Fault cause analysis system and method using numerical data of vehicle equipment

Abstract

The present invention discloses a fault cause analysis system, which utilizes numerical data of vehicle equipment during vehicle driving and analyzes the equipment numerical data included in the vehicle driving data to select fault induction factors, thereby extracting numerical data of each device from the vehicle driving data even when the fault symptoms are not continuous and appear intermittently, and analyzing the equipment numerical data to select the fault induction factors, thereby reducing the time and cost required for inspection and maintenance of vehicle equipment when the fault symptoms appear, and avoiding erroneous maintenance or excessive maintenance.

Description

Technical Field

[0001] The present disclosure relates to a system and method for analyzing numerical data of equipment included in a vehicle during driving of the vehicle when a fault occurs, and more specifically, to a system and method for analyzing the cause of a fault using numerical data of vehicle equipment during driving of the vehicle. When it is determined that the numerical data value of the vehicle equipment is outside a predetermined reference range, an impact index can be calculated for each fault inducing factor. Background Art

[0002] According to the conventional fault cause analysis method, only when the fault condition can be reproduced in the vehicle after being received at the service center, can the corresponding fault cause be analyzed and repairs be performed.

[0003] Furthermore, a method of removing and disassembling a plurality of linkages to analyze the cause of a failure while reproducing the same failure condition may result in excessive time and cost required to analyze the cause of the failure and repair the failure.

[0004] Furthermore, in the case where the failed device is an important component of the vehicle, if the cause of the failure cannot be accurately determined, the entire device (or linkage) may have to be replaced, which may cause problems due to improper vehicle maintenance. Summary of the invention

[0005] The purpose of the present disclosure is to provide a fault cause analysis system based on, for example, big data of numerical data of vehicle equipment during vehicle driving, which can analyze the numerical data of vehicle equipment included in the vehicle's driving information to calculate an influence index for each expected fault induction factor, and select the fault induction factor by the relative size of the influence index and the cumulative number of times the influence index is calculated to be greater than or equal to a reference value, so that the fault induction cause can be confirmed even in the case where the occurrence of the fault induction factor is not continuous and occurs intermittently.

[0006] According to an embodiment of the present disclosure, a fault cause analysis system that utilizes numerical data of vehicle equipment during vehicle driving may include: an equipment numerical data analysis device, which generates at least one of a fault induction factor data set, a normal state data set, and a recovery state data set when the equipment numerical data value is measured to be outside a predetermined reference range by monitoring the equipment numerical data of the vehicle and transmits the data set to a big data server, receives data corresponding to the data set from the big data server and calculates an influence index for each fault induction factor, and selects a fault induction factor based on the calculated influence index; and the big data server, which transmits the data corresponding to the received data set to the equipment numerical data analysis device.

[0007] According to an embodiment of the present disclosure, a device numerical data analysis apparatus may include: a device numerical data monitoring unit, which periodically receives vehicle status information including device numerical data from multiple sensors installed in a vehicle, and monitors whether the received device numerical data value is outside a predetermined reference range; a data set generation unit, which sets a fault state interval and a normal state interval according to a predetermined reference when the device numerical data value is outside the predetermined reference range, and generates a fault induction factor data set and a normal state data set including multiple fault induction factor data for the fault state interval and the normal state interval; a data set transmission unit, which transmits the generated data set to a big data server; an influence index calculation unit, which receives data corresponding to the data set from the big data server, and calculates the influence index for each fault induction factor by using the received data; and a result information generation unit, which generates analysis result information by selecting the fault induction factor based on the calculated influence index.

[0008] According to an embodiment of the present disclosure, the data set generation unit can set the interval from the time point when the device numerical data value is measured when it is outside a predetermined reference range to a predetermined time before as a fault state interval, and set the interval from the start time point of the fault state interval to a predetermined time before as a normal state interval.

[0009] According to an embodiment of the present disclosure, the device numerical data monitoring unit can monitor whether the device numerical data value received after the fault state interval falls into the predetermined reference range again, the data set generation unit can set the interval from the time point falling into the predetermined reference range to before the predetermined time as a recovery state interval, and generate a recovery time point data set for the recovery state interval including multiple fault inducing factor data, and the data set transmission unit can transmit the generated recovery time point data set to the big data server.

[0010] According to an embodiment of the present disclosure, the result information generating unit may select the fault induction factor by reflecting the calculated influence index and according to the cumulative number of the influence index.

[0011] According to an embodiment of the present disclosure, the impact indicator calculation unit can periodically receive a fault status data set and a recovery status data set, and calculate the impact indicator for each recovery induction factor by utilizing data corresponding to the received fault status data set and recovery status data set, and the result information generation unit can select the fault induction factor based on the calculated impact indicator to reflect the fault induction factor to the analysis result information.

[0012] According to an embodiment of the present disclosure, a fault cause analysis device based on big data using numerical data of vehicle equipment during vehicle driving may include: an equipment numerical data monitoring unit, which periodically receives vehicle status information including equipment numerical data from multiple sensors installed in the vehicle, and monitors whether the received equipment numerical data value is outside a predetermined reference range; a data set generation unit, which sets a fault state interval and a normal state interval according to a predetermined reference when the equipment numerical data value is outside the predetermined reference range, and generates a fault induction factor data set and a normal state data set including multiple fault induction factor data for the fault state interval and the normal state interval; an influence index calculation unit, which calculates an influence index for each fault induction factor by using data corresponding to the fault induction factor data set and the normal state data set; and a result information generation unit, which generates analysis result information by selecting a fault induction factor based on the calculated influence index.

[0013] According to an embodiment of the present disclosure, a method for analyzing the cause of a fault based on big data utilizing numerical data of vehicle equipment during vehicle driving may include the following steps: monitoring the equipment numerical data of the vehicle, generating at least one of a fault induction factor data set, a normal state data set, and a recovery state data set when the value of the equipment numerical data is measured to be outside a predetermined reference range and transmitting the data set to a big data server, receiving data corresponding to the data set from the big data server and calculating an influence index for each fault induction factor, and selecting a fault induction factor based on the calculated influence index; and transmitting the data corresponding to the received data set to an equipment numerical data analysis device.

[0014] According to an embodiment of the present disclosure, a device numerical data analysis method may include the following steps: periodically receiving vehicle status information including device numerical data from multiple sensors installed in a vehicle, and monitoring whether the received device numerical data value is outside a predetermined reference range; when the device numerical data value is outside the predetermined reference range, setting a fault state interval and a normal state interval according to a predetermined reference, and generating a fault induction factor data set and a normal state data set including multiple fault induction factor data for the fault state interval and the normal state interval; transmitting the generated data set to a big data server; receiving data corresponding to the data set from the big data server, and calculating an influence index for each fault induction factor by using the received data; and generating analysis result information by selecting the fault induction factor based on the calculated influence index.

[0015] According to an embodiment of the present disclosure, in the step of generating a data set, the interval from the time point when the device numerical data value is measured when it is outside a predetermined reference range to a predetermined time before can be set as a fault state interval, and the interval from the start time point of the fault state interval to a predetermined time before can be set as a normal state interval.

[0016] According to an embodiment of the present disclosure, in the monitoring step, it can be monitored whether the device numerical data value received after the fault state interval falls into the predetermined reference range again, and in the step of generating a data set, the interval from the time point falling into the predetermined reference range to before the predetermined time can be set as a recovery state interval, and a recovery time point data set including multiple fault inducing factor data for the recovery state interval can be generated, and in the step of transmitting the data set to a big data server, the generated recovery time point data set can be transmitted to the big data server.

[0017] According to an embodiment of the present disclosure, in the step of generating the analysis result information, the fault induction factor may be selected by reflecting the calculated influence index and according to the cumulative number of the influence index.

[0018] According to an embodiment of the present disclosure, in the step of calculating the impact indicator, a normal state data set and a recovery state data set can be periodically received, and the impact indicator can be calculated for each recovery inducing factor by utilizing data corresponding to the received recovery state data set and normal state data set, and in the step of generating the analysis result information, a fault inducing factor can be selected based on the calculated impact indicator to reflect the fault inducing factor to the analysis result information.

[0019] According to an embodiment of the present disclosure, a device numerical data analysis apparatus may include: a device numerical data monitoring unit, which periodically receives vehicle status information including device numerical data from multiple sensors installed in a vehicle, and monitors whether the received device numerical data value is outside a predetermined reference range; a data set generation unit, which sets a fault state interval and a recovery state interval according to a predetermined reference when the device numerical data value is outside the predetermined reference range, and generates a fault induction factor data set and a normal state data set including multiple fault induction factor data for the fault state interval and the recovery state interval; a data set transmission unit, which transmits the generated data set to a big data server; an influence index calculation unit, which receives data corresponding to the data set from the big data server, and calculates the influence index for each fault induction factor by using the received data; and a result information generation unit, which generates analysis result information by selecting the fault induction factor based on the calculated influence index.

[0020] According to an embodiment of the present disclosure, the device numerical data monitoring unit can monitor whether the device numerical data value received after the fault state interval falls into the predetermined reference range again, the data set generation unit can set the interval from the time point falling into the predetermined reference range to before the predetermined time as a recovery state interval, and generate a recovery time point data set for the recovery state interval including multiple fault inducing factor data, and the data set transmission unit can transmit the generated recovery time point data set to the big data server.

[0021] According to an embodiment of the present disclosure, a device numerical data analysis method may include the following steps: periodically receiving vehicle status information including device numerical data from multiple sensors installed in a vehicle, and monitoring whether the received device numerical data value is outside a predetermined reference range; when the device numerical data value is outside the predetermined reference range, setting a fault state interval and a recovery state interval according to a predetermined reference, and generating a fault induction factor data set and a normal state data set including multiple fault induction factor data for the fault state interval and the recovery state interval; transmitting the generated data set to a big data server; receiving data corresponding to the data set from the big data server, and calculating an influence index for each fault induction factor by using the received data; and generating analysis result information by selecting the fault induction factor based on the calculated influence index.

[0022] According to an embodiment of the present disclosure, the device numerical data analysis method may further include: monitoring whether the device numerical data value received after the fault state interval falls into the predetermined reference range again; setting the interval from the time point falling into the predetermined reference range to before the predetermined time as a recovery state interval, and generating a recovery time point data set for the recovery state interval including multiple fault induction factor data; and transmitting the generated recovery time point data set to a big data server.

[0023] According to the present disclosure, numerical data of devices included in the driving data of a vehicle can be analyzed to select a fault induction factor, so that even in a case where the fault symptom is not continuous and occurs intermittently, the numerical data of each device is extracted from the driving data of the vehicle, and the numerical data is analyzed to select the fault induction factor, thereby reducing the time and cost for inspection and repair when the fault symptom occurs, and preventing erroneous repair or excessive repair. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The above and other objects, features and other advantages of the present disclosure will be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings, in which:

[0025] Figure 1 1 is a block diagram of a configuration of a failure cause analysis system based on big data utilizing numerical data of vehicle equipment during vehicle travel according to an embodiment of the present disclosure.

[0026] Figure 2 is a detailed configuration block diagram of a device numerical data analysis apparatus according to a first embodiment of the present disclosure.

[0027] Figure 3 is a detailed configuration block diagram of a device numerical data analysis apparatus according to a second embodiment of the present disclosure.

[0028] Figure 4 is a diagram illustrating a failure state interval and a normal state interval set when a device numerical data value is outside a predetermined reference range according to an embodiment of the present disclosure.

[0029] Figure 5 is a diagram illustrating a failure state interval, a normal state interval, and a recovery state interval set when a device numerical data value is outside a predetermined reference range and then returns to within a predetermined reference range according to an embodiment of the present disclosure.

[0030] Figure 6 is a diagram illustrating a data flow in a case where a device numerical data value is outside a predetermined reference range in an embodiment of selecting a recovery inducing factor to be reflected in analysis result information according to the present disclosure.

[0031] Figure 7 is a diagram illustrating a data set generated according to an embodiment of the present disclosure.

[0032] Figure 8 is a flowchart illustrating a process of calculating an impact index for each fault inducing factor according to an embodiment of the present disclosure.

[0033] Fig. 9 is a diagram illustrating a plurality of fault induction factor analysis tables generated whenever a fault symptom occurs in order to calculate an influence index for each fault induction factor according to an embodiment of the present disclosure.

[0034] Fig.10 is a diagram showing analysis result information actually generated by a failure cause analysis system based on big data using numerical data of vehicle equipment during vehicle travel according to an embodiment of the present disclosure.

[0035] Fig.11 is a flowchart of a method for analyzing a cause of a failure based on big data using numerical data of vehicle equipment during vehicle travel according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0036] It is understood that the term "vehicle" or "vehicular" or other similar terms as used herein generally include motor vehicles, such as passenger vehicles including sport utility vehicles (SUVs), buses, trucks, various commercial vehicles, watercraft including various ships and boats, aircraft, etc., and include hybrid vehicles, electric vehicles, plug-in hybrid vehicles, hydrogen-powered vehicles, and other alternative fuel (e.g., fuels 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.

[0037] The terms used herein are only for the purpose of describing a specific embodiment and are not intended to limit the present disclosure. As used herein, the singular forms "one", "an" and "the" are also intended to include plural forms, unless the context clearly indicates otherwise. It will also be understood that the terms "including" and / or "comprising" when used in this specification specify the presence of the features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their groups. As used herein, the term "and / or" includes any and all combinations of one or more related listed items. Throughout the specification, unless explicitly described to the contrary, the word "including" and variants such as "including" or "comprising" will be understood to imply the inclusion of the elements, but do not exclude any other elements. In addition, the terms "unit", "piece", "-device" and "module" described in the specification refer to units for processing at least one function and operation, and can be implemented by hardware components or software components and combinations thereof.

[0038] In addition, 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, controller, etc. Examples of computer-readable media include, but are not limited to, ROM, RAM, compact disk (CD)-ROM, magnetic tape, floppy disk, flash drive, smart card, and optical data storage device. The computer-readable 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).

[0039] Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art to which the present disclosure belongs can easily implement the present disclosure. However, the present disclosure can be implemented in various different forms and is not limited to the embodiments described herein.

[0040] Furthermore, in the drawings, in order to clearly describe the present disclosure, parts irrelevant to the description are omitted, and the same reference numerals denote the same parts throughout the specification.

[0041] Throughout the specification, when a part is described as “comprising” a certain component, it means that the part may further include other components, rather than excluding the other components, unless specifically stated otherwise.

[0042] Hereinafter, a failure cause analysis system and method based on big data using numerical data of vehicle equipment during vehicle travel according to an embodiment of the present disclosure will be described with reference to the accompanying drawings.

[0043] Figure 1 1 is a block diagram of a configuration of a failure cause analysis system based on big data utilizing numerical data of vehicle equipment during vehicle travel according to an embodiment of the present disclosure.

[0044] Reference Figure 1 According to an embodiment of the present disclosure, a fault cause analysis system based on big data utilizing numerical data of vehicle equipment during vehicle driving may include an equipment numerical data analysis device 10 and a big data server 20 .

[0045] The equipment numerical data analysis device 10 monitors the equipment numerical data of the vehicle. When the measured equipment numerical data value is outside the predetermined reference range, the equipment numerical data analysis device 10 can generate at least one of the fault induction factor data set, the normal state data set and the recovery state data set and transmit the data set to the big data server.

[0046] Furthermore, according to an embodiment, the device numerical data analyzing apparatus 10 may calculate an influence index for each fault inducing factor by receiving data corresponding to a data set, and generate analysis result information by selecting a fault inducing factor based on the calculated influence index.

[0047] Will refer to Figure 2 The device numerical data analyzing apparatus 10 is described in more detail.

[0048] The big data server 20 may transmit data corresponding to the received data set to the device numerical data analysis apparatus 10 .

[0049] According to an embodiment of the present disclosure, the big data server 20 may receive a data set from the device numerical data analysis apparatus 10 , and extract data corresponding to the data set and transmit the data to the device numerical data analysis apparatus.

[0050] Here, the big data server 20 may be a server having a large-capacity storage device and computing power, and is not limited to a server having a specific hardware standard.

[0051] According to an embodiment of the present disclosure, the big data server 20 may extract data corresponding to a data item included in a received data set by utilizing device numerical data, and perform processing by utilizing computing resources of the big data server during the extraction process.

[0052] For example, if there is an item of the average motor RPM value within an interval in the data set, the big data server can extract data corresponding to the average motor RPM value within the interval by performing a process of obtaining the average motor RPM value in the device numerical data within the corresponding interval.

[0053] According to an embodiment of the present disclosure, the big data server 20 can periodically receive driving data from the vehicle and store the driving data in the big data server 20, and extract data included in the driving data corresponding to the fault induction factor data set and the normal state data set received from the equipment numerical data analysis device 10, generate the data in the form of a data table, and transmit the data to the equipment numerical data analysis device 10.

[0054] According to an embodiment of the present disclosure, the big data server can continuously receive device numerical data from the vehicle and store the device numerical data, and process and return the device numerical data of the data items included in the data set requested by the device numerical data analysis device, but is not limited to this, and can transmit the original data used to extract the data items included in the data set to the device numerical data analysis device, so that the processing itself is also performed in the device numerical data analysis device.

[0055] Figure 2 is a detailed configuration block diagram of a device numerical data analysis apparatus according to a first embodiment of the present disclosure.

[0056] Reference Figure 2 According to the first embodiment of the present disclosure, the device numerical data analysis apparatus 10 may further include a device numerical data monitoring unit 100, a data set generating unit 200, a data set transmitting unit 300, an impact index calculating unit 400 and a result information generating unit 500.

[0057] The device numerical data monitoring unit 100 may periodically receive vehicle state information including device numerical data from a plurality of sensors installed in the vehicle, and monitor whether the received device numerical data value is outside a predetermined reference range.

[0058] Here, the device numerical data may refer to arithmetic numerical data obtained from multiple components included in the vehicle, and according to an embodiment of the present disclosure, the device numerical data may refer to motor temperature, motor RPM, heater output, air-conditioning compressor RPM, etc., but is not limited to this, and if the device numerical data is numerical data measured by sensors and other measuring devices, the device numerical data can be used without restriction.

[0059] According to an embodiment of the present disclosure, the device numerical data monitoring unit 100 can continuously monitor whether the device numerical data value of each device is outside a predetermined baseline range, and if the device numerical data value is outside the predetermined baseline range, the device numerical data monitoring unit 100 can determine that a fault symptom has occurred, and request the data set generation unit 200 to generate a fault induction factor data set including multiple fault induction factor data and a normal state data set to analyze the cause of the fault.

[0060] According to an embodiment of the present disclosure, the device numerical data monitoring unit 100 may monitor whether the device numerical data value received after the fault state interval falls within a predetermined reference range again.

[0061] According to an embodiment of the present disclosure, if the device numerical data value received after the fault state interval reaches the predetermined normal state value again, it is judged that it has recovered to the normal state and the interval from the time point when the normal state value is reached to before the predetermined time is set as the recovery state interval, and the data set generation unit 200 is requested to generate a recovery time point data set including multiple fault induction factor data for the recovery state interval.

[0062] If the device numerical data value is outside the predetermined reference range, the data set generation unit 200 can set a fault state interval or a normal state interval according to the predetermined reference, and generate a fault induction factor data set and a normal state data set including multiple fault induction factor data for the fault state interval or the normal state interval.

[0063] According to an embodiment of the present disclosure, as a monitoring result of the device numerical data monitoring unit 100, if the device numerical data value is outside a predetermined reference range, the data set generation unit 200 can determine that a fault symptom has occurred, and set a fault state interval and a normal state interval according to a predetermined reference to analyze the cause of the fault.

[0064] According to this embodiment, the data set generation unit 200 sets a fault state interval and a normal state interval, and then can generate a fault induction factor data set and a normal state data set including a plurality of fault induction factor data for the fault state interval and the normal state interval.

[0065] Here, the data set can be generated in the form of a data table, which includes data items capable of analyzing the cause of the fault among various information included in the equipment numerical data during the corresponding interval, but is not limited to this, and if item information about the data capable of inferring the fault inducing factor can be transmitted, the data set can be used without restriction.

[0066] According to an embodiment of the present disclosure, a data set may include an average motor RPM value within a range, an average generator RPM value within a range, an air-conditioning compressor RPM change slope, a PTC (high voltage heater) power average value, an LDC output average value, a vehicle speed average value, etc. as data items, and may be formed in the form of a data table.

[0067] According to an embodiment of the present disclosure, the data set generation unit 200 can set the interval from the time point when the device numerical data value is measured to be outside the predetermined reference range to a predetermined time before as a fault state interval, and set the interval from the start time point of the fault state interval to a predetermined time before as a normal state interval.

[0068] According to this embodiment, the data set generation unit 200 can set the interval from the time point when the device numerical data value is measured outside the predetermined reference range to a predetermined time before as a fault state interval, and set the interval from the start time point of the fault state interval to a predetermined time before as a normal state interval.

[0069] According to an embodiment of the present disclosure, the data set generation unit 200 can set the interval from the time point when the device numerical data value is measured when it is outside a predetermined reference range to a predetermined time before as a fault state interval, and set the interval from the start time point of the fault state interval to a predetermined time before as a normal state interval.

[0070] According to an embodiment of the present disclosure, the data set generation unit 200 may set an interval from a time point when a normal state value is reached to a predetermined time before as a recovery state interval, and generate a recovery time point data set including a plurality of fault induction factor data for the recovery state interval.

[0071] According to this embodiment, the recovery state interval may be set to be an interval from a time point when the normal state value is reached to before a predetermined time.

[0072] The data set transmitting unit 300 may transmit the generated recovery time point data set to a big data server.

[0073] According to an embodiment of the present disclosure, at least one of the generated data sets of fault state intervals, normal state intervals and recovery state intervals can be transmitted to the big data server 20, and the big data server can generate data matching the corresponding data set in the form of a data table, and transmit the data again to the device numerical data analysis device 10.

[0074] According to an embodiment of the present disclosure, the data set transmitting unit 300 may transmit the generated recovery interval data set to a big data server.

[0075] The influence index calculation unit 400 may receive data corresponding to a data set from a big data server, and calculate an influence index for each fault induction factor by using the received data.

[0076] According to an embodiment of the present disclosure, the device numerical data analysis apparatus 10 may receive data corresponding to a data set from the big data server 20 , and receive the data in the form of a data table.

[0077] Here, the data table may represent a data format including numerical data of each of a plurality of fault inducing factors, but is not limited thereto.

[0078] According to an embodiment of the present disclosure, the influence index calculation unit 400 may calculate the influence index by comparing numerical data of the fault state interval and the normal state interval respectively for the same fault induction factor.

[0079] According to an embodiment of the present disclosure, the influence index calculation unit 400 may calculate the influence index by the following formula 1:

[0080] [Formula 1]

[0081] y>x*factor (influencing index)

[0082] y: numerical data of the fault induction factor in the normal state interval; and

[0083] x: Numerical data of the fault induction factor of the fault state interval.

[0084] According to this embodiment, as the influence index moves away from 1, the influence index is considered to be relatively large.

[0085] According to an embodiment of the present disclosure, the impact index calculation unit 400 may periodically receive a fault state data set and a recovery state data set, and calculate an impact index for each recovery induction factor by using data corresponding to the received recovery state data set and fault state data set.

[0086] According to an embodiment of the present disclosure, the impact index calculation unit 400 may calculate the impact index reflecting the data of the recovery state interval by the following formula 2:

[0087] [Formula 2]

[0088] y>x*factor (influencing index)

[0089] y: numerical data of the fault induction factor in the fault state interval; and

[0090] x: Numerical data of the fault induction factor in the recovery state interval.

[0091] According to this embodiment, as the influence index moves away from 1, the influence index may be considered to be relatively large.

[0092] According to an embodiment of the present disclosure, in the calculation of the impact index, even in the case of using the numerical data of the fault induction factor of the recovery state interval, the impact index calculation unit 400 can calculate the impact index by using the same impact index calculation process as the case of using the numerical data of the fault induction factor of the fault state interval.

[0093] The result information generating unit 500 may generate analysis result information by selecting a fault induction factor based on the calculated influence index.

[0094] According to an embodiment of the present disclosure, an influence index may be calculated for each fault induction factor by formulas such as Formula 1 and Formula 2, and relatively smaller and larger influences on a fault may be judged by comparing the calculated influence indexes.

[0095] According to an embodiment of the present disclosure, result information may be generated by comparing influence indicators calculated for each fault induction factor to select a fault induction factor having a maximum influence indicator therein as a fault cause.

[0096] According to another embodiment of the present disclosure, result information may be generated by selecting a fault induction factor having a predetermined specific value or more among the influence indicators calculated for each fault induction factor as a fault cause.

[0097] According to an embodiment of the present disclosure, the result information generating unit 500 may select a fault induction factor by reflecting the calculated influence index and according to the cumulative number of the influence index.

[0098] Figure 3 is a detailed configuration block diagram of a device numerical data analysis apparatus according to a second embodiment of the present disclosure.

[0099] Reference Figure 3 The device numerical data analysis apparatus according to the second embodiment of the present disclosure may further include an analysis result output unit 600 in the device numerical data analysis apparatus according to the first embodiment.

[0100] The analysis result output unit 600 may output the analysis result information generated by the result information generating unit 500 to the user.

[0101] According to an embodiment of the present disclosure, the analysis result output unit 600 can be connected to a display installed in a vehicle, and the analysis result information can be transmitted to the display to output the analysis result information to the user, but is not limited to this, and if the analysis result output unit 600 is a device such as a speaker that can output information to the user, the analysis result output unit 600 can be used without restriction.

[0102] Figure 4 is a diagram illustrating a failure state interval and a normal state interval set when a device numerical data value is outside a predetermined reference range according to an embodiment of the present disclosure.

[0103] Reference Figure 4 , shows the fault state interval (A) and normal state interval (B-1) set when the device numerical data value is outside the predetermined reference range according to an embodiment of the present disclosure, and sets the interval from the time point when the device numerical data value is measured to the predetermined time as the fault state interval (A), and sets the interval from the start time point of the fault state interval to the predetermined time as the normal state interval (B-1).

[0104] Figure 5 1 is a diagram illustrating a failure state interval, a normal state interval, and a recovery state interval set when a device numerical data value is outside a predetermined reference range and then recovers to be within a predetermined reference range again according to an embodiment of the present disclosure.

[0105] Reference Figure 5 , shows the fault state interval (A), normal state interval (B-1) and recovery state interval (B-2) set when the device numerical data value is outside the predetermined reference range and then recovers to the predetermined reference range according to an embodiment of the present disclosure, and the recovery state interval (B-2) can be set to the interval from the time point when the normal state value is reached to before the predetermined time.

[0106] Figure 6 is a diagram showing a data flow in a case where device numerical data is above a set value in an embodiment of selecting a recovery inducing factor to reflect it in analysis result information according to the present disclosure.

[0107] Reference Figure 6 , shows a data flow in a case where a device numerical data value is outside a predetermined reference range in an embodiment of selecting a recovery inducing factor to reflect it in analysis result information.

[0108] According to an embodiment of the present disclosure, as a result of monitoring device numerical data, if it is measured that the device numerical data value is outside a predetermined reference range and then recovers to the predetermined reference range again, returning from a normal state to a fault state and a recovery state, the device numerical data analysis device can transmit a fault induction factor data set including multiple fault induction factor data for the fault state interval, normal state interval and recovery state interval to a big data server.

[0109] Furthermore, according to an embodiment, data corresponding to a data set received from a big data server may be transmitted to a device numerical data analysis apparatus.

[0110] Figure 7 is a diagram illustrating a data set generated according to an embodiment of the present disclosure.

[0111] Reference Figure 7 , showing a data set generated according to an embodiment of the present disclosure.

[0112] According to an embodiment of the present disclosure, a data set may be generated in the form of a data directory having predetermined fault inducing factors as items.

[0113] According to an embodiment of the present disclosure, the fault induction factor may be the average motor RPM within the interval, the average generator RPM within the interval, the air-conditioning compressor RPM change slope, the PTC power average, the LDC output average, the vehicle speed average, etc., but is not limited thereto. As long as the fault induction factor is a factor that can affect the fault, the fault induction factor may be used without restriction.

[0114] Figure 8 is a flowchart illustrating a process of calculating an impact index for each fault inducing factor according to an embodiment of the present disclosure.

[0115] Reference Figure 8 , shows a process of calculating an impact index for each fault induction factor according to an embodiment of the present disclosure.

[0116] According to an embodiment of the present disclosure, the impact index can be calculated by using Formula 1 using the data of each fault inducing factor within the fault state interval and the normal state interval or Formula 2 using the data of each fault inducing factor within the fault state interval and the recovery state interval, and the impact index can be compared with the impact indicators of other fault inducing factors to relatively compare the size of the impact caused by the fault.

[0117] Fig. 9 is a diagram illustrating a plurality of fault induction factor analysis tables generated whenever a fault symptom occurs in order to calculate an influence index for each fault induction factor according to an embodiment of the present disclosure.

[0118] According to an embodiment of the present disclosure, Fig. 9 A plurality of fault induction factor analysis tables generated each time a fault symptom occurs in order to calculate an influence index for each fault induction factor according to an embodiment of the present disclosure are shown.

[0119] According to an embodiment of the present disclosure, if it is determined that the measured device numerical data value is outside a predetermined reference range and a fault state occurs, as many fault induction factor analysis tables as the number of occurrences may be generated.

[0120] According to an embodiment of the present disclosure, each fault induction factor analysis table may include relative size judgment information of the calculated influencing factor of the influencing indicator, which may be expressed as suspicion, confirmation, not an inducing factor, etc., and may generate and include information based on the cumulative number of influencing indicators, that is, the cumulative number of suspected selections of the inducing factor.

[0121] According to an embodiment of the present disclosure, if the influence index is below a predetermined value, it may be determined that the influence cannot be determined, and the predetermined value may be changed and set according to the fault item, vehicle type, and the like.

[0122] Therefore, misjudgment due to measurement and calculation errors can be prevented and overfitting can be minimized.

[0123] According to an embodiment of the present disclosure, if the influence index is below a predetermined value, the cumulative number of times is still in a state where the influence cannot be determined, and therefore the number of times may not be accumulated.

[0124] Fig.10 is a diagram showing analysis result information actually generated by a failure cause analysis system based on big data using numerical data of vehicle equipment during vehicle travel according to an embodiment of the present disclosure.

[0125] Reference Fig.10 , showing that when the actual device numerical data is monitored and the actual device numerical data value is measured to be outside the predetermined reference range, by using Fig. 9 The analysis result information generated by selecting the fault induction factor in the fault induction factor analysis result table.

[0126] According to an embodiment of the present disclosure, the reliability value may be included in the analysis result information, and the reliability value may increase as the cumulative number increases, and conversely, decrease as the cumulative number decreases.

[0127] Fig.11 is a flowchart of a method for analyzing a cause of a failure based on big data using numerical data of vehicle equipment during vehicle travel according to an embodiment of the present disclosure.

[0128] It is monitored whether the received device numerical data value is outside a predetermined reference range (operation S10).

[0129] According to an embodiment of the present disclosure, it is possible to periodically receive vehicle state information including device numerical data from a plurality of sensors installed in a vehicle, and monitor whether the received device numerical data values ​​are outside a predetermined reference range.

[0130] According to an embodiment of the present disclosure, it is possible to monitor whether the device numerical data value received after the fault state interval falls within the predetermined reference range again.

[0131] It is determined whether the value of the device numerical data is outside a predetermined reference range (operation S20).

[0132] According to an embodiment of the present disclosure, it is possible to continuously monitor whether the device numerical data of each device is outside a predetermined reference range, and to determine whether a fault symptom occurs when the predetermined reference range is exceeded, so that in order to analyze the cause of the fault, it is requested to generate a fault induction factor data set and a normal state data set including multiple fault induction factor data.

[0133] According to an embodiment of the present disclosure, if the device numerical data value received after the fault state interval falls within the predetermined reference range again, it can be judged that it has recovered to a normal state, and the interval from the time point falling within the predetermined reference range to before the predetermined time is set as a recovery state interval, and a request is made to generate a recovery time point data set for the recovery state interval including multiple fault induction factor data.

[0134] If the value of the device numerical data is outside the predetermined reference range, a failure state interval and a normal state interval are set according to the predetermined reference (operation S30).

[0135] According to an embodiment of the present disclosure, if the value of the device numerical data is outside a predetermined reference range, a fault state interval or a normal state interval may be set according to a predetermined reference.

[0136] According to an embodiment of the present disclosure, if the value of the device numerical data is outside a predetermined reference range, it can be determined that a fault symptom has occurred, and thus a fault state interval and a normal state interval are set according to a predetermined reference in order to analyze the cause of the fault.

[0137] According to an embodiment of the present disclosure, the interval from the time point when the device numerical data value is measured to be outside the predetermined reference range to the predetermined time before can be set as the fault state interval, and the interval from the start time point of the fault state interval to the predetermined time before can be set as the normal state interval.

[0138] According to this embodiment, the interval from the time point when the device numerical data value is measured outside the predetermined reference range to a predetermined time before can be set as the failure state interval, and the interval from the start time point of the failure state interval to a predetermined time before can be set as the normal state interval.

[0139] According to an embodiment of the present disclosure, the interval from the time point when the device numerical data value is measured when it is outside a predetermined reference range to a predetermined time before can be set as a fault state interval, and the interval from the start time point of the fault state interval to a predetermined time before can be set as a normal state interval.

[0140] According to an embodiment of the present disclosure, an interval from a time point falling within a predetermined reference range to before a predetermined time may be set as a recovery state interval.

[0141] A fault induction factor data set including a plurality of fault induction factor data and a normal state data set for a normal state interval are generated (operation S40 ).

[0142] According to an embodiment of the present disclosure, after setting the fault state interval and the normal state interval, a fault induction factor data set and a normal state data set including a plurality of fault induction factor data for the fault state interval and the normal state interval may be generated.

[0143] According to an embodiment of the present disclosure, an interval from a time point falling within a predetermined reference range to a predetermined time before can be set as a recovery state interval, and a recovery time point data set including a plurality of fault induction factor data for the recovery state interval can be generated.

[0144] An influence index is calculated for each fault inducing factor by using data corresponding to the fault inducing factor data set and the normal state data set (operation S50 ).

[0145] According to an embodiment of the present disclosure, data corresponding to a data set may be received from a big data server, and an impact index may be calculated for each fault inducing factor by using the received data.

[0146] According to an embodiment of the present disclosure, data corresponding to a data set may be received, and the data may be received in the form of a data table.

[0147] Here, the data table may represent a data format including numerical data of each of a plurality of fault inducing factors, but is not limited thereto.

[0148] According to an embodiment of the present disclosure, the influence index may be calculated by comparing numerical data of a fault state interval and a normal state interval respectively for the same fault induction factor.

[0149] According to an embodiment of the present disclosure, the influence index may be calculated by a formula such as Formula 1.

[0150] According to this embodiment, as the influence index moves away from 1, the influence index may be considered to be relatively large.

[0151] According to an embodiment of the present disclosure, a normal state dataset and a recovery state dataset may be periodically received, and an influence index may be calculated for each recovery inducing factor by using data corresponding to the received recovery state dataset and normal state dataset.

[0152] According to an embodiment of the present disclosure, the influence index reflecting the data of the recovery state interval may be calculated by a formula such as Formula 2.

[0153] According to this embodiment, as the influence index moves away from 1, the influence index may be considered to be relatively large.

[0154] Analysis result information is generated by selecting a fault induction factor based on the calculated influence index (operation S60).

[0155] According to an embodiment of the present disclosure, analysis result information may be generated by selecting a fault induction factor based on the calculated influence index.

[0156] According to an embodiment of the present disclosure, an influence index may be calculated for each fault induction factor by formulas such as Formula 1 and Formula 2, and relatively smaller and larger influences on a fault may be judged by comparing the calculated influence indexes.

[0157] According to an embodiment of the present disclosure, result information may be generated by comparing the influence indexes calculated for each fault induction factor and selecting the fault induction factor having the largest influence index as the fault cause.

[0158] According to another embodiment of the present disclosure, result information may be generated by selecting a fault induction factor having a predetermined specific value or more among the influence indicators calculated for each fault induction factor as a fault cause.

[0159] According to an embodiment of the present disclosure, a fault induction factor may be selected by reflecting the calculated influence index and according to the cumulative number of the influence index.

[0160] The embodiments of the present disclosure are not only implemented by the above-mentioned devices and / or methods, and although the embodiments of the present disclosure are described in detail as described above, the scope of the present disclosure is not limited thereto, and various modifications and improvements of the basic concepts of the present disclosure as defined in the attached claims by those skilled in the art are also included in the scope of the present disclosure.

Claims

1. A fault cause analysis system using numerical data of vehicle equipment during vehicle driving, comprising: an equipment numerical data analyzing device, when the equipment numerical data value measured by monitoring the vehicle is outside a predetermined reference range, setting a fault state interval and a recovery state interval according to a predetermined reference, generating a fault induction factor data set and a normal state data set for the fault state interval and the recovery state interval and transmitting the generated data sets to a big data server, receiving data corresponding to the data sets from the big data server and calculating an influence index for each fault induction factor, and selecting a fault induction factor based on the calculated influence index; as well as The big data server transmits data corresponding to the received data set to the device numerical data analysis device, The equipment numerical data analysis device selects the fault induction factor by reflecting the calculated impact index and according to the cumulative number of the impact index.

2. A device for analyzing numerical data of an equipment, comprising: an equipment numerical data monitoring unit that periodically receives vehicle state information including equipment numerical data from a plurality of sensors installed in the vehicle and monitors whether the received equipment numerical data value is outside a predetermined reference range; a data set generating unit, which sets a fault state interval and a normal state interval according to a predetermined reference when the value of the device numerical data is outside the predetermined reference range, and generates a fault induction factor data set and a normal state data set including a plurality of fault induction factor data for the fault state interval and the normal state interval; A data set transmission unit transmits the generated data set to a big data server; an impact index calculation unit, receiving data corresponding to the data set from the big data server, and calculating an impact index for each fault inducing factor by using the received data; and a result information generating unit, generating analysis result information by selecting a fault inducing factor based on the calculated influencing index, The result information generating unit selects the fault induction factor by reflecting the calculated impact index and according to the cumulative number of the impact index.

3. The device numerical data analysis apparatus according to claim 2, wherein: The data set generation unit sets an interval from a time point when the device numerical data value is measured when the device numerical data value is outside the predetermined reference range to a predetermined time before as the failure state interval, and sets an interval from a start time point of the failure state interval to a predetermined time before as the normal state interval.

4. The device numerical data analysis apparatus according to claim 2, wherein: The device numerical data monitoring unit monitors whether the device numerical data value received after the failure state interval falls within the predetermined reference range again. The data set generation unit sets a section from a time point falling within the predetermined reference range to a predetermined time before as a recovery state section and generates a recovery time point data set including a plurality of fault induction factor data for the recovery state section, and The data set transmission unit transmits the generated data set at the recovery time point to the big data server.

5. The device numerical data analysis apparatus according to claim 4, wherein: The influence index calculation unit periodically receives a fault state data set and a recovery state data set, and calculates the influence index for each recovery induction factor by using data corresponding to the received fault state data set and the recovery state data set, and The result information generating unit selects the fault induction factor based on the calculated influence index to reflect the fault induction factor to the analysis result information.

6. A device for analyzing the cause of a failure using numerical data of a vehicle device during vehicle travel, comprising: an equipment numerical data monitoring unit that periodically receives vehicle state information including equipment numerical data from a plurality of sensors installed in the vehicle and monitors whether the received equipment numerical data value is outside a predetermined reference range; a data set generating unit, which sets a fault state interval and a normal state interval according to a predetermined reference when the value of the device numerical data is outside the predetermined reference range, and generates a fault induction factor data set and a normal state data set including a plurality of fault induction factor data for the fault state interval and the normal state interval; an impact index calculation unit, which calculates an impact index for each fault inducing factor by using data corresponding to the fault inducing factor data set and the normal state data set; and a result information generating unit, generating analysis result information by selecting a fault inducing factor based on the calculated influencing index, The result information generating unit selects the fault induction factor by reflecting the calculated impact index and according to the cumulative number of the impact index.

7. A method for analyzing the cause of a failure using numerical data of a vehicle device during vehicle driving, comprising the following steps: When the value of the device numerical data is measured to be outside a predetermined reference range by monitoring the device numerical data of the vehicle, a fault state interval and a recovery state interval are set according to the predetermined reference, a fault induction factor data set and a normal state data set for the fault state interval and the recovery state interval are generated and the generated data sets are transmitted to a big data server, data corresponding to the data sets are received from the big data server, an influence index is calculated for each fault induction factor, and a fault induction factor is selected based on the calculated influence index; as well as transmitting data corresponding to the received data set to the device numerical data analysis device, The fault induction factor is selected by reflecting the calculated impact index and according to the cumulative number of the impact index.

8. A method for analyzing device numerical data, comprising the following steps: periodically receiving vehicle state information including device numerical data from a plurality of sensors installed in the vehicle, and monitoring whether the received device numerical data value is outside a predetermined reference range; When the value of the device numerical data is outside the predetermined reference range, setting a fault state interval and a normal state interval according to a predetermined reference, and generating a fault induction factor data set and a normal state data set including a plurality of fault induction factor data for the fault state interval and the normal state interval; The generated data set is transmitted to the big data server; receiving data corresponding to the data set from the big data server, and calculating an impact index for each fault inducing factor by using the received data; and generating analysis result information by selecting a fault inducing factor based on the calculated impact index, Wherein, in the step of generating the analysis result information, the fault induction factor is selected by reflecting the calculated impact index and according to the cumulative number of the impact index.

9. The device numerical data analysis method according to claim 8, wherein: In the step of generating a data set, the interval from the time point when the device numerical data value is measured when the device numerical data value is outside the predetermined reference range to the predetermined time is set as the fault state interval, and the interval from the start time point of the fault state interval to the predetermined time is set as the normal state interval.

10. The device numerical data analysis method according to claim 8, wherein: In the monitoring step, it is monitored whether the device numerical data value received after the fault state interval falls within the predetermined reference range again. In the step of generating a data set, an interval from a time point falling within the predetermined reference range to a predetermined time before is set as a recovery state interval, and a recovery time point data set including a plurality of fault induction factor data for the recovery state interval is generated, and In the step of transmitting the data set to the big data server, the generated data set at the recovery time point is transmitted to the big data server.

11. The device numerical data analysis method according to claim 8, wherein: In the step of calculating the impact index, a normal state data set and a recovery state data set are periodically received, and the impact index is calculated for each recovery inducing factor by using data corresponding to the received recovery state data set and the normal state data set, and In the step of generating the analysis result information, the fault induction factor is selected based on the calculated influence index to reflect the fault induction factor in the analysis result information.

12. A device for analyzing numerical data of an equipment, comprising: an equipment numerical data monitoring unit that periodically receives vehicle state information including equipment numerical data from a plurality of sensors installed in the vehicle and monitors whether the received equipment numerical data value is outside a predetermined reference range; a data set generating unit, which sets a fault state interval and a recovery state interval according to a predetermined reference when the value of the device numerical data is outside the predetermined reference range, and generates a fault induction factor data set and a normal state data set including a plurality of fault induction factor data for the fault state interval and the recovery state interval; A data set transmission unit transmits the generated data set to a big data server; an impact index calculation unit, receiving data corresponding to the data set from the big data server, and calculating an impact index for each fault inducing factor by using the received data; and a result information generating unit, generating analysis result information by selecting a fault inducing factor based on the calculated influencing index, The result information generating unit selects the fault induction factor by reflecting the calculated impact index and according to the cumulative number of the impact index.

13. The device numerical data analysis apparatus according to claim 12, wherein: The device numerical data monitoring unit monitors whether the device numerical data value received after the failure state interval falls within the predetermined reference range again. The data set generating unit sets an interval from a time point falling within the predetermined reference range to a predetermined time before as a recovery state interval, and generates a recovery time point data set including a plurality of fault induction factor data for the recovery state interval, and The data set transmission unit transmits the generated data set at the recovery time point to the big data server.

14. A method for analyzing device numerical data, comprising the following steps: periodically receiving vehicle state information including device numerical data from a plurality of sensors installed in the vehicle, and monitoring whether the received device numerical data value is outside a predetermined reference range; When the value of the device numerical data is outside the predetermined reference range, setting a fault state interval and a recovery state interval according to a predetermined reference, and generating a fault induction factor data set and a normal state data set including a plurality of fault induction factor data for the fault state interval and the recovery state interval; The generated data set is transmitted to the big data server; receiving data corresponding to the data set from the big data server, and calculating an impact index for each fault inducing factor by using the received data; and generating analysis result information by selecting a fault inducing factor based on the calculated impact index, The fault induction factor is selected by reflecting the calculated impact index and according to the cumulative number of the impact index.

15. The device numerical data analysis method according to claim 14, further comprising: monitoring whether the device numerical data value received after the fault state interval falls within the predetermined reference range again; setting an interval from a time point falling within the predetermined reference range to a predetermined time before as the recovery state interval, and generating a recovery time point data set including a plurality of fault induction factor data for the recovery state interval; and The generated recovery time point data set is transmitted to the big data server.

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