KR20230137170A - Method for monitoring failure of motor in a car and system using the same - Google Patents

Abstract

The present invention relates to a method for determining whether a motor in a vehicle has failed, in a state where a diagnostic system including a sensing module for sensing the state of a motor in the vehicle is installed in the vehicle, the diagnostic system includes the sensing module. sensing the state of the motor in the vehicle and obtaining a sensing value; The diagnostic system includes converting the sensing value acquired by the sensing module to extract a feature value; and determining, by the diagnostic system, whether the motor has failed based on the characteristic values.

Description

Method for determining whether a motor in a vehicle has failed and a system using the same {METHOD FOR MONITORING FAILURE OF MOTOR IN A CAR AND SYSTEM USING THE SAME}

The present invention relates to a method for determining whether a motor in a vehicle has failed and a system using the same. More specifically, when a diagnostic system including a sensing module for sensing the status of a motor in a vehicle is installed in the vehicle, the The diagnostic system includes: sensing the state of a motor in the vehicle through the sensing module and obtaining a sensing value; The diagnostic system includes converting the sensing value acquired by the sensing module to extract a feature value; and determining, by the diagnostic system, whether the motor is broken based on the characteristic value.

The technology to diagnose the state of the driving system and predict failure in advance is called PHM (Prognostic and health management). PHM has already been applied to automobiles, but it was only used to transmit data from a fault diagnosis center to an external cloud server through a network for analysis, and has never been implemented on a single chip inside the automobile.

In other words, rather than diagnosing the state of the car's driving system on the car's internal system (SoC), the state of the car was diagnosed through an external server. Accordingly, there was a problem that it had to be linked to an external server through a network, and it may be difficult to diagnose in real time.

In order to solve the above problem, the present inventor would like to propose a method for determining whether a motor in a vehicle has failed and a system using the same.

The present invention may have the following purposes.

The purpose of the present invention is to diagnose the condition of a motor, etc. in a vehicle through a diagnostic system located in the vehicle.

Additionally, the purpose of the present invention is to monitor the status of the driving system in a vehicle in real time using AI technology.

Additionally, the purpose of the present invention is to predict in advance whether a driving system in a vehicle will fail.

According to an embodiment of the present invention, in a method of determining whether a motor in a vehicle has failed, when a diagnostic system including a sensing module for sensing the state of a motor in a vehicle is installed in the vehicle, the diagnostic system Sensing the state of the motor in the vehicle through the sensing module and obtaining a sensing value; The diagnostic system includes converting the sensing value acquired by the sensing module to extract a feature value; and a method comprising determining, by the diagnostic system, whether the motor has failed based on the characteristic values.

In addition, according to another embodiment of the present invention, a system for determining whether a motor in a vehicle has malfunctioned includes a sensing module that senses the state of a motor in a vehicle and obtains a sensing value; It includes a signal processing module and a failure determination module that convert the sensing value acquired by the sensing module to extract a feature value and determine whether the motor is broken based on the feature value, and are installed in the vehicle. Includes a diagnostic system that

In this way, according to the present invention, the following effects are achieved.

According to the present invention, there is an effect of diagnosing the condition of a motor, etc. in a vehicle through a diagnostic system located in the vehicle.

In addition, according to the present invention, there is an effect of monitoring the status of the driving system in the vehicle in real time using AI technology.

In addition, according to the present invention, there is an effect of predicting in advance whether the driving system in the vehicle will fail.

1 is a diagram showing the schematic configuration of a diagnostic system according to an embodiment of the present invention.
Figure 2 is a diagram showing the specific configuration of a diagnostic system according to an embodiment of the present invention.
Figure 3 is a diagram showing the process of determining whether a motor has failed according to an embodiment of the present invention.
Figure 4 is a diagram showing results according to different reference values for each class according to an embodiment of the present invention.
Figure 5 is a diagram showing the learning process of an AI module according to an embodiment of the present invention.
Figure 6 is a diagram showing the process of extracting feature values according to an embodiment of the present invention.

The detailed description of the present invention described below refers to the accompanying drawings, which show by way of example specific embodiments in which the present invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention. It should be understood that the various embodiments of the present invention are different from one another but are not necessarily mutually exclusive. For example, specific shapes, structures and characteristics described herein with respect to one embodiment may be implemented in other embodiments without departing from the spirit and scope of the invention. Additionally, it should be understood that the location or arrangement of individual components within each disclosed embodiment may be changed without departing from the spirit and scope of the invention. Accordingly, the detailed description that follows is not intended to be taken in a limiting sense, and the scope of the invention is limited only by the appended claims, together with all equivalents to what those claims assert, if properly described. Similar reference numbers in the drawings refer to identical or similar functions across various aspects.

Hereinafter, in order to enable those skilled in the art to easily practice the present invention, preferred embodiments of the present invention will be described in detail with reference to the attached drawings.

1 is a diagram showing the schematic configuration of a diagnostic system according to an embodiment of the present invention.

As shown in FIG. 1, the diagnostic system 100 of the present invention may include a sensing module 110, a signal processing module 120, and a failure determination module 130. The diagnostic system 100 can be installed inside a vehicle and can perform various functions in the form of a System on Chip (SoC). In other words, it is a type of semiconductor that can play a role in diagnosing devices inside a vehicle.

Specifically, the diagnostic system 100 of the present invention can diagnose the state of an electric vehicle driving system, and in particular, can diagnose the state of electric motors, actuators, etc.

Additionally, although not shown in the drawing, the diagnostic system 100 may include a communication unit, a processor, a database, etc. The communication unit may be implemented using various communication technologies. That is, WIFI, WCDMA (Wideband CDMA), HSDPA (High Speed Downlink Packet Access), HSUPA (High Speed Uplink Packet Access), HSPA (High Speed Packet Access), Mobile WiMAX, and WiBro. , LTE (Long Term Evolution), 5G, 6G, Bluetooth, IrDA (infrared data association), NFC (Near Field Communication), Zigbee, wireless LAN technology, etc. can be applied. Additionally, when providing services by connecting to the Internet, TCP/IP, the standard protocol for information transmission on the Internet, can be followed.

Figure 2 is a diagram showing the specific configuration of a diagnostic system according to an embodiment of the present invention.

For reference, Figure 2 is a diagram showing the diagnostic system (Figure 1) of the present invention in more detail. The sensing module 110 corresponds to the fault diagnosis and control unit, and the signal processing module 120 corresponds to the signal processing unit. The fault determination module 130 may correspond to a fault classification unit.

Below, the processes performed in the diagnostic system of the present invention will be described, focusing on the sensing module 110, signal processing module 120, and failure determination module 130.

Figure 3 is a diagram showing the process of determining whether a motor has failed according to an embodiment of the present invention.

First, it can be assumed that the diagnostic system 100 including the sensing module 110 that senses the state of the motor in the vehicle is installed in the vehicle.

Additionally, the diagnostic system 100 can sense the status of the motor in the vehicle through the sensing module 110 and obtain a sensing value (S310). Here, the sensing module 110 can measure the temperature (Temp), voltage (V), and current (I) of the motor using various sensors, and the measured values will correspond to the sensing values. For reference, the obtained temperature, voltage, and current of the motor will correspond to analog values, as can be seen in FIG. 2.

Additionally, the diagnostic system 100 may convert the sensing value acquired by the sensing module 110 to extract a feature value (S320). The sensing value may be transmitted to the signal processing module 120 and the failure determination module 130 and converted into a feature value. Here, the signal processing module 120 may include a plurality of Analog Front Ends (AFEs), Mode Selectors, Analog Digital Converters (ADCs), MCUs, etc.

Specifically, the sensing values (analog temperature, voltage, and current) obtained through the sensing module 110 may be transmitted to the ADC through several AFEs and converted into digital form. Here, the measured sensing values of temperature, voltage, and current are transmitted through different AFEs and can pass through Amp, Filter, etc.

In addition, the sensing values (temperature, voltage, current) converted to digital form are transmitted to the failure determination module 130, go through an FFT processor, etc., and then can be extracted as feature values through an AI module (not shown). In other words, the characteristic values can be derived through an AI module, etc. based on the sensed temperature, voltage, and current.

Here, the AI module may be included in the failure determination module 130, and in some cases, may be present on the chip of the diagnostic system 100 as a separate module.

The characteristic value is calculated using the calculation formula {Ax+By+Cz+D = feature value (A, B, C, D are variables (parameters), x is the converted value of the sensed temperature, y is the converted value of the sensed voltage, and z is the converted value of the sensed voltage. converted value of the sensed current), and the AI module can refer to the above formula. When the temperature, voltage, current, etc. are measured by time zone, the feature values can also be extracted by time zone and implemented as a two-dimensional graph (x-axis: time, y-axis: feature value). The above formula may vary depending on settings.

Additionally, the diagnosis system 100 may determine whether the motor is broken based on the extracted feature value and reference value (S330). We will look at this in detail later.

Figure 4 is a diagram showing results according to different reference values for each class according to an embodiment of the present invention.

For reference, the reference value may be set in advance as a standard for comparing the size with the extracted feature value. It is possible to check in advance the vehicle's motor failure status (using the formula Ax+By+Cz+D = feature value) for the vehicle's motor, and a standard value can be set based on this.

For example, when the motor of vehicle A (measured temperature: t+1) is in a faulty state and the motor of vehicle B (measured temperature: t) is in a normal state, the reference value is the characteristic value related to vehicle B (the measured temperature t is expressed as It can be preset to a value between (applied to) and A vehicle-related characteristic value (measured temperature t+1 applied to the formula).

As above, when the reference value is preset to 10, the diagnosis system 100 determines that if the extracted feature value is greater than 10, the motor of the vehicle is in a failure state, and if the extracted feature value is less than or equal to 10, the motor of the vehicle is in a normal state. You can judge. Depending on the setting, if the characteristic value is smaller than the reference value, it may be determined that the vehicle's motor is in a failure state.

Additionally, for the purpose of explanation regarding customized reference values, it can be assumed that the vehicle is divided into a plurality of classes based on its situation. The plurality of classes may include a first class, a second class, etc.

Here, the vehicle situation may include the vehicle's age, the surrounding temperature of the vehicle, etc. For example, a vehicle with a model year of more than 5 years may be the first class, and a vehicle with a model year of less than 5 years may be the second class. , vehicles with an ambient temperature of 20 degrees or higher may fall into the first class, and vehicles with a vehicle ambient temperature of below 20 degrees may fall into the second class.

Of course, the plurality of classes is not limited to the first class and the second class, and other additional classes (ex. third, fourth class, etc.) may exist, and the situation of the vehicle also includes the vehicle's year, vehicle It may include factors other than the ambient temperature.

Additionally, it can be assumed that a customized reference value is set for each class. Here, the customized reference value is an object compared with the extracted feature value and can be a standard for determining whether a motor, etc. (driving system) has failed.

Specifically, it may be assumed that the reference value corresponding to the first class is set as the first reference value, and the reference value corresponding to the second class is set as the second reference value. At this time, if the vehicle is included in the first class, the diagnostic system 100 may determine whether the motor is broken based on the characteristic value and the first reference value. For example, if the feature value is greater than the first reference value, it may be determined that the vehicle's motor is in a failure state, and if the feature value is less than the first reference value, it may be determined that the vehicle's motor is in a normal state. can do.

Additionally, if the vehicle is included in the second class, it can be determined whether the motor is broken based on the characteristic value and the second reference value. For example, if the characteristic value is greater than the second reference value, it can be determined that the vehicle's motor is in a failure state, and if the characteristic value is smaller than the second reference value, it can be determined that the vehicle's motor is in a normal state. there is.

In a state where the vehicle situation includes the year of the vehicle and the manufacturing time of the vehicle included in the first class is earlier than the manufacturing time of the vehicle included in the second class, the first reference value may be greater than the second reference value. In other words, the reference value of an older vehicle is smaller than the reference value of a vehicle of a younger age.

This is because, in the case of older vehicles (or vehicles with high ambient temperatures), one or more of temperature, voltage, and current may be increased even during normal times even if they are not in a malfunction state, so there is a need to be more flexible in determining malfunctions. Because there is.

As can be seen in FIG. 4, since the first reference value (b) of the first class is greater than the second reference value (a) of the second class, the failure range for each class may also be different. A vehicle corresponding to the first class may be in a failure state when the characteristic value is b or more, and a vehicle corresponding to the second class may be in a failure state if the characteristic value is a or more.

In the end, the younger the vehicle in the second class is or the vehicle is located in a lower temperature area, the wider the scope of the failure can be determined.

Figure 5 is a diagram showing the learning process of an AI module according to an embodiment of the present invention. Below, we will look at the learning process of the AI module that extracts feature values with Figure 5.

First, for learning of the AI module, it can be assumed that a plurality of learning vehicles are divided into classes (e.g. first class, second class, etc.) that match based on each situation (e.g. age, ambient temperature).

Next, the diagnostic system 100 may extract first learning feature values from a plurality of first learning vehicles included in the first class using the AI module. Additionally, the diagnosis system 100 may obtain the first correct answer feature value from the correct answer vehicle included in the first class.

Here, the first characteristic value for the correct answer may correspond to a value representing the actual state of the vehicle for the correct answer (failure or normal state confirmed from temperature, voltage, and current). In the case of the vehicle for the actual correct answer, it corresponds to a vehicle whose failure has already been recognized, and accordingly, the feature value for the first correct answer may also be specified as a value smaller than the reference value (ex. If the reference value is preset to 10, the first correct answer The characteristic value is specified as one of the numbers below 10).

Additionally, the diagnosis system 100 may obtain a first difference value by comparing the first correct answer feature value and the first learning feature value, and update the parameters of the AI module based on the first difference value. That is, the diagnosis system 100 checks the difference between the feature value for the first correct answer and the first learning feature value, and ensures that there is no difference (so that the first learning feature value matches the feature value for the first answer). The process of updating parameters can be performed. The above process of updating the parameters of the AI module can be repeated, and as the number of repetitions increases, more accurate judgment (feature value extraction) of the AI module will be possible.

Additionally, as in the first class, the diagnostic system 100 may extract second learning feature values from a plurality of second learning vehicles included in the second class using an AI module.

Additionally, the diagnostic system 100 may obtain a second correct answer feature value from the answer vehicle included in the second class, and compare it with the second learning feature value to obtain a second difference value. Additionally, a process of updating the parameters of the AI module based on the second difference value may be performed.

As above, the parameters of the AI module can be repeatedly updated using the learning vehicle and the answer vehicle for each of the first class and the second class, and through this, the AI module can be trained.

Although it has been described above that the diagnostic system 100 determines whether the motor is broken based on the characteristic values, in some cases, it is determined whether the motor is in a broken state based on the characteristic values or whether the motor is expected to break down within a certain period (ex. 1 year). It is also possible to determine whether the motor is in normal condition or not. The predetermined period may be set in advance and may be changed depending on the situation.

Specifically, two or more reference values (ex p, q) may be set in advance, and the normal state, a state in which a failure is expected within a predetermined period, or a failure state can be confirmed based on the reference values and extracted feature values.

The diagnosis system 100 is configured to: If the extracted feature value is equal to or smaller than the reference value p, the installed motor is in a normal state, and if the extracted feature value is greater than the reference value p and less than or equal to the reference value q, the installed motor fails within a predetermined period of time. If this expected state and the extracted feature value are greater than the reference value q, the installed motor can be determined to be in a faulty state.

Here, the diagnostic system 100 may set the above reference value using a learned AI module (which may be different from the feature value extraction AI module). In other words, the learning process of processing data obtained from measured values such as the temperature of the vehicle (in a broken or normal state) and then passing it through the AI module to extract a reference value can be repeated. Of course, the reference value may be set arbitrarily without going through a learned AI module, etc.

Meanwhile, in the case of frequently used vehicles or vehicles where safety issues must be more strictly considered (ex. taxi, kindergarten bus, etc.), the standard values are set in advance to three or more, and the standard values are set to three or more: normal state, state in which failure is expected within 6 months, The condition of the installed motor can be judged based on whether it is expected to fail within 3 years or in a failure state. In other words, it is possible to make a more detailed judgment within a certain period (ex. 6 months, 3 years, etc.).

Figure 6 is a diagram showing the process of extracting feature values according to an embodiment of the present invention.

As described above, feature values extracted through an AI module, etc. can be extracted by time period and implemented as a two-dimensional graph (x-axis: time, y-axis: feature value) (see FIG. 6).

At this time, the feature values extracted through the calculation formula using conversion values such as temperature, current, and voltage as input values are in the form of a wave, and may fluctuate at first but gradually converge to a constant value.

At this time, the diagnostic system 100 does not extract feature values when the distance (wave height) between the crest and trough of the corresponding wave shape is greater than r, but extracts the intermediate value (convergence value) between the crest and trough when it is smaller than r. It can be extracted as a feature value. Referring to FIG. 6, the wave height has become smaller than r since t1 (time), and the diagnostic system 100 can extract the characteristic value of the vehicle as K since t1. Here, since K is greater than a, which is the reference value, the motor of the vehicle may be determined to be in a failure state.

Usually, the vehicle fluctuates waves related to characteristic values immediately after starting the engine, immediately after applying the brakes, immediately after pressing the accelerator pedal, etc., and the diagnostic system 100 adjusts the waves to a constant value after a certain time (ex t1). When converging, feature values can be extracted and compared with reference values to determine whether the motor has failed.

According to an embodiment of the present invention, the size of r, which is a factor that determines the feature value, may vary depending on the current situation of the vehicle (ex. immediately after starting the engine, immediately after pressing the brake, immediately after pressing the accelerator pedal, etc.). This is because when driving a vehicle, immediately after applying the brakes may be a more dangerous time for safety than other times, so it is necessary to quickly determine whether there is a breakdown at this time.

Accordingly, the diagnostic system 100 can set the size of r to be larger at the time immediately after applying the brakes than at other times, and for convenience of explanation, it can be set to R as shown in FIG. 6.

In this state, immediately after pressing the brake, the characteristic value fluctuates in the form of a wave, and the distance (wave height) between the crest and the valley becomes smaller than R after T1, and the diagnostic system 100 detects the motor of the vehicle after T1. The characteristic value for can be extracted, and the failure state can be determined by comparing it with the reference value a.

In the end, as can be seen in FIG. 6, immediately after applying the brakes, the characteristic value can be determined at a time point T1, which is earlier than t1, and accordingly, the presence of a failure can also be quickly determined. As a driver, you will be able to quickly determine whether there is a breakdown and take quicker action.

Additionally, in some cases, the diagnostic system 100 may set the size of r to be smaller immediately after starting the engine than at other times. This is because right after starting the engine, there may be more time than other times, so it is possible to more accurately determine whether there is a breakdown. Therefore, after waiting until the fluctuation in the wave form of the characteristic value disappears (after time t1), the diagnosis system 100 extracts the characteristic value for the motor of the vehicle and compares it with the reference value a to determine the failure state. You can judge.

In addition, the status of the installed motor, etc. can be diagnosed in real time by the diagnostic system 100 of the present invention installed in the vehicle (PHM soc type), and this can be communicated to the driver through the vehicle infotainment. In other words, the diagnostic system 100 can prevent accidents by delivering information to the driver, such as whether there is a problem with the vehicle motor and when repairs are required.

In addition, when the diagnostic system 100 determines that the motor is in a failure state or a state in which a failure is expected, it can guide nearby repair shops through vehicle infotainment or automatically transmit vehicle information (ex. location, status, etc.) to nearby repair shops. there is.

Embodiments according to the present invention described above may be implemented in the form of program instructions that can be executed through various computer components and recorded on a computer-readable recording medium. The computer-readable recording medium may include program instructions, data files, data structures, etc., singly or in combination. Program instructions recorded on the computer-readable recording medium may be specially designed and configured for the present invention, or may be known and usable by those skilled in the computer software field. Examples of computer-readable recording media include hardware devices specially configured to store and perform program instructions, such as hard disks, ROM, RAM, and the like. Examples of program instructions include not only machine language code such as that created by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like. The hardware device may be configured to operate as one or more software modules to perform processing according to the invention and vice versa.

In the above, the present invention has been described with specific details such as specific components and limited embodiments and drawings, but this is only provided to facilitate a more general understanding of the present invention, and the present invention is not limited to the above embodiments. , a person skilled in the art to which the present invention pertains can make various modifications and variations from this description.

Therefore, the spirit of the present invention should not be limited to the above-described embodiments, and the scope of the patent claims described below as well as all modifications equivalent to or equivalent to the scope of the claims fall within the scope of the spirit of the present invention. They will say they do it.

100: Diagnostic system
110: Sensing module
120: signal processing module
130: Failure judgment module

Claims (6)

In a method of determining whether a motor in a vehicle has failed,
When a diagnostic system including a sensing module that senses the status of a motor in the vehicle is installed in the vehicle,
(a) the diagnostic system senses the state of the motor in the vehicle through the sensing module and obtains a sensing value;
(b) the diagnostic system converting the sensing value acquired by the sensing module to extract a feature value; and
(c) determining, by the diagnostic system, whether the motor is broken based on the characteristic values;
A method comprising: According to paragraph 1,
In a state where the vehicle is divided into a plurality of classes based on the situation, a customized reference value is set for each class, and the feature value is extracted through an AI module,
Assuming that the plurality of classes include a first class and a second class, and the reference value corresponding to the first class is set as a first reference value and the reference value corresponding to the second class is set as a second reference value,
The diagnostic system is,
i) If the vehicle is included in the first class, determine whether the motor is broken based on the feature value and the first reference value, ii) If the vehicle is included in the second class, the feature A method characterized in that it determines whether the motor is broken based on the value and the second reference value. According to paragraph 2,
With a plurality of learning vehicles divided into classes that match based on each situation,
The diagnostic system is,
i) After extracting the first learning feature value from the plurality of first learning vehicles included in the first class using the AI module, the first answer feature value obtained from the answer vehicle included in the first class and a process of comparing the first learning feature value to obtain a first difference value and updating parameters of the AI module based on the first difference value, and ii) included in the second class using the AI module. After extracting the second learning feature value from the plurality of second learning vehicles, comparing the second answer feature value obtained from the answer vehicle included in the second class with the second learning feature value to obtain a second difference value. A method characterized by learning the AI module by acquiring and performing a process of updating parameters of the AI module based on the second difference value. According to paragraph 2,
In a state where the condition of the vehicle includes the year of the vehicle, and the manufacturing time of the vehicle included in the first class is earlier than the manufacturing time of the vehicle included in the second class,
The method is characterized in that the first reference value is greater than the second reference value. According to paragraph 1,
In step (c) above,
The diagnostic system determines whether i) the motor is in a failure state, ii) the motor is expected to fail within a predetermined period, and iii) the motor is in a normal state based on the characteristic values. How to. In a system that determines whether a motor in a vehicle has failed,
A sensing module that senses the status of the motor in the vehicle and obtains sensing values;
A signal processing module and a failure determination module that convert the sensing value acquired by the sensing module to extract a feature value and determine whether the motor is broken based on the feature value,
A diagnostic system installed in a vehicle.