KR20210132352A - Method and system of diagnosing defect and calculating remaining life of components of railway vehicle - Google Patents

Abstract

Disclosed are a method for diagnosing defects of components of a railway vehicle and calculating remaining life to increase accuracy of diagnosis and an apparatus thereof. According to the present invention, the method comprises the following steps: setting parameters and state values for diagnosis of each part; constructing a database of indexes and reference indexes for each class of parameters; comparing a detected index of the detected class with a reference index of the corresponding class in the database; and when the detected index of the detected class is greater than the reference index of the corresponding class in the database, storing a fault code for a corresponding part. The index is the state value or is calculated by processing the state value in a time domain or a frequency domain and the reference index is calculated by applying a setting factor to the index.

Description

Method and device for diagnosing defects in rail vehicle parts and calculating remaining lifespan

The present invention relates to a diagnostic technology for railway vehicle parts, and more particularly, to a railway vehicle component capable of quickly and accurately diagnosing a failure of a railway vehicle component, and preventing an increase in the amount of data required and memory resources required. It relates to a method and apparatus for diagnosing a fault and calculating the remaining life.

Currently, parts used in railway vehicles are regularly diagnosed, and repairs or replacements are made when abnormalities are found during diagnosis. However, since the railway system is a mass transport system, if an accident occurs in the railway system, it is highly likely to be a major accident. Accordingly, there is a growing need to introduce a Condition Based Maintenance (CBM) system in periodic diagnosis in order to prevent accidents and reduce maintenance/repair costs.

In addition, when minor breakdowns of parts occur in addition to breakdowns of railroad parts that cause major accidents, the railroad vehicle may not arrive at a destination on time. The on-time arrival rate is a very important item for the benefit of the people and is strictly managed by the government. Therefore, it is necessary to diagnose the condition of railway vehicle parts that may cause a decrease in the on-time arrival rate. Furthermore, since a minor breakdown of these railway parts may cause inconvenience to passengers, it is necessary to diagnose the condition of all parts of the railway vehicle.

Meanwhile, diagnostic methods for a diagnostic target have been developed in various industrial fields. The possibility of a defect in the diagnosis target may be confirmed through heat, sound, vibration, or the like. In the diagnosis method using heat or sound, a user can easily identify a defect in the diagnosis target, whereas the defect in the diagnosis target can be identified only after the defect in the diagnosis target has significantly progressed. Since the diagnosis method using vibration can predict the possibility of a defect in the diagnosis target the fastest, the diagnosis method using vibration has been mainly used in the prior art.

However, in order to use the diagnostic method using vibration, the data sampling time must be set very short and the data must be collected. In addition, since frequency conversion was required to analyze the measured data, memory resources for various calculations were excessively increased.

However, since the railway vehicle has limited memory resources, it is difficult to perform each diagnosis method for various parts of the railway vehicle. In addition, when the diagnostic server outside the railroad vehicle is to perform each diagnostic method for various parts of the railroad car, the amount of data to be transmitted is too large when data must be transmitted from the railroad car to the diagnostic server. there was. Accordingly, it is also very difficult to implement each diagnostic method for various parts of the railroad car in the diagnostic server outside the railroad car.

In addition, since the railroad car is a means of transportation, data measured from the railroad car are greatly affected by disturbances, which deteriorates the reliability of diagnosis of railroad car parts. In particular, since railroad vehicles receive a voltage of about 1500V to 2600V DC through a pantograph, they may be greatly affected by disturbance caused by electromagnetic noise. In addition, since the railroad car moves on the track, when a problem occurs on the track, it may act as a disturbance, greatly impairing the reliability of the measured data.

Matters described in this background section are prepared to enhance understanding of the background of the invention, and may include matters that are not already known to those of ordinary skill in the art to which this technology belongs.

An embodiment of the present invention classifies state values for diagnosis of various parts of a railway vehicle into first, second, and third state values according to a setting criterion, and provides a diagnosis method for the first, second, and third state values, respectively. Provided are a method and apparatus for diagnosing defects of parts of a railway vehicle and calculating the remaining lifespan, which can improve the accuracy of diagnosis and prevent an increase in the amount of data required and the amount of memory resources or communication required.

In addition, another embodiment of the present invention provides a method and apparatus for diagnosing defects of parts of a railway vehicle that can accurately predict the remaining life of each part based on the index trend and maintenance history for each part and calculating the remaining life. to provide.

A method of diagnosing defects of parts of a railway vehicle and calculating the remaining lifespan according to an embodiment of the present invention includes setting parameters and state values for diagnosis of each part; constructing a database of indexes and reference indexes for each class of parameters; comparing the detected index of the detected class with a reference index of the corresponding class in the database; and if the detected index of the detected class is greater than the reference index of the corresponding class in the database, storing the failure code of the corresponding part, wherein the index is the state value, or the state value in the time domain or frequency domain It is calculated by processing, the reference index is calculated by applying a setting factor to the index, and the state values are affected by the driving environment and the first state value processed in the time domain to calculate the index, and the driving environment Including a second state value that is not subject to and requires an integral value for a set time to calculate an index, a first index that is affected by the driving environment, and the index is processed in the time domain and a second index that is processed in the frequency domain may be classified as a third state value, and when the state value is affected by the driving environment, the parameters may include at least one of a driving speed, power consumption, driving distance, and outdoor temperature.

The database may include a buffer database and a diagnostic database.

The step of constructing a database of indexes and reference indexes for each class of parameters includes: setting the maximum number of state values that can be stored in the buffer database for each class; detecting data including parameters and state values; classifying the detected data into a corresponding class and storing a state value of the corresponding class in a buffer database; determining whether the number of state values stored in the buffer database for each class is greater than or equal to the number of maximum state values of the corresponding class; If the number of state values stored in the buffer database for each class is equal to or greater than the maximum number of state values of the corresponding class, transferring the state value of the corresponding class in the buffer database to the diagnostic database and deleting the state value of the corresponding class in the buffer database; In addition, the method may include calculating an index and a reference index of the corresponding class by processing the state value of the corresponding class in the diagnosis database.

The index of the corresponding class and the reference index may be updated until the number of state values of the corresponding class stored in the diagnostic database becomes the set number.

If the number of state values of the corresponding class stored in the diagnosis database is equal to or greater than the set number, updating of the index of the corresponding class and the reference index may be prohibited.

If the state value is the first state value, calculating the index and reference index of the class by processing the state value of the corresponding class in the diagnostic database is a probability distribution function that defines the distribution of the state values of the corresponding class in the diagnostic database. calculating; calculating an index defining the calculated probability distribution function; In addition, the method may include calculating a reference index of the corresponding class by applying a setting factor to the index of the corresponding class.

If the state value is the second state value, calculating the index of the class and the reference index by processing the state value of the corresponding class in the diagnosis database may include: integrating the state values of the corresponding class in the diagnosis database with respect to time; setting the main operating area corresponding to the set time; calculating an index by processing an integral value with respect to time in the main operating area; In addition, the method may include calculating a reference index of the corresponding class by applying a setting factor to the index of the corresponding class.

If the state value is the third state value, the step of processing the state value of the corresponding class in the diagnosis database and calculating the index and reference index of the class is the probability of defining the distribution of the state values of the corresponding class in the diagnosis database every first cycle calculating a distribution function; calculating a first index defining the calculated probability distribution function; calculating a first reference index of the corresponding class by applying a setting factor to the first index of the corresponding class; processing the state value of the corresponding class in the diagnosis database into a frequency domain value every second period longer than the first period; calculating a value of the frequency domain of the corresponding class as a second index; The method may include calculating a second reference index of the corresponding class by applying a setting factor to the second index of the corresponding class.

The step of comparing the detected index of the detected class with the reference index of the corresponding class in the database may be performed only when the reference index of the corresponding class exists in the diagnostic database.

When the state value is the first state value or the second state value, the step of comparing the detected index of the detected class with the reference index of the corresponding class in the database is to compare the index of the corresponding class of the vehicle as the reference of the corresponding class of the vehicle. comparing with the index; comparing the index of the corresponding class of the corresponding vehicle with the reference index of the corresponding class of other vehicles of the same railroad vehicle; and comparing the index of the corresponding class of the corresponding vehicle with the reference index of the corresponding class of any vehicle of another railroad vehicle.

When the state value is the third state value, the step of comparing the detected index of the detected class with the reference index of the corresponding class in the database is to compare the first index of the corresponding class of the vehicle with the first reference index of the corresponding class of the vehicle. comparing with; comparing the first index of the corresponding class of the corresponding vehicle with the first reference index of the corresponding class of another vehicle of the same railroad vehicle; comparing the first index of the corresponding class of the corresponding vehicle with the first reference index of the corresponding class of any vehicle of another railroad vehicle; comparing a second index of the corresponding class of the corresponding vehicle with a second reference index of the corresponding class of the corresponding vehicle; comparing a second index of the corresponding class of the corresponding vehicle with a second reference index of the corresponding class of another vehicle of the same railroad vehicle; and comparing the second index of the corresponding class of the corresponding vehicle with the second reference index of the corresponding class of any vehicle of another railroad vehicle.

The method may further comprise calculating the remaining life of the component.

Calculating the remaining life of the part may include: calculating an index influence through trend analysis of an index related to the part; calculating a maintenance history influence degree based on the maintenance history of the parts; and calculating the remaining lifespan based on the target operating time for the part, the current operating time of the part, the index influence degree, and the maintenance history influence degree.

When two or more state values are set for diagnosis of a part, the remaining lifespan of the part may be calculated based on each state value, and a minimum value of the calculated remaining lifespans may be determined as the remaining lifespan of the part.

Calculating the remaining life of the part may further include notifying the calculated remaining life.

A system for diagnosing defects of parts of a railway vehicle and calculating the remaining lifespan according to another embodiment of the present invention includes: a data detector for measuring data including parameters and state values for diagnosis of each part; and a buffer database and a diagnostic database, and a controller configured to diagnose a failure of each part and calculate the remaining life using the data detected by the data detector, wherein the controller is a railway according to an embodiment of the present invention. It may be configured to perform a method of diagnosing defects of parts of the vehicle and calculating the remaining life.

The controller calculates the remaining life based on the index influence through trend analysis of the index related to the part, the maintenance history effect based on the maintenance history of the part, the target operating time for the part, and the current operating time of the part may be made to do.

According to an embodiment of the present invention, the parts can be quickly and accurately diagnosed by classifying the state values for the diagnosis of various parts of the railway vehicle into three state values according to the setting criteria, and providing a diagnosis method for each state value, respectively. have.

In addition, when the number of state values stored in the buffer database for each class reaches the maximum number of state values, the state value stored in the buffer database is transferred to the diagnostic database and the state value of the class in the buffer database is deleted. An increase in memory resources can be prevented.

In addition, since the state values transferred to the diagnostic database are also stored in the form of an average or variance, an increase in memory resources of the diagnostic database can be prevented.

In addition, if the number of state values of the corresponding class stored in the diagnostic database is equal to or greater than the set number, the update of the index of the corresponding class and the reference index is prohibited, thereby further preventing an increase in memory resources of the diagnostic database.

In addition, since the remaining lifespan of each part can be accurately predicted based on the index trend and maintenance history for each part, it is possible to accurately determine the time of replacement or repair.

In addition, the effects obtainable or predicted by the embodiments of the present invention are to be disclosed directly or implicitly in the detailed description of the embodiments of the present invention. That is, various effects predicted according to an embodiment of the present invention will be disclosed in the detailed description to be described later.

Embodiments herein may be better understood by reference to the following description in connection with the accompanying drawings in which like reference numerals refer to identical or functionally similar elements.
1 is a schematic diagram illustrating a railway vehicle according to an embodiment of the present invention.
2 is a schematic cross-sectional view illustrating an axle assembly according to an embodiment of the present invention;
3 is a block diagram of an apparatus for diagnosing defects of parts of a railway vehicle and calculating the remaining lifespan according to an embodiment of the present invention.
4 is a block diagram of a data detector according to an embodiment of the present invention.
5 is a flowchart of a method of diagnosing defects of parts of a railway vehicle and calculating the remaining lifespan according to an embodiment of the present invention.
6 is a flowchart of step S210 of FIG. 5 .
7 is a flowchart of step S360 of FIG. 6 when the state value is the first state value.
8 is a flowchart of step S360 of FIG. 6 when the state value is a second state value.
9 is a graph illustrating examples of a motor current applied to a driving motor for driving a door.
10 is a graph illustrating an integral value obtained by integrating the motor current of FIG. 9 with respect to time.
11 is a flowchart of step S360 of FIG. 6 when the state value is a third state value.
12 is a flowchart of step S380 of FIG. 6 .
13 is a flowchart of step S230 of FIG. 5 when the state value is a first state value or a second state value.
14 is a flowchart of step S230 of FIG. 5 when the state value is a third state value.
15 is a flowchart of step S240 of FIG. 5 .
16 is a graph illustrating an example of a trend of an index with respect to time.
17 is an exemplary flowchart of step S210 of FIG. 5 in the case of a main transformer.
18 is a graph illustrating a cooling device temperature with respect to an amount of power consumption and an outside air temperature for each driving speed at a specific driving distance.
19 is a graph showing a cooling device temperature for each class.
20 is a graph comparing the probability distribution of indices of a specific class with a representative probability distribution function.
21 is an exemplary flowchart of steps S220 and S230 of FIG. 5 in the case of a main transformer.
It is to be understood that the drawings referenced above are not necessarily drawn to scale, but rather present a rather simplified representation of various preferred features illustrating the basic principles of the present disclosure. Certain design features of the present disclosure, including, for example, particular dimensions, orientations, locations, and shapes will be determined in part by the particular intended application and environment of use.

The terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the present disclosure. As used herein, singular forms are intended to also include plural forms unless the context clearly dictates otherwise. The terms "comprises" and/or "comprising," when used herein, specify the recited features, integers, steps, acts, elements, and/or the presence of components, but other It will also be understood that this does not exclude the presence or addition of one or more of features, integers, steps, acts, components, components, and/or groups thereof. As used herein, the term “and/or” includes any one or all combinations of the associated listed items.

As used herein, terms such as “vehicle” or “of a vehicle” or other similar terms refer to passenger cars, buses, trucks, various commercial vehicles, including sports utility vehicles (SUVs) as well as rail vehicles. It is understood to include vehicles.

Additionally, it is understood that one or more of the methods below or aspects thereof may be executed by at least one or more control units (eg, electronic control units (ECUs), etc.), controllers, or control servers. . The terms “control unit”, “controller”, or “control server” may refer to a hardware device comprising a memory and a processor. The memory is configured to store program instructions, and the processor is specifically programmed to execute the program instructions to perform one or more processes described in more detail below. A control unit, controller, or control server may control the operation of units, modules, parts, devices, or the like, as described herein. It is also understood that the methods below may be executed by an apparatus comprising a control unit or controller together with one or more other components, as will be appreciated by those skilled in the art.

In addition, the control unit, controller, or control server of the present disclosure may be implemented as a non-transitory computer-readable recording medium including executable program instructions executed by a processor. Examples of computer-readable recording media include ROM, RAM, compact disk (CD) ROM, magnetic tapes, floppy disks, flash drives, smart cards, and optical data storage devices, The present invention is not limited thereto. The computer-readable recording medium may also be distributed throughout a computer network so that program instructions can be stored and executed in a distributed manner, for example, in a telematics server or a controller area network (CAN).

1 is a schematic diagram illustrating a railway vehicle according to an embodiment of the present invention.

As shown in FIG. 1 , a railway vehicle 1 includes a plurality of vehicles 5 connected to each other. The vehicle 5 is configured to load passengers or cargo, and a plurality of bogies 10 are installed at the lower portion of the vehicle 5 to enable the vehicle to move on the track. An engine room is formed in one of the plurality of vehicles 5 , and a control server or first controller 160 and a display 170 may be disposed in the engine room. In the present specification, although it is exemplified that the first controller 160 is installed only in the engine room, it is not limited thereto. A first controller 160 may be installed in each of the plurality of vehicles 5 .

A truck 10 typically consists of two or three axle assemblies 70 and supports a vehicle body. The bogie 10 includes a bogie frame, an axle assembly 70 mounted on the bogie frame, a shock absorber, a braking device, a traction motor 14 , a gear box 16 , and the like.

The traction motor 14 generates power to move the railway vehicle 1 on the track by the electric energy supplied through the collector 68 . The current collector 68 is connected to an electric car line installed on the railway vehicle 1 to receive electric energy from the electric car line. The catenary may form an electric circuit together with the railway vehicle 1 and the track. A circuit breaker 66 may be mounted between the collector 68 and the railway vehicle 1 to protect a circuit in the railway vehicle 1 from overcurrent.

The gear box 16 includes a plurality of gears meshed with each other, converts power generated by the traction motor 14 according to a gear ratio, and transmits the converted power to the wheel 12 through the axle assembly 70 . Since the bogie 10 is well known to those skilled in the art, further detailed description thereof will be omitted.

2 is a schematic cross-sectional view illustrating an axle assembly according to an embodiment of the present invention;

As shown in FIG. 2 , axle assembly 70 includes axle 72 . The axle 72 is connected to a power source (eg, a traction motor 14, etc.), and rotates by receiving power from the power source.

A gearbox 16 may be disposed between the power source and the axle 72 . The gearbox 16 changes the rotational speed of the power of the power source, and transmits the changed power to the axle 72 . A gearbox sensor module 74 may be mounted on the gearbox 16 , and the gearbox sensor module 74 is configured to detect one of acceleration, rotational speed, and temperature of the plurality of gears. The first controller 160 may diagnose the state of the gearbox 16 based on the physical quantity of the gearbox 16 detected by the gearbox sensor module 74 .

Wheels 12 are fixedly mounted on both sides of the axle 72 . The wheel 12 may be secured to the axle 72 in a variety of ways, such as press fit, weld, or spline. Each of the wheels 12 may be rotatable on the track. When the axle 72 rotates by receiving power from the power source, the wheel 12 rotates on the track. Accordingly, the railway vehicle 1 can move.

Bearings 76 are mounted at both ends of the axle 72 . Each of the bearings 76 rotatably supports the axle 72 relative to the vehicle body. The bearing 76 includes an inner ring, an outer ring, and a plurality of rolling elements. The inner ring is fixed to the axle (72) and rotates with the axle (72). The outer ring surrounds the inner ring outside the radius of the inner ring and is fixed to the vehicle body. The plurality of rolling elements are rotatably disposed between the inner ring and the outer ring. The plurality of rolling elements allow the inner ring to rotate relative to the outer ring. A speed sensor 155 is mounted on the bearing 76 to measure the rotational speed of the axle 72 , and an acceleration sensor 145 is mounted to measure the acceleration of the bearing 76 , that is, the vibration of the bearing 76 . can be measured

A cover assembly 78 is mounted at both ends of the axle 102 . The cover assembly 78 primarily prevents foreign substances such as water or dust from entering the bearing 76 .

Referring back to FIG. 1 , the railway vehicle 1 further includes a door 20 through which passengers can get on and off, and a door control unit 22 for controlling the operation of the door 20 . The door 20 may be opened or closed under the control of the door control unit 22 .

The railway vehicle 1 further includes a battery 30 and an air compressor 32 . The battery 30 supplies electric power to the air compressor 32 , and the air compressor 32 operates by the electric power of the battery 30 to compress air. The compressed air in the air compressor 32 is used for braking, suspension, or the current collector 68 . To this end, the air compressor 32 may be controlled by a brake control unit 36 .

The railway vehicle 1 further includes a broadcast communication device 40 , a wireless communication device 50 , and a fire alarm 62 . The broadcast communication device 40 is configured to transmit information visually or aurally to the passenger, and the wireless communication device 50 is a control server or second controller 82 of another railway vehicle 1, and a control server 80 of the control room. , or to wirelessly communicate data with the user computing device 90 . For example, the wireless communicator 50 may communicate with the external server 80 , the other railway vehicle 1 , or the user computing device 90 through a wireless communication protocol such as Bluetooth, Zigbee, Wi-Fi, or LTE. The fire alarm 62 may detect whether a fire has occurred in the vehicle 5 and transmit a signal corresponding thereto to the first controller 160 , the external server 80 , or the user computing device 90 .

The railway vehicle 1 further includes a heating, ventilation, and air conditioning (HVAC) 60 . The air conditioner 60 maintains the indoor temperature of the vehicle 5 at an appropriate temperature regardless of external temperature change to maintain a comfortable indoor environment.

The railway vehicle 1 further includes a main transformer 34 and a switchboard 64 . The main transformer 34 converts the high voltage received through the current collector 68 into a voltage required by each electrical component and supplies it. Various switches 150 are provided in the switchboard 64, and the switches 150 control the connection between the main transformer 34 and each electric component.

3 is a block diagram of an apparatus for diagnosing defects of parts of a railway vehicle and calculating the remaining lifespan according to an embodiment of the present invention, and FIG. 4 is a block diagram of a data detector according to an embodiment of the present invention.

As shown in FIG. 3 , the apparatus for diagnosing defects of parts of a railway vehicle and calculating the remaining lifespan according to an embodiment of the present invention includes at least one railway vehicle 1 , an external server 80 , and a user computing device. (90) may be included.

Each railway vehicle 1 includes a data detector 100 , a wireless communication device 50 , a first controller 160 , a display 170 , and a speaker 180 .

The data detector 100 detects data for performing a method according to an embodiment of the present invention. For example, the data may include a parameter for classifying a class and a state value for diagnosing each part. The state value means data for evaluating the performance of each component of the railway vehicle 1, and accordingly, each component of the railway vehicle 1 can be diagnosed by comparing an index according to the state value with a reference index. In addition, a state value for diagnosing each component is set. One state value may be set for one component, but is not limited thereto. That is, two or more state values may be set for one component. In addition, since the state values may be affected by various factors, in order to diagnose the state of the parts, an index under an arbitrary factor should be compared with a reference index under the same factor. Accordingly, data affecting the state value is defined as a parameter, and parameters may be set according to the state value, that is, the part. For example, parameters for diagnosing the main transformer 34 may be set to travel speed, power consumption, outside air temperature, and travel distance, and the state value is the operating time of the cooling device for cooling the main transformer 34 . , the temperature of the cooling device, and the number of operations of the oil pump for supplying the cooling medium to the cooling device. In addition, the parameter for diagnosing the broadcast communication device 40 may be set as the driving distance, and the state value may be set as the number of warning occurrences. The data detector 100 is connected to the first controller 160 and transmits the detected data to the first controller 160 .

The data detector 100 includes at least one temperature sensor 110 , at least one pressure sensor 115 , at least one load sensor 120 , at least one power meter 125 , as shown in FIG. 4 , at least one It may include one ammeter 130 , a position sensor 135 , a timer 140 , at least one acceleration sensor 145 , at least one switch 150 , and at least one speed sensor 155 .

At least one temperature sensor 110 is mounted at a set position of the vehicle 5 to measure the temperature of the set position or the temperature of a fluid or gas flowing in the set position, and transmit a signal thereto to the first controller 160 send to For example, the at least one temperature sensor 110 may include an external air temperature, a cooling device for cooling the main transformer 34 or a temperature of a cooling medium circulating in the cooling device, and a motor for operating the air compressor 32 . It is possible to detect the temperature or the temperature of the oil in the motor.

At least one pressure sensor 115 is mounted at a set position of the vehicle 5 to measure the pressure of the fluid or gas at the set position, and transmits a signal for this to the first controller 160 . For example, the at least one pressure sensor 115 may detect the pressure of the air produced by the air compressor 32 , the pressure of the air supplied to the collector 68 , and the medium produced by the compressor of the air conditioner 60 . pressure can be detected.

At least one load sensor 120 is mounted on one or more of the plurality of bogies 10 to detect the total load of the railway vehicle 1 , and transmits a signal corresponding thereto to the first controller 160 . The first controller 160 may estimate the number of passengers of the railway vehicle 1 based on the total load of the railway vehicle 1 detected by the at least one load sensor 120 .

At least one power meter 125 is mounted on various electrical components of the railway vehicle 1 to measure power consumed by each electrical component, and transmits a signal corresponding thereto to the first controller 160 . For example, the at least one power meter 125 may detect the power consumption of the air compressor 32 , the power consumption of the main transformer 34 , the power consumption of the railway vehicle 1 , and the like.

At least one ammeter 130 is mounted on various electrical components of the railway vehicle 1 to measure the current supplied to each electrical component, and transmits a signal corresponding thereto to the first controller 160 . For example, the at least one ammeter 130 may detect a current supplied to the traction motor 14 , a current supplied to a motor operating the door 20 , and the like.

The position sensor 135 measures the GPS coordinates of the railway vehicle 1 and transmits a signal corresponding thereto to the first controller 160 . Since the railroad car 1 moves on a pre-installed track, the GPS coordinates of the railroad car 1 make it possible to predict on which track the railroad car 1 is moving. In addition, the first controller 160 may calculate the mileage of the railway vehicle 1 based on the change in GPS coordinates.

The timer 140 measures the time taken from the start time of a specific event, and transmits a signal corresponding thereto to the first controller 160 . For example, the start time of a specific event may be a time when a cooling device for cooling the main transformer 34 starts to operate, a time when the air conditioner 60 starts to operate, and a time when the door 20 starts to open or close. have.

At least one acceleration sensor 145 is mounted on a set position or component of the vehicle 5 to measure the acceleration of the set position or component, and transmits a signal corresponding thereto to the first controller 160 . The at least one acceleration sensor 145 is a three-axis acceleration sensor capable of measuring x-axis, y-axis, and z-axis acceleration, or measures x-axis, y-axis and z-axis acceleration and angular acceleration on the x-axis, y-axis, and z-axis It may be a 6-axis acceleration sensor that can do this, but is not limited thereto. For example, the at least one acceleration sensor 145 may measure the acceleration of the bearing 76 of the axle assembly 70 , the acceleration of the gearbox 16 , and the like.

At least one switch 150 may be mounted at a set position of the switchboard 64 , the engine room, or the railway vehicle 1 to operate each component or set an operating level. A signal for whether each component is operated or an operation level of each component is transmitted from the at least one switch 150 to the first controller 160 .

At least one speed sensor 155 is mounted on one or more of the plurality of wheels 12 to detect the traveling speed of the railway vehicle 1 , and transmits a signal corresponding thereto to the first controller 160 . For example, the speed sensor 155 is mounted on a bearing 76 or axle 72 of the axle assembly 70 to measure the rotational speed of the axle 72 , and from the rotational speed of the axle 72 , the rolling stock The running speed of (1) can be detected. On the other hand, the traveling speed of the railway vehicle 1 may be calculated based on the detection value of the position sensor 135 .

The data detector 100 may further include various sensors in addition to the sensors shown in FIG. 4 , and their measured values are transmitted to the first controller 160 .

The first controller 160 may be connected to the data detector 100 , the wireless communicator 50 , the display 170 , and/or the speaker 180 through a railway vehicle network. The first controller 160 may collect data for diagnosis of parts of the railway vehicle 1 from the data detector 100 and/or the data detector 100 of another railway vehicle 1 . The first controller 160 is configured to diagnose the parts of the railway vehicle 1 using the signal received from the data detector 100 and calculate the remaining life. For this purpose, the first controller 160 may be implemented with one or more processors 162 operating by a set program, and the set program diagnoses defects of parts of a railway vehicle according to an embodiment of the present invention, and It may be programmed to perform each step of the method for calculating the remaining life.

The first controller 160 includes a database 164 , and the database 164 includes a buffer database 166 and a diagnostic database 168 .

When it is determined that the air conditioner 60 is faulty, the first controller 160 stores a fault code and displays a warning signal to the display 170, the speaker 180, the external server 80, and/or the user computing device ( 90) can be transmitted. The mechanic may repair or replace the part based on the warning signal.

The wireless communicator 50 enables wireless communication with devices external to the railway vehicle 1 , for example, another railway vehicle 1 , an external server 80 , and/or a user computing device 90 . The wireless communicator 50 can communicate with components inside the railway vehicle 1 through a vehicle network, and wirelessly communicate with devices outside the railway vehicle 1 . For example, the wireless communicator 50 wirelessly communicates with the other railway vehicle 1, the external server 80, and/or the user computing device 90 through a wireless communication protocol such as Bluetooth, Zigbee, Wi-Fi, and LTE. can do.

The display 170 is installed in the railway vehicle 1 where the driver's or crew member's field of vision reaches, and may display various information provided to the engineer or crew member. In particular, the display 170 may display various types of information predefined in relation to defects and remaining lifespans of parts of the railway vehicle 1 . The display 170 may be any one or more of various display devices that can be installed in the railway vehicle 1 .

The speaker 180 is a device capable of providing various information audibly to an engineer or crew member in the railway vehicle 1 , and may be mounted at an appropriate position in the railway vehicle 1 . The speaker 180 may also be any one or more of various speaker devices that can be installed in the railway vehicle 1 .

The external server 80 may include a server communication unit 86 , a second controller 82 , and a server display 88 . In the present specification, the external server 80 may be a device that receives various data from the railway vehicle 1 , performs diagnosis of parts of the corresponding railway vehicle 1 , and calculates the remaining lifespan, but is not limited thereto. As mentioned above, the apparatus for diagnosing defects of railway vehicle parts and calculating the remaining lifespan according to an embodiment of the present invention is mounted only on the railway vehicle 1 or an external device (eg, the railway vehicle 1). For example, it may be distributedly mounted on the external server 80 and/or the user computing device 90 , or it may be mounted only on an external device of a railroad vehicle. In addition, the external server 80 is installed at a specific location outside the railway vehicle 1 and is a server capable of wireless communication with the railway vehicle 1 , or communicates with the railway vehicle 1 wirelessly or by wire from outside the railway vehicle 1 . It can be a mobile server that does

The server communication unit 86 allows the external server 80 to communicate with external devices. The server communication unit 86 is connected to the second controller 82 to transmit various information and signals from the railway vehicle 1 and/or the user computing device 90 to the second controller 82, and the second controller ( Various information/data and signals from 82 may be transferred to the rolling stock 1 and/or the user computing device 90 . For example, the server communication unit 86 wirelessly communicates with at least one railway vehicle 1 and/or the user computing device 90 through a wireless communication protocol such as Bluetooth, Zigbee, Wi-Fi, LTE, or the like, Wired communication may be performed with the user computing device 90 through a wired cable.

The second controller 82 uses various information/data and signals transmitted from the railway vehicle 1 and/or the user computing device 90 through the server communication unit 86 to detect defects in parts of the railway vehicle 1 . Diagnose and calculate the remaining lifespan. In particular, data processing that is difficult to be performed by the first controller 160 may be performed by the second controller 82 , but is not limited thereto. In addition, the second controller 82 may transmit various control signals to the railway vehicle 1 and/or the user computing device 90 through the server communication unit 86 .

The second controller 82 includes a server database 84 . The server database 84 stores various information/data received from the railway vehicle 1 and/or the user computing device 90 and information/data processed by the second controller 82 .

The server display 88 may display various types of information predefined in relation to defects and remaining lifespans of parts of the railway vehicle.

The user computing device 90 may include a device communication unit 96 , a third controller 92 , and a device display 98 . The user computing device 90 is a device capable of wireless and/or wired communication with the railway vehicle 1 and/or the external server 80 that is portable or accessible to the user at a fixed location, and includes smart devices. , smartphones, cell phones, tablets, PDAs, laptops, etc. The user computing device 90 may be registered in advance with the railway vehicle 1 and/or the external server 80 .

The device communication unit 96 enables communication with devices external to the user computing device 90 . The device communication unit 96 is connected to the third controller 92 and transmits various information and signals from the railway vehicle 1 and/or the external server 80 to the third controller 92 , and the third controller 92 . ) and transmits various information/data and signals from the railway vehicle 1 and/or the external server 80 . For example, the device communication unit 96 wirelessly communicates with the railway vehicle 1 and/or the external server 80 through a wireless communication protocol such as Bluetooth, Zigbee, Wi-Fi, and LTE, or an external server ( 80) and can communicate by wire.

The third controller 92 controls the operation of the user computing device 90 using various information/data and signals received from the railway vehicle 1 and/or the external server 80 through the device communication unit 96 . . In addition, when the user operates a specific program of the user computing device 90 (eg, an app for diagnosing defects of railway vehicle parts and calculating the remaining life, etc.), information/data related thereto is transmitted to the railway vehicle 1 and / or make a request to the external server 80 .

The third controller 92 includes a device database 94 . The device database 94 stores various information/data received from the railway vehicle 1 and/or the external server 80 and information/data processed by the third controller 92 .

The device display 98 may display to the owner or user of the user computing device 90 a variety of predefined information regarding defects and remaining life of rail vehicle parts.

5 is a flowchart of a method of diagnosing defects of parts of a railway vehicle and calculating the remaining lifespan according to an embodiment of the present invention.

A method for diagnosing defects of parts of a railway vehicle and calculating the remaining lifespan according to an embodiment of the present invention includes the steps of setting parameters and state values for diagnosis of each part (S200), and setting a plurality of state values to the set standards. The step of classifying according to (S205), the step of building a database of indexes for each class of parameters (S210), and the step of comparing the detected index of the detected class with the reference index of the corresponding class in the database (S220); Comparing the detected index of the detected class with the reference index of the corresponding class in the database, diagnosing the defect of the part (S230), and calculating the remaining life of the part through the trend analysis of the index (S240) include If the detected index of the class detected in the step (S230) of diagnosing the defect of the corresponding part is greater than the reference index of the corresponding class in the database, the step of storing the failure code is included. In addition, the method of diagnosing defects of parts of a railway vehicle and calculating the remaining lifespan according to an embodiment of the present invention displays a warning signal on the display 170 , the speaker 180 , the external server 80 , and/or the user computing device The step of transmitting to 90 , and outputting a warning signal visually through the display 170 , audibly through the speaker 180 , or by using the external server 80 and/or the user computing device 90 . It may further include the step of outputting visually, auditory or tactile through.

Here, the state value means data for evaluating the performance of each part of the railway vehicle 1 , and a state value for diagnosis is set for each part. One state value may be set for one component, but is not limited thereto. That is, two or more state values may be set for one component. For accuracy of diagnosis, the state value may be processed as an index, and the index may be processed as a reference index. Accordingly, each component of the railway vehicle 1 can be diagnosed by comparing the index according to the state value with the reference index. Here, the index may be the state value or may be calculated by processing the state value in a time domain or a frequency domain, and the reference index may be calculated by applying a setting factor to the index. In addition, since the state values may be affected by various factors, in order to diagnose the state of the parts, an index under an arbitrary factor should be compared with a reference index under the same factor. Accordingly, data affecting the state value is defined as a parameter, and parameters may be set according to the state value, that is, the part.

State values for diagnosing defects of parts in step S205 may be classified into first, second, and third state values according to a set standard. For example, the first state value is affected by the driving environment and is processed in the time domain to calculate the index, and the second state value is not affected by the driving environment and the integral value for the set time to calculate the index is It is required, and the third state value is influenced by the driving environment and may include a first index processed in a time domain and a second index processed in a frequency domain. Here, the driving environment means an environment in which the railway vehicle 1 is operating, the driving environment is some of the parameters, and the some parameters include at least one of traveling speed, power consumption, driving distance, and outdoor temperature. can do.

Step S210 may be performed when the database is not established because the corresponding part is initially mounted on the railway vehicle 1 , or the database is reset because the corresponding part of the railway vehicle 1 is repaired or replaced. In addition, step S220 may be performed only when the reference index exists in the diagnostic database 168 .

6 is a flowchart of step S210 of FIG. 5 .

As shown in FIG. 6 , the step of constructing a database of indexes for each class of parameters ( S210 ) starts by setting the maximum number of state values that can be stored in the buffer database 166 for each class ( S310 ). For example, the user may set the maximum number of state values in consideration of the memory capacity of the buffer database 166 . Alternatively, the first controller 160 may set the maximum number of state values in consideration of the memory capacity of the buffer database 166 .

When the number of maximum state values is set, the data detector 100 detects data for diagnosis of parts (S320). Here, the data includes parameters and state values. For example, if the component requiring diagnosis is the main transformer 34, the speed sensor 155 measures the running speed of the railway vehicle 1, and the power meter 125 measures the power consumption of the railway vehicle 1, , the temperature sensor 110 measures the outside temperature, and the position sensor 135 or the speed sensor 155 measures the mileage of the railway vehicle 1 . Here, the mileage of the railway vehicle 1 means the total distance traveled by the railway vehicle 1 after the main transformer 34 is first installed, the main transformer 34 is repaired, or the main transformer 34 is replaced. . In addition, the timer 140 and the switch 150 detect the operating time of the cooling device for cooling the main transformer 34 and the operation number of the oil pump for supplying the cooling medium to the cooling device, and the temperature sensor 110 ) detects the temperature of the cooling device for cooling the main transformer 34 .

The first controller 160 classifies the data detected by the data detector 100 into a corresponding class and stores the state value of the corresponding class in the buffer database 166 (S330). The performance of the main transformer 34 can be evaluated by the operating time of the cooling device, the number of operations of the oil pump for supplying the cooling medium to the cooling device, and the temperature of the cooling device. In addition, the operating time of the cooling device, the number of times of operation of the oil pump for supplying the cooling medium to the cooling device, and the temperature of the cooling device are determined by the traveling speed of the railway vehicle 1, the power consumption of the railway vehicle 1, the outside air temperature, And it can be affected by mileage. Therefore, the operating time of the cooling device for evaluating the performance of the main transformer 34, the number of operations of the oil pump for supplying the cooling medium to the cooling device, and the temperature of the cooling device are defined as the state value, and the state value is The running speed of the railroad car 1 that affects it, the power consumption of the railroad car 1 , the outside air temperature, and the mileage are defined as parameters. In addition, each state value may be evaluated in order to evaluate the performance of the main transformer 34 .

Also, the parameters may be classified into a plurality of classes. For example, the running speed is between the first running speed and the second running speed, the electric power consumption is between the first electric power consumption and the second electric power consumption, the outdoor air temperature is between the first outdoor air temperature and the second outdoor air temperature, If the distance is between the first mileage and the second mileage, the parameters may be classified into a class (1, 1, 1, 1). In contrast, the running speed is between the second running speed and the third running speed, the electric power consumption is between the first electric power consumption and the second electric power consumption, the outdoor air temperature is between the first outdoor air temperature and the second outdoor air temperature, and the driving distance is between the first driving distance and the second driving distance, the parameters may be classified into a class (2, 1, 1, 1), the driving speed being between the first driving speed and the second driving speed, and the amount of power consumption being If it is between the second power consumption and the third power consumption, the outside temperature is between the first outside temperature and the second outside temperature, and the driving distance is between the first driving distance and the second driving distance, the parameters are (1, 2, 1, 1) can be classified into classes.

Accordingly, the first controller 160 determines a class including the detected parameters, and stores the detected state value as a state value of the corresponding class in the buffer database 166 .

Thereafter, the first controller 160 determines whether the number of state values stored in the buffer database 166 for each class is equal to or greater than the maximum number of state values of the corresponding class ( S340 ).

If the number of state values stored in the buffer database 166 for each class is less than the number of maximum state values of the corresponding class, the data detector 100 continues to detect data for component diagnosis (S320).

If the number of state values stored in the buffer database 166 for each class is equal to or greater than the number of maximum state values of the corresponding class, the first controller 160 sets the state value of the corresponding class in the buffer database 166 to the diagnostic database 168 and deletes the state value of the corresponding class in the buffer database 166 (S350). Since the number of state values stored in the buffer database 166 is the product of the maximum number of state values and the number of classes, the memory resource of the buffer database 166 can be managed.

When the state values of the corresponding class are transferred from the buffer database 166 to the diagnostic database 168, the first controller 160 processes the state values of the corresponding class in the diagnostic database 168 to calculate an index and a reference index ( S360), the index of the corresponding class and the reference index are stored in the diagnostic database 168 .

According to an embodiment of the present invention, a method for calculating an index and a reference index varies according to the type of state value. Hereinafter, a method of calculating an index and a reference index according to a type of a state value will be described with reference to FIGS. 7 to 11 .

7 is a flowchart of step S360 of FIG. 6 when the state value is the first state value.

7, when the state value is the first state value, the first controller 160 calculates a probability distribution function close to the distribution of state values of the corresponding class in the diagnosis database 168 (S363), and the An index defining the probability distribution function is calculated (S364), and a reference index of the corresponding class is calculated by applying a setting factor to the index of the corresponding class (S362). For example, in the case of the main transformer 34, the parameters are traveling speed, power consumption, outside temperature, and traveling distance, and the state values are the operating time of the cooling device, the temperature of the cooling device, and the number of times of operation of the oil pump. Here, the operating time of the cooling device, the temperature of the cooling device, and the number of times of operation of the oil pump correspond to the first state values, respectively. In the case of the first state value, the index may be a mean, standard deviation, or maximum value defining a probability distribution function of the state values. In addition, the reference index may be defined as a product or sum of the index and a setting factor. Here, a plurality of setting factors may be set. For example, one setting factor may be set to 2, another setting factor may be set to 3, and another setting factor may be set to 4. In this case, a plurality of reference indices are calculated, and each reference index may be calculated as a product or sum of an index and a setting factor. In addition, if the index of the class is greater than the reference index of the class to which the setting factor of 2 is applied, it is determined that the state of the part is 'attention' and the corresponding fault code is saved, and the setting factor with the setting factor of 3 is applied. If it is greater than the reference index of the corresponding class, it is determined that the state of the part is in 'Warning' state and the corresponding fault code is saved. ' status, and the corresponding fault code can be saved.

8 is a flowchart of step S360 of FIG. 6 when the state value is a second state value.

As shown in FIG. 8 , when the state value is the second state value, the first controller 160 integrates the state value of the corresponding class in the diagnosis database 168 with respect to time (S365), and sets the main operating area. do (S366). The first controller 160 calculates an index by processing an integral value with respect to time in the main operation area (S367), and calculates a reference index of the corresponding class by applying a setting factor to the index of the corresponding class (S362).

For example, in the case of the door 20 , the parameters are the number of times the door 20 is opened, the number of operations of the driving motor that operates the door 20 , the number of times the door 20 is closed, and the warning of the door control unit 22 is generated. It can be the number of times, the number of times the bypass (a device that allows the safety door to be opened but allows the train to enter the platform normally), the number of times the emergency handle (a device for manually opening and closing the door in an emergency) is operated, and the status value is It may be an operating time of the driving motor and a motor current supplied to the driving motor. Here, the operating time of the driving motor corresponds to the first state value, and the motor current supplied to the driving motor corresponds to the second state value. The second state value is a value that is changed according to a preset pattern in the operation region (that is, for a set time after the operation signal is input), and in order to detect an abnormal second state value, the second state value at any point in the operation region The value should be compared to the value on the preset pattern at that point in time. However, in this case, since the amount of data in the database is very large, it may be necessary to increase the memory capacity. In addition, even if the second state value is smaller or greater than the value on the pattern preset at the point in time at any point in the operation area, the corresponding part may operate normally, so that the accuracy of diagnosis may be reduced. In order to solve this problem, in the embodiment of the present invention, a value obtained by integrating the second state value with respect to time in the main operating region is used.

9 is a graph illustrating examples of a motor current applied to a driving motor driving a door, and FIG. 10 is a graph illustrating an integral value obtained by integrating the motor current of FIG. 9 with respect to time. In FIG. 9 , a dotted line indicates a motor current applied to the driving motor when the door 20 operates abnormally, and a solid line indicates a motor current applied to the driving motor when the door 20 operates normally. In addition, in FIG. 10 , the dotted line represents the integral value obtained by integrating the motor current (dashed line in FIG. 9 ) with respect to time when the door 20 operates abnormally, and the solid line represents the motor current ( The solid line in FIG. 9) represents an integral value integrated with respect to time.

9 and 10 , the motor current applied to the driving motor for driving the door 20 is changed according to a set pattern. When the door 20 operates abnormally, the motor current applied to the drive motor is generally similar to the motor current applied to the drive motor when the door 20 operates normally, but in a specific area (for example, 1.5 seconds to 2.5 seconds). ), the difference is large. In addition, the integral value obtained by integrating the motor current with respect to time when the door 20 operates abnormally at the beginning of operation (t<T1) and at the end of operation (t>T2) is the motor current when the door 20 operates normally. is similar to the integral value integrated with respect to time, but when the door 20 operates abnormally in the main operating region (T1 < t < T2), the integral value obtained by integrating the motor current with respect to time indicates that the door 20 operates normally. In the case of operation, the difference is large from the integral value of the motor current integrated with respect to time. Therefore, in order to accurately evaluate the performance of the door 20, it is necessary to process an integral value obtained by integrating the motor current with respect to time in the main operating region (T1<t<T2). Here, the main operation region T1 < t < T2 may be preset through an operation history and experiment of the door 20 . In addition, the index may be an average (Ia, Ic, etc.) of an integral value obtained by integrating the motor current with respect to time in the main operating region (T1 < t < T2), and the reference index is the product or sum of the index and the set factor. can be defined. As mentioned above, a plurality of setting factors may be set according to the state of the component (eg, 'caution', 'warning', 'alarm', etc.).

11 is a flowchart of step S360 of FIG. 6 when the state value is a third state value.

12 , when the state value is the third state value, the first controller 160 calculates a probability distribution function close to the distribution of state values of the corresponding class in the diagnostic database 168 every first period ( S371), a first index defining the probability distribution function is calculated (S372), and a first reference index of the corresponding class is calculated by applying a setting factor to the first index of the corresponding class (S373). In addition, the first controller 160 processes the state values of the corresponding class in the diagnosis database 168 into a frequency domain value every second cycle longer than the first cycle (S374), and generates the frequency domain value of the corresponding class. It is calculated with two indexes (S375), and a second reference index of the corresponding class is calculated by applying a setting factor to the second index of the corresponding class (S376).

For example, in the case of the bearing 76 of the axle assembly 70 , the parameters are travel speed, power consumption, outside air temperature, and travel distance, and the state values are vibration of the bearing 76 and the temperature of the bearing 76 . Here, the temperature of the bearing 76 corresponds to the first state value, but the vibration of the bearing 76 corresponds to the third state value. In the case of the third state value, since it is greatly affected by disturbance, it is impossible to diagnose the defect of a part only with the value processed in the time domain. Accordingly, in the case of the third state value, the index includes a first index processed in the time domain and a second index processed in the frequency domain. For example, in the case of vibration of the bearing 76, it may be machined with bearing momentum in the time domain and machined with bearing envelope spectrum on bearing defect frequencies in the frequency domain. In this case, the first index may be an average, peak, rms value, or impulse factor of bearing momentum, and the second index may be a bearing envelope. Also, the first reference index may be defined as the product or sum of the first index and the setting factor, and the second reference index may be defined as the product or sum of the second index and the setting factor. In addition, the setting factor for the first reference index and the setting factor for the second reference index may be different from each other, and depending on the state of the part (eg, 'Caution', 'Warning', 'Alarm', etc.), a plurality of Setting factors may be set.

As described above, after calculating the index and the reference index according to each state value, the first controller 160 updates the diagnosis database 168 ( S380 ).

12 is a flowchart of step S380 of FIG. 6 .

As shown in FIG. 12 , the update of the diagnostic database 168 ( S380 ) is performed when an index and a reference index are stored in the diagnostic database 168 . That is, if the index and the reference index are stored in the diagnostic database 168, the data detector 100 detects data for diagnosis of the part (S320). Here, the data includes parameters and state values.

The first controller 160 classifies the data detected by the data detector 100 into a corresponding class and stores the state value of the corresponding class in the buffer database 166 (S330). The first controller 160 determines whether the number of state values stored in the buffer database 166 for each class is greater than or equal to the number n of the maximum state values of the corresponding class (S340).

If the number of state values stored in the buffer database 166 for each class is less than the number of maximum state values of the corresponding class, the data detector 100 continues to detect data for component diagnosis (S320).

If the number of state values stored in the buffer database 166 for each class is equal to or greater than the number of maximum state values of the corresponding class, the first controller 160 sets the state value of the corresponding class in the buffer database 166 to the diagnostic database 168 and deletes the state value of the corresponding class in the buffer database 166 (S350).

Thereafter, the first controller 160 updates the index and reference index of the corresponding class in the diagnosis database 168 ( S382 ).

For example, if the state value is the first state value, the index defining a probability distribution function that approximates the distribution of the state values in the diagnostic database 168 is updated. For example, the number, mean, and variance of the state values of the corresponding class in the diagnostic database 168 before update are Nc, Ac, σ _c ² , respectively, and the number of state values of the corresponding class transferred from the buffer database 166; If the mean and variance are n, a, and σ ² , respectively, the number, mean, and variance of the state values of the corresponding class in the diagnostic database 168 after update are as follows.

Number of state values of the class after update = Nc+n

Average of state values of the class after update = (Ac*Nc + a*n)/(Nc + n)

Variance of state values of the class after update = (σ _c ² *Nc + σ ² *n)/(Nc + n)

As mentioned above, in the case of the first state value, since the index may be defined as the average or standard deviation of the state values, when the average and variance of the state values of the corresponding class are updated, the index of the corresponding class is also updated. Also, when the index of the corresponding class in the diagnostic database 168 is updated, the reference index of the corresponding class in the diagnostic database 168 is also updated accordingly.

If the state value is the second state value, the average of the integral value of the state value with respect to time in the main operating region is newly calculated as in the above equation, and the newly calculated average is updated as an index. Also, when the index of the corresponding class in the diagnostic database 168 is updated, the reference index of the corresponding class in the diagnostic database 168 is also updated accordingly.

If the state value is the third state value, the first index processed in the time domain is updated in the same manner as the first state value. In addition, the second index is updated by newly processing all state values of the corresponding class in the diagnostic database 168 into the frequency domain. When the first and second indexes of the corresponding class in the diagnostic database 168 are updated, the first and second reference indexes of the corresponding class in the diagnostic database 168 are updated accordingly.

Thereafter, the first controller 160 determines whether the number of state values of the corresponding class stored in the diagnosis database 168 is equal to or greater than the number of set state values (S384). The performance of the parts is high at the beginning of installation, at the beginning of replacement, or at the beginning of maintenance, and decreases as the service life increases. Accordingly, the performance of the initial installation, replacement, or maintenance of the part may be used as a criterion for evaluating the performance of the part. By limiting the number of state values of a corresponding class stored in the diagnostic database 168 , the memory resource of the diagnostic database 168 can be managed. In addition, by using the performance of the initial installation, replacement, or maintenance of the part as a standard for evaluating the performance of the part, it is possible to accurately diagnose the part.

If the number of state values of the corresponding class stored in the diagnosis database 168 in step S384 is less than the number of set state values, the data detector 100 continues to detect data for component diagnosis (S320).

If the number of state values of the corresponding class stored in the diagnostic database 168 in step S384 is equal to or greater than the number of set state values, the first controller 160 prohibits the update of the index of the corresponding class and the reference index in the diagnostic database 168. do (S386). In some embodiments, an update period in which updates of the diagnostic database 168 are possible is set, and when the update period elapses from the time when database construction is started, the update of the index of the corresponding class and the reference index in the diagnostic database 168 is prohibited. can Also, if the update of the index and reference index of the corresponding class in the diagnosis database 168 is prohibited, the first controller 160 may delete the state values of the corresponding class stored in the diagnosis database 168 . Accordingly, it is possible to manage the memory resources of the diagnostic database 168 . Furthermore, if the update of the index of the corresponding class and the reference index in the diagnosis database 168 is prohibited, the first controller 160 may prohibit the transfer of the state values of the corresponding class from the buffer database 166 . In this case, if the number of state values stored in the buffer database 166 for each class is equal to or greater than the maximum number of state values of the class, the first controller 160 sets the index of the class in the buffer database 166 to the diagnostic database ( 168), it is compared with the reference index of the corresponding class, and then it can be deleted without transferring the state value of the corresponding class in the buffer database 166.

13 is a flowchart of step S230 of FIG. 5 when the state value is a first state value or a second state value.

As shown in FIG. 13 , when the state value is the first state value or the second state value, the first controller 160 determines that the index of the corresponding class of the corresponding vehicle 5 is the corresponding vehicle 5 in the diagnostic database 168 . ) is greater than or equal to the reference index of the corresponding class (S410). That is, the index of the corresponding class of the corresponding vehicle 5 is compared with the reference index of the corresponding class of the corresponding vehicle 5 in the diagnosis database 168 . Here, the index of the corresponding class of the vehicle 5 may be an index obtained by processing a state value in the buffer database 166 or an updated index in the diagnosis database 168 .

In step S410 , if the index of the corresponding class of the corresponding vehicle 5 is equal to or greater than the reference index of the corresponding class of the corresponding vehicle 5 in the diagnostic database 168 , the first controller 160 determines the corresponding class of the corresponding vehicle 5 . It is determined whether the index is equal to or greater than the reference index of the corresponding class of the other vehicle 5 of the same railroad vehicle 1 ( S420 ). That is, the index of the corresponding class of the corresponding vehicle 5 is compared with the reference index of the corresponding class of the other vehicle 5 of the same railroad vehicle 1 . Here, the reference index of a corresponding class of another vehicle 5 of the same railroad car 1 is stored in the same diagnostic database 168 of the same railroad car 1 or another diagnostic database 168 of the same railroad car 1 can be stored in

In step S420, if the index of the corresponding class of the vehicle 5 is equal to or greater than the reference index of the corresponding class of the other vehicle 5 of the same railroad vehicle 1, the first controller 160 controls the corresponding class of the vehicle 5 It is determined whether the index of is equal to or greater than the reference index of the corresponding class of any vehicle 5 of the other railway vehicle 1 ( S430 ). That is, the index of the corresponding class of the corresponding vehicle 5 is compared with the reference index of the corresponding class of any vehicle 5 of the other railroad vehicle 1 . Here, the reference index of the corresponding class of any vehicle 5 of the other railway vehicle 1 is stored in the diagnostic database 168 of the other railway vehicle 1 and the corresponding railway vehicle 1 via the wireless communicator 50 may be transmitted to the diagnostic database 168 of In addition, the other railway vehicle 1 may be the railway vehicle 1 operating on the same route as that on which the corresponding railway vehicle 1 is operating.

If the index of the corresponding class of the corresponding vehicle 5 is greater than or equal to the reference index of the corresponding class of any vehicle 5 of the other railroad vehicle 1 in step S430, the first controller 160 determines that the corresponding part is defective and store the fault code (S440). Also, the first controller 160 may transmit a warning signal to the display 170 , the speaker 180 , the external server 80 , and/or the user computing device 90 . The mechanic may repair or replace the part based on the warning signal.

In this way, the index of the corresponding class of the vehicle 5 is the reference index of the corresponding class of the vehicle 5, the reference index of the corresponding class of the other vehicle 5 of the same railroad vehicle 1, and another railroad vehicle By comparing each with the reference index of the corresponding class of the arbitrary vehicle 5 in (1), the influence of the disturbance can be minimized. Accordingly, it is possible to improve the accuracy of diagnosis of parts.

On the other hand, if the determination result is 'No' in steps S410, S420, and S430, the first controller 160 ends step S230.

14 is a flowchart of step S230 of FIG. 5 when the state value is a third state value.

14 , when the state value is the third state value, the first controller 160 determines that the first index of the corresponding class of the corresponding vehicle 5 corresponds to the corresponding vehicle 5 in the diagnostic database 168 . It is determined whether the class is equal to or greater than the first reference index (S412). That is, the first index of the corresponding class of the corresponding vehicle 5 is compared with the first reference index of the corresponding class of the corresponding vehicle 5 in the diagnosis database 168 . Here, the first index of the corresponding class of the corresponding vehicle 5 may be a first index obtained by processing a state value in the buffer database 166 in the time domain, or an updated first index in the diagnosis database 168 .

In step S412, if the first index of the corresponding class of the corresponding vehicle 5 is equal to or greater than the first reference index of the corresponding class of the corresponding vehicle 5 in the diagnosis database 168, the first controller 160 controls the corresponding vehicle 5 It is determined whether the first index of the corresponding class of the railway vehicle 1 is equal to or greater than the first reference index of the corresponding class of the other vehicle 5 of the same railroad vehicle 1 (S422). That is, the first index of the corresponding class of the corresponding vehicle 5 is compared with the first reference index of the corresponding class of the other vehicle 5 of the same railroad vehicle 1 . Here, the first reference index of a corresponding class of another vehicle 5 of the same railroad car 1 is stored in the same diagnostic database 168 of the same railroad car 1 or another diagnostic database ( 168) can be stored.

In step S422, if the first index of the corresponding class of the corresponding vehicle 5 is equal to or greater than the first reference index of the corresponding class of the other vehicle 5 of the same railroad vehicle 1, the first controller 160 controls the corresponding vehicle 5 ), it is determined whether the first index of the corresponding class is equal to or greater than the first reference index of the corresponding class of any vehicle 5 of the other railroad vehicle 1 ( S432 ). That is, the first index of the corresponding class of the corresponding vehicle 5 is compared with the first reference index of the corresponding class of any vehicle 5 of the other railroad vehicle 1 . Here, the first reference index of the corresponding class of any vehicle 5 of the other railway vehicle 1 is stored in the diagnostic database 168 of the other railway vehicle 1 and the corresponding railway vehicle ( 1) to the diagnostic database 168. In addition, the other railway vehicle 1 may be the railway vehicle 1 operating on the same route as that on which the corresponding railway vehicle 1 is operating.

Also, the first controller 160 determines whether the second index of the corresponding class of the corresponding vehicle 5 is equal to or greater than the second reference index of the corresponding class of the corresponding vehicle 5 in the diagnosis database 168 (S414). That is, the second index of the corresponding class of the corresponding vehicle 5 is compared with the second reference index of the corresponding class of the corresponding vehicle 5 in the diagnosis database 168 . Here, the second index of the corresponding class of the corresponding vehicle 5 may be a second index obtained by processing a state value in the buffer database 166 in the frequency domain, or an updated second index in the diagnosis database 168 .

In step S414, if the second index of the corresponding class of the corresponding vehicle 5 is equal to or greater than the second reference index of the corresponding class of the corresponding vehicle 5 in the diagnosis database 168, the first controller 160 controls the corresponding vehicle 5 It is determined whether the second index of the corresponding class of is equal to or greater than the second reference index of the corresponding class of the other vehicle 5 of the same railroad vehicle 1 (S424). That is, the second index of the corresponding class of the corresponding vehicle 5 is compared with the second reference index of the corresponding class of the other vehicle 5 of the same railroad vehicle 1 . Here, the second reference index of the corresponding class of the other vehicle 5 of the same railroad car 1 is stored in the same diagnostic database 168 of the same railroad car 1 or another diagnostic database ( 168) can be stored.

In step S424, if the second index of the corresponding class of the corresponding vehicle 5 is equal to or greater than the second reference index of the corresponding class of the other vehicle 5 of the same railroad vehicle 1, the first controller 160 controls the corresponding vehicle 5 ), it is determined whether the second index of the corresponding class is equal to or greater than the second reference index of the corresponding class of any vehicle 5 of another railroad vehicle 1 ( S434 ). That is, the second index of the corresponding class of the corresponding vehicle 5 is compared with the second reference index of the corresponding class of any vehicle 5 of the other railroad vehicle 1 . Here, the second reference index of the corresponding class of any vehicle 5 of the other railway vehicle 1 is stored in the diagnostic database 168 of the other railway vehicle 1 and the corresponding railway vehicle ( 1) to the diagnostic database 168. In addition, the other railway vehicle 1 may be the railway vehicle 1 operating on the same route as that on which the corresponding railway vehicle 1 is operating.

In step S432, the first index of the corresponding class of the corresponding vehicle 5 is equal to or greater than the first reference index of the corresponding class of any vehicle 5 of another railroad vehicle 1, and in step S434, the corresponding class of the corresponding vehicle 5 If the second index of is greater than or equal to the second reference index of the corresponding class of any vehicle 5 of the other railway vehicle 1, the first controller 160 determines that the corresponding part is defective and stores the failure code ( S440). Also, the first controller 160 may transmit a warning signal to the display 170 , the speaker 180 , the external server 80 , and/or the user computing device 90 . The mechanic may repair or replace the part based on the warning signal.

In this way, the first and second indexes of the corresponding class of the corresponding vehicle 5 are compared with the first and second reference indexes of the corresponding class of the corresponding vehicle 5, and the corresponding class of the other vehicle 5 of the same railroad vehicle 1 By comparing the first and second reference indexes of , respectively, with the first and second reference indexes of the corresponding class of an arbitrary vehicle 5 of another railway vehicle 1, the influence of disturbance can be minimized. Accordingly, it is possible to improve the accuracy of diagnosis of parts.

On the other hand, if the determination result is 'No' in steps S412, S422, S432, S414, S424, and S434, the first controller 160 ends step S230.

FIG. 15 is a flowchart of step S240 of FIG. 5 , and FIG. 16 is a graph showing an example of the index trend with respect to time.

When step S240 starts, the first controller 160 calculates the index influence through trend analysis of the indexes ( S510 ). Here, a moving average method, a particle filter method, a hyper function regression method, etc. may be used as a method of analyzing the trend of the indices.

As shown in FIG. 16 , the index vibrates in the set range in the startup phase, and the index is stabilized to the set value in the normal operation phase. After that, if the period of using the parts increases, it enters the failure stage and the index increases rapidly. In this case, the gradient at which the index increases is defined as the index influence. The index influence may have a value of 1 or more.

When the index influence is calculated in step S510, the first controller 160 calculates the influence of the maintenance history based on the maintenance history (S520). In general, when parts are repaired, the lifespan of the parts is reduced according to the number of repairs. Therefore, the effect of the maintenance frequency on the reduction of the service life is defined as the maintenance history effect. For example, the maintenance history influence is a value of 1 or more, and it can be set to increase by a set value (0.1 or 0.2) according to the number of maintenance.

Thereafter, the first controller 160 calculates the remaining life based on the target operating time, the current operating time, the index influence, and the maintenance history influence (S530). For example, the remaining life may be calculated by the following equation.

Remaining time = target run time ?? Σ Current Operating Hours * Index Impact * Maintenance History Impact

Here, the target operating time is a value set in advance as the life expectancy of a new product.

Meanwhile, if a plurality of state values are set for diagnosis of a corresponding part, the remaining time for each state value may be calculated, and a minimum value among the plurality of remaining times may be determined as the final remaining time.

Thereafter, the first controller 160 may notify the calculated remaining life to the display 170 , the speaker 180 , the external server 80 , and/or the user computing device 90 .

Hereinafter, a method of diagnosing a fault in the main transformer 34 will be described in more detail with reference to FIGS. 17 to 21 .

17 is an exemplary flowchart of step S210 of FIG. 5 in the case of a main transformer, and FIG. 21 is an exemplary flowchart of steps S220 and S230 of FIG. 5 in the case of a main transformer.

As shown in FIG. 17 , the position sensor 135 or the speed sensor 155 measures the traveling speed and the traveling distance of the railway vehicle 1 ( S612 and S615 ), and the power meter 125 is the railway vehicle 1 . measures the power consumption (S613), and the temperature sensor 110 measures the outside temperature (S614).

Thereafter, the first controller 160 classifies four parameters of the traveling speed, the power consumption, the outside air temperature, and the traveling distance into a corresponding class ( S616 ). In addition, the timer 140 and the switch 150 detect the operating time of the cooling device for cooling the main transformer 34 (S617), the temperature sensor 110 detects the temperature of the cooling device (S618) , the switch 150 detects the number of operations of the oil pump for supplying the refrigerant to the main transformer 34 (S619). Here, the operating time of the cooling device, the temperature of the cooling device, and the operating frequency of the oil pump are state values for diagnosing the main transformer 34 and correspond to the first state value. That is, three state values are set for diagnosing the main transformer 34 .

Thereafter, the first controller 160 stores the operating time of the cooling device of the corresponding class, the temperature of the cooling device, and the number of operation of the oil pump in the buffer database 166 to build a database of state values for each class ( S620).

The first controller 160 repeats steps S612 to S620 until the number of state values of the corresponding class becomes the maximum number of state values of the corresponding class.

When the number of each state value of the corresponding class reaches the maximum number of state values of the corresponding class, the first controller 160 transfers each state value of the corresponding class in the buffer database 166 to the diagnostic database 168, Each state value of the corresponding class in the buffer database 166 is deleted.

Then, the first controller 160 calculates a probability distribution function defining the distribution of each state value of the corresponding class in the diagnosis database 168 (since the state value for diagnosing the main transformer is the first state value). For example, as shown in FIG. 18 , a cooling device for outdoor temperature and power consumption for each driving speed (eg, speed 1, speed 2, etc.) at a specific driving distance (eg, mileage 1) temperature can be shown. That is, in order to easily recognize a state value visually and calculate a probability distribution function defining a distribution of the state value, a function of four parameters is expressed in two dimensions.

Similarly, for each driving speed (eg, speed 1, speed 2, etc.) at a specific driving distance (eg, mileage 1), the operating time of the cooling device and the number of operations of the oil pump for the outside air temperature and power consumption can be shown.

Then, as shown in FIG. 19 , the temperature distribution of the cooling device for each class is one-dimensionally illustrated. 19 shows values (3σ) according to the average and standard deviation of the temperature of the cooling device for each class. Similarly, the distribution of the operating time of the cooling device and the operating frequency of the oil pump for each class may be shown in one dimension.

As shown in FIG. 19 , when the distribution of state values for each class is shown, the first controller 160 derives a probability distribution function close to the distribution of state values of the corresponding class in the diagnosis database 168 .

20 is a graph comparing the probability distribution of state values of a specific class with four representative probability distribution functions (Rayleigh, Gauss, Weibull, Bernbaum ? Sanders). In FIG. 20, a thin dotted line indicates a Gaussian probability distribution function, a thick dotted line indicates a Rayleigh probability distribution function, a thin solid line indicates a Weibull probability distribution function, and a thick solid line indicates a Bernbaum?? Represents the Birnbaum??Saunders probability distribution function. It can be seen that the distribution of state values of a specific class shown in FIG. 20 best matches the Bernbaum-Sanders probability distribution function.

Then, the first controller 160 calculates an index by calculating the mean, standard deviation, and/or maximum value defining the derived probability distribution function. As mentioned earlier, the index may be the mean, standard deviation, and/or maximum defining a probability distribution function of the state value. Also, the first controller 160 calculates a reference index by multiplying or adding a setting factor to the index.

As described above, after step S210 is performed, the first controller 160 performs steps S220 and S230. As shown in FIG. 21 , the position sensor 135 or the speed sensor 155 measures the traveling speed and the traveling distance of the railway vehicle 1 ( S612 and S615 ), and the power meter 125 is the railway vehicle 1 . measures the power consumption (S613), and the temperature sensor 110 measures the outside temperature (S614).

Thereafter, the first controller 160 classifies four parameters of driving speed, power consumption, outdoor temperature, and driving distance into a corresponding class (S616), and reads the reference index of the corresponding class in the diagnosis database 168 ( S621). In step S621 , the first controller 160 determines the reference index of the corresponding class of the corresponding vehicle 5 , the reference index of the corresponding class of the other vehicle 5 of the same railroad vehicle 1 , and the reference index of the other railroad vehicle 1 . The reference index of the corresponding class of any vehicle 5 is read.

In addition, the timer 140 and the switch 150 detect the operating time of the cooling device for cooling the main transformer 34 (S617), the temperature sensor 110 detects the temperature of the cooling device (S618) , the switch 150 detects the number of operations of the oil pump for supplying the refrigerant to the main transformer 34 (S619).

Thereafter, the first controller 160 stores the operating time of the cooling device of the class, the temperature of the cooling device, and the number of operations of the oil pump in the buffer database 166 , and processes the state value in the buffer database 166 . to calculate the index.

Thereafter, the first controller 160 compares the index of the corresponding class of the corresponding vehicle 5 with the reference index of the corresponding class of the corresponding vehicle 5 (S622), and compares the index of the corresponding class of the corresponding vehicle 5 (S622). The reference index of the corresponding class of the other vehicle 5 of the same railroad vehicle 1 is compared (S623), and the index of the corresponding class of the corresponding vehicle 5 is compared with that of any vehicle 5 of the other railroad vehicle 1 It is compared with the reference index of the corresponding class (S624).

Thereafter, the first controller 160 notifies the display 170 , the speaker 180 , the external server 80 , and/or the user computing device 90 of the diagnosis result of the corresponding part ( S624 ).

Here, a method for diagnosing a fault in the main transformer 34 has been specifically exemplified, but the embodiment of the present invention is not limited to a method for diagnosing a fault in the main transformer 34 . The embodiment of the present invention relates to all parts of the railway vehicle 1 requiring diagnosis (for example, an air compressor, an air conditioner, a current collector, a switchboard, a bearing, a traction motor, a door, a broadcasting device, a fire detection device, etc.) Applicable.

Although the preferred embodiment of the present invention has been described above, the present invention is not limited to the above embodiment, and it is easily changed by a person skilled in the art from the embodiment of the present invention to equivalent It includes all changes to the extent recognized as such.

Claims (15)

In the method of diagnosing defects of parts of a railway vehicle and calculating the remaining life,
setting parameters and state values for diagnosis of each part;
constructing a database of indexes and reference indexes for each class of parameters;
comparing the detected index of the detected class with a reference index of the corresponding class in the database; and
If the detected index of the detected class is greater than the reference index of the corresponding class in the database, storing the failure code of the corresponding part;
including,
The index is the state value, or is calculated by processing the state value in a time domain or a frequency domain, and the reference index is calculated by applying a setting factor to the index,
The state values are affected by the driving environment and processed in the time domain to calculate the index, and the second state is not affected by the driving environment and requires an integral value for a set time to calculate the index. It can be classified into a value and a third state value including a first index that is affected by the driving environment and the index is processed in the time domain and a second index that is processed in the frequency domain,
When the state value is affected by the driving environment, the parameters include at least one of driving speed, power consumption, mileage, and outdoor temperature. According to claim 1,
The database includes a buffer database and a diagnostic database,
The step of building a database of indexes and reference indexes for each class of parameters is
setting the maximum number of state values that can be stored in the buffer database for each class;
detecting data including parameters and state values;
classifying the detected data into a corresponding class and storing a state value of the corresponding class in a buffer database;
determining whether the number of state values stored in the buffer database for each class is greater than or equal to the number of maximum state values of the corresponding class;
If the number of state values stored in the buffer database for each class is equal to or greater than the maximum number of state values of the corresponding class, transferring the state value of the corresponding class in the buffer database to the diagnostic database and deleting the state value of the corresponding class in the buffer database; and
calculating an index and a reference index of the corresponding class by processing the state value of the corresponding class in the diagnostic database;
A method of diagnosing defects of parts of a railroad car, including a method of calculating the remaining life. 3. The method of claim 2,
A method of diagnosing defects of parts of a railway vehicle whose index and reference index of the corresponding class are updated until the number of state values of the corresponding class stored in the diagnostic database reaches a set number, and calculating the remaining lifespan. 3. The method of claim 2,
If the number of state values of the corresponding class stored in the diagnostic database is greater than or equal to the set number, a method of diagnosing defects of parts of a railway vehicle for which the update of the index of the corresponding class and the reference index is prohibited and calculating the remaining lifespan. 3. The method of claim 2,
If the state value is the first state value, calculating the index and reference index of the class by processing the state value of the corresponding class in the diagnostic database
calculating a probability distribution function defining a distribution of state values of a corresponding class in a diagnostic database;
calculating an index defining the calculated probability distribution function; and
calculating a reference index of the corresponding class by applying a setting factor to the index of the corresponding class;
A method of diagnosing defects of parts of a railroad car, including a method of calculating the remaining life. 3. The method of claim 2,
If the state value is the second state value, calculating the index and reference index of the class by processing the state value of the corresponding class in the diagnostic database
integrating the state values of the corresponding class in the diagnostic database with respect to time;
setting the main operating area corresponding to the set time;
calculating an index by processing an integral value with respect to time in the main operating area; and
calculating a reference index of the corresponding class by applying a setting factor to the index of the corresponding class;
A method of diagnosing defects of parts of a railroad car, including a method of calculating the remaining life. 3. The method of claim 2,
If the status value is the third status value, calculating the index and reference index of the class by processing the status value of the corresponding class in the diagnostic database is
calculating a probability distribution function defining a distribution of state values of a corresponding class in a diagnostic database for each first period;
calculating a first index defining the calculated probability distribution function;
calculating a first reference index of the corresponding class by applying a setting factor to the first index of the corresponding class;
processing the state value of the corresponding class in the diagnosis database into a frequency domain value every second period longer than the first period;
calculating a value of the frequency domain of the corresponding class as a second index; and
calculating a second reference index of the corresponding class by applying a setting factor to the second index of the corresponding class;
A method of diagnosing defects of parts of a railroad car, including a method of calculating the remaining life. 3. The method of claim 2,
The step of comparing the detected index of the detected class with the reference index of the corresponding class in the database is performed only when the reference index of the corresponding class exists in the diagnostic database. A method of diagnosing defects of parts of a railway vehicle and calculating the remaining lifespan . 7. The method according to claim 5 or 6,
The step of comparing the detected index of the detected class with the reference index of the corresponding class in the database is
comparing the index of the corresponding class of the corresponding vehicle with the reference index of the corresponding class of the corresponding vehicle;
comparing the index of the corresponding class of the corresponding vehicle with the reference index of the corresponding class of other vehicles of the same railroad vehicle; and
comparing the index of the corresponding class of the corresponding vehicle with the reference index of the corresponding class of any vehicle of another railroad vehicle;
A method of diagnosing defects of parts of a railroad car, including a method of calculating the remaining life. 8. The method of claim 7,
The step of comparing the detected index of the detected class with the reference index of the corresponding class in the database is
comparing the first index of the corresponding class of the corresponding vehicle with the first reference index of the corresponding class of the corresponding vehicle;
comparing the first index of the corresponding class of the corresponding vehicle with the first reference index of the corresponding class of another vehicle of the same railroad vehicle;
comparing the first index of the corresponding class of the corresponding vehicle with the first reference index of the corresponding class of any vehicle of another railroad vehicle;
comparing a second index of the corresponding class of the corresponding vehicle with a second reference index of the corresponding class of the corresponding vehicle;
comparing a second index of the corresponding class of the corresponding vehicle with a second reference index of the corresponding class of another vehicle of the same railroad vehicle; and
comparing a second index of the corresponding class of the corresponding vehicle with a second reference index of the corresponding class of any vehicle of another railroad vehicle;
A method of diagnosing defects of parts of a railroad car, including a method of calculating the remaining life. 3. The method of claim 2,
calculating the remaining life of the part;
The steps to calculate the remaining life of a part are:
calculating an index influence through trend analysis of the index related to the part;
calculating a maintenance history influence degree based on the maintenance history of the parts; and
calculating the remaining life based on the target operating time for the part, the current operating time of the part, the index influence degree, and the maintenance history influence degree;
A method of diagnosing defects of parts of a railroad car, including a method of calculating the remaining life. 12. The method of claim 11,
When two or more state values are set for diagnosis of a part, the remaining life of the part is calculated based on each state value, and a minimum value of the calculated remaining lifes is determined as the remaining life of the part. A method of diagnosing defects in parts of a railway vehicle and calculating the remaining life. 12. The method of claim 11,
Calculating the remaining life of the part further comprises the step of notifying the calculated remaining life. A method of diagnosing defects of parts of a railway vehicle and calculating the remaining life. In an apparatus for diagnosing defects of parts of a railway vehicle and calculating the remaining life,
a data detector for measuring data including parameters and state values for diagnosis of each part; and
a controller including a buffer database and a diagnostic database, the controller configured to diagnose a failure of each component and calculate the remaining life using the data detected by the data detector;
includes,
An apparatus for diagnosing defects of parts of a railway vehicle and calculating the remaining life, wherein the controller is adapted to execute the method of diagnosing defects of parts of the railway vehicle according to claim 1 or 2 and calculating the remaining life. 15. The method of claim 14,
The controller calculates the remaining life based on the index influence through trend analysis of the index related to the part, the maintenance history effect based on the maintenance history of the part, the target operating time for the part, and the current operating time of the part A device for diagnosing defects in parts of railway vehicles and calculating the remaining service life.

Images