting the generated data sets to a big data server, and receiving data corresponding to the data sets from the big data server; calculating an influence index for the failure inducing factors by using the received data; and generating analysis result information by selecting failure inducing factors based on the calculated influence index.

[0011] The method may further include: outputting the analysis result information to a user. Generating the data sets may include: when the insulation resistance value is below the minimum normal value, setting the interval from the time point of measuring the insulation resistance value to a preset time before as the failure state interval, and setting the interval from the start time point of the failure state interval to a preset time before as the normal state interval.

[0012] In addition, the method may include: monitoring whether the insulation resistance value received after the failure state interval reaches above the minimum normal value; when the insulation resistance value reaches above the minimum normal value again, setting the interval from the time point when the insulation resistance value reaches the minimum normal value to a preset time before as a recovery state interval, and generating a recovery time point data set including a plurality of failure inducing factor data for the recovery state interval; and transmitting the generated recovery time point data set to the big data server, and receiving data corresponding to the data set from the big data server.

[0013] According to an exemplary embodiment, generating the analysis result information may include: selecting failure inducing factors by reflecting the calculated influence index and the cumulative number of times according to the influence index. The failure state data sets and the recovery state data sets may be received periodically, and the data corresponding to the received failure state data sets and recovery state data sets may be used to calculate the influence index for the failure inducing factors, and the failure inducing factors may be selected based on the calculated influence index, and the selected failure inducing factors may be reflected in the analysis result information.

[0014] In addition, generating the analysis result information may include: whenever the measured insulation resistance value of the vehicle is below the minimum normal value and it is determined that a failure state occurs, generating a failure inducing factor analysis table according to the analysis result. The failure inducing factor analysis table may include at least one of information for judging the relative magnitudes of the calculated influence index and the influence, failure inducing factor suspicion information, and cumulative number of times of inducing factor suspicion selection information.

[0015] According to an exemplary embodiment, in generating analysis result information, when an influence index is below a preset value, it can be determined that the influence cannot be judged, and the number of times is not accumulated into the cumulative number of times. In generating analysis result information, the analysis result information may include a reliability value, and the reliability value increases as the cumulative number of times increases. On the contrary, the reliability value decreases as the cumulative number of times decreases. According to an embodiment, the minimum normal value may be about 1000 kΩ.

[0016] The method may further include: the big data server receives a data set, extracts data corresponding to the data set, and transmits the extracted data.

[0017] According to the present disclosure, even in the case where failure symptoms occur intermittently, numerical data of a device can be extracted from the driving data of a vehicle by analyzing driving information including the insulation resistance value of the vehicle and selecting a failure inducing factor. By analyzing the numerical data and selecting a failure inducing factor, the time and cost required for inspection and repair when failure symptoms occur can be saved, and incorrect repair or unnecessary repair can be prevented. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and other aspects, features, and advantages of the present disclosure will become more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which:

[0019] Figure 1 is a view of a device for analyzing the cause of a failure caused by insulation breakdown based on big data analysis according to a first exemplary embodiment of the present disclosure;

[0020] Figure 2 is a view of a device for analyzing the cause of a failure caused by insulation breakdown based on big data analysis according to a second exemplary embodiment of the present disclosure;

[0021] Figure 3 is a view showing a failure state interval and a normal state interval set when the insulation resistance value is below the minimum normal value according to an exemplary embodiment of the present disclosure;

[0022] Figure 4 is a view showing a failure state interval, a normal state interval, and a recovery state interval set when the insulation resistance value is below the minimum normal value and then changes to above the minimum normal value again according to an exemplary embodiment of the present disclosure;

[0023] Figure 5 is a view showing a data flow when the insulation resistance value is below the minimum normal value and then returns to above the minimum normal value again in an exemplary embodiment of the present disclosure in which a recovery inducing factor is selected and reflected in the analysis result information;

[0024] Figure 6 is a view showing a data set generated according to an exemplary embodiment of the present disclosure;

[0025] Figure 7 is a flowchart showing a process of calculating an influence index for a fault inducing factor according to an exemplary embodiment of the present disclosure;

[0026] Figure 8 is a view showing a plurality of fault inducing factor analysis tables generated whenever a fault symptom appears in order to calculate an influence index for a fault inducing factor according to an exemplary embodiment of the present disclosure;

[0027] Figure 9 is a view showing analysis result information actually generated by a device for analyzing a fault cause using driving information including an insulation resistance value according to an exemplary embodiment of the present disclosure; and

[0028] Figure 10 is a flowchart of a method for analyzing a fault cause due to insulation breakdown based on big data analysis according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0029] It is understood that as used herein, the term "vehicle" or "vehicular" or other similar terms generally include motor vehicles, such as passenger vehicles including sport utility vehicles (SUVs), buses, trucks, various commercial vehicles, watercraft including various ships and vessels, aircraft, etc., and include hybrid vehicles, electric vehicles, plug-in hybrid vehicles, hydrogen-powered vehicles, and other alternative fuel (e.g., fuel derived from resources other than petroleum) vehicles. As referred to herein, a hybrid vehicle is a vehicle having two or more power sources, such as a gasoline and electric dual-power vehicle.

[0030] Although the exemplary embodiments are described as using multiple units to perform the exemplary processes, it is understood that the exemplary processes may also be performed by one or more modules. Additionally, it is understood that the term "controller" / "control unit" refers to a hardware device including a memory and a processor. The memory is configured to store modules, and the processor is specifically configured to execute the modules to perform one or more processes described further below.

[0031] Furthermore, the control logic of the present disclosure may be implemented as a non-transitory computer-readable medium on a computer-readable medium containing executable program instructions executed by a processor, a controller / control unit, etc. Examples of the computer-readable medium include, but are not limited to, ROM, RAM, compact disc (CD)-ROM, magnetic tape, floppy disk, flash drive, smart card, and optical data storage device. The computer-readable recording medium may also be distributed in a networked computer system so that the computer-readable medium is stored and executed in a distributed manner, for example, by a telematics server or a controller area network (CAN).

[0032] The terms used herein are for describing particular embodiments only and are not intended to limit the present disclosure. Unless otherwise clearly specified in the context, as used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well. It will be further understood that the terms "comprising" and / or "including" when used in this specification, specify the presence of the described features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0033] Unless otherwise specifically stated or obvious from the context, as used herein, the term "about" is understood to be within the normal tolerances in the art, for example within 2 standard deviations of the mean. "About" can be understood to be within 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, 0.05%, or 0.01% of the stated value. Unless the context clearly indicates otherwise, all numerical values provided herein are modified by the term "about".

[0034] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings so that those skilled in the art can easily implement the present disclosure. However, the present disclosure can be implemented in various different forms and is not limited to the exemplary embodiments described herein.

[0035] In addition, components irrelevant to the present disclosure are omitted in the drawings to make the present disclosure clear, and throughout the specification, the same reference numerals refer to the same or similar components. Throughout the specification, when it is described that a component includes an element, unless a contrary description is made, this may mean that the component may further include a second element without excluding the second element.

[0036] Hereinafter, a device and a method for analyzing the cause of a failure caused by insulation breakdown based on big data analysis according to an exemplary embodiment of the present disclosure will be described with reference to the accompanying drawings.

[0037] Figure 1 is a diagram of a device for analyzing the cause of a failure caused by insulation breakdown based on big data analysis according to a first exemplary embodiment of the present disclosure. Referring to Figure 1 , a device for analyzing the cause of a failure caused by insulation breakdown based on big data analysis according to an exemplary embodiment of the present disclosure may include a resistance value data monitoring unit 100, a data set generation unit 200, a data set transmission / reception unit 300, an influence index calculation unit 400, and a result information generation unit 500. Each unit may be operated by a controller having a memory and a processor, where the processor is configured to execute the processes of the unit.

[0038] In particular, the resistance value data monitoring unit 100 may be configured to monitor whether the insulation resistance value of the vehicle decreases below the minimum normal value. The resistance value data monitoring unit 100 may be configured to periodically collect the insulation resistance value by using a sensor attached to the vehicle, and continuously monitor whether the collected insulation resistance value decreases below the minimum normal value. The insulation resistance value may be received included in the driving information of the vehicle, or may be received separately.

[0039] The driving information may refer to algorithmic numerical data obtained from a large number of components included in the vehicle, and according to an exemplary embodiment of the present disclosure, the driving information may refer to the temperature of the motor, the revolutions per minute (RPM) of the motor, the output of the heater, the RPM of the air conditioner compressor, etc., however, the present disclosure is not limited thereto, but any numerical data measured by using sensors and other measuring instruments may be used without limitation.

[0040] According to an exemplary embodiment of the present disclosure, when the insulation resistance value is below the minimum normal value, it may be determined that a fault symptom has occurred, and the dataset generation unit 200 may be requested to generate a fault inducing factor dataset and a normal state dataset including a plurality of fault inducing factor data to analyze the cause of the fault. In particular, the minimum normal value may refer to the minimum (e.g., lower limit) resistance value at which the state of the vehicle can be determined to be normal.

[0041] The minimum normal value may be about 1000 kΩ. When the insulation resistance value is below about 300 kΩ, it may be determined that an insulation breakdown fault has occurred, and when the measured insulation resistance value is between about 300 kΩ and 1000 kΩ, it may be determined as a preliminary fault step, and the fault cause analysis process of the present disclosure may be executed. When the measured insulation resistance value decreases below about 1000 kΩ, it may be determined that a fault state has occurred instead of a normal state, and a fault state interval and a normal state interval are set to analyze the cause, and a fault inducing factor dataset and a normal state dataset including a plurality of fault inducing factor data for the fault state interval and the normal state interval may be generated.

[0042] According to an exemplary embodiment of the present disclosure, the resistance value data monitoring unit 100 may be configured to monitor whether the insulation resistance value received after the fault state interval reaches a preset reference range again. When the insulation resistance value received after the fault state interval reaches the preset normal state value again, it may be determined that the normal state has been restored, and the interval from the time point when the normal state value is reached to before the preset time may be set as the restoration state interval, and the dataset generation unit 200 may be requested to generate a restoration time point dataset including a plurality of fault inducing factor data for the restoration state interval.

[0043] The data set generation unit 200 may be configured to, when the insulation resistance value is below the minimum normal value, set a fault state interval and a normal state interval according to a preset reference, and generate a fault inducing factor data set and a normal state data set including a plurality of fault inducing factor data for the fault state interval and the normal state interval. The data set generation unit 200 may be configured to, when the monitored insulation resistance value by the resistance value data monitoring unit 100 is below the minimum normal value, determine that a fault symptom has occurred, and set a fault state interval and a normal state interval according to a preset reference to analyze the cause of the fault.

[0044] According to an exemplary embodiment, after setting the fault state interval and the normal state interval, a fault inducing factor data set and a normal state data set including a plurality of fault inducing factor data for the fault state interval and the normal state interval may be generated. In particular, the data set may be generated in the form of a data table, and the data table includes data items that can analyze the cause of the fault among various information included in the driving information during the corresponding interval. However, the present disclosure is not limited thereto, and any data set that can transmit data for inferring the factor causing the fault may be used without limitation.

[0045] In addition, according to an exemplary embodiment of the present disclosure, the average RPM of the motor in the interval, the average RPM of the generator in the interval, the change slope of the RPM of the air conditioner compressor, the average power of the high voltage heater (PTC), the average output of the low voltage DC-DC converter (LDC), and the average speed of the vehicle, etc. may exist as data items in the data set and may be formed in the form of a data table. The data set generation unit 200 may be configured to set the interval from the time point when the measured insulation resistance value is below the minimum normal value to a preset time before as the fault state interval.

[0046] In addition, according to an exemplary embodiment, the interval from the start time point of the fault state interval to a preset time before may be set as the normal state interval. According to an exemplary embodiment, the data set generation unit 200 may be configured to set the interval from the time point when the measured insulation resistance value is below the minimum normal value to a preset time before as the fault state interval, and may be configured to set the interval from the start time point of the fault state interval to a preset time before as the normal state interval.

[0047] The data set generation unit 200 may be configured to, when the insulation resistance value received after the fault state interval reaches the preset reference range again, when the insulation resistance value reaches above the minimum resistance value again, set the interval from the time point when the minimum resistance value is reached to a preset time before as the recovery state interval. The data set generation unit 200 may be configured to generate a recovery time point data set including a plurality of fault inducing factor data for the recovery state interval.

[0048] When the time for measuring the insulation resistance when setting the normal state interval, the fault state interval, and the recovery state interval respectively is x seconds, the fault state interval starting from the insulation resistance fault time point can be x seconds before the time point when the insulation resistance is abnormal, the normal state interval can be 2x seconds before x seconds, and the recovery state interval can be x seconds before the insulation resistance recovery time point.

[0049] The dataset transmission / reception unit 300 may be configured to transmit the generated dataset to the big data server and receive data corresponding to the dataset from the big data server. Additionally, the dataset transmission / reception unit 300 may be configured to transmit the generated recovery time point dataset to the big data server and receive data corresponding to the dataset from the big data server. The dataset may be received in the form of a data table, where the data table may refer to a data form including numerical data of multiple fault inducing factors, however, the present disclosure is not limited thereto.

[0050] According to an exemplary embodiment of the present disclosure, at least one of the generated datasets of the fault state interval, the normal state interval, and the recovery state interval may be transmitted to the big data server, and the big data server may generate data matching the corresponding dataset in the form of a data table and transmit the generated data to the dataset transmission / reception unit 300 again. The dataset transmission / reception unit 300 may be configured to transmit the generated recovery interval dataset to the big data server.

[0051] The impact index calculation unit 400 may be configured to calculate an impact index for the fault inducing factor using the received data. In particular, the impact index calculation unit 400 may be configured to calculate the impact index by respectively comparing the numerical data of the fault state interval and the normal state interval for the same fault inducing factor. The impact index calculation unit 400 may be configured to calculate the impact index using Equation 1.

[0052] Equation 1

[0053] Factor (impact index) = y / x

[0054] Where y is the numerical data of the fault inducing factor in the normal state interval and x is the numerical data of the fault inducing factor in the fault state interval.

[0055] According to the exemplary embodiment, it can be considered that as the impact index is farther from 1, the impact index is relatively larger. The impact index calculation unit 400 may be configured to periodically receive the fault state dataset and the recovery state dataset and calculate an impact index for the recovery inducing factor using the data corresponding to the received recovery state dataset and fault state dataset.

[0056] The impact index calculation unit 400 may be configured to calculate an impact index of data reflecting the recovery state interval using Equation 2.

[0057] Equation 2

[0058] Factor (impact index) = y / x

[0059] Where y is the numerical data of the fault induction factor in the fault state interval, and x is the numerical data of the fault induction factor in the recovery state interval.

[0060] According to an exemplary embodiment, it can be considered that as the impact index moves away from 1, the impact index is relatively large. The impact index calculation unit 400 may be configured to calculate the impact index using an impact index calculation process similar to the case of using the numerical data of the fault induction factor in the fault state interval even when using the numerical data of the fault induction factor in the recovery state interval when calculating the impact index.

[0061] In addition, the impact index calculation unit 400 may be configured to periodically receive a fault state data set and a recovery state data set, and calculate an impact index for the fault induction factor using data corresponding to the received fault state data set and recovery state data set. The result information generation unit 500 may be configured to generate analysis result information by selecting a fault induction factor based on the calculated impact index.

[0062] According to an exemplary embodiment of the present disclosure, the result information generation unit 500 may be configured to calculate an impact index for the fault induction factor using Equation 1 and Equation 2, and determine whether the impact on the fault is relatively small or relatively large by comparing the calculated impact indexes. In addition, result information may be generated by comparing the impact indexes calculated for the fault induction factors and selecting the fault induction factor having the largest impact index as the fault cause.

[0063] According to another exemplary embodiment of the present disclosure, result information may be generated by selecting a fault induction factor for which the impact index calculated for the fault induction factor is above a preset value as the fault cause. When the insulation resistance is abnormal, when the calculated impact index changes by about 5% or more, the corresponding fault induction factor may be selected as the fault cause, or it may be reflected in the cumulative number of times.

[0064] The result information generation unit 500 may be configured to select a fault induction factor by reflecting the calculated impact index and the cumulative number of times according to the impact index. According to an exemplary embodiment of the present disclosure, it can be considered that as the cumulative number of times increases, the possibility that the corresponding component becomes a fault induction factor increases. On the contrary, as the cumulative number of times decreases, the accuracy relatively decreases. In addition, in the case of 10 times or more, the reliability may be set to 100%.

[0065] When the magnitude of the influence index of a specific fault inducing factor is below a preset value, it can be determined that the influence cannot be judged and the cumulative count can be not counted. In particular, when the influence index is greater than about 0.95 and less than about 1.05, it can be determined that the influence cannot be judged and the cumulative count can be not counted.

[0066] The result information generating unit 500 can be configured to calculate the final influence degree by reflecting the calculated influence index and the cumulative count based on the influence index, and can calculate the final influence degree using Equation 3.

[0067] Equation 3

[0068] Final influence degree = abs(1 - factor) * cumulative count

[0069] where factor = influence index

[0070] The result information generating unit 500 can be configured to generate a fault inducing factor analysis table according to the analysis result whenever the measured insulation resistance value of the vehicle is below the minimum normal value and it is determined that a fault state has occurred. The fault inducing factor analysis table can include at least one of information for judging the relative magnitudes of the calculated influence index and the influence, fault inducing factor suspicion information, and induced factor suspicion selection cumulative count information.

[0071] In addition, the result information generating unit 500 can be configured to determine that the influence cannot be judged when the influence index is below the preset value, and can not add the count to the cumulative count. The analysis result information can include a reliability value, and the reliability value can increase as the cumulative count increases. Conversely, the reliability value can decrease as the cumulative count decreases.

[0072] The apparatus for analyzing the cause of a fault due to insulation breakdown based on big data analysis can further include a big data server configured to receive a data set from the data set transmitting / receiving unit, extract data corresponding to the data set, and transmit the extracted data to the data set transmitting / receiving unit. The big data server can be configured to continuously receive the insulation resistance value and driving information of the vehicle and store the insulation resistance value and driving information, and can process and restore the driving information of the data items included in the data set requested by the apparatus for analyzing the cause of the fault. However, the present disclosure is not limited thereto. In other words, the big data server can be configured to transmit the raw data for extracting the data items included in the data set to the apparatus for analyzing the cause of the fault, so that the processing of the data itself can be performed by the apparatus for analyzing the cause of the fault.

[0073] Figure 2 is a diagram of an apparatus for analyzing the cause of a fault due to insulation breakdown based on big data analysis according to another exemplary embodiment of the present disclosure. Refer toFigure 2 , in addition to the elements of the apparatus for analyzing the cause of a failure due to insulation breakdown based on big data analysis according to the first exemplary embodiment, the apparatus for analyzing the cause of a failure due to insulation breakdown based on big data analysis according to an exemplary embodiment of the present disclosure may further include an analysis result output unit 600.

[0074] In particular, the analysis result output unit 600 may be configured to output the analysis result information generated by the result information generation unit 500 to the user. According to an exemplary embodiment of the present disclosure, the analysis result output unit 600 may be connected to a display installed in a vehicle and may be configured to transmit the analysis result information to the display and output the analysis result information to the user. However, the present disclosure is not limited thereto, and any device capable of outputting information to the user, such as a speaker, may be used without limitation.

[0075] Figure 3 is a view showing a failure state interval and a normal state interval set when the insulation resistance value is below the minimum normal value according to an exemplary embodiment of the present disclosure. Refer to Figure 3 , according to an exemplary embodiment of the present disclosure, a failure state interval (A) and a normal state interval (B-1) set when the insulation resistance value is below the minimum normal value are shown, and the interval from the time point when the insulation resistance value is measured to a preset time may be set as the failure state interval (A), and the interval from the start time point of the failure state interval to a preset time may be set as the normal state interval (B-1).

[0076] According to an exemplary embodiment of the present disclosure, the minimum normal value may be about 1000 kΩ, and in the exemplary embodiment, when the periodically collected insulation resistance value is about 1000 kΩ, the failure state interval and the normal state interval may be set, and a failure inducing factor data set and a normal state data set including a plurality of failure inducing factor data may be generated.

[0077] Figure 4 is a view showing a failure state interval, a normal state interval, and a recovery state interval set when the insulation resistance value is below the minimum normal value and then changes to above the minimum normal value again according to an exemplary embodiment of the present disclosure. Refer to Figure 4 , according to an exemplary embodiment of the present disclosure, a failure state interval (A), a normal state interval (B-1), and a recovery state interval (B-2) set when the insulation resistance value is below the minimum normal value and then changes to above the minimum normal value again are shown, and the interval from the time point when the value reaches above the minimum normal value to a preset time may be set as the recovery state interval (B-2).

[0078] According to an exemplary embodiment of the present disclosure, the minimum normal value may be about 1000 kΩ, and in the exemplary embodiment, when the insulation resistance value periodically collected decreases to below about 1000 kΩ and then returns above about 1000 kΩ again, a fault state interval and a recovery state interval may be set, and a fault inducing factor data set and a recovery state data set including a plurality of fault inducing factor data may be generated.

[0079] Figure 5 is a view showing a data flow when the insulation resistance value is below the minimum normal value and then returns above the minimum normal value again in an exemplary embodiment of the present disclosure in which a recovery inducing factor is selected and reflected in the analysis result information. Refer to Figure 5 , which shows a data flow when the insulation resistance value exceeds a reference range in an exemplary embodiment in which a recovery inducing factor is selected and reflected in the analysis result information.

[0080] According to an exemplary embodiment of the present disclosure, when the result of monitoring the insulation resistance value measures that the insulation resistance value is below the minimum normal value and then returns above the minimum normal value again and returns from the normal state to the fault state and then to the recovery state, a fault inducing factor data set including a plurality of fault inducing factor data for the fault state interval, the normal state interval, and the recovery state interval may be transmitted to the big data server. In addition, according to the exemplary embodiment, data corresponding to the received data set may be transmitted from the big data server to a device for analyzing the cause of the fault.

[0081] Figure 6 is a view showing a data set generated according to an exemplary embodiment of the present disclosure. Refer to Figure 6 , according to an exemplary embodiment of the present disclosure, a data set may be generated in the form of a data directory having preset fault inducing factors as items as shown in Figure 6 .

[0082] The fault inducing factors may include the average RPM of the motor in a specific interval, the average RPM of the generator in a specific interval, the change slope of the air conditioner compressor, the average power of the PTC, the average output of the LDC, and the average speed of the vehicle, etc. However, the present disclosure is not limited thereto, and any factor that can analyze the cause of the change that can affect the insulation resistance value may be used without limitation.

[0083] Figure 7 is a flowchart showing a process of calculating an influence index for a fault inducing factor according to an exemplary embodiment of the present disclosure. Refer to Figure 7, according to an exemplary embodiment of the present disclosure, Equation 1 using data of fault inducing factors in a fault state interval and a normal state interval or Equation 2 using data of fault inducing factors in a fault state interval and a recovery state interval can be used to calculate an impact index, and the magnitude of the impact caused by a fault can be compared by comparing it with the impact indexes of other fault inducing factors.

[0084] Figure 8 is a view showing a plurality of fault inducing factor analysis tables generated each time a fault symptom appears for calculating an impact index for a fault inducing factor according to an exemplary embodiment of the present disclosure. Refer to Figure 8 , according to an exemplary embodiment of the present disclosure, shows a plurality of fault inducing factor analysis tables generated each time a fault symptom appears for calculating an impact index for a fault inducing factor.

[0085] Refer to Figure 8 , according to an exemplary embodiment of the present disclosure, if the result information generation unit 500 determines that the measured insulation resistance value is below the minimum normal value and a fault state occurs, a fault inducing factor analysis table is generated according to the number of occurrences of the fault state. Each fault inducing factor analysis table may include information for judging the relative magnitude of the impact through the calculated impact index, the inducing factor may be expressed as suspected, determined, or not an inducing factor, etc., and information about the cumulative number of times according to the impact index, that is, information about the cumulative number of times of suspected selection of the inducing factor, may be generated and included.

[0086] When the impact index is below a preset value, it can be determined that the impact cannot be judged, and the preset value can be changed and set according to the fault item, the type of vehicle, etc. Therefore, misjudgment due to measurement error or calculation error can be prevented, and overfitting can be minimized. Since the impact cannot be judged when the cumulative number of times or the impact index is below the preset value, the number of times may not be accumulated.

[0087] Figure 9 is a view showing analysis result information actually generated by a device for analyzing a fault cause using driving information including an insulation resistance value according to an exemplary embodiment of the present disclosure. Refer to Figure 9 , shows analysis result information generated by selecting a fault inducing factor by using a fault inducing factor analysis table as shown in Figure 8 when the actually measured insulation resistance value is below the minimum normal value during monitoring and measurement. The analysis result information may include a reliability value, and the reliability value may increase as the cumulative number of times increases. On the contrary, the reliability value may decrease as the cumulative number of times decreases.

[0088] Figure 10It is a flowchart of a method for analyzing the cause of a failure caused by insulation breakdown based on big data analysis according to an exemplary embodiment of the present disclosure. The method described below can be executed by a controller. In particular, the controller can be configured to monitor whether the insulation resistance value of the vehicle decreases below the minimum normal value (S10). In particular, the controller can be configured to monitor whether the insulation resistance value of the vehicle decreases below the minimum normal value. The insulation resistance value can be periodically collected using sensors attached to the vehicle, and the controller can continuously monitor whether the collected insulation resistance value decreases below the minimum normal value.

[0089] The insulation resistance value can be received included in the driving information of the vehicle, or can be received separately. In particular, the driving information can refer to algorithmic numerical data obtained by a large number of components included in the vehicle, and according to an exemplary embodiment of the present disclosure, the driving information can refer to the temperature of the motor, the RPM of the motor, the output of the heater, the RPM of the air conditioning compressor, etc. However, the present disclosure is not limited thereto, but any numerical data measured by sensors and other measuring instruments can be used without limitation.

[0090] The controller can be configured to determine whether the insulation resistance value is below the minimum normal value (S20). According to an exemplary embodiment of the present disclosure, the controller can be configured to determine that a failure symptom has occurred when the insulation resistance value is below the minimum normal value. In particular, the minimum normal value can refer to the minimum (e.g., lower limit) resistance value at which the state of the vehicle can be judged to be normal.

[0091] The minimum normal value can be about 1000 kΩ, and the controller can be configured to determine that a failure state has occurred rather than a normal state when the insulation resistance value decreases below about 1000 kΩ. When the insulation resistance value exceeds the preset reference range, the failure state interval and the normal state interval can be set according to the preset reference (S30). The controller can be configured to determine that a failure state has occurred rather than a normal state when the measured insulation resistance value decreases below about 1000 kΩ, and can set the failure state interval and the normal state interval to analyze the cause of the failure, and generate a failure inducing factor dataset and a normal state dataset including a plurality of failure inducing factor data for the failure state interval and the normal state interval.

[0092] In addition, the controller can be configured to determine that the normal state has been restored when the insulation resistance value received after the fault state interval reaches above the minimum normal value again, and can set the interval from the time point when the normal state value is reached to before the preset time as the restoration state interval, and can request the generation of a restoration time point data set including multiple fault inducing factor data for the restoration state interval. When the insulation resistance value is below the minimum normal value, the fault state interval and the normal state interval can be set according to a preset criterion, and a fault inducing factor data set and a normal state data set including multiple fault inducing factor data for the fault state interval and the normal state interval can be generated.

[0093] According to an exemplary embodiment of the present disclosure, when the insulation resistance value is below the minimum normal value, the fault state interval or the normal state interval can be set according to a preset criterion, and a fault inducing factor data set and a normal state data set including multiple fault inducing factor data for the fault state interval or the normal state interval can be generated. The controller can be configured to determine that a fault symptom has occurred when the insulation resistance value is below the minimum normal value, and can set the fault state interval and the normal state interval according to a preset criterion to analyze the cause of the fault.

[0094] After setting the fault state interval and the normal state interval, a fault inducing factor data set and a normal state data set including multiple fault inducing factor data for the fault state interval and the normal state interval can be generated. In particular, the data set can be generated in the form of a data table, and the data table includes data items that can analyze the cause of the fault among various information included in the driving information during the corresponding interval. However, the present disclosure is not limited thereto, and any data set that can transmit data for inferring the factors causing the fault can be used without limitation.

[0095] In addition, the average RPM of the motor in the interval, the average RPM of the generator in the interval, the change slope of the RPM of the air conditioner compressor, the average power of the high-pressure heater (PTC), the average output of the LDC, and the average speed of the vehicle, etc. can exist as data items in the data set and can be formed in the form of a data table. The interval from the time point when the measured insulation resistance value is below the minimum normal value to before the preset time can be set as the fault state interval.

[0096] In addition, according to an exemplary embodiment, the interval from the start time point of the fault state interval to before the preset time can be set as the normal state interval. The interval from the time point when the measured insulation resistance value is below the minimum normal value to before the preset time can be set as the fault state interval, and the interval from the start time point of the fault state interval to before the preset time can be set as the normal state interval.

[0097] When the insulation resistance value received after the fault state interval reaches the preset reference range again, and when the insulation resistance value reaches above the minimum resistance value again, the interval from the time point when the minimum resistance value is reached to before the preset time is set as the recovery state interval. A recovery time point data set including multiple fault inducing factor data for the recovery state interval can be generated.

[0098] A fault inducing factor data set and a normal state data set including multiple fault inducing factor data for the normal state interval can be generated (S40). The generated data sets can be transmitted to the big data server, and data corresponding to the data sets can be received from the big data server. In addition, the generated recovery time point data set can be transmitted to the big data server, and data corresponding to the data set can be received from the big data server.

[0099] According to an exemplary embodiment of the present disclosure, the data set can be received in the form of a data table. Here, the data table can refer to a data form including numerical data of multiple fault inducing factors. However, the present disclosure is not limited thereto. At least one of the generated data sets of the fault state interval, the normal state interval, and the recovery state interval can be transmitted to the big data server, and the big data server can generate data matching the corresponding data set in the form of a data table and transmit the generated data to the device for analyzing the cause of the fault.

[0100] The generated recovery interval data set can be transmitted to the big data server. An impact index can be calculated for the fault inducing factor by using the data corresponding to the fault inducing factor data set and the normal state data set (S50). An impact index can be calculated for the fault inducing factor by using the received data. In addition, an impact index can be calculated by comparing the numerical data of the fault state interval and the normal state interval for the same fault inducing factor respectively. In particular, the impact index can be calculated by Equation 1. It can be considered that as the impact index moves away from 1, the impact index increases.

[0101] According to an exemplary embodiment of the present disclosure, the fault state data set and the recovery state data set can be received periodically, and an impact index can be calculated for the recovery inducing factor by using the data corresponding to the received recovery state data set and the fault state data set. The impact index reflecting the data of the recovery state interval can be calculated by Equation 2. It can be considered that as the impact index moves away from 1, the impact index increases.

[0102] In addition, even when numerical data of a fault inducing factor in a recovery state interval is used when calculating an impact index, an impact index calculation process similar to that in the case of using numerical data of a fault inducing factor in a fault state interval can be used to calculate the impact index. The fault state data set and the recovery state data set can be received periodically, and data corresponding to the received recovery state data set and fault state data set can be used to calculate the impact index for the recovery inducing factor.

[0103] Analysis result information can be generated by selecting a fault inducing factor based on the calculated impact index (S60). In particular, analysis result information can be generated by selecting a fault inducing factor based on the calculated impact index. Equations 1 and 2 can be used to calculate the impact index for the fault inducing factor, and the controller can be configured to judge whether the impact on the fault is relatively small or relatively large by comparing the calculated impact indices.

[0104] Result information can be generated by comparing the impact indices calculated for the fault inducing factors and selecting the fault inducing factor with the largest impact index among them as the fault cause. In particular, result information can be generated by selecting a fault inducing factor whose calculated impact index is above a preset value as the fault cause. The fault inducing factor can be selected by reflecting the calculated impact index and the cumulative number of times according to the calculated impact index.

[0105] In addition, whenever the measured insulation resistance value of the vehicle is below the minimum normal value and it is determined that a fault state has occurred, a fault inducing factor analysis table can be generated according to the analysis result. The fault inducing factor analysis table can include at least one of information for judging the calculated impact index and the relative magnitude of the impact, fault inducing factor suspicion information, and cumulative number of times of induced factor suspicion selection information.

[0106] According to an exemplary embodiment of the present disclosure, when the impact index is below the preset value, it is determined that the impact cannot be judged, and the number of times may not be accumulated to the cumulative number of times. The analysis result information can include a reliability value, and the reliability value can increase as the cumulative number of times increases. On the contrary, the reliability value can decrease as the cumulative number of times decreases. The big data server can be configured to receive a data set from a device that analyzes the fault cause due to insulation breakdown based on big data analysis, extract data corresponding to the data set, and transmit the extracted data to the device that analyzes the fault cause.

[0107] In addition, the big data server may be configured to continuously receive the insulation resistance value and driving information from the vehicle and store the insulation resistance value and driving information, and may process and restore the driving information of the data included in the dataset requested by the device for analyzing the cause of the failure. However, the present disclosure is not limited thereto, and the big data server may be configured to transmit the raw data for extracting the data items included in the dataset to the device for analyzing the cause of the failure, so that the processing of the data itself may be performed by the device for analyzing the cause of the failure.

[0108] According to an exemplary embodiment of the present disclosure, the generated analysis result information may be output to the user. Additionally, the analysis result output unit may be connected to a display installed in the vehicle, and may transmit the analysis result information to the display and output the analysis result information to the user. However, the present disclosure is not limited thereto, and any device capable of outputting information to the user, such as a speaker, may be utilized without limitation.

Claims

1. An apparatus for analyzing the cause of a failure caused by insulation breakdown based on big data analysis, comprising: a memory for storing program instructions; and a processor for executing the program instructions, wherein the program instructions, when executed, are configured to: monitor whether the insulation resistance value of the vehicle decreases below the minimum normal value; when the insulation resistance value is below the minimum normal value, set a fault state interval and a normal state interval according to a preset reference, and generate a fault inducing factor data set and a normal state data set including a plurality of fault inducing factor data for the fault state interval and the normal state interval; transmit the generated data set to a big data server, and receive data corresponding to the data set from the big data server; calculate an influence index for the fault inducing factors by using the received data; and generate analysis result information by selecting fault inducing factors based on the calculated influence index.

2. The apparatus according to claim 1, wherein the program instructions, when executed, are configured to: when the insulation resistance value is below the minimum normal value, set the interval from the time point of measuring the insulation resistance value to a preset time before as the fault state interval, and set the interval from the start time point of the fault state interval to a preset time before as the normal state interval.

3. The apparatus according to claim 1, wherein the program instructions, when executed, are configured to: monitor whether the insulation resistance value received after the fault state interval returns above the minimum normal value; when the insulation resistance value reaches above the minimum normal value again, set the interval from the time point when the insulation resistance value reaches the minimum normal value to a preset time before as the recovery state interval, and generate a recovery time point data set including a plurality of fault inducing factor data for the recovery state interval; and transmit the generated recovery time point data set to the big data server, and receive data corresponding to the data set from the big data server.

4. The apparatus according to claim 1, wherein the program instructions, when executed, are configured to: select fault inducing factors by reflecting the calculated influence index and the cumulative number of times according to the influence index.

5. The apparatus according to claim 3, wherein the program instructions, when executed, are configured to: periodically receive a fault state data set and a recovery state data set, and calculate an influence index for the fault inducing factors by using the data corresponding to the received fault state data set and the recovery state data set; and select fault inducing factors based on the calculated influence index, and reflect the selected fault inducing factors in the analysis result information.

6. The apparatus according to claim 1, wherein the program instructions, when executed, are configured to: whenever the measured insulation resistance value of the vehicle is below the minimum normal value and it is determined that a fault state occurs, generate a fault inducing factor analysis table according to the analysis result.

7. The apparatus according to claim 6, wherein The fault inducing factor analysis table includes at least one of information for judging influencing indicators and relative magnitudes of influences in calculation, fault inducing factor suspicion information, and cumulative number information of induced factor suspicion selections.

8. The apparatus according to claim 1, wherein, the program instructions are configured, when executed, to: when the influencing indicator is below a preset value, determine that the influence cannot be judged and do not accumulate the number to the cumulative number.

9. The apparatus according to claim 1, wherein, the analysis result information includes a reliability value, and the reliability value increases as the cumulative number increases, and conversely, the reliability value decreases as the cumulative number decreases.

10. The apparatus according to claim 1, wherein, the big data server receives the data set, extracts data corresponding to the data set, and transmits the extracted data to the processor.

11. A method for analyzing the cause of a fault caused by insulation breakdown based on big data analysis, comprising: the processor monitors whether the insulation resistance value of the vehicle decreases below the minimum normal value; when the insulation resistance value is below the minimum normal value, the processor sets a fault state interval and a normal state interval according to a preset criterion, and generates a fault inducing factor data set and a normal state data set including a plurality of fault inducing factor data for the fault state interval and the normal state interval; the processor transmits the generated data set to the big data server and receives data corresponding to the data set from the big data server; the processor calculates an influencing indicator for the fault inducing factor by using the received data; and the processor generates analysis result information by selecting a fault inducing factor based on the calculated influencing indicator.

12. The method according to claim 11, wherein, generating the data set includes: when the insulation resistance value is below the minimum normal value, the processor sets the interval from the time point of measuring the insulation resistance value to a preset time before as the fault state interval, and sets the interval from the start time point of the fault state interval to a preset time before as the normal state interval.

13. The method according to claim 11, further comprising: the processor monitors whether the insulation resistance value received after the fault state interval reaches above the minimum normal value; when the insulation resistance value reaches above the minimum normal value again, the processor sets the interval from the time point when the insulation resistance value reaches the minimum normal value to a preset time before as a recovery state interval, and generates a recovery time point data set including a plurality of fault inducing factor data for the recovery state interval; and the processor transmits the generated recovery time point data set to the big data server and receives data corresponding to the data set from the big data server.

14. The method according to claim 11, wherein, generating the analysis result information includes: the processor selects a fault inducing factor by reflecting the calculated influencing indicator and the cumulative number according to the influencing indicator.

15. The method according to claim 13, further comprising: The processor periodically receives a fault status data set and a recovery status data set, and calculates an impact metric for a fault inducing factor by using data corresponding to the received fault status data set and the recovery status data set; And The processor selects a fault inducing factor based on the calculated impact metric and reflects the selected fault inducing factor in the analysis result information.

16. The method according to claim 11, wherein, Generating the analysis result information includes: Whenever the measured insulation resistance value of the vehicle is below the minimum normal value and it is determined that a fault state has occurred, the processor generates a fault inducing factor analysis table according to the analysis result.

17. The method according to claim 16, wherein, The fault inducing factor analysis table includes at least one of information for judging the relative magnitudes of the calculated impact metric and the impact, fault inducing factor suspicion information, and cumulative number of times of suspected selection of the inducing factor.

18. The method according to claim 11, wherein, In generating the analysis result information, when the impact metric is below a preset value, it is determined that the impact cannot be judged and the number of times is not added to the cumulative number of times.

19. The method according to claim 11, wherein, In generating the analysis result information, the analysis result information includes a reliability value, and the reliability value increases as the cumulative number of times increases. On the contrary, the reliability value decreases as the cumulative number of times decreases.

20. The method according to claim 11, further comprising: The processor transmits the data set to the big data server; The processor receives extraction data corresponding to the data set from the big data server.

Images