KR20220097274A - Diesel Particulate Filter device and operating method therefor - Google Patents

Abstract

According to an embodiment of the present invention, a diesel particulate filter device comprises: a diesel burner for raising the temperature of exhaust gas introduced by an engine of a vehicle; a diesel oxidation catalyst oxidizing the exhaust gas passing through the diesel burner; a filter for collecting particulate matter in the exhaust gas which has passed through the diesel oxidation catalyst; a temperature measuring unit which is located between the engine and the diesel burner, and measures at least one of a first exhaust temperature (Te) of the exhaust gas, a second exhaust temperature (Tb) between the diesel burner and the diesel oxidation catalyst, and a third exhaust temperature (Tf) between the diesel oxidation catalyst and the filter, and a fourth exhaust temperature (T4) behind the filter; and a processor unit predicting the operating state of the engine based on the measured exhaust temperature. The processor unit is configured to stop the operation of the diesel burner when the operating state of the engine is predicted to be abnormal. The present invention can accurately predict the operating state of the engine by using big data.

Description

Diesel Particulate Filter device and operating method therefor

Embodiments of the present invention relate to a smoke reduction apparatus for predicting engine failure and controlling an after-treatment apparatus, and an operating method therefor.

Devices installed to reduce air pollutants emitted from diesel engines include SCR (Selective Catalytic Reduction), EGR (Exhaust Gas Recirculation System), and DPF (Diesel Particulate Filter). SCR is a catalyst, EGR is circulation, and DPF is a device that reduces exhaust gas through a filter.

Specifically, the DPF is a type of exhaust post-treatment device that physically collects and burns PM (particulate matter) in exhaust gas of a diesel engine to remove it. The DPF's PM burning operation is not always running while the car is running, but it works when the filter is filled with some dust.

When this function works, the temperature of the filter is raised to 600 degrees Celsius to burn the captured PM, and this process of burning PM is called Regeneration.

However, when an engine malfunction occurs, the amount of PM emitted from the engine may increase more than twice compared to the normal operating condition, and the amount of PM collected in the DPF filter may exceed the allowable collection limit. When the function is activated, excessively captured PM burns instantaneously and generates high heat of over 1000 degrees Celsius, which can damage the filter and have additional adverse effects on the engine.

Therefore, it is necessary to detect engine abnormalities at an early stage and to warn the user (driver) to protect the engine and DPF filter when an engine abnormality occurs, and to control DPF regeneration in this case.

As a prior proposal, reference may be made to Korean Patent Publication No. 10-2015-0173025, filed on December 7, 2015.

SUMMARY Embodiments of the present invention have an object to provide a smoke reduction device and an operation method therefor, which accurately predict the operating state of an engine by using big data to warn when an abnormality occurs, and control the operation of a post-processing device accordingly. .

It relates to a method for predicting engine failure of a vehicle equipped with a Diesel Particular Filter (DPF). In a normal vehicle, the exhaust temperature is almost the same under the same vehicle speed and back pressure conditions, and engine abnormalities such as engine fuel injection system, cooling system, and lubrication system The exhaust temperature will increase. Using this, engine (cooling system, lubrication system, injection system) failure can be predicted. Specifically,

1) measuring the temperature by installing a temperature sensor in the exhaust pipe and the filter unit to measure the exhaust gas temperature;

2) Check whether the exhaust temperature is not common (abnormally high temperature) using a deep learning AI model that combines supervised learning and unsupervised learning under the same vehicle speed and back pressure conditions among the temperatures measured in the measurement step. cognition) determining;

3) When an unusual temperature condition occurs, it may include the step of notifying OBD (On-Board Diagnosis) whether there is an engine abnormality and blocking the operation of the DPF regeneration device (diesel burner).

According to an embodiment of the present invention, there is provided a soot reduction device attached to a vehicle, comprising: a diesel burner configured to increase a temperature of exhaust gas introduced by an engine of the vehicle; Diesel Oxidation Catalyst to oxidize exhaust gas passing through diesel burners; a filter for collecting particulate matter in the exhaust gas that has passed through the diesel oxidation catalyst; Located between the engine and the diesel burner, the first exhaust temperature (Te) of the exhaust gas, the second exhaust temperature (Tb) between the diesel burner and the diesel oxidation catalyst, and the third exhaust temperature (Tf) between the diesel oxidation catalyst and the filter and a temperature measuring unit measuring at least one of a fourth exhaust temperature T4 at the rear of the filter. and a processor unit predicting an operating state of the engine based on the measured exhaust temperature, wherein the processor unit is configured to stop the operation of the diesel burner when the engine operating state is abnormally predicted.

According to an embodiment of the present invention, by utilizing big data, it is possible to accurately predict the operating state of the engine, and furthermore, it is possible to prevent a malfunction of the post-processing device.

1 is a view showing the configuration of a smoke reduction device according to an embodiment of the present invention.
2 is a block diagram illustrating and describing a computing environment including a computing device suitable for use in example embodiments.
3 is a sequence-chart illustrating a method of operating an apparatus for reducing smoke according to an embodiment of the present invention.
4 is an exemplary diagram for explaining a method for predicting an engine state according to an embodiment of the present invention.
5 is a conceptual diagram illustrating a threshold range according to an embodiment of the present invention.
6 and 7 are diagrams illustrating a learning pattern according to machine learning according to an embodiment of the present invention.

Hereinafter, various embodiments of the present invention will be described with reference to the accompanying drawings. It should be understood that the present invention is not limited to specific embodiments, and includes various modifications, equivalents, and/or alternatives of the embodiments of the present invention. In connection with the description of the drawings, like reference numerals may be used for like components.

In this document, expressions such as "have", "may have", "includes", or "may include" indicate the presence of a corresponding characteristic (eg, a numerical value, function, operation, or component such as a part). and does not exclude the presence of additional features.

In this document, expressions such as “A or B”, “at least one of A or/and B”, or “one or more of A or/and B” may include all possible combinations of the items listed together. . For example, "A or B", "at least one of A and B", or "at least one of A or B" means (1) includes at least one A, (2) includes at least one B; Or (3) it may refer to all cases including both at least one A and at least one B.

Expressions such as "first", "second", "first", or "second" used in this document may modify various elements, regardless of order and/or importance, and may modify one element to another. It is used only to distinguish it from the components, and does not limit the components. For example, without departing from the scope of the rights described in this document, a first component may be referred to as a second component, and similarly, the second component may also be renamed as a first component.

The expression "configured to (or configured to)" as used in this document, depending on the context, for example, "suitable for", "having the capacity to" It can be used interchangeably with "," "designed to", "adapted to", "made to", or "capable of". The term "configured (or set up to)" does not necessarily mean only "specifically designed to".

In this document, transmitted and received between the first electronic device(s) and the second electronic device(s), for example, “command”, “instruction”, “control information”, “message”, “ Information", "data", "packet", "data packet", "intent" and/or "signal" means any human perceptible idea or specific electrical representation (eg, digital code/analog physical quantity) or may refer to itself. It will be apparent to those skilled in the art that the above-listed exemplary expressions may be interpreted in various ways depending on the context in which they are used. In this document, “A is greater than B” means not only “A is greater than B”, but also includes “A is greater than or equal to B”.

Terms used in this document are only used to describe specific embodiments, and may not be intended to limit the scope of other embodiments. The singular expression may include the plural expression unless the context clearly dictates otherwise. Terms used herein, including technical or scientific terms, may have the same meanings as commonly understood by one of ordinary skill in the art described in this document. Among the terms used in this document, terms defined in a general dictionary may be interpreted with the same or similar meaning to the meaning in the context of the related art, and unless explicitly defined in this document, have an ideal or excessively formal meaning. not interpreted In some cases, even terms defined in this document cannot be construed to exclude embodiments of this document.

1 is a view showing the configuration of a smoke reduction device according to an embodiment of the present invention.

As shown in FIG. 1 , the smoke reduction device according to an embodiment of the present invention includes a diesel burner 102 , a pressure measurement unit 103 , a temperature measurement unit 104 , a vehicle speed measurement unit 105 , and a processor unit ( 106). The smoke reduction device according to an embodiment of the present invention may be attached to a vehicle, and the vehicle includes a diesel fuel tank 108 , a fuel borne catalyst tank (FBC tank) 110 , and a pump module 112 . ), an engine 114 , a self-diagnosis device (ODB: On-Board Diagnosis, 116 ), a diesel oxidation catalyst (DOC: Diesel Oxidation Catalyst: 118 ), and a filter 120 . Note that, according to an embodiment, a configuration included in a vehicle may be defined as a configuration of a smoke reduction device.

The diesel burner 102 is a component of the exhaust gas reduction device, and may be a point at which exhaust gas is first introduced from the engine 114 of the vehicle. For convenience, one end through which the exhaust gas is introduced from the soot reduction device may be defined as a front end, and the other end through which the exhaust gas is discharged through the soot reduction device may be defined as a rear end.

The diesel burner 102 may be configured to raise the temperature of the incoming exhaust gas. That is, the diesel burner 102 may forcibly increase the temperature of the exhaust gas for filter regeneration.

The pressure measuring unit 103 may measure the engine back pressure or the differential pressure of the filter.

The temperature measuring unit 104 may measure the temperature of the exhaust gas at at least one point in the path through which the exhaust gas passes in the soot reduction device. According to an embodiment, the temperature measuring unit 104 may include a plurality of sensors for measuring the temperature of the exhaust gas in the soot reduction device. The plurality of sensors may be installed on a path through which exhaust gas in the soot reduction device passes.

The temperature measuring unit 104 may be positioned between the engine 114 and the diesel burner 102 to measure the exhaust temperature of the exhaust gas. Referring to FIG. 1 , the exhaust temperature of the exhaust gas between the engine 114 and the diesel burner 102 may be referred to as Te.

According to one embodiment, the temperature measuring unit 104 is the exhaust gas temperature (Tb) between the diesel burner 102 and the diesel oxidation catalyst, between the diesel oxidation catalyst 118 and the filter 120 At least one of the exhaust gas temperature Tf and the exhaust gas temperature T4 behind the filter 120 may be further measured.

The speed measuring unit 105 may measure the vehicle speed of the vehicle or the rotation speed of the engine.

The processor unit 106 may predict the operating state of the engine based on the measured exhaust temperature Te. Specifically, as in the graph of a vehicle in which an actual engine abnormality has occurred shown in FIG. 4 , exhaust gas temperature and pressure within a specific range exist at a specific speed. ), it is possible to predict the operating state of the engine.

For example, the processor unit 106 may be implemented to predict the operating state of the engine through comparison of a preset threshold value and the exhaust temperature Te, or to predict the operating state of the engine through the setting and updating procedure of the critical range. can

In addition, the processor unit 106 may predict the operating state of the engine by applying continuous data of various sensors, such as vehicle speed and exhaust temperature, and at least one of deep learning and machine learning.

A method of predicting various engine operating states performed by the processor unit 106 will be described in detail with reference to the drawings to be described later.

When the engine state is predicted to be abnormal, the processor unit 106 may stop the operation of the diesel burner 102 . Also, when the engine state is predicted to be abnormal, the processor unit may generate a warning message. The warning message may be transmitted to the self-diagnosis device 116 to indicate an engine abnormality. That is, damage to the engine 114 and the filter 120 that may occur due to excessive combustion of soot generated when the engine 114 operates abnormally can be prevented in advance.

In addition, when the engine state is predicted to be abnormal, the processor unit 106 may control the operation of the pump module 112 to stop or avoid regeneration.

The processor unit 106 determines the control status of the diesel burner 102, the oxidation catalyst 118, and the filter 120 based on the temperature (Tb, Tf, T4) of the exhaust gas for each position measured by the temperature processing unit. can be checked Tf and T4 may be used for determining whether the regeneration temperature has been reached at the rear end of the DOC and for determining whether the regeneration temperature has been reached at the rear end of the DPF, respectively.

According to an embodiment, the processor unit 106 may be an electronic control unit (ECU) mounted on a vehicle.

The diesel fuel tank 108 may store fuel. According to an embodiment, the fuel may be diesel.

The fuel addition type catalyst tank 110 may store a fuel addition type catalyst (FBC, Fuel Borne Catalyst) supplied to diesel fuel. The fuel additive type catalyst tank 110 may be supplied to fuel provided from the diesel fuel tank 108 to the engine 114 through the control of the processor unit 106 .

The pump module 112 may control the diesel burner 102 . Specifically, the filter naturally regenerates when the temperature exceeds a certain temperature (about 350 degrees or more) due to the exhaust temperature. Renewable temperatures can be reached.

The engine 114 is a device that provides power to a vehicle, and after receiving fuel to generate power, exhaust gas may be discharged. The engine 114 according to an embodiment may be a diesel engine.

The self-diagnosis unit (OBD unit: 116) may indicate whether a product is operating or not, and may store operation/abnormality data like a black box. Specifically, the measured exhaust temperature and the predicted operating state of the engine may be stored in the self-diagnosis device 116 . The operating state of each of the oxidation catalyst and the filter may be stored in the self-diagnosis device 116 .

The oxidation catalyst unit (DOC: 118) may perform a function of oxidizing total hydrocarbons and carbon monoxide in exhaust gas using a catalyst, and oxidizing nitrogen monoxide to nitrogen dioxide.

The filter 120 means a Diesel Particulate Filter, and after collecting particulate matter (PM), it is possible to reduce the PM collected under a constant temperature condition.

2 is a block diagram illustrating and describing a computing environment 10 including a computing device suitable for use in example embodiments. In the illustrated embodiment, each component may have different functions and capabilities other than those described below, and may include additional components other than those described below.

The illustrated computing environment 10 includes a computing device 12 . In one embodiment, computing device 12 may be processor unit 106 .

Computing device 12 includes at least one processor 14 , computer readable storage medium 16 , and communication bus 18 . The processor 14 may cause the computing device 12 to operate in accordance with the exemplary embodiments discussed above. For example, the processor 14 may execute one or more programs stored in the computer-readable storage medium 16 . The one or more programs may include one or more computer-executable instructions that, when executed by the processor 14, configure the computing device 12 to perform operations in accordance with the exemplary embodiment. can be

Computer-readable storage medium 16 is configured to store computer-executable instructions or program code, program data, and/or other suitable form of information. The program 20 stored in the computer-readable storage medium 16 includes a set of instructions executable by the processor 14 . In one embodiment, computer-readable storage medium 16 includes memory (volatile memory, such as random access memory, non-volatile memory, or a suitable combination thereof), one or more magnetic disk storage devices, optical disk storage devices, flash It may be memory devices, other forms of storage medium accessed by computing device 12 and capable of storing desired information, or a suitable combination thereof.

Communication bus 18 interconnects various other components of computing device 12 , including processor 14 and computer readable storage medium 16 .

Computing device 12 may also include one or more input/output interfaces 22 and one or more network communication interfaces 26 that provide interfaces for one or more input/output devices 24 . The input/output interface 22 and the network communication interface 26 are coupled to the communication bus 18 . Input/output device 24 may be coupled to other components of computing device 12 via input/output interface 22 . Exemplary input/output device 24 may include a pointing device (such as a mouse or trackpad), a keyboard, a touch input device (such as a touchpad or touchscreen), a voice or sound input device, various types of sensor devices, and/or imaging devices. input devices, and/or output devices such as display devices, printers, speakers and/or network cards. The exemplary input/output device 24 may be included in the computing device 12 as a component constituting the computing device 12 , and may be connected to the processor unit 106 as a separate device distinct from the computing device 12 . may be

3 is a sequence diagram illustrating a method of operating an apparatus for reducing smoke according to an embodiment of the present invention.

1 to 3 , the processor unit 106 performs a preset operation interval (eg, 60 ms) from a plurality of sensors included in the pressure measurement unit 103 , the temperature measurement unit 104 , and the vehicle speed measurement unit 105 . ) can be implemented to predict the operating state of the engine by processing the measured sensing data.

For reference, the sensing data measured by the plurality of sensors included in the pressure measuring unit 103 , the temperature measuring unit 104 , and the vehicle speed measuring unit 105 is transmitted to an on-board diagnosis unit (OBD unit). can be stored and updated by

In step S302 , the temperature measuring unit 104 is located between the engine 114 of the vehicle and the diesel burner 102 configured to increase the temperature of the exhaust gas introduced by the engine 114 and the exhaust gas of the exhaust temperature (Te) can be measured.

In step S304 , the process unit 106 may predict whether the operating state of the engine 114 is abnormal based on the measured exhaust temperature.

A method of predicting the operating state of the engine 114 using the exhaust temperature Te will be described in more detail with reference to FIGS. 4 to 7 to be described later.

In step S306 , the process unit 106 may stop the operation of the diesel burner 102 when the operating state of the engine 114 is predicted to be abnormal. The processor unit 106 may transmit a warning message to an on-board diagnosis unit (OBD unit) when the operating state of the engine is predicted to be abnormal.

The smoke reduction device includes: a diesel oxidation catalyst for oxidizing the exhaust gas that has passed through the diesel burner (Diesel Oxidation Catalyst); and a filter for collecting particulate matter of the exhaust gas that has passed through the diesel oxidation catalyst, wherein the temperature measuring unit 104 includes an exhaust gas temperature (Tb) between the diesel burner and the diesel oxidation catalyst. , at least one of an exhaust gas temperature (Tf) between the diesel oxidation catalyst and the filter and an exhaust gas temperature (T4) behind the filter may be further measured.

The processor unit 106 may include the diesel burner 102 based on the pressure, temperature, and speed information collected by the exhaust pressure measurement unit 103 , the temperature measurement unit 104 , and the speed measurement unit 105 . , the oxidation catalyst 118 and the filter 120 may be configured to predict the operating state of each.

Operation states of the diesel burner 102 , the oxidation catalyst 118 , and the filter 120 may be stored in an on-board diagnostic unit (OBD unit).

4 is an exemplary diagram for explaining a method for predicting an engine state according to an embodiment of the present invention.

The X-axis of FIG. 4 corresponds to the time t associated with the engine operation, and may be associated with a plurality of sections T1 to T5 associated with the operation cycle of the engine. Meanwhile, the time shown on the X-axis may be associated with a preset operation interval (eg, 60 ms) for a plurality of sensors included in the pressure measuring unit 103 , the temperature measuring unit 104 , and the vehicle speed measuring unit 105 . have.

For example, the preset operation interval may preferably be 60 ms, in which case the time interval (4 minutes and 15 seconds) shown on the X-axis of FIG. 4 is 4,250 times the preset operation interval (60 ms) (ie, 255 seconds). /0.06 seconds) may mean a time interval enlarged on a time scale.

The left side of the y-axis in FIG. 4 corresponds to the unit (°C) for the exhaust temperature (Te), the unit (kg/h) related to the intake air flow rate (MAF) of the engine, and the unit (mbar) related to the pressure of the exhaust gas (P1) can do. Also, the right side of the y-axis may correspond to the unit (km/h) associated with the rotational speed of the engine (ie, rpm).

When it is determined that the operating state of the engine is abnormal, the processor unit 106 may control the operation of at least one of the diesel burner 102 , the diesel oxidation catalyst 118 , and the filter 120 as a post-processing device. .

For example, each time section T1 to T5 corresponding to one engine cycle shown in FIG. 4 may be set based on the engine rotation speed (ie, rpm).

As shown in FIG. 4 , the first section ( T1 to T2 ) and the second section ( T2 to T3 ) are sections in which the engine is in a normal state, and the third section ( T3 to T4 ) and the fourth section ( T4 to T5 ) ) and the fifth sections T5 to T6 may be understood as sections in which the engine is in an abnormal state.

For example, it can be seen that in the first section (T1 to T2) and in the second section (T2 to T3), the intake air flow rate (MAF) of the engine and the pressure (P1) of the exhaust gas tend to be similar according to the engine rotation speed. can In addition, in the first section (T1 to T2) and the second section (T2 to T3), the exhaust temperature (Te) shows a tendency to fluctuate in the vicinity of 200 °C to 400 °C.

On the other hand, in the third section (T3 to T4) to the fifth section (T5 to T6), the exhaust temperature ( Te) shows a tendency to fluctuate around 400°C to 600°C, which is higher than before. In addition, it can be seen that similar trends in the intake air flow rate MAF of the engine and the pressure P1 of the exhaust gas fall in the third sections T3 to T4 and the fourth sections T4 to T5.

In particular, in the vicinity of the sixth time point T6, it may be understood as a time when engine failures are accumulated and the engine is stopped. Accordingly, the third section T3 to T4 to the fifth section T5 to T6 may be regarded as a time when an abnormality warning according to an engine operating state is required.

In the present embodiment, the prediction that the operating state of the engine is abnormal means that a warning about the operation of the engine is required.

In the present embodiment, the prediction of the operating state of the engine may be largely implemented in four ways as follows.

In a first implementation manner, the processor unit 106 corresponding to step S304 of FIG. 3 may determine whether the exhaust temperature Te is higher than a preset single threshold value. For example, the single threshold value may be preset to a specific value between 400°C and 600°C.

If it is determined that the exhaust temperature Te is higher than a preset single threshold value (eg, 400° C.), the processor unit 106 performs step S306 and transmits a warning message to the self-diagnostic device 116 and post-processing. You can control the operation of the device.

In a second implementation manner, the processor unit 106 corresponding to step S304 of FIG. 3 may determine whether the exhaust temperature Te falls within a critical range. For example, the time interval between the respective time points t-1, t, and t+1 may be understood as the aforementioned preset operation interval (eg, 60 ms).

In this case, the threshold range may be newly defined based on sensing data (eg, Tf, T4) measured for each time point (t-1, t, t+1).

For example, the intermediate value M2 of the critical range corresponding to the current time point t may be defined as an average value of Tf and T4 measured at the current time point t. Similarly, the median value M1 of the critical range corresponding to the previous time point t-1 is defined as the average value of Tf and T4 measured at the previous time point t-1, and corresponds to the next time point t+1. The intermediate value M3 of the critical range may be defined as an average value of Tf and T4 measured at a next time point t+1.

That is, the intermediate values M1, M2, and M3 of the critical range may be newly updated every operation interval (eg, 60 ms).

In other words, the threshold range is defined as a value obtained by subtracting and adding a preset boundary value (a) based on an intermediate value (M1, M2, M3) newly defined for each time point (t-1, t, t+1). It can mean the range of Here, the boundary value (a) may be set to a different value according to an implementation method and an operating environment.

If it is determined that the exhaust temperature Te measured at a specific time point t+1 as shown in FIG. 5 is out of the critical range (ie, M3-a to M3+a) corresponding to the specific time point t+1, as shown in FIG. , the processor 106 may transmit a warning message to the self-diagnosis apparatus 116 by performing step S306 and control the operation of the post-processing apparatus.

As a third implementation method, the processor unit 106 corresponding to step S304 of FIG. 3 may predict the operating state of the engine using a pre-trained model as shown in FIGS. 6 and 7 using a machine learning technique.

For example, the pre-trained model uses sensing data (eg, Te, P1) acquired through a plurality of sensors included in the pressure measuring unit 103 , the temperature measuring unit 104 , and the vehicle speed measuring unit 105 . Therefore, it can be understood as corresponding to the learning pattern performed by machine learning.

In this case, the learning pattern of the pre-trained model of FIG. 6 may correspond to the normal state of the engine, and the learning pattern of the pre-trained model of FIG. 7 may correspond to the abnormal state of the engine.

Specifically, measuring the distance between the learning pattern of the pre-trained model as in FIG. 6 or 7 and the exhaust temperature Te corresponding to the same pressure condition measured at each time point (t-1, t, t+1) The operating state of the engine can be predicted using various methods (Euclidean method, Mahalonovis method).

As an example, a Euclidean distance, which is a method of calculating the distance between two points in a multidimensional space, or a Mahalanobis distance, which is a method of calculating a distance on a probability distribution may be applied.

If the exhaust temperature Te is determined to be an abnormal pattern as shown in FIG. 7 , the processor unit 106 performs step S306, transmits a warning message to the self-diagnosis device 116, and controls the operation of the post-processing device. can

As a fourth implementation method, the processor unit 106 corresponding to step S304 of FIG. 3 may predict the operating state of the engine using the following deep learning technique.

1. Anomaly detection technology

- Statistical-based: A method of finding outliers that deviate from statistics (mean & variance) - Data must have a normal distribution, and it is difficult to find outliers in high-dimensional data

- Proximity, clustering, tree-based: Judging how far the given data is from a large number of sufficiently dense data - Difficult to apply when the data density is not high enough

- Analysis technique to find the principal component that maximizes the data variance - When the data contains outliers, the principal component cannot be found. Labeling for normal data should be performed in a clearly organized state.

2. Classification according to anomaly sample definition (Anomaly detection)

- Outlier detection: A method of detecting data that deviate significantly from the normal data distribution as an outlier compared to the existing data distribution. A technique that is more effective when the deviation of normal data is not large and it is closer to a normal distribution. While the daily deviation is large, it is not appropriate to apply the abnormal data if it has a characteristic that does not deviate significantly from the distribution of the normal interval data.

3. Classification according to the use of abnormal samples and the presence or absence of labels during training

- Supervised Anomaly Detection: Although the accuracy is high, the occurrence of abnormal samples is low, so class-imbalance problems are frequently encountered. To solve this problem, concurrent research such as Data Augmentation (Augmentation), Loss function redesign, and Batch Sampling

- Semi-supervised Anomaly detection (One-class classification): Sets a discriminative boundary surrounding normal samples and narrows this boundary as much as possible so that all samples outside are considered abnormal. (Refer to papers such as One-Class SVM, Deep SVDD, etc.)

- Unsepervised Anomaly Detection: Assume that most of the data are normal samples and train without acquiring labels. Abnormal data detection using PCA. However, it is sensitive to hyper parameters.

- Isolation forest for anomaly detection: This is a decision tree-based anomaly detection technique that detects anomalies by randomly selecting a dimension and partitioning the space based on random criteria.

- LOF (Local Outlier Factor): A learning method that detects anomalies based on how far a given data and neighboring data are from a nearby dense data area: High computational load because all normal values are considered

- LSTM Autoencoder Learning Method - It trains normal and abnormal data by creating a self-generated model on sequence data and learning fake data and real data. A newer learning method than isolation forest and LOF.

According to an embodiment, the processor unit 106 may perform anomaly detection in parallel with supervised learning and unsupervised learning.

Referring to FIG. 4 , after the processor unit 106 of the present invention predicts that the operating state of the engine is abnormal, a phenomenon in which the operation of the engine is actually stopped may occur.

According to an embodiment of the present invention, by utilizing big data, it is possible to accurately predict the operating state of the engine, and furthermore, it is possible to prevent malfunction of the post-processing device.

Although representative embodiments of the present invention have been described in detail above, those of ordinary skill in the art to which the present invention pertains will understand that various modifications are possible within the limits without departing from the scope of the present invention with respect to the above-described embodiments. . Therefore, the scope of the present invention should not be limited to the described embodiments, and should be defined by the claims described below as well as the claims and equivalents.

102: diesel burner
103: pressure measuring unit
104: temperature measurement unit
105: vehicle speed measurement unit
106: processor unit

Claims (6)

A smoke reduction device attached to a vehicle, comprising:
a diesel burner configured to raise a temperature of exhaust gas introduced by the engine of the vehicle;
a diesel oxidation catalyst for oxidizing the exhaust gas that has passed through the diesel burner (Diesel Oxidation Catalyst);
a filter for collecting particulate matter in the exhaust gas that has passed through the diesel oxidation catalyst;
a first exhaust temperature (Te) of the exhaust gas located between the engine and the diesel burner, a second exhaust temperature (Tb) between the diesel burner and the diesel oxidation catalyst, and a distance between the diesel oxidation catalyst and the filter. a temperature measuring unit for measuring at least one of a third exhaust temperature (Tf) and a fourth exhaust temperature (T4) behind the filter; and
A processor unit for predicting the operating state of the engine based on the measured exhaust temperature,
The processor unit is configured to stop the operation of the diesel burner when the operating state of the engine is predicted to be abnormal. According to claim 1,
The processor unit is configured to transmit a warning message to an On-board Diagnosis unit (OBD unit) when the operating state of the engine is predicted to be abnormal. According to claim 1,
The processor is implemented to predict the operating state of the engine based on a threshold range associated with the first exhaust temperature Te. 4. The method of claim 3,
The critical range is defined based on a preset boundary value for the average value of the third exhaust temperature Tf and the fourth exhaust temperature T4 obtained by the temperature measuring unit, and
The threshold range is updated every preset operation interval of the temperature measuring unit, soot reduction device. 5. The method of claim 4,
The processor unit,
When the measured first exhaust temperature Te is included in the critical range, it is determined that the operating state of the engine is normal, and
When the measured first exhaust temperature Te is not included in the critical range, the smoke reduction apparatus is implemented to determine the operating state of the engine as abnormal. A method of operating a smoke reduction device attached to a vehicle, the method comprising:
measuring an exhaust temperature (Te) of the exhaust gas positioned between an engine of the vehicle and a diesel burner configured to increase a temperature of exhaust gas introduced by the engine;
determining whether the operating state of the engine is abnormal based on the measured exhaust temperature; and
and stopping the operation of the diesel burner when the operating state of the engine is predicted to be abnormal.

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