JP6605170B1 - Learning device and estimation device - Google Patents

Abstract

A learning apparatus and an estimation apparatus for estimating a physical quantity that supports a determination result are provided. A knock determination device 10 removes a noise component included in an input physical quantity, estimates a desired physical quantity from the input physical quantity from which the noise component is removed, and determines whether knocking has occurred. The knocking determination device 10 uses a neural network to generate a mask for removing a noise component included in the input physical quantity, and / or transfer the input physical quantity from which the noise component has been removed by the mask into a desired physical quantity. A learning unit 21 for learning a function is provided. [Selection] Figure 3

Description

  The present invention relates to a learning device and an estimation device for removing a noise component included in an input physical quantity and estimating a desired physical quantity from the input physical quantity from which the noise component has been removed.

  In general, an ignition timing in an internal combustion engine such as a gasoline engine is advanced as much as possible within a crank angle range in which knocking does not occur for the purpose of improving output torque. Therefore, in the process of adjusting the ignition timing, whether or not knocking has occurred is determined by the tester or the knocking determination device. An example of such a knocking determination device is described in Patent Document 1.

  The knocking determination device described in Patent Document 1 is a condition in which a target signal to be compared with a determination signal for determining the presence or absence of knocking is determined by a relationship with the determination signal (for example, a temporal relationship or a relationship in operating conditions). Select based on.

JP 2017-44148

  However, in the knock determination device described in Patent Document 1, only the determination result of knocking presence / absence can be presented to the outside, and it is difficult to support the determination result. Therefore, it is desired that the above-described knocking determination apparatus estimates a desired physical quantity that supports the determination result.

  In general, the evaluation of knocking is performed based on the magnitude of knocking sound and the frequency of occurrence of knocking. In the above-described knock determination device, only the frequency of occurrence of knocking is evaluated based on the presence or absence of knocking, the knocking sound cannot be presented to the outside, and it is difficult to evaluate the magnitude of the knocking sound.

The present invention has been made in view of such circumstances, and an object thereof is to provide a learning apparatus and an estimation apparatus for estimating a desired physical quantity that supports a determination result .

A learning device that solves the problem is a learning device for removing a noise component included in an input physical quantity and estimating a desired physical quantity from the input physical quantity from which the noise component is removed, wherein the noise component is an internal combustion engine Noise other than knocking noise generated in the engine, the input physical quantity is a sound pressure of the internal combustion engine including the noise and the knocking sound, and the desired physical quantity is a cylinder of the internal combustion engine when knocking occurs. Internal pressure at the time of occurrence of knocking , which is an internal pressure, a weight of a network that generates a mask for removing the noise and extracting the knocking sound by a neural network, and / or a knocking sound extracted by the mask The learning part which learns the transfer function converted into in- cylinder pressure is provided.

  According to such a configuration, the learning device can generate a mask for estimating a desired physical quantity, and learns a transfer function. As a result, a noise component included in the input physical quantity can be removed using the mask, and a desired physical quantity can be estimated from the input physical quantity from which the noise component has been removed using the transfer function. That is, a desired physical quantity that supports the determination result can be presented to the outside using the above-described mask and transfer function.

  Here, the learning device may learn both the weight and transfer function of the network that generates the mask, or may learn only one of the weight or transfer function of the network that generates the mask. Further, the input physical quantity and the desired physical quantity may be the same type of physical quantity or different types of physical quantities. Examples of physical quantities include sound pressure, pressure, or vibration (displacement, speed, acceleration).

Further, the learning apparatus regards the in-cylinder pressure estimation of the internal combustion engine as a signal separation problem (sound separation problem) and a transfer function estimation problem, and learns the weight and transfer function of the network that generates the mask by a neural network. Thus, the knocking sound is separated and extracted from the sound pressure of the internal combustion engine using the mask, and the in-cylinder pressure of the internal combustion engine when the knocking occurs can be estimated from the knocking sound using the transfer function. That is, the in-cylinder pressure of the internal combustion engine at the time of occurrence of knocking, which supports the determination result of the presence or absence of knocking, can be presented to the outside using the above-described mask or transfer function.

  In the learning apparatus, the learning unit learns a weight of a network that generates the mask by a convolutional neural network, and learns a weight to be multiplied by an input physical quantity from which the noise component is removed by the mask as the transfer function.

  According to such a configuration, it is possible to appropriately learn the weight and transfer function of the network that generates the mask, so that a desired physical quantity can be estimated more accurately. An example of a convolutional neural network is U-Net. When the weight of the network that generates a mask with this U-Net is learned, the mask changes appropriately according to the input physical quantity. The transfer function functions as a time-invariant filter in the frequency axis direction.

In the learning device, a first estimation unit for estimating the desired physical quantity from the input physical quantity using the mask and the transfer function by a neural network, and a desired physical quantity estimated by the first estimation unit for a predetermined time And a threshold value calculation unit that calculates a median value of the desired physical quantity that is summed up every time, and sets a threshold value based on the calculated median value.
According to such a configuration, the threshold value can be set appropriately, so that the threshold value determination for a desired physical quantity can be performed more accurately.

In the learning apparatus, a first estimation unit that estimates an input physical quantity obtained by removing the noise component from an input physical quantity including the noise component using the mask by a neural network, and the first estimation unit estimates A threshold calculating unit that sums up the input physical quantities every predetermined time, calculates a median value of the summed input physical quantities, and sets a threshold based on the calculated median value;
According to such a configuration, the threshold value can be set appropriately, so that it is possible to perform the threshold value determination for the input physical quantity from which the noise component has been removed more accurately.

An estimation apparatus that solves the problem is an estimation apparatus that removes a noise component included in an input physical quantity and estimates a desired physical quantity from the input physical quantity from which the noise component has been removed, wherein the noise component is an internal combustion engine. The noise other than the knocking noise that is generated, the input physical quantity is a sound pressure of the internal combustion engine including the noise and the knocking sound, and the desired physical quantity is an in-cylinder pressure of the internal combustion engine at the time of occurrence of knocking. Yes, using a mask that removes the noise and extracts the knocking sound by a neural network , and a transfer function that converts the knocking sound extracted by the mask into an in-cylinder pressure of the internal combustion engine when the knocking occurs And a second estimation unit for estimating the desired physical quantity from the input physical quantity.

  According to such a configuration, it is possible to estimate a desired physical quantity from the input physical quantity from which the noise component has been removed using a transfer function by removing a noise component included in the input physical quantity using a mask. That is, a desired physical quantity that supports the determination result can be presented to the outside using the above-described mask and transfer function.

  For example, the knocking sound can be separated and extracted from the sound pressure of the internal combustion engine using a mask, and the in-cylinder pressure of the internal combustion engine when knocking can be estimated from the knocking sound using a transfer function. Thereby, the in-cylinder pressure of the internal combustion engine at the time of occurrence of knocking that supports the determination result of the presence or absence of knocking can be presented to the outside.

The estimation apparatus further includes a threshold determination unit that performs threshold determination between a preset threshold and a desired physical quantity estimated by the second estimation unit.
According to such a configuration, it is possible to more accurately perform threshold determination for a desired physical quantity. For example, the presence or absence of knocking can be determined more accurately with respect to the estimated in-cylinder pressure of the internal combustion engine.

According to the present invention, the physical quantity that supports the determination result can be estimated .

(A) is explanatory drawing explaining the sound separation problem, (b) is explanatory drawing explaining the estimation problem of a transfer function. The block diagram which shows the whole structure of the knock determination system which concerns on 1st Embodiment. The block diagram which shows the structure of the knock determination apparatus which concerns on 1st Embodiment. Explanatory drawing explaining cutting of a sound pressure signal in 1st Embodiment. Explanatory drawing explaining learning of the weight and transfer function of the network which produces | generates a mask in 1st Embodiment. In the first embodiment, (a) is an example of a time-frequency map obtained by performing a short-time Fourier transform on a sound near the engine including noise, (b) is an example of a time-frequency map of a mask, and (c) is noise. An example of the time-frequency map of the sound near the removed engine, (d) is an example of the time-frequency map of the estimated in-cylinder pressure. Explanatory drawing explaining an example of the neural network which learns the weight and transfer function of the network which produce | generate a mask in 1st Embodiment. 5 is a flowchart showing data collection processing of the knocking determination device in the first embodiment. The flowchart which shows the learning process of a knock determination apparatus in 1st Embodiment. The flowchart which shows the threshold value calculation process of a knock determination apparatus in 1st Embodiment. The flowchart which shows the determination process of a knock determination apparatus in 1st Embodiment. Explanatory drawing explaining the network which produces | generates a mask in 2nd Embodiment. The block diagram which shows the structure of the knock determination apparatus which concerns on 2nd Embodiment.

(Sound separation problem, transfer function estimation problem)
First, with reference to FIG. 1, a sound separation problem, which is an example of a signal separation problem, and a transfer function estimation problem will be described as a premise for explaining the knocking determination device according to the embodiment. Thereafter, the knocking determination device will be described. In each embodiment, the same means is denoted by the same reference numeral, and description thereof is omitted.

  In the test of the vehicle engine 1 which is an example of the internal combustion engine, for example, a test such as adjustment of the advance amount of the ignition timing or a confirmation test of the adjusted ignition timing is performed. In these ignition timing tests, the presence or absence of knocking is determined so that the piston is not damaged by knocking. The presence or absence of this knocking is determined by the subjectivity of a skilled tester, and information that supports the determination result cannot be presented to the outside. Therefore, there is a desire to estimate information that supports the determination result of the presence or absence of knocking.

  Here, the knocking is a phenomenon in which a large fluctuation is generated in the engine 1 by amplifying the pressure fluctuation (shock wave) caused by the abnormal combustion generated in the cylinder of the engine 1 at the natural frequency of the cylinder. That is, the engine 1 generates vibrations based on pressure fluctuations that occur during abnormal combustion in the cylinder, and the vibrations are emitted from the engine 1 as sound. Therefore, it is desired to learn the nature of the knocking sound from the sound measured near the engine 1 and to separate the knocking sound, but it is difficult to measure the ideal knocking sound as teacher data. For this reason, a method of estimating the in-cylinder pressure of the engine 1 by examining the combustion noise of the engine 1 is examined. Hereinafter, the sound measured in the vicinity of the engine 1 may be referred to as “neighboring sound”, and the estimated in-cylinder pressure of the engine 1 may be referred to as “estimated in-cylinder pressure”.

  The combustion noise of the engine 1 is obtained by multiplying the time frequency component of the in-cylinder pressure of the engine 1 by the structure attenuation correction amount. The structural damping correction amount is a transfer characteristic until the in-cylinder pressure during engine combustion passes through the engine 1 and becomes sound and reaches the sound pressure sensor. Here, attention is paid to the fact that the combustion noise level of the engine 1 is a sound pressure and that the structure attenuation correction amount is multiplied. That is, the estimated in-cylinder pressure of the engine 1 can be obtained by multiplying the sound near the engine 1 that is the sound pressure by the reciprocal of the structural attenuation correction amount. Therefore, a method for estimating the in-cylinder pressure of the engine 1 from the vicinity sound of the engine 1 is examined.

  As shown in FIG. 1 (a), when knocking occurs, the sound near the engine 1 includes an engine sound that is the sound of the engine 1 itself, a knocking sound, and a noise component that is noise other than knocking. . By multiplying the vicinity sound of the engine 1 by a mask, only the knocking sound can be separated. The mask removes noise (noise component = 0) and extracts knocking sound (knocking sound component = 1). Therefore, if the mask can be correctly estimated, the knocking sound can be separated and extracted from the sound near the engine 1 and can be regarded as a sound separation problem for separating the knocking sound.

As shown in FIG. 1B, the in-cylinder pressure of the engine 1 at the time of knocking can be estimated by multiplying the knocking sound by the reciprocal of the structural attenuation correction amount. It can be said that the reciprocal of this structural attenuation correction amount is a transfer function for converting a certain input physical quantity (for example, sound pressure) into a desired physical quantity (for example, in-cylinder pressure). Therefore, if the transfer function can be estimated correctly, the in-cylinder pressure at the time of occurrence of knocking can be obtained from the knocking sound, so that it can be regarded as a transfer function estimation problem.
In FIG. 1, the near sound and in-cylinder pressure of the engine 1 represent a time-frequency map obtained by performing a short-time Fourier transform on these sounds.

The sound separation problem and the transfer function estimation problem described above can be expressed by the following equations. The power spectrum | X | ² of the in-cylinder pressure x of the engine 1 is obtained from the power spectrum | Y | ² of the vicinity sound y of the engine 1. In this case, the power spectrum | Y | ² is expressed by Expression (1) to Expression (4).

However, in each formula, it is defined as follows.
x: In-cylinder pressure of the engine X: Fourier spectrum of the in-cylinder pressure of the engine y: Near sound of the engine Y: Fourier spectrum of the near sound of the engine h: Impulse response H: Transfer function of the impulse response e: Noise signal E other than knocking sound : Fourier spectrum of noise signal

Here, as shown in equations (5) to (7), it is assumed that the spectral additivity is approximately established. In this case, the power spectrum estimated value | X ^ | ² of the in-cylinder pressure x of the engine 1 is expressed by the following equation (8). Α is a mask of the power spectrum (0 ≦ α ≦ 1).

  Therefore, the in-cylinder pressure of the engine 1 can be estimated by the following procedure. First, the near sound y of the engine 1 and the actually measured in-cylinder pressure x of the engine 1 are collected as teacher data. Hereinafter, the actually measured in-cylinder pressure of the engine 1 may be referred to as “measured in-cylinder pressure”. Using the neighborhood sound y of the engine 1 and the actually measured in-cylinder pressure x of the engine 1, the weight of the network and the transfer function H for generating the mask α, which is an unknown number in the equation (8), are learned by a neural network. Then, the in-cylinder pressure x of the engine 1 is estimated from the vicinity sound y of the engine 1 measured during the test, using the mask α and the transfer function H generated by the learned network.

(First embodiment)
[Overall configuration of knocking determination system]
With reference to FIG. 2, the overall configuration of the knock determination system 100 according to the first embodiment will be described. As shown in FIG. 2, the knock determination system 100 determines whether or not the engine 1 to be tested is knocked, and includes a sound pressure sensor 4, an in-cylinder pressure sensor 5, a data collection device 6, and a monitor 7. And a knocking determination device 10.

  As shown in FIG. 2, the engine 1 to be tested is mounted on a vehicle 3. The engine 1 to be tested may be used in a state where it is not mounted on the vehicle 3, for example, in a single state. An engine ECU (Electronic Control Unit) 2 that controls driving of the engine 1 is connected to the engine 1. The engine ECU 2 is an engine control unit, and includes a central processing unit (CPU), a read only memory (ROM), a random access memory (RAM), and other storage devices. The engine ECU 2 controls the driving of the engine 1 while acquiring various information necessary for driving control of the engine 1 from outside the engine ECU 2 by performing arithmetic processing on the program stored in the ROM or the storage device.

  The engine ECU 2 outputs angle information representing the current rotation angle of the engine 1 to the data collection device 6. The angle information includes, for example, a rotation pulse and a crank angle pulse. The rotation pulse is a signal that is output at an angle in which the rotation angle of the crankshaft is determined as the origin, and for example, one pulse is output each time the crankshaft rotates once. The crank angle pulse is a signal that is output every time the rotation angle of the crankshaft advances by a unit angle. For example, when one pulse is output every 1 °, four strokes of intake, compression, combustion, and exhaust are performed by one. In a 4-stroke engine having a cycle, 720 pulses are output between two rotations in which the crankshaft rotates in one cycle. If the angle information is input to the data collection device 6, the angle from the origin sensor that detects the origin of the rotation angle of the crankshaft or the angle sensor that detects the rotation angle of the crankshaft without passing through the engine ECU 2. Information may be input to the data collection device 6.

  A sound pressure sensor 4 is installed near the engine 1. The sound pressure sensor 4 detects the sound generated from the engine 1 and outputs a sound pressure signal based on the detected sound to the data collection device 6. More specifically, the sound pressure sensor 4 detects a sound pressure that is an example of a physical quantity based on a pressure fluctuation generated in the engine 1 and generates a sound pressure signal that indicates the magnitude of the detected sound pressure. Therefore, when the engine 1 is not knocked, the sound pressure signal output from the sound pressure sensor 4 does not include sound correlated with knocking. On the other hand, when knocking occurs in the engine 1, the sound pressure signal output from the sound pressure sensor 4 includes a sound correlated with knocking.

  An in-cylinder pressure sensor 5 that detects the in-cylinder pressure of the engine 1 is attached to the engine 1. The in-cylinder pressure sensor 5 outputs an in-cylinder pressure signal including a waveform component corresponding to the combustion gas vibration in the cylinder to the data collection device 6. Here, when knocking does not occur in the engine 1, the in-cylinder pressure signal output from the in-cylinder pressure sensor 5 does not include a component correlated with knocking. On the other hand, when knocking occurs in the engine 1, the in-cylinder pressure signal output from the in-cylinder pressure sensor 5 includes a component correlated with knocking. The in-cylinder pressure sensor 5 may be integrated with the spark plug, or may be configured separately from the spark plug.

  The data collection device 6 inputs the sound pressure signal from the sound pressure sensor 4 and performs A / D conversion. Further, the data collection device 6 acquires current angle information from the engine ECU 2 at the timing of inputting the sound pressure signal. The data collection device 6 acquires a sound pressure signal for one cycle of the engine 1 based on the angle information. Therefore, taking a four-stroke single cylinder engine as an example, the data collection device 6 is a number corresponding to the rotational speed of the engine 1 per unit time, for example, 1500 per minute if the rotational speed is 3000 [r / min]. A sound pressure signal is generated. Further, the data collection device 6 associates angle information with part or all of the sound pressure signals. Then, the data collection device 6 outputs a sound pressure signal associated with the angle information to the knocking determination device 10. Note that the data collection device 6 may temporarily hold the sound pressure signal or temporarily store the sound pressure signal and then output it to the knocking determination device 10. The data collection device 6 may associate time information with the sound pressure signal.

  The monitor 7 displays the in-cylinder pressure of the engine 1 estimated by the knocking determination device 10 and the determination result of the presence or absence of knocking by the knocking determination device 10. An example of the monitor 7 is a general flat panel display.

  The knocking determination device 10 learns the weight of the network that generates the mask α and the transfer function H. Further, the knock determination device 10 estimates the in-cylinder pressure of the engine 1 using the mask α and the transfer function H generated by the learned network, and determines the presence or absence of knocking based on the estimated in-cylinder pressure of the engine 1. To do. The knocking determination device 10 displays the estimated in-cylinder pressure of the engine 1 and the determination result of the presence or absence of knocking on the monitor 7.

  Here, the knock determination device 10 includes a CPU, a ROM, a RAM, and other storage devices. The knocking determination device 10 performs arithmetic processing on a program stored in a ROM or a storage device by a CPU. The knocking determination device 10 may be a personal computer (PC) having a program for executing the following processing.

  In the present embodiment, the knocking determination device 10 operates in three operation modes: a learning mode, a threshold calculation mode, and a determination mode. The first learning mode is an operation mode in which the network weight for generating the mask α and the transfer function H are learned. The second threshold value calculation mode is an operation mode for calculating a threshold value when determining whether or not knocking is present after learning the weight of the network for generating the mask α and the transfer function H. The third determination mode is an operation mode in which the presence or absence of knocking is determined using the mask α and transfer function H generated by the learned network. These three operation modes can be switched arbitrarily. For example, the operation mode of the knock determination device 10 can be switched by a management device (not shown) via a CAN (Controller Area Network). Further, the operation mode of the knocking determination device 10 may be switched using an operation unit such as a mouse or a keyboard (not shown).

In the learning mode, the engine 1 is operated under the driving conditions where knocking occurs and the driving conditions where knocking does not occur, and the data collection device 6 collects the in-cylinder pressure signal as teacher data. At this time, the data collection device 6 inputs the in-cylinder pressure signal from the in-cylinder pressure sensor 5, performs A / D conversion, and associates this with the sound pressure signal.
In the threshold calculation mode, the engine 1 is operated under an operation condition in which knocking does not occur, and the data collection device 6 collects a sound pressure signal.
In the threshold calculation mode or determination mode, the data collection device 6 does not need to collect the in-cylinder pressure signal.

[Configuration of knock determination device]
With reference to FIG. 3, the structure of the knock determination apparatus 10 is demonstrated. As illustrated in FIG. 3, the knock determination device 10 includes a signal extraction unit 11, a spectrum calculation unit 12, a signal storage unit 13, a switch 14, a learning processing unit (learning device) 20, and a determination processing unit ( Estimation device) 30. Here, the knock determination device 10 receives the sound pressure signal and the angle information associated with the sound pressure signal from the data collection device 6. Furthermore, in the learning mode, the knocking determination device 10 receives an in-cylinder pressure signal from the data collection device 6.

  The signal cutout unit 11 cuts out a predetermined cutout angle range from the sound pressure signal based on the angle information input from the data collection device 6. For example, the cut-out angle range is an angle range of about 90 ° from the ignition position or near the TDC (Top Dead Center). In the present embodiment, the cut-out angle range remains fixed even when the ignition timing is changed, but the cut-out angle range may be changed according to the change in the ignition timing.

As shown in FIG. 4, generally, when the engine 1 has four cylinders, the combustion strokes of the first cylinder and the second cylinder are shifted by 540 °. Further, the combustion strokes of the first cylinder and the third cylinder are shifted by 180 °, and the combustion strokes of the first cylinder and the fourth cylinder are shifted by 360 °. Therefore, the signal cutout unit 11 calculates the combustion stroke timing of each cylinder from the angle information, and cuts out sound pressure signals for four cylinders in accordance with the calculated combustion stroke timing. At this time, the signal cutout unit 11 may associate identification information for uniquely identifying each cylinder with the cut out sound pressure signal.
Thereafter, the signal cutout unit 11 outputs the cut out sound pressure signal to the spectrum calculation unit 12.

The spectrum calculation unit 12 performs discrete Fourier transform on the sound pressure signal cut out by the signal cutout unit 11 in the time frequency domain, and calculates a time frequency component (spectrum) of the sound pressure signal. The discrete Fourier transform is performed by, for example, a fast Fourier transform that calculates the discrete Fourier transform at high speed.
Thereafter, the spectrum calculation unit 12 writes the sound pressure signal converted into the time frequency domain into the signal storage unit 13.

  The signal storage unit 13 is a storage device such as a memory that stores the sound pressure signal converted by the spectrum calculation unit 12, an HDD (Hard Disk Drive), or an SSD (Solid State Drive). In the learning mode, the signal storage unit 13 may store the in-cylinder pressure signal input from the data collection device 6 as teacher data.

  The switch 14 selects the output destination of the signal stored in the signal storage unit 13 according to the operation mode of the knocking determination device 10, and outputs the signal to the selected output destination. In the learning mode, the switch 14 selects a learning unit 21 described later as an output destination, and outputs the sound pressure signal and the in-cylinder pressure signal stored in the signal storage unit 13 to the learning unit 21. In the threshold calculation mode, the switch 14 selects a first estimation unit 23 described later as an output destination, and outputs the sound pressure signal stored in the signal storage unit 13 to the first estimation unit 23. In the determination mode, the switch 14 selects a second estimation unit 31 described later as an output destination, and outputs the sound pressure signal stored in the signal storage unit 13 to the second estimation unit 31.

<Learning processing unit>
The learning processing unit 20 performs a learning process (FIG. 9) for learning the weight of the network for generating the mask α and the transfer function H in the learning mode (FIG. 9), and is used for threshold determination of the estimated in-cylinder pressure of the engine 1 in the threshold calculation mode. A threshold value calculation process (FIG. 10) for calculating the threshold value is performed. As illustrated in FIG. 3, the learning processing unit 20 includes a learning unit 21, a learned parameter storage unit 22, a first estimation unit 23, a threshold calculation unit 24, and a threshold storage unit 25.

In the learning mode, the learning unit 21 uses the neural network to remove the noise and extract the knocking sound extracted by the mask α generated by the learned network and the network weight for generating the mask α for extracting the knocking sound. A transfer function H that is converted into in-cylinder pressure of the engine 1 when knocking occurs is learned. In the present embodiment, the learning unit 21 learns the network weight and transfer function H for generating the mask α using the sound pressure signal and the in-cylinder pressure signal input from the switch 14.
Thereafter, the learning unit 21 writes the weight of the network that generates the learned mask α and the transfer function H in the learned parameter storage unit 22.

<< Learning of network weight and transfer function H for generating mask α >>
The learning of the weight of the network (mask generation network 94A) that generates the mask α and the transfer function H will be described with reference to FIGS. As shown in FIG. 5, when the engine vicinity sound 90 including noise is multiplied by a mask α, the engine vicinity sound 91 from which the noise is removed can be obtained. When the noise near the engine vicinity sound 91 is multiplied by the transfer function H, the physical quantity is converted from the sound pressure to the in-cylinder pressure, and the estimated in-cylinder pressure 92 of the engine 1 can be obtained.

  An example of a time-frequency map obtained by performing a short-time Fourier transform on the engine vicinity sound 90 including noise is shown in FIG. 6A, and an example of a time-frequency map of the mask α is shown in FIG. 6B. An example of the time-frequency map of the engine vicinity sound 91 from which noise is removed is shown in FIG. 6C, and an example of the time-frequency map of the estimated in-cylinder pressure 92 is shown in FIG.

  Therefore, the learning unit 21 learns the network weight and the transfer function H for generating the mask α by the neural network 94 so that the error between the measured in-cylinder pressure 93 and the estimated in-cylinder pressure 92 that is the teacher data is minimized. That's fine. In the present embodiment, the learning unit 21 learns the weight of the network that generates the mask α by using a convolutional neural network (CNN). Further, the learning unit 21 learns the weight to be multiplied by the time frequency component of the sound pressure signal as the transfer function H.

  As shown in FIG. 7, U-Net 95 is an example of a convolutional neural network that learns the weight of the network that generates the mask α. This U-Net95 is a kind of Encoder-Decoder model, and is a method of deep learning used for image recognition and sound separation. In the sound separation, the U-Net 95 performs convolution (stride is 2 or more) in the downward path 96 (Encoder), and extracts the sound features as the layer 97 becomes deeper. On the other hand, in the upward path 98 (Decoder), a mask α is generated by performing deconvolution and UP sampling (expansion) from the extracted sound features. Up to this point, the configuration is a general Encoder-Decoder model. Further, in U-Net 95, the output 99 from each Encoder's convolution layer is merged with the Decoder's convolution layer. As a result, the U-Net 95 can generate a mask α with higher accuracy than a general Encoder-Decoder model.

  In this way, the learning unit 21 learns the weight of the network that generates the mask α using U-Net, so that the mask α generated by the learned network changes appropriately according to the nearby sound of the engine 1. . Thereby, knock determination device 10 can accurately estimate the in-cylinder pressure of engine 1.

Since U-Net is described in detail in the following references, further explanation is omitted.
References: SINGING VOICE SEPARATION WITH DEEP U-NET CONVOLUTIONAL NETWORKS, Andreas Jansson, Proceedings of the 18th ISMIR Conference, Suzhou, China, October 23-27, 2017

  Also, as shown in FIG. 7, the transfer function H functions as a time-invariant filter in the frequency axis direction. In the present embodiment, the transfer function H is replicated up to the same time width as the knocking sound extracted by the mask α (the engine vicinity sound 91 from which noise has been removed), and the knocking sound extracted from the mask α (the vicinity of the engine from which noise has been removed). The element product of the sound 91) and the copied transfer function H is obtained.

Returning to FIG. 3, the description of the configuration of knock determination device 10 will be continued.
The learned parameter storage unit 22 is a storage device such as a memory, an HDD, or an SSD that stores learned parameters (a network weight for generating the mask α and a transfer function H).

In the threshold calculation mode, the first estimation unit 23 estimates the in-cylinder pressure of the engine 1 from the sound pressure signal using the mask α and the transfer function H generated by the learned network by the neural network. The in-cylinder pressure estimated by the first estimation unit 23 is used when calculating a threshold value to be described later. Specifically, the first estimation unit 23 inputs the sound pressure signal input from the switch 14 to the neural network in which the weight of the network that generates the mask α of the learned parameter storage unit 22 and the transfer function H are reflected. To do. Then, the mask α generated by the learned network removes noise included in the sound pressure signal and extracts a knocking sound from the sound pressure signal. Then, the transfer function H converts the knocking sound extracted by the mask α into the in-cylinder pressure when knocking occurs.
Thereafter, the first estimation unit 23 outputs the estimated in-cylinder pressure to the threshold value calculation unit 24.

The threshold calculation unit 24 calculates a threshold based on the in-cylinder pressure of the engine 1 estimated by the first estimation unit 23. Specifically, the threshold value calculation unit 24 sums up the time frequency components of the estimated in-cylinder pressure every predetermined time (for example, one cycle of the engine 1). For example, when the estimated in-cylinder pressure in FIG. 6D is summed, one score is obtained for each cylinder. Subsequently, the threshold value calculation unit 24 calculates the median value of the estimated in-cylinder pressures summed up every predetermined time. For example, the threshold calculation unit 24 calculates the median value of the sum of the estimated in-cylinder pressures for all cycles. At this time, the threshold value calculation unit 24 adds a margin preset with an arbitrary value to the median value to obtain a threshold value. The threshold calculation unit 24 may calculate the threshold for each cylinder by summing the estimated in-cylinder pressure for each cylinder to obtain a median value. On the other hand, the threshold value calculation unit 24 may sum the estimated in-cylinder pressure for each cylinder, obtain a median value for all cylinders, and calculate a common threshold value for all cylinders.
Thereafter, the threshold value calculation unit 24 writes the calculated threshold value in the threshold value storage unit 25.

  The threshold storage unit 25 is a storage device such as a memory, HDD, or SSD that stores the threshold calculated by the threshold calculation unit 24.

  In the determination mode, the determination processing unit 30 estimates the in-cylinder pressure of the engine 1 from the sound pressure signal, and performs a determination process (FIG. 11) for determining the presence or absence of knocking. As illustrated in FIG. 3, the determination processing unit 30 includes a second estimation unit 31 and a threshold determination unit 32.

In the determination mode, the second estimation unit 31 estimates the estimated in-cylinder pressure from the sound pressure signal using the mask α and the transfer function H generated by the learned network by the neural network. The in-cylinder pressure estimated by the second estimation unit 31 is used for threshold determination described later. Note that the processing content of the second estimation unit 31 is the same as that of the first estimation unit 23, and thus the description thereof is omitted.
Thereafter, the second estimation unit 31 outputs the estimated in-cylinder pressure to the threshold determination unit 32.

In the determination mode, the threshold determination unit 32 determines the presence / absence of knocking by performing threshold determination between the threshold stored in the threshold storage unit 25 and the in-cylinder pressure estimated by the second estimation unit 31. Specifically, the threshold determination unit 32 sums up the time frequency components of the estimated in-cylinder pressure every predetermined time, like the threshold calculation unit 24. Then, the threshold determination unit 32 compares the total estimated in-cylinder pressure with the threshold, determines that knocking is present when the estimated in-cylinder pressure exceeds the threshold, and does not knock when the estimated in-cylinder pressure is less than the threshold. Is determined.
Thereafter, the threshold determination unit 32 outputs the determination result of the presence / absence of knocking and the time-frequency map of the estimated in-cylinder pressure input from the second estimation unit 31 to the monitor 7 (FIG. 2).

[Operation of knocking determination device]
Hereinafter, the operation of the knock determination device 10 will be described. In the learning mode, the knocking determination apparatus 10 performs the learning process of FIG. 9 after performing the data collection process of FIG. In the threshold calculation mode, the knocking determination apparatus 10 performs the threshold calculation process of FIG. 10 after performing the data collection process of FIG. In the determination mode, the knocking determination apparatus 10 performs the determination process of FIG. 11 after performing the data collection process of FIG.

<Data collection processing>
With reference to FIG. 8, the data collection process executed in the learning mode, the threshold value calculation mode, and the determination mode will be described.
As shown in FIG. 8, in step S <b> 20, the data collection device 6 inputs a sound pressure signal, an in-cylinder pressure signal (teacher data), and angle information to the signal cutout unit 11. In the threshold calculation mode or determination mode, it is not necessary to input an in-cylinder pressure signal in step S20.
In step S <b> 21, the signal cutout unit 11 calculates the combustion stroke timing of each cylinder from the angle information.
In step S22, the signal cutout unit 11 cuts out a sound pressure signal near the TDC in accordance with the combustion stroke timing calculated in step S21.
In step S23, the spectrum calculation unit 12 performs fast Fourier transform on the sound pressure signal cut out in step S22 to calculate a time frequency component.
In step S <b> 24, the spectrum calculation unit 12 writes the sound pressure signal converted into the time frequency domain in the signal storage unit 13.

<Learning process>
The learning process executed in the learning mode will be described with reference to FIG.
As shown in FIG. 9, in step S <b> 30, the switch 14 reads out the sound pressure signal and the in-cylinder pressure signal from the signal storage unit 13 and inputs them to the learning unit 21.
In step S31, the learning unit 21 learns the network weight and transfer function H for generating the mask α using the sound pressure signal and the in-cylinder pressure signal input in step S30. The learning unit 21 learns the weight of the network that generates the mask α using a convolutional neural network (for example, U-Net), and learns the weight to be multiplied by the time frequency component of the sound pressure signal as the transfer function H.
In step S <b> 32, the learning unit 21 writes the weight of the network that generates the learned mask α and the transfer function H in the learned parameter storage unit 22.

<Threshold calculation processing>
With reference to FIG. 10, the threshold value calculation process executed in the threshold value calculation mode will be described.
As shown in FIG. 10, in step S40, the first estimation unit 23 estimates the in-cylinder pressure of the engine 1 from the sound pressure signal using the mask α and the transfer function H of the signal storage unit 13 by a neural network.
In step S <b> 41, the threshold value calculation unit 24 sums up the time frequency components of the estimated in-cylinder pressure in each cycle of the engine 1.
In step S42, the threshold value calculation unit 24 calculates the median value of the estimated estimated in-cylinder pressure in all cycles.
In step S43, the threshold value calculation unit 24 adds a preset margin to the median value to obtain a threshold value.
In step S <b> 44, the threshold calculation unit 24 writes the calculated threshold in the threshold storage unit 25.

<Judgment process>
The determination process executed in the determination mode will be described with reference to FIG.
As shown in FIG. 11, in step S50, the second estimation unit 31 uses the network weight and transfer function H for generating the mask α of the learned parameter storage unit 22 by the neural network, from the signal storage unit 13. The in-cylinder pressure of the engine 1 is estimated from the input sound pressure signal.
In step S <b> 51, the threshold determination unit 32 sums up the time frequency components of the estimated in-cylinder pressure in each cycle of the engine 1.
In step S52, the threshold determination unit 32 compares the threshold stored in the threshold storage unit 25 with the total estimated in-cylinder pressure, and determines whether the estimated in-cylinder pressure exceeds the threshold.
When the estimated in-cylinder pressure exceeds the threshold value (Yes in step S52), the threshold value determination unit 32 determines that knocking is present (step S53).
If the estimated in-cylinder pressure is less than the threshold (No in step S52), the threshold determination unit 32 determines that there is no knocking (step S54).
In step S55, the threshold determination unit 32 outputs the result of the threshold determination and the estimated in-cylinder pressure to the monitor 7.

[Action / Effect]
As described above, according to the present embodiment, the following effects can be obtained.
(1) According to such a configuration, the learning processing unit 20 learns a network weight and a transfer function H for generating a mask α for estimating a desired physical quantity. Thereby, the noise component included in the input physical quantity is removed using the mask α generated by the learned network, and the desired physical quantity is estimated from the input physical quantity from which the noise component is removed using the transfer function H. Can do. That is, by using the mask α and the transfer function H described above, a desired physical quantity that supports the determination result can be presented to the outside.

(2) The learning processing unit 20 regards the estimation of the in-cylinder pressure of the engine 1 as a sound separation problem and a transfer function estimation problem, and learns the network weight and transfer function H for generating the mask α by a neural network. As a result, the knocking sound is extracted from the vicinity sound of the engine 1 using the mask α generated by the learned network, and the in-cylinder pressure of the engine 1 when the knocking occurs is estimated from the knocking sound using the transfer function H. be able to. That is, the in-cylinder pressure of the engine 1 at the time of occurrence of knocking, which supports the determination result of the presence or absence of knocking, can be presented to the outside using the mask α and the transfer function H described above. Thereby, the presence or absence of knocking subjectively evaluated by the tester can be objectively evaluated by the estimated in-cylinder pressure of the engine 1. Furthermore, by applying a mask α and presenting a sound source from which only the knocking sound is extracted, it is possible to make use of the skill of the tester.

(3) Since the learning processing unit 20 can appropriately learn the weight of the network that generates the mask α and the transfer function H, the in-cylinder pressure of the engine 1 can be estimated more accurately.
(4) Since the learning processing unit 20 can appropriately set the threshold value, the threshold value determination for the estimated in-cylinder pressure of the engine 1 can be performed more accurately.

(5) The determination processing unit 30 uses the mask α generated by the learned network to remove the noise component included in the input physical quantity, and uses the transfer function H to obtain a desired value from the input physical quantity from which the noise component has been removed. Can be estimated. That is, a desired physical quantity that supports the determination result can be estimated using the mask α and the transfer function H described above. For example, a knocking sound can be extracted from the vicinity sound of the engine 1 using the mask α, and the in-cylinder pressure of the engine 1 when the knocking occurs can be estimated from the knocking sound using the transfer function H. Thereby, the estimated in-cylinder pressure of the engine 1 at the time of occurrence of knocking that supports the determination result of the presence or absence of knocking can be presented to the outside.

(6) The determination processing unit 30 can more accurately perform threshold determination for a desired physical quantity. For example, the presence or absence of knocking can be more accurately determined for the estimated estimated in-cylinder pressure of the engine 1.

(Second Embodiment)
The first embodiment described above can also be carried out in the following forms that are appropriately modified.
In the first embodiment, the case where the estimated in-cylinder pressure 92 of the engine 1 is obtained from the engine vicinity sound 90 including noise using the mask α and the transfer function H is exemplified (FIG. 5). However, as shown in FIG. 12, the engine vicinity sound (knocking sound) 91 not including noise may be estimated from the engine vicinity sound 90 including noise using a mask α. Thereby, the magnitude of the engine vicinity sound 91 that does not include noise can be presented to the outside.

[Configuration of knock determination device]
With reference to FIG. 13, the difference of the knocking determination apparatus 10B according to the second embodiment from the first embodiment will be described.

As illustrated in FIG. 13, the knock determination device 10B includes a signal extraction unit 11, a spectrum calculation unit 12, a signal storage unit 13, a switch 14, a learning processing unit (learning device) 20B, and a determination processing unit ( Estimation device) 30B.
In addition, since it is the same as that of 1st Embodiment except the learning process part 20B and the determination process part 30B, description is abbreviate | omitted.

  The learning processing unit 20B performs a learning process for learning the network weight and transfer function H for generating the mask α in the learning mode, and performs a threshold calculation process for calculating a threshold value used for threshold determination of the knocking sound in the threshold calculation mode. Do. As illustrated in FIG. 13, the learning processing unit 20B includes a learning unit 21, a learned parameter storage unit 22, a first estimation unit 23B, a threshold calculation unit 24B, and a threshold storage unit 25. Note that the processing content of the learning mode is the same as that of the first embodiment, and thus the description thereof is omitted.

  In the threshold calculation mode, the first estimation unit 23B estimates a knocking sound from the sound pressure signal by using the mask α generated by the learned network by the neural network. Then, the first estimation unit 23B outputs the estimated knocking sound to the threshold value calculation unit 24B. The knocking sound estimation method is the same as that in the first embodiment, and a description thereof will be omitted.

  The threshold calculation unit 24B calculates a threshold based on the knocking sound estimated by the first estimation unit 23B. Then, the threshold value calculation unit 24B writes the calculated threshold value in the threshold value storage unit 25. The threshold value calculation method is the same as that of the first embodiment except that knocking sound is used instead of the estimated in-cylinder pressure of the engine 1, and thus the description thereof is omitted.

  In the determination mode, the determination processing unit 30B estimates a knocking sound from the sound pressure signal and performs a determination process for determining the presence or absence of knocking. As illustrated in FIG. 13, the determination processing unit 30B includes a second estimation unit 31B and a threshold determination unit 32B.

  In the determination mode, the second estimation unit 31B estimates a knocking sound from the sound pressure signal using the mask α generated by the learned network by the neural network. Then, the second estimation unit 31B outputs the estimated knocking sound to the threshold determination unit 32B. The knocking sound estimation method is the same as that in the first embodiment, and a description thereof will be omitted.

  In the determination mode, the threshold determination unit 32B determines the presence or absence of knocking by performing threshold determination between the threshold stored in the threshold storage unit 25 and the knocking sound estimated by the second estimation unit 31B. Then, the threshold determination unit 32B outputs the determination result of the presence / absence of knocking and the time-frequency map of the knocking sound input from the second estimation unit 31B to the monitor 7 (FIG. 2).

  Note that the threshold determination method is the same as that of the first embodiment except that knocking sound is used instead of the estimated in-cylinder pressure of the engine 1, and thus description thereof is omitted. Further, the threshold determination unit 32 of FIG. 3 may be configured to output a knocking sound time-frequency map to the monitor 7 as in the second embodiment.

  As described above, the knocking determination device 10B can present the amount of knocking sound necessary for the evaluation of knocking to the outside and determine the presence or absence of knocking.

(Modification)
As mentioned above, although embodiment of this invention was explained in full detail, this invention is not limited to above-described embodiment, The design change etc. of the range which does not deviate from the summary of this invention are included.
In the above-described embodiment, it has been described that the input physical quantity is the engine vicinity sound (sound pressure) and the desired physical quantity is the estimated in-cylinder pressure (pressure) of the engine 1, but the type of the physical quantity is not limited thereto. That is, the desired physical quantity that can be estimated by the present invention may be any physical quantity that can be handled as the signal separation problem and the transfer function estimation problem. Examples of these desired physical quantities include sound pressure, pressure, or vibration (displacement, speed, acceleration).
Further, the input physical quantity and the desired physical quantity may be the same kind of physical quantity as well as different kinds of physical quantities. For example, the input physical quantity may be a sound outside the vehicle, and the desired physical quantity may be a sound inside the vehicle interior.

In the above-described embodiment, it has been described that both the weight and transfer function of the network that generates the mask are learned. However, only one of the weight or transfer function of the network that generates the mask may be learned.
For example, the time frequency characteristics of the knocking sound do not change due to the relationship such as the bore diameter, while the transfer function may change depending on the relationship between the body structure, the position of the microphone and the frequency characteristics. In this case, the transfer function may be learned without learning the network weight necessary for mask generation.
In addition, while the transfer function is known, the time frequency characteristic of the knocking sound may be unknown. In this case, it is only necessary to learn the network weight necessary for mask generation without learning the transfer function.

  In the above-described embodiment, the signal cutout unit has been described as cutting out the sound pressure signal based on the angle information. However, the present invention is not limited to this. For example, the data collection device may not collect the angle information, and the signal cutout unit may cut out the sound pressure signal at random.

  In the above-described embodiment, the knocking determination device has been described as including the learning processing unit and the determination processing unit, but the present invention is not limited to this. That is, the learning processing unit may be a learning device, the determination processing unit may be an estimation device, and each may be configured as an independent device.

  In the above-described embodiment, the engine is described as having four cylinders, but the number of cylinders is not particularly limited. Furthermore, you may estimate a desired physical quantity about test objects other than an engine.

  In the above-described embodiment, the knocking determination device has been described as an independent device, but the present invention is not limited to this. For example, the present invention can also be realized by a program that causes hardware resources such as a CPU, a memory, and a hard disk included in a computer to operate cooperatively as the above-described knocking determination device. These programs may be distributed via a communication line, or may be distributed by writing in a recording medium such as a CD-ROM or a flash memory.

  DESCRIPTION OF SYMBOLS 1 ... Engine, 2 ... Engine ECU, 3 ... Vehicle, 4 ... Sound pressure sensor, 5 ... In-cylinder pressure sensor, 6 ... Data collection device, 7 ... Monitor, 10, 10B ... Knocking determination device, 11 ... Signal extraction part, DESCRIPTION OF SYMBOLS 12 ... Spectrum calculation part, 13 ... Signal storage part, 14 ... Switch, 20, 20B ... Learning process part (learning apparatus), 21 ... Learning part, 22 ... Learned parameter storage part, 23, 23B ... 1st estimation part, 24, 24B ... threshold calculation unit, 25 ... threshold storage unit, 30, 30B ... determination processing unit (estimation device), 31, 31B ... second estimation unit, 32, 32B ... threshold determination unit, 100, 100B ... knocking determination system

Claims (6)

A learning device for removing a noise component included in an input physical quantity and estimating a desired physical quantity from the input physical quantity from which the noise component has been removed,
The noise component is noise other than knocking noise generated in an internal combustion engine,
The input physical quantity is a sound pressure of the internal combustion engine including the noise and the knocking sound,
The desired physical quantity is an in-cylinder pressure of the internal combustion engine at the time of occurrence of knocking,
The weight of the network that generates the mask for removing the noise and extracting the knocking sound by the neural network, and / or the knocking sound extracted by the mask is used as the in-cylinder pressure of the internal combustion engine when the knocking occurs. A learning unit for learning a transfer function to be converted,
A learning apparatus comprising: The learning unit
Learning the weight of the network that generates the mask by means of a convolutional neural network;
The learning apparatus according to claim 1, wherein a weight that multiplies an input physical quantity from which the noise component has been removed by the mask is learned as the transfer function. A first estimation unit that estimates the desired physical quantity from the input physical quantity using the mask and the transfer function by a neural network;
A threshold calculation unit that sums up the desired physical quantities estimated by the first estimation unit every predetermined time, calculates a median value of the summed desired physical quantities, and sets a threshold value based on the calculated median value;
Learning device according to claim 1 or claim 2, further comprising a. A first estimation unit for estimating an input physical quantity obtained by removing the noise component from an input physical quantity including the noise component using the mask by a neural network;
A threshold calculation unit that sums up the input physical quantities estimated by the first estimation unit every predetermined time, calculates a median value of the summed input physical quantities, and sets a threshold based on the calculated median value;
Learning device according to claim 1 or claim 2, further comprising a. An estimation device for removing a noise component contained in an input physical quantity and estimating a desired physical quantity from the input physical quantity from which the noise component has been removed,
The noise component is noise other than knocking noise generated in an internal combustion engine,
The input physical quantity is a sound pressure of the internal combustion engine including the noise and the knocking sound,
The desired physical quantity is an in-cylinder pressure of the internal combustion engine at the time of occurrence of knocking,
Using a neural network to remove the noise and extract the knocking sound, and using a transfer function that converts the knocking sound extracted by the mask into the in-cylinder pressure of the internal combustion engine when the knocking occurs , A second estimation unit that estimates the desired physical quantity from the input physical quantity;
An estimation apparatus comprising: A threshold value determination unit for performing threshold value determination between a preset threshold value and a desired physical quantity estimated by the second estimation unit;
The estimation apparatus according to claim 5 , further comprising: